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Record W2928918839 · doi:10.1039/c8cp07806h

DFT study of furfural conversion on a Re/Pt bimetallic surface: synergetic effect on the promotion of hydrodeoxygenation

2019· article· en· W2928918839 on OpenAlexafffund
He Dong, Ying Zheng, P. Hu

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrodeoxygenationFurfuralBimetallic stripChemistryCatalysisPromotion (chess)Organic chemistrySelectivity

Abstract

fetched live from OpenAlex

Density functional theory (DFT) calculations of furfural conversion were performed via hydrogenation and hydrodeoxygenation pathways on a bimetallic surface, namely, a thin oxygen-covered Re film on Pt(111). In the most stable adsorption conformation, the furyl ring component adsorbs on the Re edge site and the carbonyl oxygen also plays an important role in the adsorption strength. It was found that, while furfural conversion is kinetically favoured in the hydrogenation route to generate furfuryl alcohol, the hydrodeoxygenation mechanism to generate 2-methylfuran and water is thermodynamically favoured. Our results show that the hydrodeoxygenation product 2-methylfuran is achievable via the hydrogenation of furfural into hydroxyalkyl species, followed by C-OH bond cleavage, and successive hydrogenations of the furyl-CH intermediate. However, the production of 2-methylfuran is prohibited as the oxidised Re surface cannot accept further oxygen deposition, due to the oxygen-related species are difficult to remove in the form of water via hydrogenation. By comparing the results from the Re/Pt system to those on a monometallic flat Pt surface, we were able to demonstrate that incorporation of the oxophilic metals to active metals for hydrogenation could promote the hydrodeoxygenation route by reducing the barrier of C-O bond cleavage.

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 score0.058
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.206
Teacher spread0.199 · 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

Citations18
Published2019
Admission routes2
Has abstractyes

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