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Record W4324060326 · doi:10.3390/c9010031

Graphene-like Carbon Structure Synthesis from Biomass Pyrolysis: A Critical Review on Feedstock–Process–Properties Relationship

2023· review· en· W4324060326 on OpenAlexafffund
Farhan Chowdhury Asif, Gobinda C. Saha

Bibliographic record

VenueC – Journal of Carbon Research · 2023
Typereview
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaNew Brunswick Innovation Foundation
KeywordsPyrolysisRaw materialBiomass (ecology)Renewable energyGrapheneProcess (computing)MicrowaveProcess engineeringMaterials scienceCarbon fibersWaste managementNanotechnologyComputer scienceEngineeringOrganic chemistryChemistryComposite materialTelecommunications

Abstract

fetched live from OpenAlex

Biomass pyrolysis is a promising route for synthesizing graphene-like carbon (GLC) structures, potentially offering a cost-effective and renewable alternative to graphene. This review paper responds to the call for highlighting the state of the art in GLC materials design and synthesis from renewable biomass microwave pyrolysis. This paper includes an introduction of the microwave pyrolysis technology, information on feedstock variability and selection, discussion on the correlation between microwave pyrolysis process conditions and pyrolyzed product characteristics, and, more importantly, a section identifying any differences between pyrolyzing feedstock using the microwave pyrolysis method vs. conventional pyrolysis method. Furthermore, this work concludes by detailing the knowledge currently missing with the recommendation for future research/innovation directions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.249
GPT teacher head0.443
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations39
Published2023
Admission routes2
Has abstractyes

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