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Record W4408070060 · doi:10.1002/cssc.202402509

Sustainable Graphite and Jet Fuel from Biorefinery Residue

2025· article· en· W4408070060 on OpenAlexaff
Lillian Lower, Steven M. Rowland, Michael Regula, Kristiina Iisa, Zachary A. Combs, Sunkyu Park, Tijmen Vries, Ton R. Vries, Mark R. Nimlos, William Joe Sagues

Bibliographic record

VenueChemSusChem · 2025
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsCarbon Engineering (Canada)
FundersBioenergy Technologies Office
KeywordsJet fuelAviation fuelRaw materialGraphiteWaste managementMaterials scienceFossil fuelRenewable energyFuel oilRenewable fuelsEnvironmental scienceChemistryOrganic chemistryMetallurgyEngineering

Abstract

fetched live from OpenAlex

Abstract Battery‐grade graphite and aviation fuel are traditionally produced from non‐renewable, fossil carbon feedstocks and result in substantial greenhouse gas emissions. Biomass holds exciting potential as a renewable and sustainable feedstock for the production of graphite and aviation fuel, but challenges exist including the necessity of a catalyst when producing graphite and low selectivity when producing aviation fuel. A process to convert a biomass‐derived feedstock into graphite without the use of a catalyst and fuels with high selectivity towards sustainable aviation fuel (SAF) is innovated. Heavy bio‐oil undergoes a conversion process similar to the commercial production of synthetic graphite including coking at 500 °C, calcination at 1000 °C, and graphitization at 2800 °C. The resulting biographite exhibits excellent performance in lithium‐ion battery configurations with specific capacity of ~330 mAh g −1 and a 96.8 % capacity rebound after high rate cycling. The liquid hydrocarbon co‐product from coking is suitable for hydrotreating into SAF. The aviation fuel fraction (70 wt % of the fuel produced) meets ASTM standards and is composed primarily of cycloalkanes (~80 wt %) which improves energy density compared to paraffins produced by other SAF pathways and may replace aromatics for elastomer swelling in traditional jet fuel with less soot production.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.185
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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