Biomass SAF Sustainability: Chain-of-Custody Models and Their Critical Role in Meeting CORSIA and ESG Mandates

Abstract

This article provides a comprehensive analysis of chain-of-custody (CoC) models for ensuring the sustainability and traceability of biomass-derived Sustainable Aviation Fuel (SAF). Tailored for researchers and drug development professionals familiar with rigorous quality and provenance standards, we explore foundational concepts, methodological implementation, system optimization, and validation frameworks. The content bridges environmental science with traceability protocols, offering insights into digital ledger technologies, mass balance accounting, and compliance strategies crucial for advancing low-carbon aviation and biomaterial supply chains.

The Pillars of Provenance: Understanding Chain-of-Custody Fundamentals for Biomass SAF

Chain-of-Custody (CoC) is a procedural and documentary chronology that records the sequence of custody, control, transfer, analysis, and disposition of physical or digital evidence. In forensic science, it is the bedrock of evidentiary integrity, ensuring that evidence presented in court is authentic and has not been tampered with. This concept is directly analogous to sustainable biomass supply chains, particularly for Sustainable Aviation Fuel (SAF), where the "evidence" is the sustainability claim (e.g., carbon footprint, sustainable land use). The CoC model provides the verifiable audit trail linking the final product back to the certified source of sustainable biomass.

Core CoC Models: A Comparative Analysis

Three primary CoC models are applied in certified sustainable supply chains, each with varying levels of traceability and mixing allowances.

Table 1: Comparison of Primary Chain-of-Custody Models

Model	Principle	Traceability Level	Mixing Allowance	Key Audit Focus
Identity Preserved	Material from a single certified source is kept physically segregated throughout the supply chain.	Highest	None	Documentation for segregation at every transfer point.
Segregation	Material from multiple certified sources can be mixed but is kept separate from non-certified material.	High	Only between certified sources.	Mass balance calculations and separation protocols.
Mass Balance	Certified and non-certified material can be mixed physically, but the volume of certified material is tracked and allocated to outputs.	Bookkeeping	Permitted. Certified & non-certified inputs mixed.	Robust accounting system, verifiable purchase & sales invoices.
Crediting/Book & Claim	Sustainability attributes are decoupled from the physical flow and traded as certificates.	Lowest (Attribute only)	Complete physical separation of attribute from material.	Integrity of certificate issuance, trading, and redemption registry.

Application Notes for Biomass SAF Research

Note 1: System Boundary Definition A CoC system for biomass SAF must define explicit boundaries: from biomass cultivation or collection (forest, farm, waste facility) through harvesting, pre-processing, transportation, conversion (to bio-intermediates like bio-crude or alcohols), upgrading to SAF, blending, and distribution to airports. Each node in this chain is a custody transfer point requiring documentation.

Note 2: Critical Tracking Events & Key Data Elements At each transfer point (Critical Tracking Event), specific data must be captured.

Table 2: Key Data Elements at a Custody Transfer Point

Data Element	Example	Purpose in CoC
Material Identifier	Batch #: BRZ-2023-087	Unique identifier for the lot.
Product Description	Torrefied Pine Wood Chips, 500 MT	Type, form, and quantity of material.
Sustainability Attributes	Certificate #: SBP-12345, GHG saving: 85%	Linked sustainability credentials.
Date/Time of Transfer	2023-11-15, 14:30 GMT	Timestamps the event.
Custodian (From/To)	From: BioHarvest Inc. To: GreenLogistics LLC	Documents change of control.
Transfer Documentation	Bill of Lading #: BL7712, Mass Balance Report	Legal and accounting proof.
Storage/Conditions	Warehouse A, controlled humidity	For quality and degradation tracking.

Note 3: Integrating Forensic-Inspired Analytical Chemistry The use of biomarkers and isotopic fingerprinting (( \delta^{13}C ), ( \delta^{2}H )) can serve as a "forensic" tool to geolocate biomass origin or verify feedstock type. This physicochemical traceability provides an independent verification layer to supplement documentary CoC.

Experimental Protocols for CoC Verification

Protocol 1: Mass Balance Audit for a Conversion Facility Objective: To verify that the volume of certified sustainable material claimed in outputs is supported by certified inputs and robust accounting. Materials: Scale/weighbridge calibration records, purchase invoices (certified/non-certified), production logs, sales invoices, inventory management system. Methodology:

• Define Audit Period: Select a discrete time period (e.g., one month).
• Record Inputs: Quantify mass/volume of all biomass inputs, segregating certified (C) and non-certified (NC) batches via purchase documents.
• Record Outputs: Quantify mass/volume of all output products (e.g., bio-crude, fuel).
• Apply Mass Balance Formula: Allocate certified content to outputs based on the proportion of certified inputs. Certified Content in Output = (Mass of C Inputs / Total Mass of Inputs) * Mass of Output
• Reconcile: Compare calculated certified volumes with volumes claimed on sales documentation. Discrepancy must be <5%.
• Trace Sample Batches: Select 3-5 random certified input batches and trace their calculated contribution through production logs to specific output batches.

Protocol 2: Isotopic Fingerprinting for Feedstock Origin Verification Objective: To determine the geographic origin of a biomass sample and cross-check against its claimed origin in the CoC documentation. Materials: Ground biomass sample, elemental analyzer, isotope ratio mass spectrometer (IRMS), certified isotopic reference standards, solvent for cellulose extraction. Methodology:

• Sample Preparation: a. Mill biomass to a fine, homogeneous powder. b. For ( \delta^{13}C ) and ( \delta^{15}N ): Lipid removal via Soxhlet extraction (methanol/chloroform). Optional cellulose extraction for ( \delta^{18}O ) analysis. c. Weigh 1.0-1.5 mg of prepared sample into a tin capsule.
• IRMS Analysis: a. Analyze samples in duplicate alongside laboratory reference standards calibrated against IAEA international standards. b. Pass sample capsules to an elemental analyzer (combusted at 1020°C for ( \delta^{13}C ), ( \delta^{15}N ); pyrolyzed at 1450°C for ( \delta^{2}H ), ( \delta^{18}O )). c. The resulting gases (CO₂, N₂, H₂, CO) are separated by GC and introduced to the IRMS.
• Data Calculation: Express results in standard delta (δ) notation per mil (‰) relative to VPDB (( \delta^{13}C )), VSMOW (( \delta^{2}H ), ( \delta^{18}O )), and Air (( \delta^{15}N )).
• Comparison: Compare the isotopic signature of the sample to a reference database of known geographic origins using discriminant analysis.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Key Reagents & Materials for CoC Verification Research

Item	Function in Research	Example Application
Stable Isotope Reference Standards	Calibrate the isotope ratio mass spectrometer, ensuring accurate δ-value measurements.	IAEA-CH-3 (Cellulose, ( \delta^{13}C )), USGS34 (( \delta^{15}N )), VSMOW2 (( \delta^{2}H ), ( \delta^{18}O )).
Certified Reference Biomass	Provide a material with known isotopic or biomarker profile for method validation.	NIST SRM 1547 (Peach Leaves) for elemental & isotopic analysis.
Lipid Extraction Solvents	Remove lipids that can skew ( \delta^{13}C ) measurements, isolating structural carbohydrates.	Methanol, Chloroform, Dichloromethane for Soxhlet extraction.
Cellulose Extraction Kit	Isolate pure cellulose for hydrogen and oxygen isotope analysis, removing exchangeable H.	Acidified sodium chlorite (NaClO₂) and acetic acid series.
Unique Molecular Tracers	Synthetic or natural biomarkers added to biomass for unequivocal physical tracing.	Perdeuterated n-alkanes or DNA-based tracers for segregation model testing.
Chain-of-Custody Seals	Tamper-evident seals for research sample bags during transport, mimicking forensic practice.	Serialized barcoded security seals for sample integrity.

Visualizations

Mass Balance CoC Flow for SAF

Forensic Isotopic Verification Workflow

Application Notes: Regulatory & ESG Traceability Drivers

Traceability in sustainable aviation fuel (SAF) derived from biomass is not merely an operational best practice; it is a mandatory requirement driven by a complex landscape of regulations and voluntary corporate commitments. Effective chain-of-custody (CoC) models must be designed to satisfy these overlapping, and sometimes divergent, data requirements for sustainability attestation.

Table 1: Key Regulatory & ESG Driver Comparison

Driver	Full Name	Primary Scope	Key Traceability & Data Requirements	GHG Reduction Threshold (Typical)
CORSIA	Carbon Offsetting and Reduction Scheme for International Aviation	International Aviation (ICAO)	Mass Balance CoC mandatory. Requires lifecycle GHG calculation (via ICAO's CORSIA Eligible Fuels lifecycle assessment methodology), sustainability criteria compliance, and issuance of Sustainability Certification.	≥10% reduction vs. fossil jet fuel baseline (for lower threshold). Up to ~80% for advanced pathways.
EU RED II	Revised EU Renewable Energy Directive (2018/2001/EU)	EU Energy & Transport	Prefers Mass Balance, permits others. Mandates compliance with sustainability criteria (land, GHG, biodiversity). Requires certification via voluntary schemes (e.g., ISCC, RSB). GHG savings calculated per EU methodology.	65% for biofuels from 2021, 70% for biofuels from 2026.
CA-LCFS	California Low Carbon Fuel Standard	California Transportation Fuels	Accepts various CoC models (physical segregation, mass balance, book & claim). Requires a pathway-specific Carbon Intensity (CI) score (gCO₂e/MJ) certified by the California Air Resources Board (CARB).	CI score must be below the annually declining carbon intensity benchmark.
Corporate ESG	Environmental, Social, and Governance Goals	Voluntary Corporate Targets	Demands high-integrity data (often book & claim or enhanced mass balance) for Scope 3 emission reporting (e.g., SBTi). Focus on additionality, environmental co-benefits (biodiversity, water), and social equity.	Varies by target (e.g., 30% SAF usage by 2030, net-zero by 2050).

Core Implication for Researchers: The CoC model is the data architecture that collects, allocates, and preserves the specific attributes (e.g., GHG savings, sustainable land use) of sustainable biomass through complex supply chains to the final fuel blend. Research must optimize CoC models for data fidelity, auditability, and cost to enable compliance across these multiple frameworks simultaneously.

Experimental Protocols for CoC Model & Attribute Verification

Protocol 2.1: Simulated Mass Balance Chain-of-Custody Audit for Multi-Feedstock SAF

• Objective: To validate the robustness of a mass balance CoC model in accurately tracing sustainability attributes from diverse biomass feedstocks (e.g., used cooking oil (UCO), agricultural residues) through co-processing in a conventional hydrotreater to final SAF blend.
• Materials: Simulated transaction datasets (feedstock purchase, transportation, processing, fuel sales), sustainability attribute certificates (GHG value, land use status), digital CoC platform or ledger software.
• Procedure:
  • Define Batches: Create virtual "sustainable attribute batches" for each feedstock type, assigning unique IDs and quantified GHG reduction values (gCO₂e/MJ) per relevant methodology (e.g., ICAO, EU RED).
  • Simulate Co-Processing: Model the input of 1,000 MT of UCO (Batch A) and 1,000 MT of fossil crude (Batch B) into a hydroprocessing unit with a 90% liquid yield.
  • Apply Mass Balance Rules: Allocate the output of 1,800 MT of liquid hydrocarbons proportionally (50:50) to the input batches. Thus, 900 MT of output carries the sustainability attributes of Batch A.
  • Blending & Claiming: Simulate the blending of the 900 MT "sustainable" intermediate with fossil jet fuel. Track the transfer of attribute claims to the final blended SAF volume.
  • Audit Trail Generation: Use the software to generate an immutable audit trail for a random claim, tracing it back to the original feedstock batch, including all intermediate transactions and compliance documents.
• Outcome Analysis: Measure the time, data completeness, and potential for double-counting or attribute dilution under different allocation scenarios.

Protocol 2.2: Molecular Tracer Analysis for Physical Segregation Verification

• Objective: To experimentally verify the physical segregation of a biomass-derived SAF blendstock from fossil counterparts using stable isotope ratio analysis.
• Materials: SAF sample (e.g., from hydroprocessed esters and fatty acids pathway), fossil Jet A-1 reference, elemental analyzer, isotope ratio mass spectrometer (EA-IRMS), certified isotope standards.
• Procedure:
  • Sample Preparation: Precisely weigh 0.5-1.0 mg of each fuel sample into tin capsules.
  • Combustion & Analysis: Introduce samples into the EA-IRMS. The EA combusts the sample to CO₂, N₂, and H₂O. The CO₂ is purified and introduced into the IRMS.
  • Isotopic Measurement: The IRMS measures the ratio of stable carbon isotopes (¹³C/¹²C) relative to an international standard (Vienna Pee Dee Belemnite, VPDB).
  • Data Calculation: Express results as δ¹³C values in per mil (‰). Biomass-derived fuels have a distinct δ¹³C signature (typically -25‰ to -35‰) compared to fossil fuels (typically -28‰ to -32‰, but regionally variable) due to different photosynthetic pathways.
  • Statistical Comparison: Perform a Student's t-test to determine if the δ¹³C value of the SAF blend is significantly different from the fossil reference, indicating the presence of biogenic carbon.
• Outcome Analysis: A significantly different δ¹³C value provides forensic proof of biogenic content, supporting claims under physical segregation or identity-preserved CoC models.

Visualization of Traceability Systems & Pathways

Title: Interaction of Physical Supply Chain and Data Flow in SAF CoC

Title: Regulatory Compliance Pathway for SAF Certification

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for Biomass SAF Traceability Research

Item / Reagent	Function in Research Context	Example/Notes
Stable Isotope Standards (¹³C, ²H)	Calibration of IRMS for precise measurement of biogenic vs. fossil carbon and hydrogen origin.	VPDB for δ¹³C, VSMOW for δ²H. Certified reference materials from NIST or IAEA.
Synthetic DNA Tracers	Unique, inert oligonucleotide tags for physically tracing specific biomass batches through conversion processes.	Custom-designed, non-coding DNA sequences applied to feedstock. Detected via qPCR.
Chain-of-Custody Software Platform	Digital system for modeling, implementing, and auditing different CoC models (mass balance, book & claim).	Platforms like Insurgo, Chainparency, or custom blockchain-based ledgers.
Lifecycle Assessment (LCA) Database	Source of emission factors for calculating GHG intensity of feedstocks, transport, and conversion.	Commercial (e.g., GaBi, SimaPro) or public (e.g., GREET, EC LCA database).
Certified Reference Fuel Samples	Calibrated samples of fossil and advanced biofuels for analytical method validation and instrument calibration.	Supplied by organizations like NREL, NIST, or commercial specialty gas/certified reference material providers.

Within the context of advancing chain-of-custody (CoC) models for biomass-derived sustainable aviation fuel (SAF), rigorous assessment of sustainability criteria is paramount. This document provides application notes and experimental protocols for researchers and scientists to quantify and validate four key pillars: Carbon Intensity, Land Use, Biodiversity, and Social Responsibility. The methodologies support the traceability and verification requirements of robust CoC systems.

Application Notes & Quantitative Data

Carbon Intensity (CI) Assessment

Objective: Measure the lifecycle greenhouse gas (GHG) emissions from biomass feedstock production through to final SAF combustion (Well-to-Wake). Data is critical for certification under schemes like CORSIA and the EU Renewable Energy Directive II.

Table 1: CI Values for Representative Biomass Feedstocks (gCO₂e/MJ SAF)

Feedstock Type	Cultivation & Harvest	Processing & Conversion	Transport & Distribution	Total CI (Low)	Total CI (High)	Key Variables
Used Cooking Oil	5	25	3	33	40	Collection efficiency, hydrogen source
Non-food Cellulosic (e.g., Miscanthus)	15	35	8	58	75	Fertilizer input, soil C change, conversion yield
Forestry Residues	7	32	12	51	65	Harvesting intensity, transport distance

Land Use Change (LUC) & Biodiversity Impact

Objective: Evaluate direct and indirect land use change (dLUC/iLUC) emissions and assess impacts on local biodiversity indices.

Table 2: Land Use and Biodiversity Metrics for Feedstock Systems

Metric	Unit	Managed Forest (Residues)	Dedicated Energy Crop (Marginal Land)	Palm Oil (Controversial Source)
dLUC Carbon Stock Change	tCO₂/ha/yr	-2 (net sequestration)	+0.5 (initial loss, then gain)	-35 (high loss)
Biodiversity Intactness Index (BII)	% (vs. primary habitat)	85%	65%	40%
Simpsons Diversity Index (Post-cultivation)	Ratio (0-1)	0.75	0.60	0.25

Social Responsibility Indicators

Objective: Quantify socio-economic impacts in biomass sourcing regions to ensure compliance with social sustainability standards.

Table 3: Core Social Responsibility Performance Indicators

Indicator	Measurement Method	Benchmark for Positive Performance
Land Tenure Security	% of suppliers with documented, uncontested land rights	>95%
Labor Rights Compliance	No. of violations per audit (ILO core conventions)	0
Community Health & Safety (Air/Water Quality)	Pollutant concentrations vs. WHO guidelines	Within limits
Local Economic Benefit	% of operational spend within local community (>50km)	>20%

Experimental Protocols

Protocol 2.1: CI Calculation via Life Cycle Assessment (LCA)

Method: Attributional LCA following ISO 14044:2006.

• Goal & Scope: Define functional unit (e.g., 1 MJ of delivered SAF), system boundaries (Well-to-Wake).
• Inventory Analysis (LCI):
  • Collect primary data from CoC documentation: fuel/energy inputs, fertilizer, transport logs.
  • Use secondary data from databases (e.g., Ecoinvent, GREET).
  • Activity: For "Transport," calculate: Emissions = Σ (Distanceₓ * Mode Emission Factorₓ)
• Impact Assessment (LCIA): Apply IPCC AR6 GWP100 factors to convert inventory to CO₂ equivalents.
• Interpretation: Conduct sensitivity analysis on key parameters (e.g., N₂O emission factors, co-product allocation method).

Protocol 2.2: Biodiversity Field Assessment

Method: Modified PREDICTS project protocol for agroecosystems.

• Site Selection: Stratify sampling across feedstock plots and adjacent natural reference plots.
• Taxon Sampling:
  • Flora: Deploy 10 randomly placed 1m² quadrats per 100ha. Identify species, count individuals, and estimate cover %.
  • Soil Macrofauna: Five 10x10x20cm soil cores per plot. Hand-sort, identify to order/family.
  • Avifauna: Two 10-minute point counts at dawn per plot.
• Data Analysis: Calculate richness (S), abundance (N), and Simpsons Diversity Index (D = 1 - Σ(n/N)²).
• BII Calculation: Utilize the model: BII = 0.49 + (0.42 / (1 + exp(-1.1 * (D - 1.5)))) (Site-specific calibration required).

Protocol 2.3: Social Impact Survey

Method: Structured household surveys and key informant interviews (KIIs).

• Design: Develop questionnaire covering demographics, livelihood assets, perceived impacts (5-point Likert scale), and grievance mechanisms.
• Sampling: Random stratified sample of households (n≥50) in communities within 50km of feedstock operations. Purposive sample for KIIs (community leaders, labor union reps).
• Ethics: Obtain prior informed consent. Ensure anonymity.
• Analysis: Quantitative data analyzed for descriptive statistics. Qualitative data from KIIs analyzed via thematic coding.

Diagrams

Biomass SAF Sustainability Assessment Workflow

Title: SAF sustainability assessment workflow from feedstock to certificate.

Key Signaling in Sustainability Governance

Title: Governance signaling from policy to market via CoC and data.

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Materials for Sustainability Field & Lab Research

Item	Function in Research	Example Product/Supplier
Life Cycle Inventory (LCI) Database	Provides secondary emission factors for background processes in CI calculation.	Ecoinvent v3.9, GREET Model 2023 (Argonne National Lab).
Soil Carbon Analysis Kit	Measures soil organic carbon (SOC) for land use change carbon stock calculations.	LECO CN928 Dry Combustion Analyzer.
Biodiversity Survey Toolkit	Standardized collection and identification of flora and fauna.	NEON Biocollection Kit (quadrats, pitfall traps, vials, GPS).
GIS Software & Land Cover Data	Analyzes historical land use change (iLUC/dLUC) via satellite imagery.	ArcGIS Pro with ESA WorldCover 10m resolution dataset.
Social Survey Platform	Enables digital, structured data collection for social indicators in remote areas.	KoBoToolbox (open-source).
Stable Isotope Analyzer	Can be used to trace biogenic vs. fossil carbon in fuels and verify feedstock origin.	Picarro G2201-i isotopic Analyzer for δ¹³C.
Chain-of-Custody Documentation Software	Digitally records, tracks, and manages custody transfer data from field to refinery.	SAP S/4HANA (Sustainability Modules).

Within the thesis on "Advanced Chain-of-Custody (CoC) Models for Verifying Sustainability in Biomass-to-Sustainable Aviation Fuel (SAF) Value Chains," these four archetypes represent the foundational methodological framework. They are critical for tracking and quantifying the flow of certified, sustainable biomass feedstocks (e.g., used cooking oil, agricultural residues) through complex conversion pathways to the final SAF molecule. The choice of model directly impacts the integrity of sustainability claims, the cost of compliance, and the flexibility of supply chains, forming a central research axis in scaling SAF production credibly.

Model Archetypes: Definitions & Comparative Analysis

Table 1: Core CoC Model Archetypes Comparison

Model Archetype	Core Principle	Physical Mixing Allowed?	Claim Specificity	Supply Chain Flexibility	Traceability Level	Premium/Cost Impact
Book & Claim (B&C)	Decouples sustainability attributes from physical flow via a certificate trading system.	Yes, unrestricted.	Attribute-only; not tied to specific physical volume.	Very High	Low (Chain-of-Custody)	Low (transaction cost only)
Mass Balance (MB)	Sustainability attributes are allocated across mixed physical flows using a defined ruleset (e.g., percentage-based).	Yes, certified & non-certified feedstocks can be mixed.	Volumetric (e.g., X liters of SAF contain Y% sustainable attributes).	High	Moderate (Chain-of-Custody)	Moderate
Segregation	Certified materials are kept separate from non-certified materials throughout the supply chain.	No, parallel streams are maintained.	Batch-specific to a certified stream.	Lower	High (Chain-of-Custody)	Higher
Identity Preservation (IP)	The unique identity and specific properties of a single batch are maintained from source to end product.	No, strict physical isolation.	Product is fully traceable to a single, unique batch of origin.	Very Low	Very High (Full Traceability)	Highest

Application Notes & Experimental Protocols for Research Validation

Protocol 3.1: Simulating Mass Balance Allocation in a Biorefinery Context

• Objective: To quantitatively model and validate the allocation of sustainability attributes (e.g., GHG savings, certified feedstock units) in a co-processing facility using a mass balance approach.
• Materials: Supply chain influx data (volumes of certified/non-certified feedstocks), reactor output data, allocation rule set (e.g., gravimetric, market-value-based).
• Methodology:
  • Input Stream Characterization: Measure and record total mass (M_total) of input. Quantify mass of certified sustainable feedstock (M_cert).
  • Output Stream Analysis: Measure total mass of output product slate (e.g., SAF, diesel, naphtha) (P_total).
  • Allocation Factor Calculation: Apply the chosen allocation rule.
    • Gravimetric: Allocation Factor (AF) = M_cert / M_total.
  • Attribute Assignment: For each output product P_x, assign certified volume = Mass of P_x * AF.
  • Verification: Ensure sum of all certified volumes assigned to outputs equals M_cert. Document audit trail.

Protocol 3.2: Designing an Identity-Preserved Supply Chain Pilot

• Objective: To establish a protocol for maintaining the physical and documentary identity of a specific biomass batch from origin to SAF conversion.
• Materials: Unique batch identifiers (e.g., QR codes, RFID tags), dedicated storage and transport containers, blockchain or secure database for logging.
• Methodology:
  • Source Batch Definition: At point of origin, assign a globally unique ID to a homogeneous biomass batch. Record all batch-specific data (geocoordinates, feedstock type, sustainability metrics).
  • Physical Handling Protocol: Ensure batch is physically isolated in dedicated, labeled containers during storage and transport. Cleanout procedures must be documented between batches.
  • Custody Transfer Documentation: At each transfer point (loader, carrier, receiver), scan batch ID and log transaction (timestamp, mass, responsible party) into the secure ledger.
  • Conversion in Dedicated Campaign: Schedule the IP batch for processing in a dedicated time window at the biorefinery, following equipment cleanout procedures. Link output SAF tank to the source batch ID.
  • Chain-of-Custody Audit: Provide auditors with access to the full digital trail and physical audit points to verify uninterrupted identity preservation.

Visualizing CoC Model Decision Pathways & Workflows

Diagram 1: CoC Model Selection Decision Tree (94 chars)

Diagram 2: Identity Preservation Physical & Data Flow (99 chars)

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Research Tools for CoC Model Analysis

Item / Solution	Function in CoC/SAF Research
Stable Isotope Tracers (e.g., 13C, 2H)	Serve as physical markers in biomass to experimentally validate traceability and detect mixing in IP/Segregation models via isotopic ratio mass spectrometry (IRMS).
Blockchain/DLT Platform (e.g., Hyperledger Fabric)	Provides an immutable, transparent ledger for simulating and testing the digital Chain-of-Custody documentation across B&C, MB, and IP models.
Life Cycle Assessment (LCA) Software (e.g., OpenLCA)	Critical for calculating the precise GHG savings values that are tracked and allocated through different CoC models in SAF certification.
Radio-Frequency Identification (RFID) Tags & Scanners	Enable automated, high-fidelity tracking of physical batch movement in pilot-scale Segregation and IP supply chain experiments.
Process Simulation Software (e.g., Aspen Plus)	Models mass and energy flows in biorefineries to generate accurate data for developing and testing Mass Balance allocation algorithms.
Secure Database with API Access (e.g., SQL-based)	Acts as the central repository for custody certificates, transaction logs, and sustainability attributes in Book & Claim and Mass Balance simulations.

Application Notes on Stakeholder Roles & Data Requirements

The integrity of biomass-based Sustainable Aviation Fuel (SAF) supply chains relies on a robust Chain-of-Custody (CoC) model linking five core stakeholder groups. Each entity is responsible for generating, transmitting, and verifying critical data points that attest to the fuel's sustainability, from land to wing. The following notes detail their primary functions and the data they must provide within a mass-balance or book-and-claim CoC system.

Table 1: Core Stakeholder Functions & Data Responsibilities

Stakeholder	Primary Role in SAF CoC	Key Data Generated / Transmitted	Critical Sustainability Attribute Verified
Feedstock Producer	Cultivates/harvests biomass.	Land coordinates, crop type, yield (t/ha), agricultural practice logs, soil carbon data, initial GHG calculation.	Land use change (direct/indirect), soil health, biodiversity impact, carbon stock change.
Converter	Processes feedstock into refined SAF (HEFA, FT, ATJ, etc.).	Feedstock input mass, processing efficiency (%), SAF output mass, energy use, emission factors, chain-of-custody certificates.	GHG emissions from processing (Scope 1 & 2), feedstock conversion efficiency.
Trader	Handles physical and/or certificate logistics between producer, converter, and airline.	Transaction certificates (e.g., RSB, ISCC), volume traded, batch IDs, transaction history (blockchain or registry entries).	Guarantee of origin, prevention of double-counting, audit trail integrity.
Airline	End-user of SAF, claims environmental benefits.	Fuel uplift volume, blending ratio, claim statement (CO2e reduction), corresponding certificate retirement.	Accurate attribution of GHG savings (Scope 3), compliance with CORSIA/RSB.
Verifier	Independent third-party auditor.	Audit reports, verification statements, certificate issuance, non-conformity reports.	Compliance with chosen standard (e.g., RED II, CORSIA), entire chain data integrity.

Table 2: Quantitative Data Flow Summary for a Mass-Balance CoC Model

Data Point	Example Value	Unit	Source Stakeholder	Verification Method
Feedstock Yield	15.6	t/ha (dry matter)	Producer	Remote sensing, yield logs.
Carbon Stock Change (Soil)	+0.3	t C/ha/yr	Producer	Soil sampling & modeling.
Feedstock-to-SAF Conversion	72	% (energy basis)	Converter	Process mass/energy balance.
Well-to-Wake GHG Reduction	78	% vs. fossil baseline	Converter/Verifier	LCA using GREET/CORSIA model.
Traded SAF Certificate Volume	1,250,000	MT CO2e saved	Trader	Registry transaction log.
Blending Ratio at Uplift	34	% SAF in blend	Airline	Fuel ticket, batch analysis.

Experimental Protocols for CoC Data Integrity & GHG Validation

Protocol 2.1: Remote Sensing & GIS Verification of Feedstock Sustainability

Objective: To independently verify feedstock producer data regarding land use history and crop type without direct field audit.

Materials:

• Satellite imagery (Sentinel-2, Landsat 8/9) with high temporal resolution.
• Historical land cover datasets (e.g., ESA CCI, USGS).
• GIS software (e.g., QGIS, ArcGIS Pro).
• Ground truth point data (coordinates) for calibration.

Methodology:

• Define Area of Interest (AOI): Input producer-provided geospatial coordinates (polygon) for the feedstock production plot.
• Historical Land Use Analysis: Access satellite imagery archives for the AOI for a minimum of 10 years prior to the current cultivation date. Classify land cover for each year using a supervised classification algorithm (e.g., Random Forest in Google Earth Engine).
• Change Detection: Apply a change detection algorithm (e.g., NDVI time-series breakpoint analysis) to identify the date of any significant land cover conversion. Compare against the date provided by the producer.
• Crop Type Validation: For the current season, analyze the spectral signature (NDVI, LAI) growth curve of the cultivated crop. Match the curve against known signature libraries for the claimed feedstock (e.g., camelina, carinata, switchgrass).
• Accuracy Assessment: Validate classification results against ground truth points with a confidence interval of ≥95%. Generate a verification report detailing findings on indirect land use change (iLUC) risk.

Protocol 2.2: Carbon Isotopic Analysis for SAF Blending Verification

Objective: To empirically verify the blending ratio of biogenic SAF with conventional Jet A-1 fuel at the point of airline uplift.

Materials:

• Fuel samples (pre- and post-blend).
• Elemental Analyzer coupled to an Isotope Ratio Mass Spectrometer (EA-IRMS).
• High-purity helium, oxygen.
• Reference standards for carbon isotopes (VPDB scale).

Methodology:

• Sample Preparation: Collect triplicate fuel samples (1 mL each) from the blended fuel batch and the neat Jet A-1 supply. Store in sealed vials.
• Combustion & Purification: Inject 0.5 µL of sample into the EA, where it is combusted at 1020°C in an oxygen-rich environment. The resulting CO₂ gas is carried by helium through a series of chemical traps (to remove water and other gases) and a GC column for purification.
• Isotopic Measurement: The purified CO₂ is introduced into the IRMS. The instrument measures the ratio of ¹³C/¹²C relative to the reference CO₂ gas pulse.
• Data Analysis: Calculate the δ¹³C value (‰) for each sample. Biogenic feedstocks (C3 plants) have a distinct δ¹³C signature (approx. -28 to -32‰) compared to fossil-derived carbon (approx. -24 to -28‰ for Jet A-1).
• Blend Calculation: Using a two-component isotopic mixing model, calculate the proportion of biogenic carbon in the blended fuel. Compare this result to the blending ratio declared on the fuel certificate.

Protocol 2.3: Blockchain-Based CoC Transaction Integrity Audit

Objective: To audit the immutability and completeness of the transaction trail from converter to airline via the trader.

Materials:

• Access to the permissioned blockchain node or distributed ledger registry (e.g., based on Hyperledger Fabric).
• The unique batch ID of the SAF certificate.
• Query tools (APIs, block explorers).

Methodology:

• Trace Forward: Input the batch ID at its point of creation (Converter node). Query the ledger to list all subsequent transactions (e.g., "issued to Trader A," "split into two lots," "transferred to Airline B"). Record all transaction hashes, timestamps, and participating wallet addresses.
• Trace Backward: Starting from the certificate retirement transaction (Airline node), perform a reverse trace to confirm the certificate's origin links back to the correct feedstock batch and converter.
• Double-Counting Check: Query the ledger's global state for the status (active/retired) of all certificate IDs linked to the original feedstock batch. Confirm that the sum of retired volumes does not exceed the originally issued volume.
• Integrity Verification: For a sample of 10% of traced transactions, cryptographically verify the digital signature using the public key of the transacting stakeholder. Confirm the transaction hash is unaltered.
• Audit Report: Document the complete, unbroken chain of custody and the absence of double-counting or tampering.

Mandatory Visualizations

Title: SAF Chain of Custody Stakeholder & Audit Flow

Title: Multi-Protocol Verification Workflow for SAF Claims

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Key Reagents & Materials for SAF CoC and LCA Research

Item Name & Supplier Example	Function in Research	Application in Described Protocols
Elemental Analyzer-Isotope Ratio Mass Spectrometer (EA-IRMS)e.g., Thermo Scientific Delta V Plus System	Precisely measures the stable isotope ratios (¹³C/¹²C, ¹⁸O/¹⁶O) in fuel samples.	Protocol 2.2: Differentiates biogenic vs. fossil carbon to verify SAF blending ratios.
Certified Isotopic Reference Materialse.g., IAEA-600 Caffeine (δ¹³C Certified)	Calibrates the IRMS, ensuring accuracy and traceability of isotopic measurements to international scales (VPDB).	Protocol 2.2: Used as a running standard for quality control during fuel sample batch analysis.
Satellite Imagery Data Platformse.g., Google Earth Engine, Copernicus Open Access Hub	Provides multi-temporal, multi-spectral remote sensing data for land monitoring.	Protocol 2.1: Source for Sentinel-2/Landsat imagery to conduct historical land use change analysis.
Hyperledger Fabric Blockchain Framework(Open Source)	Provides a permissioned, modular blockchain platform to develop CoC tracking applications with high transaction privacy.	Protocol 2.3: The underlying infrastructure for creating the immutable, auditable ledger of certificate transactions.
GREET Model Software(Argonne National Laboratory)	Lifecycle analysis tool specifically for transportation fuels. Calculates well-to-wake GHG emissions.	Thesis Context: Used to generate the GHG reduction values that are attached to and verified for SAF certificates.

From Theory to Practice: Implementing and Deploying Robust CoC Systems

Effective Chain-of-Custody (CoC) models are critical for ensuring the integrity, traceability, and sustainability of biomass feedstocks used in Sustainable Aviation Fuel (SAF) production. Within a broader research thesis, this system design provides a verifiable framework to track physical custody, legal ownership, and sustainability characteristics (e.g., carbon intensity, land use) from biomass origin through pre-processing, conversion, and final fuel blending. This is foundational for credible life-cycle analysis (LCA), regulatory compliance (e.g., EU RED II, US IRA), and premium market claims.

Core System Documentation Framework

A robust CoC system requires multi-layered documentation, digitally integrated where possible.

Table 1: Core CoC Documentation Layers

Document Layer	Primary Function	Key Data Points Captured	Custody Link
1. Declarations of Origin	Attests to the source and initial sustainability attributes.	Geospatial coordinates, land type, biomass species, harvest date, initial mass.	Links biomass to point of harvest/origin.
2. Transfer Certificates (TC)	Legal and physical custody transfer between entities.	Date, parties involved, transferred quantity, unique batch ID, transportation method.	Creates an immutable link between successive custodians.
3. Sustainability Credits/Mass Balance Ledger	Tracks allocation of sustainability attributes under mass balance accounting.	Credit ID, corresponding physical batch ID, credit value (e.g., MJ, tons CO2eq saved), retirement claim.	Decouples sustainability attributes from physical flow for flexible yet auditable allocation.
4. Laboratory Analysis Reports	Provides objective, quantitative data on feedstock/fuel properties.	Moisture content, carbon content, lipid profile, isotopic signature, contaminant levels.	Provides scientific anchor points for validating declarations and process conversions.
5. System Audit Logs	Automated record of all digital transactions and data modifications.	User ID, timestamp, action performed, data before/after change.	Ensures digital trail integrity and non-repudiation.

Critical Data Points & Quantitative Benchmarks

Data must be collected at each Custody Transfer Point (CTP). The following table summarizes mandatory and optional data points based on current SAF certification schemes (e.g., ISCC, RSB).

Table 2: Data Points at Key Custody Transfer Points

Custody Transfer Point	Mandatory Quantitative Data	Recommended Analysis/Protocol	Purpose in Sustainability Research
Field/Forest to Collection Yard	Wet mass (kg), Area harvested (ha), GPS polygon.	NIRS scan for initial moisture & composition.	Baselines carbon stock change and land-use efficiency.
Collection Yard to Preprocessor	Dry mass (kg), Moisture content (%).	ISO 18134 for moisture, TGA for volatiles.	Calculates energy expended in drying; basis for yield.
Preprocessor to Biorefinery	Torrefied mass / Pyrolysis oil volume (L), Energy density (MJ/kg).	ASTM E1131 for proximate analysis, Bomb calorimetry.	Tracks mass/energy yield through intermediate conversion.
Biorefinery to Blender	Hydroprocessed Esters and Fatty Acids (HEFA) or Alcohol-to-Jet (ATJ) volume (L), Carbon number distribution.	GC-MS (ASTM D2887), 14C Analysis (ASTM D6866) for biogenic carbon.	Verifies final fuel spec and biogenic carbon content.
Blender to Airport	Blended SAF volume (L), Blending ratio %, Final CI score (gCO2e/MJ).	FT-IR for blend verification, LCA model calculation.	Final claim substantiation for offtake agreements.

Detailed Experimental Protocols for Key Data Validation

These protocols provide the scientific underpinning for data points in Table 2.

Protocol 1: Feedstock Composition via Thermogravimetric Analysis (TGA)

• Objective: Determine moisture, volatile matter, fixed carbon, and ash content in solid biomass.
• Methodology:
  • Sample Prep: Grind feedstock to <250 µm. Dry at 105°C for 1 hr (optional pre-step).
  • Equipment: Standard TGA apparatus with inert (N2) and oxidative (air) gas capability.
  • Procedure: a. Load 10-20 mg sample into platinum crucible. b. Heat from ambient to 105°C at 20°C/min under N2 (50 mL/min). Hold for 10 min. % Weight Loss = Moisture Content. c. Heat to 900°C at 20°C/min under N2. % Weight Loss = Volatile Matter. d. Switch gas to air (50 mL/min). Hold at 900°C for 10 min. Residual oxidation occurs. Final Residue = Ash Content. e. Fixed Carbon % = 100% - (Moisture% + Volatile% + Ash%).
• Relevance: Critical for mass balance and predicting conversion yields in pyrolysis/gasification.

Protocol 2: Verification of Biogenic Carbon Content via Radiocarbon (14C) Analysis

• Objective: Distinguish biogenic from fossil carbon in intermediate or final fuel products per ASTM D6866.
• Methodology:
  • Sample Combustion: Precisely measure ~1 mg of carbon from the liquid fuel sample. Convert it to CO2 via sealed tube combustion or automated elemental analyzer.
  • Purification & Graphitization: Purify the CO2, then catalytically reduce it to graphite solid targets.
  • AMS Measurement: Analyze targets using an Accelerator Mass Spectrometer (AMS). The 14C/12C ratio is measured.
  • Data Analysis: Compare the sample's 14C/12C ratio to a modern oxalic acid standard. Report as Fraction Modern (F14C) or Percent Modern Carbon (pMC). A 100% biogenic sample will yield ~100 pMC (varies with atmospheric nuclear testing).
• Relevance: Scientifically validates the renewable origin of carbon atoms in SAF, essential for carbon accounting.

Custody Transfer Protocol Workflow

The following diagram outlines the logical sequence and decision points for a physical custody transfer, integrating documentation and data verification.

Diagram Title: Physical Custody Transfer Verification Workflow

Mass Balance CoC Model for Sustainability Attribute Tracking

This diagram illustrates how physical and informational custody are tracked in parallel under a mass balance CoC model, which is prevalent in SAF certification.

Diagram Title: Mass Balance Chain of Custody Model

The Scientist's Toolkit: Key Research Reagent Solutions & Materials

Table 3: Essential Materials for Biomass SAF CoC Research & Validation

Item/Category	Function in CoC Research	Example/Notes
Stable Isotope Standards	For tracing feedstock origin via isotopic fingerprinting (δ13C, δ2H, δ18O).	VPDB (Vienna Pee Dee Belemnite) for δ13C, VSMOW (Vienna Standard Mean Ocean Water) for δ2H/δ18O. Essential for geolocation verification.
NIST SRMs for Biofuels	Calibrating instruments for accurate composition & property analysis.	NIST SRM 2770 (Biodiesel Fatty Acid Methyl Esters), NIST 2773 (Crude Oil). Ensures data interoperability between labs.
Certified Reference Materials for 14C	Quantifying biogenic carbon content via AMS.	IAEA C6 (sucrose, modern carbon), NIST SRM 4990C (Oxalic Acid). Primary standards for ASTM D6866.
Solid Sorbents for VOC Sampling	Capturing volatile organic compounds from headspace for process emission tracking.	Tenax TA, Carbotrap. Used in TD-GC-MS to link emissions to specific batch processes.
Derivatization Reagents for GC	Enabling analysis of non-volatile compounds in pyrolysis oils or intermediates.	N,O-Bis(trimethylsilyl)trifluoroacetamide (BSTFA) with TMCS. Silanizes -OH and -COOH groups for accurate yield analysis.
Solid-Phase Extraction (SPE) Kits	Cleaning and fractionating complex biomass hydrolysates or fermentation broths.	C18, ion-exchange, and mixed-mode cartridges. Isolates target molecules for yield tracking through conversion steps.
Sealed Sampling Equipment	Maintaining custody integrity during field sampling.	Triple-seal sample bags, tamper-evident seals, chain-of-custody tags. Preserves sample integrity from field to lab.

Application Notes

This document details the integration of blockchain, IoT sensors, and Digital Product Passports (DPPs) to establish a verifiable, real-time chain-of-custody (CoC) model for biomass-derived Sustainable Aviation Fuel (SAF). The system is designed to meet the stringent traceability and sustainability verification needs of researchers and industrial partners in advanced biofuel development.

1.1 Core System Architecture The proposed model creates a tamper-evident ledger of biomass feedstock from point of origin (e.g., agricultural residue collection, algae harvest) through pre-processing, conversion, refining, and final fuel blending. IoT sensors provide real-time physical data (location, temperature, mass), which is cryptographically hashed and written to a permissioned blockchain. A DPP serves as the dynamic, standardized digital twin of the physical batch, aggregating CoC data, sustainability attributes (GHG emissions, land use), and lab analysis certificates for researcher access.

1.2 Key Applications in SAF Research

• Provenance Verification: Immutable recording of feedstock type, origin, and handling to prevent fraudulent sustainability claims.
• Process Optimization: Correlating real-time sensor data (e.g., storage conditions) with downstream conversion efficiency and fuel properties analyzed in the lab.
• Automated Sustainability Accounting: Programmatic calculation of life-cycle GHG emissions based on verified primary data flows, crucial for research on fuel pathways.
• Sample Integrity for R&D: Ensuring physical samples taken for catalyst testing or compositional analysis are accurately linked to their specific supply chain event and batch.

Table 1: Comparison of Digital CoC Platform Components

Component	Primary Function	Typical Data Points	Latency	Key Metric for SAF Research
IoT Sensors	Physical-to-digital data capture	GPS: ±3m accuracy; Temp: ±0.5°C; Mass: ±0.1% FS	Real-time (<2 min)	Data granularity, sensor failure rate
Blockchain	Immutable data ledger & smart contracts	Transaction finality: <30 sec; Throughput: 100-1000 tps	Near-real-time	Auditability, node decentralization
Digital Product Passport	Human/machine-readable data interface	GHG data, certificates, custody events	On-demand	Interoperability (GS1 EPCIS), data schema compliance

Table 2: Representative IoT Data for Biomass Feedstock Monitoring

Feedstock Stage	Sensor Type	Measured Parameter	Target Range	Impact on SAF Yield/Quality
Storage	Temperature/Humidity	Biomass moisture content	10-15% w.b.	Prevents degradation, preserves carbohydrate content
Transport	GPS/Accelerometer	Location, journey shocks	<5g shocks	Ensures route compliance, detects handling anomalies
Pre-processing	NIR Spectrometer	Lignin/Cellulose ratio	Cellulose >40%	Predicts hydroprocessing severity required

Experimental Protocols

Protocol 3.1: Integrating IoT Sensor Data with Blockchain for CoC Verification Objective: To validate the integrity and timeliness of sensor data recorded on-chain for a batch of woody biomass. Materials: GPS/Temperature sensor tag (e.g., Bluetooth or LPWAN-enabled), Permissioned blockchain node (e.g., Hyperledger Fabric client), DPP backend server. Procedure:

• Affix a sensor tag to a defined biomass batch (≥ 100 kg) at the harvesting site.
• Configure the tag to log geographic coordinates and ambient temperature every 5 minutes.
• Each data packet is hashed (SHA-256) locally by the tag's edge module. The hash and a timestamp are broadcast to a blockchain gateway.
• The gateway submits the hash as a transaction to the blockchain, triggering a smart contract that records the batch ID, timestamp, and data hash.
• The full sensor data is stored off-chain in the DPP database, referenced by the on-chain hash.
• At the biorefinery gate, a reader scans the tag. The current data hash is compared to the last on-chain hash. Mismatch indicates tampering.
• Researchers can query the DPP via API to retrieve the full time-series data for correlation with lab analysis of the received feedstock.

Protocol 3.2: Validating Sustainability Attributes via a Smart Contract Objective: To automate the verification of GHG threshold compliance for a SAF batch using on-chain primary data. Materials: Emission factor database, Smart contract development environment (e.g., Solidity), Pre-defined biomass lifecycle model. Procedure:

• Encode a GHG calculation model (e.g., based on ARTM) into a blockchain smart contract. Set a threshold (e.g., 50% reduction vs. fossil baseline).
• As the biomass moves through the chain, key data (diesel use for transport, natural gas for drying) are written to the blockchain by authorized nodes (e.g., transporter, processor).
• Upon receipt of the final process energy data from the biorefinery, the smart contract is automatically executed.
• The contract retrieves the verified data points, applies the emission factors, and computes the total lifecycle GHG intensity.
• The contract writes the result and a compliance flag (Pass/Fail) to the blockchain and updates the DPP status.
• Researchers can audit the calculation by inspecting the contract code and all input transactions.

Diagrams

Title: Digital CoC System Data Flow for Biomass SAF

Title: Automated GHG Verification Workflow via Smart Contract

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for Digital CoC-Integrated SAF Research

Item	Function in CoC-Integrated Research
Programmable IoT Data Logger (e.g., with GPS, Temp)	Attached to biomass samples to generate the primary spatial/temporal data stream for CoC validation.
Permissioned Blockchain Testnet Access (e.g., Hyperledger Besu)	Provides a sandbox for developing and testing smart contracts for automated sustainability accounting without cryptocurrency costs.
DPP Schema Validator (e.g., for EU DPP proposal)	Ensures research data packaging complies with emerging regulatory standards for interoperability.
Chain-of-Custody Sample Kit (RFID/NFC tags, scanners)	Links physical lab samples (e.g., of intermediate bio-oil) to digital CoC events, preserving sample integrity for catalyst testing.
API Client for DPP/Blockchain Node (e.g., Python web3.py)	Allows researchers to programmatically query CoC data for integration into data analysis pipelines (e.g., in R or Python).
Digital Twin Simulation Software	Models the impact of feedstock handling variables (from IoT data) on conversion process kinetics and final fuel properties.

Within a broader thesis investigating chain-of-custody (CoC) models for sustainable aviation fuel (SAF) derived from biomass, the mass balance model represents a critical, market-enabling mechanism. This model facilitates the scaling of sustainable supply chains by allowing the mixing of certified sustainable and non-certified materials, with credits representing the sustainable attributes traded separately from the physical flow. This document provides detailed application notes and experimental protocols for researching the efficacy, integrity, and traceability of the mass balance approach as governed by leading standards like the International Sustainability and Carbon Certification (ISCC) and the Roundtable on Sustainable Biomaterials (RSB).

Core Principles & Allocation Rules

Mass balance is an auditable accounting method that tracks the total volume of sustainable material through a complex supply chain. Certified and non-certified materials may be mixed, but an equivalent amount of end-product can be sold as certified.

Key Allocation Rules:

• Segregation: Physical mixing permitted within certified systems.
• Verification: Requires independent, third-party auditing of input, throughput, and output.
• Allocation Methods: Credits can be allocated based on:
  • Volume/Mass: Proportional to the certified input volume.
  • Greenhouse Gas (GHG) Savings: Allocating based on the certified material's superior GHG performance.
  • Economic Value: Rarely used, based on relative market value.

Table 1: Quantitative Comparison of ISCC and RSB Mass Balance Core Rules

Parameter	ISCC PLUS (Mass Balance)	RSB (Mass Balance / Book & Claim)
Minimum Chain-of-Custody Unit	Single site (e.g., refinery, mill)	Single site or entire supply chain
Allocation Period	Maximum 3 months	Defined by operator, audited annually
Credit Tradability	Yes, via ISCC certificates	Yes, via RSB Certificates of Sustainability
GHG Calculation Requirement	Mandatory (RED II/III compliance)	Mandatory (RSB-specific methodology)
Permitted Feedstock Mixing	Yes, with defined conversion factors	Yes, with material balance & conversion factors
Claim on Final Product	"Contains x% sustainable material"	"Supported by RSB-certified materials"

Credit Trading Mechanisms & Integrity Protocols

Credit trading decouples the environmental attributes from the physical commodity. Research must verify the prevention of double-counting and fraud.

Experimental Protocol 3.1: Simulating and Auditing a Credit Trade Objective: To model and validate a single credit trade within a simulated biomass-to-SAF supply chain. Materials: Database server (SQL), standardized credit token (JSON-LD format), audit log blockchain testnet (e.g., Hyperledger Fabric), ISCC/RSB rulebooks. Procedure:

• System Setup: Establish three virtual nodes: Producer (Biorefinery), Trader (Commodity Broker), Claimant (Airline/Fuel Supplier).
• Credit Generation:
  • Input 1000 kg of ISCC-certified used cooking oil (UCO) into the simulated system.
  • Apply the ISCC-defined conversion factor (e.g., 0.85) to calculate 850 kg of HVO-SAF equivalent.
  • Generate one digital certificate (credit) for 850 kg SAF with metadata (Batch ID, GHG saving 85%, feedstock type, origin).
• Trade Execution:
  • Producer lists credit on a simulated registry.
  • Trader purchases credit. The registry records ownership change, timestamp, and transaction hash on the testnet.
  • The original physical SAF batch is blended into the general fuel pool.
• Claim & Retirement:
  • Claimant purchases credit from Trader and "retires" it to make a sustainability claim for 850 kg of physical SAF uplifted.
  • Registry marks credit status as Retired, preventing further transaction.
• Integrity Verification:
  • Run a reconciliation script comparing total credits issued vs. retired across all nodes.
  • Audit the blockchain ledger for immutable sequence and consensus-validated entries.
  • Attempt to generate a duplicate credit with identical Batch ID; system must reject.

Expected Outcome: A verifiable, tamper-evident chain of custody for the sustainability attribute, enabling accurate reporting without physical segregation.

Chain-of-Custody Standards: Comparative Analysis

Detailed Protocol 4.1: Material Balance Calculation under ISCC & RSB Objective: To perform a comparative material balance calculation for a co-processing refinery using both ISCC and RSB methodologies. Workflow: See Diagram 1.

Procedure:

• Define Inputs: For a given allocation period (e.g., 1 month), gather:
  • Input_Cert: Mass of certified biomass (e.g., 6000 kg UCO).
  • Input_NonCert: Mass of fossil crude (e.g., 94000 kg).
  • Total_Output: Mass of total diesel/SAF output (e.g., 97000 kg).
  • Conversion_Factor_ISCC: From ISCC list (e.g., UCO to HVO: 1.0).
  • Yield_RSB: Mass balance yield from RSB-approved process scheme.
• Calculate Certified Output (ISCC Method):
  • Output_Claim_ISCC = Input_Cert * Conversion_Factor_ISCC
  • Example: 6000 kg * 1.0 = 6000 kg claimable output.
• Calculate Certified Output (RSB Method):
  • Determine total input: Total_Input = Input_Cert + Input_NonCert
  • Calculate allocation factor: Allocation_Factor = Input_Cert / Total_Input
  • Apply to output: Output_Claim_RSB = Total_Output * Allocation_Factor
  • Example: (6000 kg / 100000 kg) * 97000 kg = 5820 kg claimable output.
• Analysis: Tabulate results and compare the percentage claim (Claim/Total_Output) under each system. Discuss implications for credit generation and economic incentive.

Diagram 1: Mass Balance Calculation Workflow for ISCC vs. RSB.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Research Materials for CoC Model Analysis

Item / Reagent	Function in Research Context	Example / Specification
Digital Registry Sandbox	Simulates credit issuance, trading, and retirement in a controlled environment.	ISCC or RSB demonstration registry; Custom blockchain testnet (Ethereum, Hyperledger).
Sustainability Standard Rulebooks	Definitive source documents for allocation and audit rules.	ISCC PLUS System Documents v3.5+; RSB Standard & Procedures v4.0+.
Feedstock & Conversion Factor Database	Enables accurate calculation of material and energy balances.	ISCC List of Conversion Factors; RSB EU RED Compliance Package.
GHG Calculation Engine	Computes lifecycle emissions for certified and reference pathways.	Open-source LCA software (openLCA) with EU RED-compliant plugins.
Isotopic or Molecular Tracers	For physical validation studies (e.g., tracing biogenic carbon).	14C analysis (for biogenic carbon); Stable isotope-labeled compounds (13C).
Secure Audit Trail Logger	Immutably records experimental transactions and data changes.	Blockchain ledger or cryptographically hashed SQL database with WORM properties.

Advanced Protocol: Tracing Attribute Integrity via Synthetic DNA Taggants

Protocol 6.1: Validating Physical-Credit Linkage in Bulk Storage Objective: To experimentally test the feasibility of using synthetic DNA tracers to create a physical link to mass balance credits in bulk liquid storage.

Materials:

• Synthetic DNA sequences (unique 100-base pair oligos, inert, stable in hydrocarbon).
• Hydrocarbon-compatible encapsulation medium (e.g., silica microcapsules).
• Model fluid system (e.g., dodecane as SAF surrogate).
• qPCR machine and specific primers/probes.
• Simulated credit registry.

Procedure:

• Tagging: Encapsulate unique DNA Sequence A. Inject a known quantity (e.g., 10^12 capsules) into 1000 L of certified model bio-fuel (Batch_Cert).
• Blending: Mix Batch_Cert with 9000 L of untagged fossil model fuel (Batch_Fossil) in a simulated tank farm. Homogenize.
• Credit Generation: Issue a digital credit for 1000 L of sustainable fuel linked to DNA_ID_A.
• Sampling & Detection: Periodically sample from the blended tank. Extract DNA, amplify via qPCR with primers for Sequence A.
• Quantification: Use qPCR standard curve to estimate the concentration of tagged fuel in any sample.
• Correlation: Correlate the quantified proportion of tagged fuel in withdrawn batches with the redemption of digital credits from the registry.

Significance: This protocol provides a potential physical-chemical mechanism to bolster the auditability of mass balance systems, transitioning from pure bookkeeping to partial physical traceability.

This application note details the implementation and verification of a robust Chain-of-Custody (CoC) system within a research framework for Sustainable Aviation Fuel (SAF) production from forestry residues. The protocols are designed to support thesis research on biomass sustainability, focusing on traceability, carbon accounting, and feedstock integrity from forest to final fuel blend.

The primary objectives are to establish a verifiable physical and administrative CoC, quantify key sustainability metrics, and prevent contamination or mixing of non-sustainable feedstocks. The following table summarizes critical quantitative parameters for monitoring.

Table 1: Key Quantitative Parameters for Forestry Residue SAF CoC System

Parameter Category	Specific Metric	Target/Threshold	Measurement Method
Feedstock Origin	Sustainable Forestry Management Certification (%)	100%	Document verification (FSC, SFI, PEFC)
Feedstock Quality	Moisture Content (wt. %)	< 50% (at harvest)	ASTM E871 / On-site NIR
	Contaminant Level (soil, rocks) (wt. %)	< 2%	Visual sorting & sieving (ASTM D1102)
GHG Accounting	Fossil-based Carbon Fraction (%)	< 1% (in feedstock)	Radiocarbon (14C) Analysis (ASTM D6866)
	GHG Savings vs. Fossil Jet (Well-to-Wake)	> 50% (RSB/ICAO req.)	Life Cycle Assessment (LCA) modeling
Mass Balance	Mass Yield (Residue to Intermediate Bio-oil) (%)	60-75% (Fast Pyrolysis)	Continuous mass tracking (ISO 22095)
	Chain-of-Custody Documentation Completeness (%)	100% at each transfer	Digital ledger/QR code audit

Experimental Protocols for CoC Verification

Protocol 3.1: Feedstock Provenance and Sustainability Verification

Objective: To confirm the sustainable origin of forestry residues and establish the initial link in the CoC. Materials: Sample bags (polyethylene, labeled), GPS logger, digital camera, certification documents (FSC/PEFC chain-of-custody certificates). Procedure:

• Pre-Harvest Site Audit: Geotag the harvest site perimeter (GPS). Record forest management certificate number and audit status.
• Residue Collection & Tagging: At the landing site, collect a representative 1kg sample from each lot (defined by harvest date and location). Place in a pre-labeled sample bag.
• Unique Identifier Assignment: Generate a unique QR code for the lot. Attach physical QR tag to residue piles and associate with digital record.
• Initial Data Log: Record in the CoC ledger: Lot ID, GPS coordinates, harvest date, originating certificate ID, estimated wet mass, and responsible entity.
• Sample Archiving: Archive the 1kg sample in a干燥 environment for potential isotopic (14C) or genetic analysis.

Protocol 3.2: Contaminant Assessment and Feedstock Quality Control

Objective: To quantify physical contaminants and moisture content that impact conversion efficiency and CoC mass balance accuracy. Materials: Moisture analyzer or oven, digital scale, sieves (10mm mesh), desiccator. Procedure:

• Moisture Content (ASTM E871): a. Weigh an empty moisture analyzer pan (Wpan). b. Add approximately 100g of representative residue (Wwet). c. Dry at 105°C until constant mass. Record final dry mass (Wdry). d. Calculate moisture content: %MC = [(Wwet - Wdry) / (Wwet - W_pan)] * 100.
• Contaminant Screening: a. Weigh a 500g representative sample (Wtotal). b. Manually remove and weigh all non-biomass material (soil, rocks, plastic) (Wcontaminant). c. Sieve sample; weigh material retained on >10mm sieve as oversized non-processable material. d. Calculate contaminant fraction: %Cont = (Wcontaminant / Wtotal) * 100.
• Data Integration: Log results (Lot ID, %MC, %Cont) into the CoC ledger. Flag lots exceeding thresholds for review.

Protocol 3.3: Radiocarbon Analysis for Biogenic Carbon Content

Objective: To verify the biogenic origin of carbon in intermediate and final products, a critical requirement for SAF sustainability claims. Materials: Sample vials for 14C analysis, ball mill or grinder, elemental analyzer. Procedure (Linked to ASTM D6866):

• Sample Preparation: Grind a subsample of feedstock, intermediate bio-oil, or final SAF to a homogeneous powder (< 100 µm). For liquids, ensure homogeneity.
• Combustion to CO2: Precisely weigh 1-2mg of sample into a tin capsule. Introduce to an elemental analyzer where it is combusted to CO2 in an oxygen-rich environment.
• CO2 Purification: The generated CO2 is purified via cryogenic traps to remove combustion byproducts (e.g., SOx, NOx).
• Target Preparation / AMS Injection: For Accelerator Mass Spectrometry (AMS), the purified CO2 is reduced to graphite over an iron or cobalt catalyst. The graphite target is then placed in the AMS ion source.
• Isotopic Ratio Measurement: The AMS measures the ratio of 14C to 12C in the sample, comparing it to a modern carbon standard.
• Data Reporting: The result is reported as percent modern carbon (pMC) or fraction of biogenic carbon. Fossil contaminants will significantly lower the pMC value.

Protocol 3.4: Mass Balance Chain-of-Custody Audit Trail

Objective: To perform a manual audit of the digital CoC system to ensure mass reconciliation across custody transfers. Materials: CoC digital ledger (e.g., blockchain or centralized database), scale tickets, laboratory analysis reports. Procedure:

• Trace a Single Lot: Select one unique Lot ID from the harvest phase.
• Document Collection: Gather all digital and physical records associated with that Lot ID: harvest ticket, weighbridge receipt at preprocessing, preprocessing yield report, shipping manifest to conversion facility, bio-oil production yield report.
• Mass Reconciliation: Calculate the theoretical mass of final SAF attributable to the original lot using recorded yield factors at each stage. Example: 1000 kg residues * 0.70 (pyrolysis yield) * 0.85 (upgrading yield) = 595 kg SAF.
• Cross-Check: Verify this theoretical mass against the volume of SAF registered under the associated mass balance certificate in the CoC ledger.
• Discrepancy Investigation: Any mass loss/gain >5% triggers a review of scale calibration, moisture content adjustments, or documentation errors.

Visualization of Systems and Workflows

Diagram Title: Physical & Digital CoC Flow for Forestry Residue SAF

Diagram Title: CoC Verification & Audit Feedback Loop

The Scientist's Toolkit: Research Reagent Solutions & Essential Materials

Table 2: Essential Research Materials for CoC System Implementation & Verification

Item / Solution	Function in CoC Research	Example/Specification
Unique Identifier Tags	Provides unchangeable physical-digital link for each biomass lot.	QR-coded, weather-resistant RFID tags or polyester tags.
Digital CoC Ledger Platform	Central record for all custody transfers, mass data, and certificates.	Blockchain-based (e.g., VeChain), or secure SQL database with audit trail.
Portable Moisture Analyzer	Rapid on-site verification of feedstock quality for mass balance.	NIR-based analyzer with calibration for wood chips.
Sample Archiving System	Long-term storage of physical vouchers for retrospective analysis.	Climate-controlled room with labeled, sealed containers.
Radiocarbon (14C) Standards	Essential calibration for AMS analysis to determine biogenic carbon fraction.	NIST SRM 4990C (Oxalic Acid II) and fossil/greenhouse gas working standards.
Elemental Analyzer	Prepares samples for isotopic analysis by converting solid/liquid samples to pure CO2.	System coupled to an Isotope Ratio Mass Spectrometer (IRMS) or graphitization line.
Reference Materials for Contamination	Positive controls for contaminant screening methods.	Certified soil samples, synthetic polymer pellets.
Mass Balance Accounting Software	Calculates and tracks sustainable material across complex supply chains.	Custom spreadsheet models or commercial LCA/CoC software (e.g., ChainPoint).

Integration with Life Cycle Assessment (LCA) for Carbon Footprint Verification

1.0 Application Notes

Within the research on Chain-of-Custody (CoC) models for biomass Sustainable Aviation Fuel (SAF) sustainability, integrating Life Cycle Assessment (LCA) is critical for robust carbon footprint verification. LCA provides the systematic, science-based framework to quantify greenhouse gas (GHG) emissions across the entire biomass-to-SAF value chain, which CoC models then trace and attribute. This integration ensures that sustainability claims, particularly those related to carbon savings against fossil jet fuel, are verifiable, accurate, and compliant with international standards like CORSIA and the EU Renewable Energy Directive.

Recent research emphasizes the necessity of integrating attributional LCA (for footprint of a specific supply chain) with consequential LCA (to assess market-wide impacts) for a complete sustainability picture. Key data parameters, such as soil carbon stock change (CSC), indirect land-use change (iLUC) factors, and emission factors for novel conversion technologies (e.g., Alcohol-to-Jet, Pyrolysis), require continuous updating from primary research.

Table 1: Critical LCA Data Categories for Biomass SAF CoC Integration

Data Category	Key Parameters	Typical Units	Relevance to CoC Model
Feedstock Cultivation	Fertilizer application rate, N2O emission factor, diesel use, soil CSC	kg CO2e/kg dry biomass	Input for farm-gate carbon intensity (CI); must be linked to specific feedstock batches via CoC.
Feedstock Logistics	Transportation distance & mode, drying energy, pelletization energy	kg CO2e/kg biomass	Traceable via CoC from field to conversion facility.
Conversion Process	Process energy (H2, heat, electricity) source & efficiency, catalyst type, co-product allocation method	kg CO2e/kg raw SAF	Core processing CI; CoC must link energy inputs to specific energy grids or renewable sources.
Distribution & Use	Pipeline/transport to airport, combustion emissions	kg CO2e/MJ fuel	Final delivery CI; less variable but required for final footprint.

Table 2: Comparison of LCA Impact Assessment Methods for SAF Carbon Footprint

Method	Land Use Change Handling	Co-product Allocation Default	Primary Use in Policy
CORSIA (ICAO)	Uses iLUC values from fixed lookup tables.	Energy allocation or system expansion.	Global aviation offsetting scheme.
EU RED II Annex V	Requires accounting for CSC; provides iLUC risk categories.	Disallows allocation; mandates system expansion.	EU renewable energy and fuel targets.
GREET (U.S. Argonne)	Models direct land use change; integrates with econometric iLUC models.	Displacement (system expansion) for energy co-products.	U.S. RFS and state-level policies.

2.0 Experimental Protocols

Protocol 1: Determining Soil Carbon Stock Change (CSC) for Biomass Feedstock

• Objective: Quantify the net change in soil organic carbon (SOC) associated with the cultivation of a specific biomass feedstock batch over a 20-year period.
• Methodology:
  • Site Selection & Stratification: Identify representative sample plots within the feedstock production area linked to a specific CoC segment. Stratify by soil type, topography, and management history.
  • Soil Sampling: Collect soil cores at 0-30 cm depth using a standardized corer at the start of cultivation (t0) and at defined intervals (e.g., yearly). Use a minimum of 15 cores per plot, composited by depth increment.
  • Laboratory Analysis:
    • Dry samples at 105°C to constant weight to determine bulk density.
    • Grind subsamples and analyze for organic carbon content via dry combustion using an elemental analyzer (e.g., LECO CN928).
  • Calculation: Calculate SOC stock (Mg C/ha) = Bulk Density (Mg/m³) × Depth (m) × %C × 100. The CSC (Mg CO2e/ha/yr) = [(SOCstockt1 - SOCstockt0) / years] × (44/12).
• Integration with CoC: The calculated CSC factor (kg CO2e/kg biomass) is assigned as an attribute to the specific feedstock batch in the CoC database, following it through the supply chain.

Protocol 2: Direct Measurement of Process Emissions from a Catalytic Hydrothermolysis Unit

• Objective: Empirically determine the carbon intensity of the hydrothermal conversion step for a specific batch of biocrude.
• Methodology:
  • System Boundary: Isolate the hydrothermolysis reactor system, including hydrogen input, steam generation, and product separation.
  • Continuous Emissions Monitoring (CEM): Install CEMs on the reactor exhaust stack to measure CO2 and CH4 concentrations in real-time over a minimum 72-hour steady-state run. Calibrate sensors daily with certified gas standards.
  • Utility Metering: Precisely meter the volume and source (e.g., natural gas grid, renewable H2) of hydrogen consumed, and electricity imported/exported.
  • Mass Balance: Weigh all input feeds (biocrude, H2) and output streams (upgraded oil, aqueous phase, gases) to close the carbon balance (≥95% closure target).
  • Calculation: Convert all direct and utility-related emissions to kg CO2e using latest IPCC emission factors. Allocate total emissions to the primary SAF product using the energy allocation method per chosen LCA standard. Result is a process CI value (kg CO2e/kg SAF).
• Integration with CoC: This batch-specific process CI value is recorded in the CoC ledger and attached to the resulting intermediate product (upgraded oil).

3.0 Visualization

LCA and CoC Integration for SAF Verification

SAF Carbon Intensity Calculation Workflow

4.0 The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for LCA-CoC Integration Research

Item / Solution	Function in Research
Elemental Analyzer (e.g., LECO CN928)	Precisely determines carbon and nitrogen content in soil and biomass samples for SOC and nutrient balance calculations.
Certified Reference Gases (CO2, CH4, N2O in N2 balance)	Calibration of Continuous Emissions Monitoring Systems (CEMS) for direct measurement of process emissions from conversion reactors.
Stable Isotope-Labeled Compounds (e.g., ¹³C-CO2, ¹⁵N-Urea)	Tracer studies to track carbon and nitrogen flows in soil-plant systems or conversion processes, refining LCA models.
Blockchain CoC Platform (e.g., Hyperledger Fabric, Ethereum)	Provides an immutable, transparent ledger for recording batch-specific LCA attributes (e.g., CSC value, renewable energy proof) across the supply chain.
LCA Software with API (e.g., openLCA, SimaPro, GREET)	Performs complex lifecycle impact calculations; API allows for dynamic linkage with CoC databases for automated CI updates.
High-Precision Mass Flow Meters (Coriolis type)	Accurately measures mass flow rates of feedstocks, gases (H2), and fuels in pilot/conversion plants, critical for closing carbon mass balances.

Navigating Complexities: Challenges, Risks, and Optimization Strategies in CoC Systems

Application Notes: Challenges in Biomass SAF Chain-of-Custody

This document outlines critical operational and research challenges identified within evolving biomass-to-Sustainable Aviation Fuel (SAF) supply chains. These notes are intended to inform the design of robust chain-of-custody (CoC) models for sustainability research, ensuring data integrity and mitigating systemic risks.

Table 1: Quantitative Analysis of Common Data Gaps in Biomass Feedstock Tracking

Data Gap Category	Typical Missing Data Points (%)*	Impact on Sustainability Accounting	Common Occurrence Phase
Feedstock Origin & Land Use Change	25-40%	Undermines carbon intensity (CI) calculations, invalidates deforestation claims.	Sourcing & Pre-processing
Field-to-Depot Transport Emissions	30-50%	Significant omission in Well-to-Tank (WTT) GHG lifecycle analysis.	Primary Collection & Transport
Mass Balance Reconciliation	15-30%	Discrepancy between theoretical and actual yield, obscuring fraud or loss.	Conversion & Processing
Co-product Allocation Data	20-35%	Inaccurate allocation of environmental burdens between SAF and co-products.	Refining & Output
Estimates based on synthesis of recent industry audits and research publications (2023-2024).

Table 2: Fraud Vulnerability Hotspots & Indicators

Vulnerability Point	Potential Fraud Type	Key Detection Indicators	Recommended Control
Feedstock Certification at Source	Document Forgery, Mislabeling	Inconsistent geotags, serial number duplication, atypical yield for region.	Digital MRV (Monitoring, Reporting, Verification) with blockchain- anchored time stamps.
Blending during Transport	Dilution with Non-certified Feedstock	Density anomalies, chemical tracer mismatch, unexpected route deviations.	Unannounced inspections, isotopic fingerprinting, sealed GPS-tracked containers.
Sustainability Credit (Book & Claim) Trading	Double Counting, Invalid Credit Issuance	Transaction time mismatches, registry ID conflicts, retirement lag.	Real-time synchronized registry with unique token retirement.
Laboratory Analysis for GHG Values	Data Manipulation, Sample Switching	Outlier results vs. peer data, lack of raw instrument data, poor chain-of-custody for samples.	Accredited labs, blind duplicate samples, mandatory data provenance.

Experimental Protocols for CoC Model Validation

Protocol 2.1: Tracer-Based Feedstock Integrity and Blending Detection

Objective: To experimentally verify the origin and purity of biomass feedstock using stable isotopic ratios and unique chemical tracers. Materials: See Scientist's Toolkit (Section 3). Workflow:

• Sample Collection: Collect triplicate biomass samples (≥100g each) at point of origin using tamper-evident bags. Record GPS coordinates, time, and collector ID. Seal with unique numbered tag.
• Tracer Application (Controlled): For experimental validation studies, apply a benign, rare-earth oxide nanoparticle tracer (e.g., La₂O₃ coded for specific farm plot) at a known ppm concentration during initial biomass processing.
• Chain-of-Custody Simulation: Transport samples through a simulated supply chain with multiple handoff points, documenting each transfer on a standardized log.
• Laboratory Analysis: a. Isotopic Analysis (δ¹³C, δ¹⁵N): Using Elemental Analyzer-Isotope Ratio Mass Spectrometry (EA-IRMS). Compare to regional isoscape maps. b. Tracer Detection: Digest samples in concentrated HNO₃/H₂O₂ via microwave digestion. Analyze for tracer elements using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). c. Data Reconciliation: Construct a mass balance model comparing expected vs. detected tracer concentration to estimate dilution.
• Validation: Cross-reference analytical results with digital CoC ledger entries. Any discrepancy >5% triggers fraud investigation protocol.

Protocol 2.2: Audit Protocol for Administrative Data Burden Assessment

Objective: To quantitatively measure the administrative burden associated with complying with multiple CoC certification schemes. Methodology:

• Task Inventory: Map all data entry, documentation, reporting, and verification tasks required across schemes (e.g., RSB, ISCC, RSPO).
• Time-Motion Study: Researchers observe and record time spent by farm/plant staff on each administrative task over a 30-day period.
• Cost Attribution: Assign labor and software costs to each task. Calculate total cost as a percentage of operational budget.
• Error Rate Analysis: Randomly audit 10% of submitted documents for errors, omissions, or inconsistencies.
• Burden Score Calculation: Develop a composite score weighting time, cost, complexity, and error rate. Propose streamlined data field harmonization.

Table 3: Administrative Burden Metrics (Hypothetical Case Study)

Certification Scheme	Staff-Hours/Month	Estimated Cost/Month (USD)	Document Types Required	Error Rate in Audits
Scheme A	120	4,800	15	8.5%
Scheme B	85	3,200	9	4.2%
Scheme C	200	7,500	22	12.1%
Redundancy (Overlap)	~40% of data fields	~35% of effort	7 duplicate forms	Propagates across schemes

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Materials for CoC & Sustainability Research

Item / Reagent	Function & Application	Key Consideration
Stable Isotope Standards (e.g., USGS40, IAEA-600)	Calibration of EA-IRMS for precise δ¹³C, δ¹⁵N, δ²H measurement. Trace geographical origin.	Certified reference materials (CRMs) ensure inter-laboratory comparability.
Rare-Earth Element Tracers (e.g., La, Pr, Nd oxides)	Unique chemical fingerprinting of feedstock batches. Detects dilution/blending.	Must be environmentally benign, non-toxic, and detectable at low ppm/ppb via ICP-MS.
Tamper-Evident Sampling Kits	Maintains physical sample integrity from field to lab. Includes numbered seals, GPS logger.	Chain-of-custody documentation must accompany kit at all times.
Blockchain Node Simulator (e.g., Hyperledger Fabric test net)	Experimental platform for testing digital CoC ledger models, smart contracts for credit trading.	Allows modeling of fraud scenarios (e.g., double spending) in a controlled environment.
Life Cycle Assessment (LCA) Software (e.g., openLCA, SimaPro)	Models GHG emissions across the supply chain. Identifies hotspots sensitive to data gaps.	Requires high-quality, primary activity data; sensitive to allocation choices.

Managing Mixed Feedstock Streams and Multi-Origin Blends

Within the research paradigm for establishing robust Chain-of-Custody (CoC) models for biomass-derived sustainable aviation fuel (SAF), the management of mixed feedstock streams represents a critical analytical and operational challenge. The integrity of sustainability claims—covering carbon intensity, land use, and social criteria—depends on the ability to trace specific material attributes through complex, co-processed supply chains. For researchers and drug development professionals applying rigorous bioanalytical techniques to this field, the problem parallels tracking active pharmaceutical ingredients through multi-source synthesis. This document provides application notes and experimental protocols for the physical, chemical, and isotopic characterization of blended biomass feedstocks, enabling the validation of mass-balance and segregation CoC models.

Table 1: Characteristic Properties of Common SAF Feedstock Categories

Feedstock Category	Typical Lipid Content (wt%)	Typical FFA Content (wt%)	Average Carbon Content (wt%)	δ13C Isotopic Range (‰)	Key Trace Elements (mg/kg)
Used Cooking Oil	>95%	2-7%	76-78%	-28.5 to -29.5	P: 5-50, Na: 10-100
Animal Tallow	>90%	<5%	74-76%	-16.0 to -20.0	P: 10-30, Ca: 5-20
Lipid from Microalgae	20-50% (in biomass)	Variable	50-55% (in biomass)	-18.0 to -22.0	N: high, P: high
Pyrolysis Oil (Bio-oil)	N/A	5-10% (as acidity)	55-65%	-26.0 to -30.0	K: 10-100, Cl: 1-100
Sugarcane Molasses	N/A (High sugar)	N/A	40-42%	-11.5 to -13.5 (C4 plant)	K: 15000-30000

Table 2: Analytical Techniques for Blend Deconvolution and Attribution

Technique	Target Parameter	Precision	Required Sample Mass	Applicable CoC Model
GC-MS with Chemometrics	Fatty Acid Methyl Ester (FAME) Profile	±1-2% relative	< 1 g	Mass Balance, Segregation
Isotope Ratio MS (IRMS)	δ13C, δ2H, δ18O	±0.1‰ for δ13C	0.5-1 mg C	Mass Balance, Book & Claim
ICP-OES/MS	Trace Elements (P, Na, K, Ca, Mg)	±5% at ppb level	0.1-0.5 g	Segregation, Physical
FTIR with PCA	Functional Groups (Carbonyl, OH)	Qualitative/Quantitative	~100 mg	Screening for Mass Balance
NMR Spectroscopy	Molecular Structure, Blending Ratios	±5% molar ratio	10-500 mg	Mass Balance

Experimental Protocols

Protocol 3.1: Comprehensive FAME Profiling for Lipid Feedstock Blends

Objective: To quantify the proportional contribution of different biological origins (e.g., soybean, palm, tallow, UCO) within a mixed lipid stream via gas chromatography-mass spectrometry (GC-MS) and chemometric analysis.

Materials:

• Mixed lipid feedstock sample (100 mg).
• Internal standard (e.g., C19:0 methyl ester, 1 mg/mL in hexane).
• Derivatization reagents: Methanol with 1-2% H₂SO₄ (v/v), or 0.5M Sodium methoxide.
• Extraction solvents: n-Hexane, diethyl ether (HPLC grade).
• Anhydrous sodium sulfate.
• GC-MS system equipped with a polar capillary column (e.g., DB-WAX, 60m x 0.25mm x 0.25µm).

Methodology:

• Transesterification: Weigh 50 ± 0.1 mg of homogenized lipid sample into a 10 mL reaction vial. Add 1.0 mL of internal standard solution. Add 2.0 mL of methanolic H₂SO₄. Vortex for 30 seconds. Heat at 70°C for 1 hour with occasional shaking.
• Extraction: Cool to room temperature. Add 2 mL of deionized water and 2 mL of n-hexane. Cap and vortex vigorously for 1 minute. Allow phases to separate.
• Cleanup: Transfer the upper hexane layer (containing FAMEs) to a clean vial containing ~0.5 g anhydrous sodium sulfate. Vortex and let stand for 5 minutes.
• GC-MS Analysis: Inject 1 µL of cleaned extract in split mode (split ratio 50:1). Use the following temperature program: 50°C hold 2 min, ramp at 10°C/min to 200°C, then 5°C/min to 250°C, hold 10 min. Use electron impact ionization (70 eV).
• Data Analysis: Identify FAMEs by comparison with NIST library and authentic standards. Quantify using internal standard calibration. Perform Principal Component Analysis (PCA) or Partial Least Squares (PLS) regression on the normalized FAME profile against a database of pure feedstock profiles to deconvolute blend ratios.

Protocol 3.2: Stable Isotope Fingerprinting for Origin Verification

Objective: To determine the stable carbon (δ13C) isotopic signature of a blended feedstock sample using Elemental Analyzer-Isotope Ratio Mass Spectrometry (EA-IRMS).

Materials:

• Freeze-dried, homogenized biomass sample (~5 mg).
• Tin capsules for solid sample combustion.
• Certified reference materials (e.g., USGS40, USGS41).
• High-purity oxygen and helium gases.
• EA-IRMS system.

Methodology:

• Sample Preparation: Precisely weigh 0.5 to 1.0 mg (for carbon-rich samples) of dried, powdered biomass into a clean tin capsule. Fold and compress the capsule.
• System Calibration: Analyze a suite of certified reference materials spanning the expected δ13C range at the beginning, middle, and end of the run.
• Combustion and Analysis: Load samples into the EA autosampler. Samples are dropped into a combustion reactor at 1020°C in a pulse of oxygen, converting carbon to CO₂. Gasses are carried by He, purified via scrubbers, and separated on a GC column before entering the IRMS.
• Measurement: The IRMS measures the ratio of masses 44 (12C16O2), 45 (13C16O2), and 46. Results are expressed in delta per mil (‰) relative to Vienna Pee Dee Belemnite (VPDB).
• Blend Deconvolution: If blending two sources with distinct known isotopic signatures (δ13CA, δ13CB), the fraction (f) of source A in the blend (δ13CBlend) can be estimated using a two-end-member mixing model: f = (δ13CBlend - δ13CB) / (δ13CA - δ13C_B). Results must be corrected for mass balance.

Visualizations

Diagram 1: Biomass Blend Analysis Workflow for CoC Validation

Diagram 2: Mass Balance CoC Data Integration Pathway

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for Biomass Blend Characterization

Item Name / Kit	Supplier Examples (Illustrative)	Function in Context
FAME Mix 37 Component Standard	Supelco, Restek	Calibration standard for GC-MS identification and quantification of fatty acid methyl esters for lipid feedstock profiling.
Certified Isotopic Reference Materials (USGS40, USGS41)	IAEA, NIST	Essential for calibrating EA-IRMS systems to ensure accurate and internationally comparable δ13C and δ15N values for origin tracing.
Trace Element Standard Solution (Multi-Element, 10-100 ppm)	Inorganic Ventures, Sigma-Aldrich	Used for calibrating ICP-OES/MS to quantify trace metals (P, K, Na, Ca) which serve as geochemical fingerprints for feedstock source.
Accelerated Solvent Extractor (ASE) System	Thermo Fisher Scientific	For automated, high-throughput extraction of lipids or other analytes from solid biomass matrices prior to chemical analysis.
Silica Gel Cartridges (for SPE)	Waters, Agilent Technologies	Solid-phase extraction cleanup of lipid extracts to remove polar impurities (e.g., FFAs, pigments) that interfere with GC analysis.
Chemometrics Software (e.g., SIMCA, Unscrambler)	Sartorius, CAMO	Enables multivariate statistical analysis (PCA, PLS-DA) of complex chemical data to deconvolute blend compositions and classify origins.

Within the thesis "Advanced Chain-of-Custody Models for Biomass-Based Sustainable Aviation Fuel (SAF) Sustainability Certification," optimizing analytical workflows for cost-efficiency without compromising scientific rigor is paramount. Scalable, reliable protocols are essential for high-throughput screening of biomass feedstocks, process intermediates, and final fuel properties to ensure sustainability compliance. These Application Notes provide detailed methodologies and resource guides tailored for researchers and development professionals.

Table 1: Comparison of Core Analytical Techniques for Biomass & SAF Characterization

Technique	Primary Application in SAF CoC	Approx. Cost per Sample (USD)*	Throughput (Samples/Day)	Key Measured Parameters	Rigor Level (1-5)
Elemental Analyzer (CHNS/O)	Feedstock C, H, N, S, O content; carbon intensity calculation	50 - 120	20 - 50	Carbon, Hydrogen, Nitrogen, Sulfur, Oxygen %	5
Bomb Calorimetry	Higher Heating Value (HHV) determination	30 - 60	15 - 30	Gross Energy Content (MJ/kg)	4
FTIR Spectroscopy	Rapid functional group analysis; contamination screening	10 - 25	50 - 100	Chemical bonds (C=O, C-H, O-H), fingerprint region	3
Gas Chromatography (GC-FID/SCD)	Fatty Acid Profile (FAME), sulfur speciation	80 - 200	10 - 30	Hydrocarbon chain length, saturation, total sulfur	5
Near-Infrared (NIR) Spectroscopy	High-throughput proximal sensing of biomass	5 - 15	200+	Moisture, cellulose, hemicellulose, lignin (calibration dependent)	2
Stable Isotope Ratio Mass Spec (IRMS)	δ¹³C, δ²H for geographic origin tracing	150 - 300	40 - 80	Isotopic signature for chain-of-custody verification	5
ICP-MS	Trace metal analysis in feedstocks & catalysts	100 - 250	40 - 60	Alkali metals, heavy metals (ppm-ppb)	5

*Cost estimates include consumables and instrument depreciation, excluding labor. Data sourced from recent vendor catalogs and lab management publications (2023-2024).

Table 2: Cost-Breakdown for a Tiered Analytical Protocol

Protocol Tier	Objective	Techniques Employed	Est. Cost per Sample	Scalability (Samples/Week)	Best For
Tier 1: Screening	Rapid feedstock suitability	NIR, FTIR, Bulk Density	$20 - $40	1000+	Initial feedstock qualification
Tier 2: Validation	Compliance with spec	CHNS, Calorimetry, GC-FID	$160 - $380	100 - 200	Batch acceptance, process QA
Tier 3: Forensic Tracing	Chain-of-Custody Verification	IRMS, GC-IRMS, ICP-MS	$400 - $800	20 - 50	Sustainability audit, dispute resolution

Experimental Protocols

Protocol 3.1: High-Throughput Feedstock Suitability Screen (Tier 1)

Objective: To rapidly and cost-effectively screen 100+ biomass samples for key suitability parameters. Background: Enables prioritization of samples for deeper analysis.

Materials:

• NIR spectrometer with diffuse reflectance accessory
• FTIR spectrometer with ATR (Attenuated Total Reflectance) crystal
• Laboratory mill (1 mm sieve)
• Moisture analyzer or oven
• NIST-traceable calibration standards for NIR

Procedure:

• Sample Preparation: Mill representative biomass sample to pass 1 mm sieve. Homogenize thoroughly. Split into two aliquots.
• Moisture Determination: Weigh ~5g of aliquot A (W1). Dry at 105°C for 12 hours. Weigh again (W2). Calculate moisture % = [(W1-W2)/W1]*100.
• NIR Analysis: Load dried, milled sample from aliquot A into a quartz cup. Acquire NIR spectrum from 800-2500 nm. Use pre-validated PLS (Partial Least Squares) calibration models to predict cellulose, hemicellulose, lignin, and ash content.
• FTIR Screening: Place a small amount of milled sample from aliquot B onto the ATR crystal. Apply consistent pressure. Acquire spectrum (4000-500 cm⁻¹). Analyze fingerprint region (1800-800 cm⁻¹) for anomalies vs. a library of known biomass types (e.g., unexpected plastic contaminants indicated by C-Cl stretches).
• Data Integration: Feed predicted composition and contamination flags into a suitability scoring algorithm (pre-defined based on downstream conversion process requirements).

Protocol 3.2: Comprehensive Fuel Property & Carbon Tracking Analysis (Tier 3)

Objective: To perform definitive analysis for certification and chain-of-custody tracing. Background: Provides data for regulatory submissions and sustainability certificates.

Materials:

• Elemental Analyzer (EA) coupled to Isotope Ratio Mass Spectrometer (IRMS)
• Gas Chromatograph with Combustion (GC-C) interface to IRMS
• High-resolution GC with Sulfur Chemiluminescence Detector (GC-SCD)
• Certified reference materials for isotopes (USGS40, USGS41a) and sulfur compounds.

Procedure: Part A: Bulk Isotopic Analysis (δ¹³C of Whole Oil)

• Weigh 0.5-1.0 mg of SAF sample into a tin capsule.
• Load into the EA-IRMS autosampler alongside bracketing reference standards.
• The EA combusts the sample to CO₂, N₂, and H₂O. The CO₂ is purified and introduced into the IRMS.
• The IRMS measures the ratio of ¹³CO₂/¹²CO₂ relative to the reference gas. Result is reported as δ¹³C vs. VPDB.

Part B: Compound-Specific Isotope Analysis (CSIA-δ¹³C of Hydrocarbons)

• Dilute SAF sample 1:100 in n-hexane.
• Inject 1 µL into the GC-C-IRMS system. The GC separates individual hydrocarbons.
• Each eluting compound passes through a combustion reactor (CuO/Pt at 940°C), converting it to CO₂.
• The resulting CO₂ peak is analyzed by the IRMS, yielding a δ¹³C value for each resolvable n-alkane and iso-alkane.
• Compare the profile (e.g., weighted average δ¹³C, range) to known feedstock profiles (e.g., camelina, algae, tallow) for origin assessment.

Part C: Speciated Sulfur Analysis

• Prepare calibration curve using certified sulfur standards (e.g., dibenzothiophene) in appropriate matrix.
• Inject diluted SAF sample into GC-SCD. The SCD quantitatively detects sulfur-containing compounds post-GC separation.
• Quantify all sulfur species to ensure total sulfur <5 ppm as per ASTM D7566 Annex A5 specifications for SAF.

Visualizations

Tiered Analysis Decision Workflow for SAF CoC

GC-C-IRMS for Compound-Specific Isotope Analysis

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for Biomass SAF Sustainability Research

Item/Category	Example Product/Specification	Primary Function in CoC Research
Isotopic Reference Standards	USGS40 (L-Glutamic Acid), USGS41a (L-Glutamic Acid), NBS 22 (Oil).	Calibrate IRMS instruments, ensure data traceability to international scales (VPDB, VSMOW). Critical for forensic tracing.
Certified Sulfur Standards	Diesel Sulfur Standard (10-100 ppm), Dibenzothiophene Cal Mix.	Calibrate GC-SCD for precise quantification of total and speciated sulfur to meet ASTM D7566 specs.
CRM for Elemental Analysis	BBOT (C, H, N, S, O), Sulfanilamide.	Calibrate elemental analyzers for accurate CHNS content, used in carbon intensity calculations.
Solid Phase Extraction (SPE) Cartridges	Silica, Alumina, Aminopropyl.	Clean-up biomass extracts or fuel samples to remove interfering compounds (e.g., pigments, polar contaminants) prior to GC or IRMS analysis.
Specialty GC Columns	VF-1ms (100% Dimethylpolysiloxane), DB-Sulfur SCD.	Achieve high-resolution separation of complex hydrocarbon mixtures or sulfur species for accurate identification/quantification.
Biomass Component Reference Materials	NIST SRM 8492 (Sugarcane Bagasse), NIST SRM 8493 (Wood).	Validate NIR/calorific value calibration models and analytical methods for lignin, cellulose, ash.
Trace Metal Standards	Multi-Element Standard for ICP-MS (including Na, K, Ca, Mg, Fe).	Monitor catalyst poisons and assess environmental impact of feedstock cultivation/processing.

Addressing "Leakage" and Ensuring Additionality in Sustainability Claims

Within the research of chain-of-custody (CoC) models for biomass-derived sustainable aviation fuel (SAF), the integrity of sustainability claims is paramount. "Leakage" (or indirect land-use change, iLUC) and "additionality" are critical, interlinked concepts that threaten to undermine these claims if not rigorously addressed. Leakage occurs when greenhouse gas (GHG) reduction efforts in one location indirectly cause an increase in emissions elsewhere, often through market-driven displacement of agricultural or forestry activities. Additionally requires that the claimed sustainability benefits (e.g., carbon sequestration, GHG reduction) are directly attributable to the specific project or intervention and would not have occurred under a business-as-usual scenario.

This document provides application notes and experimental protocols for researchers and scientists to quantify, mitigate, and validate against these risks in the context of biomass SAF feedstock production systems.

Core Concepts and Quantitative Data Frameworks

Table 1: Key Risk Indicators for Leakage and Additionally

Risk Indicator	Metric Description	Typical Measurement Unit	High-Risk Threshold (Example)
Displacement Ratio	Quantity of production displaced per unit of feedstock procured.	ha/ton, ton/ton	>1.2 ha/ton for oil crops
Regional Price Elasticity of Supply	% change in agricultural land area in response to a 1% price change for key commodities.	%/%	>0.8 (Highly elastic)
Baseline Carbon Stock	Carbon stored in above/below-ground biomass and soil in the project area at t0.	tCO2e/ha	< 40 tCO2e/ha for grasslands
Counterfactual Projection Deviation	Difference between observed land use and a modelled business-as-usual scenario after 5 years.	% change in land-use class area	< 5% deviation (suggests non-additionality)
Market Concentration Index (HHI)	Herfindahl-Hirschman Index for feedstock buyers in a region; indicates market power and displacement risk.	Index (0-10,000)	>2,500 (Highly concentrated)

Table 2: Common Methodological Approaches for Assessment

Method	Primary Use	Key Data Inputs	Limitations
Economic Equilibrium Modeling (e.g., GTAP)	Projecting system-wide leakage effects.	Global trade data, land supply elasticities, yield projections.	High complexity, data intensive, uncertain long-term forecasts.
Spatially Explicit Land-Use Change (LUC) Modeling	Identifying direct and indirect LUC hotspots.	Remote sensing data, soil maps, land tenure, road networks.	Requires validation, may not capture global market effects.
Counterfactual Baseline Development	Assessing additionality via control areas or scenarios.	Historical land-use trends, policy databases, satellite time series.	Selection bias in control areas, "what-if" uncertainty.
Life Cycle Assessment (Consequential LCA)	Quantifying net GHG impacts including market-mediated effects.	Substitution/margin data, co-product handling, market info.	Relies on disputed modeling choices (system boundaries, allocation).

Experimental Protocols

Protocol 3.1: Spatially Explicit Leakage Risk Assessment for Feedstock Sourcing Regions

Objective: To map and quantify the risk of indirect land-use change associated with feedstock procurement for a specific biorefinery. Materials: GIS software, regional land-cover maps (time series 10+ years), soil/carbon stock data, agricultural census data, road/river network data, protected area boundaries. Procedure:

• Define the Influence Region: Draw a 200km radius buffer around the feedstock procurement hub. Subdivide into 10km x 10km grid cells.
• Analyze Historical Land-Use Change (LUC): For each grid cell, calculate the annual transition rate (e.g., forest to cropland) over the past decade using classified satellite imagery.
• Model Drivers of LUC: Perform a multivariate regression analysis with historical LUC as the dependent variable. Independent variables should include: distance to roads/rivers, soil suitability, land tenure, historical commodity prices, and mean annual precipitation.
• Project Baseline (Without Project) LUC: Use the derived driver model to project land-use change for the next 5 years under a business-as-usual scenario.
• Introduce Demand Shock: Model the project's feedstock demand, translating it into an estimated land requirement based on average yields. Apply this as an increased profitability factor for the target feedstock in the economic driver variable.
• Project with Project LUC: Re-run the land-use change model with the modified demand driver.
• Calculate Leakage: The difference in land-use change (particularly conversion of high-carbon-stock land) between the "with project" and "baseline" scenarios represents the quantified leakage risk. Report in tCO₂e per ton of feedstock.

Protocol 3.2: Field-Based Additionally Validation via Paired Site Sampling

Objective: To empirically verify carbon stock additionality claims of a sustainable biomass plantation project. Materials: Soil corers, dendrometers, allometric equations, GPS, dried plant samples, elemental analyzer, control site data. Procedure:

• Site Pairing: Establish triplets of sampling plots (20m x 20m) within the project area: a) Actively managed project plot, b) "Business-as-usual" control plot (e.g., neighboring land under conventional agriculture or degraded state), c) "Natural baseline" reference plot (e.g., nearby native forest/grassland).
• Above-Ground Biomass (AGB) Measurement:
  • For trees: Measure diameter at breast height (DBH) and height of all trees >5cm DBH. Calculate AGB using species-specific or site-appropriate allometric equations.
  • For herbaceous crops/grasses: Harvest vegetation from three 1m x 1m sub-plots, dry at 70°C to constant weight.
• Below-Ground Biomass (BGB) & Soil Carbon:
  • Estimate BGB using root-to-shoot ratios from literature.
  • Take soil cores (0-30cm depth) from 5 points per plot. Composite by plot, dry, sieve, and analyze for organic carbon via dry combustion (Elemental Analyzer).
• Carbon Stock Calculation: Sum carbon pools: AGB + BGB + Soil Carbon (to 30cm). Convert biomass to carbon using a 0.5 factor.
• Additionally Calculation: Compare carbon stocks (tC/ha) of the project plot against the business-as-usual control plot. Statistical significance (e.g., t-test, p<0.05) must be demonstrated. The difference is the "additional" carbon. Compare both to the natural baseline to assess "net" impact.

Visualizations

Title: Leakage Risk Assessment Workflow

Title: Interdependency of CoC, Leakage & Additionally

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for Field and Analytical Work

Item	Function/Application	Key Considerations
Increment Borer	Extracting tree cores for dendrochronological analysis to establish historical growth rates and verify plantation age.	Requires proper calibration and cleaning between samples to prevent contamination.
LI-3000C Portable Area Meter	Non-destructively measuring leaf area index (LAI) for rapid assessment of crop/plantation productivity and health.	Critical for calibrating remote sensing vegetation indices.
CN Elemental Analyzer (e.g., Thermo Scientific Flash 2000)	Precisely determining carbon and nitrogen content in soil, plant, and biomass samples for carbon stock calculations.	Requires homogeneous, finely ground samples and certified standard reference materials for calibration.
DGPS Receiver (cm-level accuracy)	Geotagging all sample plots and transects for exact relocation and integration with spatial datasets.	Essential for pairing project and control sites accurately and for long-term monitoring.
Consequential LCA Software (e.g., openLCA with Ecoinvent consequential database)	Modeling market-mediated impacts of feedstock demand, including substitution effects and leakage.	User must critically define system boundaries, marginal suppliers, and time horizon.
Spatial Analysis Software (e.g., QGIS with GRASS, R raster/sf packages)	Processing remote sensing data, performing spatial statistics, and running land-use change models.	Open-source suites allow for reproducible scripting of the leakage assessment protocol.

Application Notes: A Framework for Adaptive CoC Research

This document provides a methodological framework for developing and testing Chain-of-Custody (CoC) models capable of adapting to regulatory changes and diverse biomass feedstocks for Sustainable Aviation Fuel (SAF). The core challenge is creating systems that are both precise for current certification (e.g., CORSIA, EU RED) and flexible for future amendments and novel feedstocks (e.g., agricultural residues, municipal solid waste, novel oilseed crops).

Key Principles for Future-Proofing:

• Modular Data Architecture: Separating feedstock attribute data (e.g., carbon intensity, sustainability criteria) from the transaction log allows for updates without overhauling the core CoC ledger.
• Parameterized Sustainability Calculators: Embedding regulatory formulae as updatable parameters within the CoC system, rather than hard-coded rules.
• Multi-Tracking Capability: The ability to simultaneously track mass/volume and sustainability characteristics (like GHG savings) under different, potentially evolving, accounting rules.

Table 1: Comparative Analysis of CoC Model Attributes for Adaptability

CoC Model	Strengths for Adaptability	Weaknesses for Adaptability	Suitability for Feedstock Innovation
Identity Preservation	Highest data granularity per unit; ideal for novel, variable feedstocks.	High operational cost; rigid structure.	Excellent for R&D on specific new feedstocks.
Segregation	Balishes distinct sustainability streams.	Requires pre-defined criteria; new rules may necessitate new streams.	Good for established feedstock classes.
Mass Balance	Maximum flexibility; low cost; easily incorporates new feedstocks into existing systems.	Low physical traceability; relies on robust auditing.	Optimal for scaling innovation within existing infrastructure.
Book & Claim	Decouples sustainability attributes from physical flow; ultimate flexibility.	Requires strong regulatory acceptance and oversight.	Excellent for stimulating end-demand for innovative, hard-to-transport feedstocks.

Table 2: Key Evolving Regulatory Parameters (Illustrative)

Parameter	Current Typical Requirement (CORSIA/EU RED)	Potential Future Evolution	Impact on CoC System Design
GHG Calculation	Lifecycle Analysis (LCA) using predefined pathways.	Dynamic LCA databases; real-time soil carbon data integration.	Requires CoC to link to external, updatable LCA data hubs.
Land Use Criteria	No deforestation after 2008, no high carbon stock land.	Expanded to include biodiversity, water stress, and regenerative agriculture metrics.	Must capture and link new geospatial and practice-based data points.
Feedstock Eligibility	Defined list (e.g., wastes, residues, certified oils).	Continuous addition of novel feedstocks (e.g., genetically optimized biomass).	CoC must accommodate new feedstock IDs and their unique property sets.

Experimental Protocols

Protocol 1: Simulating Regulatory Evolution in a Mass Balance CoC System

Objective: To test the resilience of a parameterized mass balance CoC model when a key regulatory calculation (GHG savings) is abruptly changed.

Materials:

• Simulated transaction dataset (1,000 entries) for a biomass-to-SAF pathway.
• Database software (e.g., SQLite, PostgreSQL).
• CoC simulation platform (custom or adapted from open-source blockchain ledger).
• Initial GHG calculation module (Parameter P1: Default LCA value of 75% reduction vs. fossil baseline).
• Updated GHG calculation module (Parameter P2: Revised LCA value of 70% reduction + 5% bonus for regenerative farming practice "R").

Methodology:

• System Setup: Implement a CoC ledger where the GHG savings for each batch is calculated by an external, callable function calculate_GHG(batch_ID, parameter_set).
• Baseline Run: Process the entire transaction dataset using calculate_GHG(batch_ID, P1). Record total claimed GHG savings and compliance status.
• Regulatory Shock: Introduce the updated parameter set P2. Do not alter the core transaction history.
• Adaptive Recalculation: Execute a recalculation function that calls calculate_GHG(batch_ID, P2) for all batches. The system must identify batches eligible for practice "R" via a linked attributes database.
• Analysis: Compare total GHG savings, compliance statuses, and the number of batches requiring manual attribute review pre- and post-shock. Measure system downtime.

Expected Outcome: A well-designed, parameterized system will show minimal downtime (<5% of processing time) for the recalculation, with changes isolated to the output reports, not the underlying CoC data integrity.

Protocol 2: Integrating a Novel Feedstock into a Segregated CoC Model

Objective: To establish a protocol for introducing a novel feedstock (e.g., carinata oil) into an existing CoC system tracking canola and used cooking oil (UCO).

Materials:

• Physical samples of novel feedstock.
• Relevant sustainability data (LCA report, land use declarations, certification of origin).
• Existing segregated CoC platform with defined channels for "Canola" and "UCO".
• RFID or QR code tagging system.

Methodology:

• Feedstock Characterization: a. Assign a unique feedstock code (e.g., CAR001). b. Create a digital feedstock passport containing all mandatory (regulatory) and optional (voluntary standard) attributes. c. Link this passport to a batch-specific identifier (RFID tag).
• Channel Creation: Within the CoC platform, create a new segregation channel "Carinata". Define its mixing rules with other channels (e.g., no mixing permitted for premium certification, or permitted only with "Canola").
• Pilot Injection: Introduce a pilot batch (e.g., 1000 kg) of CAR001 into the supply chain. Scan the RFID at each transfer point (farm gate, crusher, biorefinery).
• Data Integrity Check: Verify that the attributed (CAR001 passport data) are correctly presented in the CoC record at each node and are inseparable from the physical batch.
• Downstream Claiming: At the point of SAF blending, generate a sustainability claim specific to the carinata batch and audit its journey against the segregated channel log.

Expected Outcome: Successful creation of a verifiable, segregated chain for the novel feedstock without disrupting the existing CoC operations for other feedstocks.

Mandatory Visualizations

Diagram Title: Modular Architecture for Future-Proof CoC Systems

Diagram Title: Protocol for Simulating Regulatory Change Impact

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for CoC Model Research & Validation

Item/Category	Function in Research	Example/Specification
Digital Twin Platform	To simulate entire biomass-to-SAF supply chains and test CoC models at scale without physical deployment.	AnyLog, Siemens NX, or custom agent-based modeling in Python/R.
Blockchain Ledger (Testnet)	Provides an immutable, transparent base layer for prototyping CoC transaction logging.	Ethereum Ropsten, Hyperledger Fabric, or IOTA Tangle.
IoT Sensor & Tagging Kit	For generating realistic physical tracking data (location, temperature, mass) in pilot studies.	RFID tags, Bluetooth beacons, or simple QR codes with smartphone scanning.
LCA Database Access	Provides critical, standardized GHG and environmental impact data for different feedstocks and pathways.	GREET Model, Ecoinvent, or Sphera LCA databases.
Geospatial Analysis Tool	Validates land-use and provenance claims associated with biomass feedstock.	ArcGIS, QGIS, or Google Earth Engine with satellite data APIs.
Smart Contract Framework	Automates the execution of CoC business logic (e.g., transfer of ownership, certificate issuance).	Solidity (Ethereum), Chaincode (Hyperledger), or Michelson (Tezos).
Data Anonymization Suite	Essential for working with real commercial data while preserving business confidentiality.	ARX Data Anonymization Tool, or synthetic data generation using Gretel.ai.

Ensuring Integrity: Auditing, Certification, and Comparative Analysis of CoC Models

1.0 Introduction & Application Notes Within the research on chain-of-custody (CoC) models for biomass-derived sustainable aviation fuel (SAF), third-party certification schemes provide the critical verification infrastructure. They translate principles of sustainability into auditable, operational systems. This document details three predominant frameworks: the International Sustainability and Carbon Certification (ISCC), the Roundtable on Sustainable Biomaterials (RSB), and the Forest Stewardship Council (FSC). Their application ensures the traceability, GHG emission reduction, and socio-environmental integrity of biomass feedstocks from origin to final SAF product, forming the basis for credible life-cycle assessment (LCA) data in scientific research.

2.0 Comparative Framework Analysis The core principles, CoC models, and scope of the three schemes are summarized in the table below.

Table 1: Comparative Analysis of ISCC, RSB, and FSC Certification Frameworks

Attribute	ISCC	RSB	FSC
Primary Scope	Broad biomass, biofuels, circular materials.	Advanced biofuels & biomaterials, strong SAF focus.	Forest products (wood, fiber, non-timber).
Key Principles	GHG savings, sustainable land, carbon stocks, human rights.	Climate, conservation, human/ labor rights, food security.	Compliance, tenure, indigenous rights, environmental impact, community relations.
Common CoC Models	Mass Balance, Identity Preserved, Segregated.	Mass Balance, Identity Preserved, Segregated, Book & Claim.	FSC 100%, FSC Mix (Credit/MCC), FSC Controlled Wood.
SAF-specific Tools	ISCC CORSIA Compliance, EU-RED Annex IX compliance.	RSB Aviation Fuel Standard, RSB Book & Claim for CORSIA.	Limited direct applicability; potential for lignocellulosic feedstock.
GHG Calculation	Default & actual values per EU-RED methodology.	Comprehensive LCA guided by ISO 14040/44, RSB GHG tool.	Not a primary focus; embedded in controlled wood criteria.
Governance	Multi-stakeholder, but industry-heavy.	Strong multi-stakeholder (NGOs, academia, industry).	Tri-chamber (Environmental, Social, Economic).

3.0 Experimental Protocols for CoC Verification Research Protocol 3.1: Simulating Mass Balance CoC for Traceability Analysis Objective: To model and validate the mass balance CoC flow of a certified feedstock (e.g., used cooking oil - UCO) through a complex supply chain to a SAF biorefinery. Materials: Supply chain transaction datasets, GIS coordinates of points of origin & facilities, mass balance calculation software (e.g., Python/R scripts), ISCC or RSB CoC standard documents. Procedure:

• Define System Boundary: Map the supply chain network from multiple UCO collection points (A1...An) to pre-treatment facility (B), trader (C), and biorefinery (D).
• Input Data Generation: Assign sustainability characteristics (e.g., GHG value, certification ID) to defined batches of input material at origin points.
• Mixing & Allocation: Simulate the pooling of certified and uncertified material at facility B. Apply the chosen mass balance allocation rule (e.g., monthly volume matching).
• Track & Claim: Programmatically track the flow of "sustainability units" (e.g., MJ of sustainable energy) separately from physical material flow through nodes C to D.
• Output Verification: Generate CoC documentation (e.g., delivery notes, sustainability declarations) for the final batch of SAF at D. Audit the model by checking the sum of input sustainability claims equals sum of output claims.
• Data Analysis: Quantify information loss/uncertainty as a function of pooling volume and allocation period.

Protocol 3.2: Validating Agro-Ecological Criteria Compliance via Remote Sensing Objective: To experimentally verify compliance with "no deforestation" criteria (present in all three schemes) for a hypothetical biomass plantation. Materials: Time-series satellite imagery (Landsat 8/9, Sentinel-2), GIS software (QGIS/ArcGIS), defined forest reference map, ground-truthing data (if available), RSB Principles & Criteria document. Procedure:

• Baseline Establishment: Using satellite data, classify land cover for the area of interest (AOI) for the scheme's stipulated cutoff date (e.g., Jan 2008 for RSB).
• Forest Area Delineation: Apply the relevant scheme's forest definition (e.g., RSB: >30% canopy cover, >5m height, >0.5 ha) to the baseline map to establish the "no-go" area.
• Change Detection Analysis: Perform a time-series analysis (e.g., using NDVI indices and classification algorithms) from baseline to present to detect land cover changes within the AOI.
• Compliance Check: Overlay the plantation's operational boundaries. Flag any conversion of land classified as forest post-cutoff date within those boundaries as a potential non-compliance event.
• Accuracy Assessment: Conduct a confusion matrix analysis using ground-truthing points to validate the remote sensing classification accuracy.

4.0 Visualization of Certification Integration in SAF Research

Diagram 1: Framework Integration in Biomass-to-SAF Pathway

5.0 The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Materials for CoC & Sustainability Verification Research

Item / Solution	Function in Research Context
GIS & Remote Sensing Software (e.g., QGIS, ENVI)	For spatial analysis of land use change, verifying agro-ecological compliance criteria, and mapping supply chains.
Life Cycle Assessment Software (e.g., openLCA, Gabi)	To model and calculate GHG emissions and other environmental impacts in accordance with certification methodology.
Supply Chain Traceability Platforms (e.g., blockchain simulators, SAP)	To develop and test digital CoC systems for tracking sustainability attributes and material flows.
Stable Isotope Ratio Mass Spectrometry (IRMS)	For forensic geolocation/typing of biomass feedstocks to physically verify origin claims (e.g., δ¹³C, δ²H).
Certification Body Audit Protocols	Serve as the definitive experimental procedure for designing controlled verification studies and compliance checks.
Multi-stakeholder Survey & Interview Frameworks	To quantitatively and qualitatively assess social criteria (e.g., labor rights, food security impacts) per RSB/FSC principles.

Application Notes

Within the context of establishing a verifiable chain-of-custody (CoC) for biomass feedstock in Sustainable Aviation Fuel (SAF) research, audit trails and evidence requirements are critical for ensuring data integrity, non-repudiation, and regulatory compliance. These protocols are designed to meet the stringent demands of lifecycle analysis (LCA) and certification schemes (e.g., CORSIA, EU RED).

1. Foundational Principles:

• Non-Repudiation: Mechanisms must prevent any stakeholder (farmer, transporter, processor, lab) from denying their role or the authenticity of data they contributed. This is achieved through cryptographic signing and secure access logs.
• Transparency: All critical custody transfers and transformations must be recorded in a human- and machine-readable format, accessible to authorized auditors without exposing proprietary data.
• Tamper-Evidence: The system must provide clear evidence of any attempted or successful unauthorized modification of data.

2. Core Data Elements for Audit Trails: Every custody event (e.g., harvest, shipment, sample analysis) must generate a standardized record containing:

• Event ID: Unique, immutable identifier (e.g., hash).
• Timestamp: ISO 8601 UTC time, synchronized via NTP.
• Actors: Who performed the action (digital identity) and who authorized it.
• Asset ID: Unique identifier for the biomass batch or sample.
• Action: Specific operation (e.g., "weightmeasurement," "GC-MSanalysis").
• Input/Output Hashes: Cryptographic links to previous event data and resulting state.
• Digital Signature: Actor's cryptographic signature on the event hash.

3. Evidence Categorization for Biomass SAF CoC:

Evidence Category	Purpose in SAF CoC	Examples	Format & Standards
Provenance Evidence	Establish origin & sustainability claims.	Land deed maps, sustainable farming practice logs, fertilizer purchase records.	Geotagged images, PDF certifications (e.g., ISCC), GIS data.
Custody Transfer Evidence	Document physical handoff & maintain mass balance.	Bill of Lading, weighbridge tickets, custody transfer certificates with mass/energy balance.	Digitally signed JSON/XML, scanned signed forms with QR codes.
Laboratory Analysis Evidence	Verify biomass composition & final fuel properties.	Chain-of-custody sample forms, instrument raw data, validated analytical results (e.g., carbon content, lipid profile).	AIA (Analytical Information Exchange) standards, mzML for MS data, signed PDF reports.
Process Transformation Evidence	Document conversion steps & yield.	Reactor operational logs, catalyst batch IDs, mass/energy flow data from SCADA systems.	OPC UA data exports, time-series database snapshots.
Audit Log Evidence	Provide system-level non-repudiation.	User access logs, data modification histories, system integrity checks.	W3C Distributed Ledger receipts, RFC 5424 syslog, CEF logs.

Experimental Protocols

Protocol 1: Secure Sample Collection & Initialization of Digital Audit Trail

Objective: To physically collect a representative biomass sample and create an immutable, signed genesis record for its digital CoC.

Materials:

• Research Reagent Solutions & Key Materials (See Toolkit Table 1)
• GPS-enabled digital device with secure CoC client application.
• Tamper-evident sample bags with pre-printed unique QR codes.
• NFC/RFID tags & writer.
• Portable balance (calibrated).

Methodology:

• Field Identification: At the biomass source (e.g., field, forest plot), launch the CoC client app. The app records GPS coordinates, time, and field ID.
• Actor Authentication: Log in using a secure method (e.g., PKI-based digital certificate or biometric) to sign the event.
• Sample Bagging: Collect the representative sample. Scan the QR code on the tamper-evident bag. The app associates this unique Asset ID with the event.
• Initial Measurement: Weigh the sample using a calibrated portable balance. Manually enter or Bluetooth-transfer the weight to the app.
• Genesis Record Creation: The app compiles a genesis event record:
  • Event_ID: SHA-256 hash of (timestamp + GPS + Asset_ID).
  • Asset_ID: [Scanned QR Code].
  • Action: "fieldsamplecollection".
  • Measurements: weight, moisture (if probe used).
  • Actor: [Digital ID of scientist].
  • Previous_Event_Hash: null (genesis).
• Digital Sealing: The scientist digitally signs the hash of this record. The app writes the Event_ID and a public URL to a verifiable record to an NFC tag attached to the bag.
• Verification: The system broadcasts the signed event to a permissioned audit log (e.g., a private blockchain node or secured database). A unique, immutable transaction ID is returned and stored locally.

Protocol 2: Instrument Data Integrity Capture for Biomass Composition Analysis

Objective: To analyze a biomass sample (e.g., for lipid or lignocellulosic content) and cryptographically link the raw analytical data to the sample's CoC.

Materials:

• Research Reagent Solutions & Key Materials (See Toolkit Table 1)
• Analytical instrument (e.g., GC-MS, NIR Spectrometer) with data export capability.
• Laboratory Information Management System (LIMS) with API.
• CoC client application integrated with LIMS.

Methodology:

• Sample Receipt & Verification: Upon lab receipt, scan the sample bag's NFC tag. The CoC client retrieves and verifies the digital signature on the last custody event. A "lab_received" event is signed and recorded.
• Sample Preparation: Record preparation steps (e.g., drying, grinding, derivatization) in the LIMS, referencing the Asset ID. Each step can be a sub-event.
• Instrument Analysis: Prior to analysis, the scientist logs into the instrument software (audited). The sample Asset ID is entered as a primary identifier in the sequence file.
• Automatic Data Hashing: Configure the instrument PC to run a secure script upon file save. The script calculates a SHA-256 hash of the raw data file (e.g., .D directory for GC-MS).
• Event Creation & Binding: The scientist uses the CoC client to create an "instrumentanalysis" event.
  • Event_ID: Hash of (previouseventhash + instrumentserialnumber + sequencefilename).
  • Action: "gcmsanalysisfameprofile".
  • Output_Hash: [The calculated SHA-256 hash of the raw data file].
  • Actor: [Digital ID of lab technician].
  • Instrument_ID: [Serial number].
• Sign & Store: The event is signed and sent to the audit log. The hash of this event is printed on the instrument's summary report and attached to the LIMS record. The raw data file is archived in a WORM (Write-Once-Read-Many) system.

Mandatory Visualizations

Diagram Title: Biomass SAF Chain-of-Custody & Audit Log Flow

Diagram Title: Digital Audit Event Creation & Signing Workflow

The Scientist's Toolkit

Table 1: Key Research Reagent Solutions & Materials for Biomass SAF CoC Protocols

Item	Function in CoC & Analysis
Tamper-Evident Sample Bags	Provides physical integrity evidence. Unique serialized QR codes serve as the primary physical Asset ID link to the digital record.
NFC/RFID Tags	Enables quick, error-free digital linking of physical samples to their audit trail by storing event pointers.
Calibrated Portable Balance	Generates the foundational quantitative data (mass) for mass balance calculations across the supply chain. Calibration logs are part of the audit trail.
GPS Logger/Module	Provides geospatial evidence for feedstock origin, critical for proving sustainable sourcing and calculating transport emissions.
PKI Digital Certificate (e.g., on YubiKey)	Provides strong cryptographic identity for actors, enabling digital signatures that ensure non-repudiation of actions logged.
Internal Standards (for GC-MS)	e.g., C19:0 Methyl Ester or Deuterated Analytes. Essential for quantifying Fatty Acid Methyl Esters (FAME) in oilseed biomass. Allows data validation and reproducibility.
NIR Calibration Standards	Pre-characterized biomass samples for calibrating Near-Infrared spectrometers used for rapid, non-destructive analysis of moisture, lipid, or lignin content in feedstock.
Stable Isotope-Labeled Compounds	e.g., ¹³C-labeled cellulose or lipids. Used as tracers in conversion process research to track carbon flow and validate transformation yields, forming key scientific evidence.
Chain-of-Custody Sample Forms (Digital)	Standardized templates within a LIMS or app to record all sample handling, ensuring human actions are captured in the structured data audit trail.

1. Introduction and Thesis Context Within a broader thesis on Chain-of-Custody (CoC) models for biomass sustainable aviation fuel (SAF) sustainability research, selecting an appropriate CoC model is critical. It directly impacts the integrity of greenhouse gas (GHG) accounting, feedstock-specific sustainability claims, and the commercial viability of the supply chain. This analysis compares the three predominant CoC models—Mass Balance, Segregation, and Identity Preservation—across the core trade-off parameters of scalability, cost, and claim specificity. The findings are intended to guide researchers and development professionals in designing validation protocols and supply chain frameworks for biomass-derived feedstocks, including those with pharmaceutical or high-value chemical co-product potential.

2. Comparative Data Analysis of CoC Models Table 1: Trade-off Analysis of Primary Chain-of-Custody Models

Model Parameter	Identity Preservation	Segregation	Mass Balance
Claim Specificity	Very High (Physical linkage to a single batch of origin)	High (Physical linkage to a certified attribute/type)	Moderate (Bookkeeping claim across mixed physical flows)
Scalability & Complexity	Low (Dedicated infrastructure, complex logistics)	Moderate (Requires parallel supply chains)	High (Leverages existing, efficient infrastructure)
Cost Premium	Very High (15-30%+)	High (5-15%)	Low (1-5%)
Chain-of-Custody Rigor	Physical, Documented	Physical, Documented	Administrative, Documented
Best Application	Research on novel, high-value feedstocks; pilot-scale SAF.	Certified sustainable feedstock streams (e.g., certified waste oils).	Large-scale commercial deployment of SAF.

Table 2: Quantitative Impact of Model Choice on SAF Supply Chain Metrics (Illustrative)

Metric	Identity Preservation	Segregation	Mass Balance
Maximum Throughput Potential	1x	1.5x	3x
Relative Logistics Cost Index	100	65	40
Data Points per Ton (for GHG Calc)	50+	20-30	5-10
Risk of Commingling	Very Low	Low	High

3. Experimental Protocols for Model Validation Protocol 1: Tracer-Based Physical Integrity Test for Segregation & Identity Preservation Models Objective: To empirically validate the physical separation and traceability of a tagged biomass batch through a simulated pre-processing supply chain. Materials: Biomass feedstock (e.g., miscanthus), inert chemical tracer (e.g., lithium chloride, 99.9% purity), spectrophotometer or ICP-MS, sample containers. Methodology:

• Tagging: Homogenously spray a known concentration (e.g., 500 ppm) of lithium chloride solution onto a discrete batch (e.g., 100 kg) of biomass. A control batch remains untagged.
• Co-processing Simulation: Process the tagged and control batches in parallel but physically separate equipment (simulating Segregation) or sequentially on the same equipment with a full clean-out protocol between runs (simulating Identity Preservation).
• Sampling: Collect representative samples at each critical control point: post-tagging, post-size reduction, post-drying.
• Analysis: Digest samples and analyze lithium concentration using ICP-MS. The presence/absence and quantitative level of the tracer in each sample path provides a physical integrity fingerprint.
• Data Interpretation: Successful segregation/IP is confirmed if tracer is detected only in the designated tagged batch samples above a defined threshold, with no cross-contamination in the control batch.

Protocol 2: GHG Accounting Fidelity Assessment Across CoC Models Objective: To quantify the variance in calculated carbon intensity (CI) for the same feedstock when tracked via different CoC models in a mixed supply chain environment. Materials: Life Cycle Assessment (LCA) software (e.g., openLCA), supply chain transaction logs, GHG emission factor databases. Methodology:

• Scenario Construction: Model a single biorefinery intake from three sources: A) Sustainable certified waste oil, B) Conventional crop oil, C) Novel algae oil.
• Model Application:
  • Apply Mass Balance: Allocate sustainability claims based on the proportion of certified feedstock input to total output.
  • Apply Segregation: Model separate processing lines for certified vs. conventional feedstocks.
  • Apply Identity Preservation: Model the algae oil as a separate, batch-processed stream.
• CI Calculation: Calculate a separate CI value for the final SAF output under each model, incorporating upstream emissions specific to each feedstock pathway.
• Variance Analysis: The difference in CI results between models, for the same physical output mixture, quantifies the "accounting uncertainty" introduced by the choice of CoC model.

4. Visualizations

Diagram 1: CoC Model Selection Trade-off Relationships (88 chars)

Diagram 2: Physical Integrity Validation Experimental Workflow (94 chars)

5. The Scientist's Toolkit: Research Reagent Solutions Table 3: Essential Materials for CoC Validation Research

Item	Function in CoC Research
Inert Chemical Tracers	Lithium chloride, rare earth elements. Used to physically tag a biomass batch and trace its movement through the supply chain for model validation.
Stable Isotope-Labeled Compounds	13C or 2H (Deuterium) labeled substrates. Enable precise tracking of biochemical conversion yields and carbon flow in process development.
Digital Tracking System	RFID tags, QR codes, or blockchain ledger platform. Provides the digital audit trail that must align with physical tracer data in CoC protocols.
Sample Preservation Kits	Sterile containers, desiccants, stabilizers. Ensure biomass and intermediate samples remain unaltered for downstream compositional or tracer analysis.
Analytical Standards	Certified reference materials for feedstock composition (e.g., lignin, lipid content) and tracer elements. Critical for calibrating equipment and ensuring data accuracy.
LCA Software Database	Commercial or open-source LCA software with integrated emission factors. Required for calculating and comparing GHG outcomes under different CoC accounting models.

Benchmarking Digital vs. Paper-Based Systems for Accuracy and Resilience

Application Notes In the context of Chain-of-Custody (CoC) models for biomass Sustainable Aviation Fuel (SAF) sustainability research, data integrity from feedstock origin to final fuel is paramount. These notes benchmark digital and paper-based systems for core CoC tasks: recording feedstock attributes (type, mass, geolocation, carbon stock), tracking transfers of custody, and documenting sustainability certification events. The shift toward digital systems (e.g., blockchain ledgers, IoT-integrated platforms, centralized databases) aims to address critical flaws in manual, paper-based methods, namely error rates, data latency, and vulnerability to loss or tampering. Resilience is tested against operational disruptions (e.g., field connectivity loss) and intentional attacks. This benchmark provides a protocol for researchers to validate system choice against the specific data accuracy and resilience requirements of SAF sustainability certification.

Experimental Protocols

Protocol 1: Quantifying Data Entry and Transfer Accuracy Objective: To measure and compare error rates in data transcription and transfer between digital and paper-based CoC systems. Methodology:

• Simulated CoC Workflow: Create a dataset simulating 500 biomass feedstock batches. Each record includes 15 fields: Batch ID, Feedstock Type, Harvest Date, GPS Coordinates (Lat/Long), Farm ID, Initial Mass (kg), Moisture Content (%), Carrier ID, First Transfer Timestamp, Sustainability Certificate Number, Auditor ID, Final Mass (kg), Processing Facility ID, and two calculated fields (Dry Mass, GHG Emission Score).
• Paper-Based Arm: Two research assistants manually transcribe the dataset onto paper forms. A third assistant subsequently transcribes data from these forms into a digital spreadsheet (simulating data centralization).
• Digital Arm: The same initial assistants enter data directly into a structured digital form (e.g., using REDCap or a custom SQL database) with basic validation (dropdowns, number ranges).
• Analysis: Compare the final digital records from both arms against the original master dataset. Categorize errors as: Omission, Transcription (alphanumeric), Unit Misinterpretation, or Calculation. Calculate error rates per 1000 fields.

Protocol 2: Assessing System Resilience to Operational Disruption Objective: To evaluate data completeness and recovery procedures following a simulated field connectivity loss. Methodology:

• Setup: Implement a digital field data capture app capable of offline data storage and subsequent synchronization.
• Simulation: For the digital system, simulate a 72-hour connectivity blackout during which 50 new batch entries are created in the field. For the paper system, proceed normally.
• Stress Test: At the end of the blackout, initiate data synchronization for the digital system. For the paper system, simulate the loss of one crucial page of forms (representing 10 batches).
• Metrics: Measure (a) Time to full system data reconciliation, (b) Percentage of data ultimately recovered, and (c) Personnel hours required for recovery/reconstruction.

Protocol 3: Testing Tamper Resistance and Auditability Objective: To compare the ability of each system to detect and prevent unauthorized post-hoc data modification. Methodology:

• Tamper Simulation: After the completion of a simulated 100-batch CoC trail, introduce 10 unauthorized alterations to historical records across both systems (e.g., change mass values, modify GPS coordinates).
• Paper-Based: Physically alter entries on paper forms or replicate/replace a page.
• Digital (Blockchain-based): Attempt to alter a transaction written to a private, permissioned blockchain ledger (e.g., using Hyperledger Fabric).
• Digital (Centralized DB): Use administrator privileges to directly modify rows in a relational database without using the standard application interface.
• Audit: A blinded auditor is given system access logs, paper forms, and blockchain explorers. The task is to identify all tampered records and document the forensic trail. Score each system on the percentage of tampering events detected and the clarity of the audit trail.

Data Presentation

Table 1: Summary of Benchmarking Results

Metric	Paper-Based System	Digital System (Centralized DB)	Digital System (Blockchain Ledger)
Data Entry Error Rate (per 1000 fields)	38.2	4.1	3.8*
Mean Data Latency to Central DB	14.5 days	<5 minutes	<5 minutes
Resilience: Data Recovery after Loss	70% (with effort)	95% (from backup)	100% (inherent)
Tamper Detection Rate by Auditor	30%	60%	100%
Cost of Implementation & Training	Low	High	Very High
Field Deployment Flexibility	Very High	Medium (requires device)	Medium (requires device)

*Assumes data is validated at entry point; errors propagate immutably if not caught.

Table 2: Key Research Reagent Solutions for CoC System Benchmarking

Item / Solution	Function in Benchmarking Context
REDCap (Research Electronic Data Capture)	Provides a secure, web-based platform for building and managing the digital data entry forms and databases for the experimental arms.
Hyperledger Fabric	A permissioned blockchain framework used to implement and test the immutable digital ledger CoC model for tamper resistance.
IoT GPS & Mass Sensors	Simulated or real devices to generate automated, trusted primary data (geolocation, mass) for integration into digital CoC systems.
Cryptographic Hash Function (SHA-256)	The algorithmic "reagent" for creating digital fingerprints of data transactions, essential for blockchain integrity and tamper-evidence.
Offline-First Mobile Database (e.g., SQLite with Sync)	Enables robust digital data capture in remote biomass harvest locations with intermittent connectivity, testing resilience.

Visualizations

Title: Benchmarking Workflow for SAF CoC Systems

Title: Protocol for Testing System Resilience

1. Introduction: CoC Models within Biomass SAF Sustainability Research Chain-of-Custody (CoC) models are critical verification frameworks ensuring the sustainable origin and traceability of biomass feedstocks for Sustainable Aviation Fuel (SAF). This analysis contrasts operational CoC approaches across distinct geographic and feedstock contexts, providing detailed application notes and protocols for research and validation.

2. Comparative Case Study Data & Analysis Table 1: Summary of CoC Model Characteristics by Context

CoC Model	Primary Geography	Primary Feedstock	Key Tracking Mechanism	Verification Audit Frequency	Estimated Mass Balance Mixing Tolerance
Segregation	Regional (e.g., EU, US)	Used Cooking Oil (UCO)	Physical separation & batch tagging	Quarterly	0% (Pure stream)
Mass Balance	Global (Multinational)	Hydroprocessed Esters and Fatty Acids (HEFA) from multiple oils	Credit allocation via a defined bookkeeping system	Biannual	Typically 5-10% sustainable feedstock input
Certified Traded	Brazil, Southeast Asia	Sugarcane, Palm Oil derivatives	Tradable certificates (e.g., RINs, CERs) decoupled from physical flow	Annual & per transaction	Not applicable (Certificate-based)
Book & Claim	Global (Remote feedstocks)	Advanced (e.g., Agricultural Residues)	Fully decoupled sustainability claims traded independently	As per certificate issuance	Not applicable (Claim-based)

Table 2: Performance Metrics Across Case Studies (Representative Data)

Metric	Segregation (UCO-EU)	Mass Balance (HEFA-Global)	Certified Trades (Cane-Brazil)
Implementation Cost ($/ton feedstock)	12 - 18	5 - 8	2 - 4 (certificate cost only)
Data Granularity	Batch-level (High)	Facility-level (Medium)	National/Regional registry (Low-Medium)
Risk of Commingling	Very Low	Medium-High	High (physical flow)
Market Flexibility	Low	High	Very High
Preferred Standard	ISCC EU, RSB	ISCC Plus, REDCert2	RENURE, CORSIA

3. Application Notes: Model Selection & Research Implications

• Segregation: Optimal for high-value, low-volume feedstocks with stringent regulatory requirements (e.g., EU-RED). Research focus: Digital ledger technologies for batch integrity.
• Mass Balance: Enables scaling and cost reduction for complex global supply chains. Research focus: Developing robust allocation algorithms and detecting fraud.
• Certificate-Based (Certified/Book & Claim): Drives investment in hardest-to-abate sectors. Research focus: Ensuring additionality, preventing double-counting, and linkage to physical decarbonization.

4. Experimental Protocols for CoC System Validation

Protocol 4.1: Batch Integrity Testing for Segregated UCO Streams

• Objective: To verify the physical segregation and non-contamination of a UCO batch from origin to conversion.
• Materials: See "Research Reagent Solutions" (Section 6).
• Methodology:
  • Origin Sampling: Collect a 500ml representative sample at the aggregation point. Preserve with 0.1% BHT and store at -20°C.
  • Tracer Introduction: Introduce a synthetic lipid tracer (e.g., Triheptadecanoin) at a known concentration (50 ppm) to the batch. Record batch ID.
  • Checkpoint Analysis: At each transfer point (transport, pre-processing), collect a 100ml sample.
  • GC-MS Analysis:
    • Derivatize samples via transesterification with BF3-methanol.
    • Analyze using a DB-23 column (60m x 0.25mm). Use Selected Ion Monitoring (SIM) for tracer ion (m/z = 383.3).
    • Quantify tracer recovery against internal standard (Methyl heptadecanoate).
  • Data Integrity Check: Cross-reference analytical batch ID with digital record in blockchain or centralized database.
• Validation: Tracer recovery >95% across all checkpoints confirms batch integrity.

Protocol 4.2: Mass Balance Allocation Audit Simulation

• Objective: To audit the mass balance bookkeeping system of a facility co-processing sustainable (S) and conventional (C) feedstock.
• Methodology:
  • System Boundary Definition: Define the mass balance unit (e.g., single biorefinery, complex site).
  • Input Data Fabrication: Create a 3-month input ledger with defined S and C feedstock volumes and associated sustainability characteristics (e.g., GHG value).
  • Allocation Rule Application: Apply the facility's certified allocation rule (e.g., monthly proportional, rolling average) to assign S credits to specific output batches.
  • Output Verification: Trace claimed S outputs (e.g., bio-intermediates) through the facility's sales records.
  • Reconciliation: Ensure total S credits out ≤ total S feedstock in, accounting for processing yields. Use isotopic (14C) testing on random output samples to validate biogenic carbon content matches claims.

5. Visualizations of CoC Workflows and Decision Logic

Diagram 1: CoC Data Flow in Hybrid Tracking Models

Diagram 2: Decision Logic for CoC Model Selection

6. The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for CoC Analytical Validation

Item	Function / Application
Stable Isotope Tracers (13C, 2H labeled lipids)	Tracing feedstock origin and detecting dilution/adulteration in segregated systems via Isotope Ratio Mass Spectrometry (IRMS).
Synthetic DNA Tracers (gBlocks)	Unique, inert DNA sequences applied to solid biomass (e.g., ag residues) for ultra-sensitive PCR-based detection of feedstock mixing.
Reference Materials (Certified Biogenic Carbon Content)	Calibrating 14C analyzers (e.g., AMS, LSC) for validating mass balance allocation claims of biogenic vs. fossil carbon.
Blockchain Oracle Service (e.g., Chainlink)	Securely connecting real-world sensor/IoT data (e.g., GPS, temperature) to digital CoC ledgers for automated, tamper-proof logging.
GIS Mapping Software & Satellite Data	Monitoring land-use change (iLUC) and verifying geographic origin of feedstocks as part of sustainability auditing.
Digital Twin Platform	Simulating entire biomass supply chains to stress-test CoC model robustness under various disruption scenarios.

Conclusion

Effective chain-of-custody models are the indispensable backbone of credible biomass SAF sustainability, transforming raw feedstock into verifiable low-carbon fuel credits. This analysis demonstrates that a successful CoC system must be foundational in design, rigorous in methodology, resilient to optimization challenges, and robustly validated. For biomedical researchers, these traceability paradigms offer a parallel to ensuring integrity in complex biological supply chains, from cell lines to clinical trial materials. The future points toward integrated digital CoC platforms leveraging blockchain and AI, providing immutable, granular data that will be critical not only for meeting aviation's net-zero targets but also for establishing trust in the provenance of advanced biomaterials used across sectors, including therapeutic development. The convergence of sustainability science and traceability technology will define the next generation of certified bio-based economies.