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Record W4206072180 · doi:10.3389/fenrg.2021.758744

A Review: Depolymerization of Lignin to Generate High-Value Bio-Products: Opportunities, Challenges, and Prospects

2022· article· en· W4206072180 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

VenueFrontiers in Energy Research · 2022
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsAgriculture and Agri-Food CanadaDalhousie University
Fundersnot available
KeywordsDepolymerizationLigninBioproductsBiochemical engineeringRenewable energyRenewable resourceBiotechnologyChemistryOrganic chemistryBiofuelEngineeringBiology

Abstract

fetched live from OpenAlex

Lignin is identified as a promising candidate in renewable energy and bioproduct manufacturing due to its high abundance, polymeric structure, and biochemical properties of monomers. Thus, emerging opportunities exist in generating high-value small molecules from lignin through depolymerization. This review aims at providing an overview of the major technologies of lignin depolymerization. The feasibility of large-scale implementation of these technologies, including thermal, biological, and chemical depolymerizations, are discussed in relation to potential industrial applications. Lignin as a renewable alternative to petroleum-based chemicals has been well documented. This review attempts to emphasize potential applications of lignin-derived monomers and their derivatives as bioactives in food, natural health product, and pharmaceutical sectors. The critical review of the prospects and challenges of lignin-derived bioproducts reveals that the advancement of research and development is required to explore the applications of depolymerization of lignins to their full potential.

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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.666
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.262
Teacher spread0.212 · 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