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Record W4288489180 · doi:10.1007/s44246-022-00009-1

Lignin derived carbon materials: current status and future trends

2022· article· en· W4288489180 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCarbon Research · 2022
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsLigninCarbonizationCarbon fibersLignocellulosic biomassBiomass (ecology)Materials scienceBiochemical engineeringNanotechnologyOrganic chemistryChemistryEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract Developing novel techniques to convert lignin into sustainable chemicals and functional materials is a critical route toward the high-value utilization of lignocellulosic biomass. Lignin-derived carbon materials hold great promise for applications in energy and chemical engineering, catalysis and environmental remediation. In this review, the state-of-art sciences and technologies for controllable synthesis of lignin-derived carbon materials are summarized, pore structure engineering, crystalline engineering, and morphology controlling methodologies are thoroughly outlined and critically discussed. Green chemical engineering with cost-effectiveness and precise carbonization tuning microstructure are future research trends of lignin-derived carbon materials. Future research directions that could be employed to advance lignin-derived carbon materials toward commercial applications are then proposed.

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.

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 score0.083
Threshold uncertainty score0.571

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 integrity0.0000.001
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 head0.030
GPT teacher head0.306
Teacher spread0.276 · 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