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Record W3216770489 · doi:10.1021/acssuschemeng.1c04592

Plasmon-Enhanced 5-Hydroxymethylfurfural Production from the Photothermal Conversion of Cellulose in a Biphasic Medium

2021· article· en· W3216770489 on OpenAlexafffund
Aiguo Wang, Paula Bertón, Heng Zhao, Steven L. Bryant, Md Golam Kibria, Jinguang Hu

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversity of Calgary
FundersCanada First Research Excellence Fund
KeywordsCelluloseIonic liquidYield (engineering)CatalysisChemistryHydrolysisChemical engineeringBiomass (ecology)Surface plasmon resonanceLignocellulosic biomass5-hydroxymethylfurfuralOrganic chemistryMaterials scienceNanotechnologyNanoparticleComposite material

Abstract

fetched live from OpenAlex

The rigid structure of cellulose renders a challenge for the efficient transformation of cellulose into valuable chemicals under mild conditions. Here, we report a multifunctional catalyst (Ru/HY-SO 3 H) for the selective cellulose conversion to 5-hydroxymethylfurfural (HMF) with a yield of 48.4% in a biphasic medium (ionic liquid/methyl isobutyl ketone) at a relatively low temperature (120 °C) and light illumination. The high yield of HMF from direct cellulose transformation is attributed to the cooperative effect of Ru particles and acidic sites. Ru supported on zeolite Y generates the surface plasmon resonance effect to enhance the catalytic activity via light energy harvesting and provides more acid sites to enhance cellulose hydrolysis. The biphasic system is found to facilitate the production of HMF by protecting it from undergoing side reactions. Our work demonstrates a feasible and sustainable way to efficiently produce high value-added chemicals from biomass conversion driven by solar energy under mild conditions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.166
Teacher spread0.163 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations20
Published2021
Admission routes2
Has abstractyes

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Same venueACS Sustainable Chemistry & EngineeringSame topicCatalysis for Biomass ConversionFrench-language works237,207