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Record W1556900793 · doi:10.18331/brj2015.1.3.4

Efficient Conversion of Carbohydrates to 5-Hydroxymethylfurfural (HMF) Using ZrCl4 Catalyst in Nitromethane

2014· article· en· W1556900793 on OpenAlexvenueno aff
Raju S. Thombal, Vrushali H. Jadhav

Bibliographic record

VenueBiofuel Research Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsnot available
Fundersnot available
Keywords5-hydroxymethylfurfuralNitromethaneCatalysisChemistryOrganic chemistryCarbohydrateFood science

Abstract

fetched live from OpenAlex

Solvent nitromethane along with a variety of metal chloride and mineral acids as catalyst were studied for the synthesis of 5-Hydroxymethylfurfural (HMF), a key precursor in the formation of alternative fuel 2,5-dimethylfuran (DMF) and other value added chemicals. Reaction time, temperature and catalyst concentration were also systematically studied to achieve highest HMF formation. Among the carbohydrates studied for HMF synthesis, D-fructose and inulin were found particularly most productive yielding >70% and with 100% selectivity using ZrCl4 in nitromethane at 100 oC during 3h. Readily available reagents, solvents, and simple reaction conditions could mark this process promising for HMF formation from biomass.

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 score0.003

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 head0.036
GPT teacher head0.312
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

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

Citations8
Published2014
Admission routes1
Has abstractyes

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