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Record W2965416787 · doi:10.1002/cjce.23613

Efficient manganese decorated cobalt based catalysts for hydrogenation of 5‐hydroxymethylfurfural (HMF) to 2,5‐dimethylfuran (DMF) biofuel

2019· article· en· W2965416787 on OpenAlexvenueno aff
Solmaz Akmaz, Merve Esen, Esra Sezgin, Serkan Naci Koç

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsnot available
Fundersnot available
KeywordsCatalysisCobaltX-ray photoelectron spectroscopyYield (engineering)ChemistryManganese5-hydroxymethylfurfuralNuclear chemistryMetalTransition metalMaterials scienceInorganic chemistryOrganic chemistryChemical engineeringMetallurgy

Abstract

fetched live from OpenAlex

Abstract 2,5‐dimethylfuran (DMF) is a promising compound in the production of biofuel with high‐quality properties. In this study, it is aimed to develop new efficient catalysts to synthesize DMF from 5‐hydroxymethylfurfural (HMF). Co, Mn/Co, and Ru/Co catalysts were prepared using the NaBH 4 reduction method. The catalysts were subjected to activity tests for the hydrogenation of HMF to DMF by changing the reaction parameters, such as temperature and time. Mn/Co catalysts prepared from metal precursors at various molar ratios of Mn/Co were found to be effective in hydrogenation reactions of HMF to DMF. A 91.8% DMF yield was achieved in the presence of a Mn/Co (50/50) catalyst without noble metal at 180°C for 4 hours. The Brunauer‐Emmet‐Teller (BET) method, x‐ray diffraction (XRD), x‐ray photoelectron spectroscopy (XPS), and induction coupled plasma mass spectroscopy (ICP‐MS) techniques were used to characterize the efficient Mn/Co catalyst.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.692

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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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 teacher head, 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

Citations14
Published2019
Admission routes1
Has abstractyes

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