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Record W4211176692 · doi:10.1002/cssc.202102535

Mechanochemical Transformations of Biomass into Functional Materials

2022· review· en· W4211176692 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemSusChem · 2022
Typereview
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsNational Research Council CanadaMcGill UniversityCentre in Green Chemistry and Catalysis
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMitacsMcGill UniversityFonds Québécois de la Recherche sur la Nature et les TechnologiesParks CanadaNational Research Council Sri LankaMarine Environmental Observation Prediction and Response Network
KeywordsBiomass (ecology)Biochemical engineeringHemicelluloseContext (archaeology)Environmentally friendlyMechanochemistryCelluloseBiopolymerRenewable energyEnvironmental scienceNanotechnologyMaterials scienceChemistryEngineeringOrganic chemistryEcology

Abstract

fetched live from OpenAlex

Biomass is one of the promising alternatives to petroleum-derived materials and plays a major role in our fight against climate change by providing renewable sources of chemicals and materials. Owing to its chemical and structural complexity, the transformation of biomass into value-added products requires a profound understanding of its composition at different scales and innovative methods such as combining physical and chemical processes. In this context, the use of mechanochemistry in biomass valorization is currently growing owing to its potentials as an efficient, sustainable, and environmentally friendly approach. This review highlights the latest advances in the transformation of biomass (i. e., chitin, cellulose, hemicellulose, lignin, and starch) to functional materials using mechanochemical-assisted methods. We focused here on the methodology of biomass processing, influencing factors, and resulting properties with an emphasis on achieving functional materials rather than breaking down the biopolymer chains into smaller molecules. Opportunities and limitations associated this methodology were discussed accordingly for future 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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.463
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.035
GPT teacher head0.252
Teacher spread0.217 · 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