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Record W4281650431 · doi:10.1002/cssc.202200328

Diverse Applications of Biomass‐Derived 5‐Hydroxymethylfurfural and Derivatives as Renewable Starting Materials

2022· review· en· W4281650431 on OpenAlexafffund
Franklin Chacón‐Huete, Cynthia Messina, Brandon Cigana, Pat Forgione

Bibliographic record

VenueChemSusChem · 2022
Typereview
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsConcordia UniversityCentre in Green Chemistry and Catalysis
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsBiomass (ecology)Raw materialRenewable energyFine chemicalChemical industryBiochemical engineering5-hydroxymethylfurfuralNanotechnologyGreen chemistryChemistryEnvironmental scienceOrganic chemistryMaterials scienceEngineeringCatalysisIonic liquid

Abstract

fetched live from OpenAlex

This Review summarizes recent efforts to capitalize on 5-hydroxymethylfurfural (HMF) and related furans as emerging building blocks for the synthesis of fine chemicals and materials, with a focus on advanced applications within medicinal and polymer chemistry, as well as nanomaterials. As with all chemical industries, these fields have historically relied heavily on petroleum-derived starting materials, an unsustainable and polluting feedstock. Encouragingly, the emergent chemical versatility of biomass-derived furans has been shown to facilitate derivatization towards valuable targets. Continued work on the synthetic manipulation of HMF, and related derivatives, for access to a wide range of target compounds and materials is crucial for further development. Increasingly, biomass-derived furans are being utilized for a wide range of chemical applications, the continuation of which is paramount to accelerate the paradigm shift towards a sustainable chemical industry.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.270
Teacher spread0.240 · 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

Citations62
Published2022
Admission routes2
Has abstractyes

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