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Record W4396815380 · doi:10.1002/cssc.202301767

Eugenol Hydrodeoxygenation Over Mixed Mo−W Carbides

2024· article· en· W4396815380 on OpenAlexafffund
Dahi Akmach, Chi‐Cong Tran, Tatjana Stevanović, Abdelkrim El Kadib, Serge Kaliaguine

Bibliographic record

VenueChemSusChem · 2024
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversité Euromed de Fès
KeywordsHydrodeoxygenationDeoxygenationCatalysisChemistrySelectivityCarbideEugenolIsoeugenolDepolymerizationMolybdenumBenzothiopheneOrganic chemistryTransition metalThiophene

Abstract

fetched live from OpenAlex

Abstract The modification of molybdenum carbide catalysts by another transition metal has raised an increasing research interest due to the significant improvement of catalyst activity in hydrodeoxygenation of lignin derivatives. At par with the commonly used Co and Ni that add a strong hydrogenation functionality, it was found that the addition of the more oxophilic W restricts ring hydrogenation while allowing the deoxygenation of oxygenated compounds and thus yielding higher selectivity toward the formation of non‐oxygenated aromatic compounds. The coexistence of Mo 2 C with W 2 C along with metallic W altered the electronic properties of Mo 2 C which resulted in an increase of catalyst active site density and facilitated further total eugenol deoxygenation. Propyl‐benzene selectivity of up to 83 % was reached at close to 100 % eugenol conversion. These findings will allow a better overview of the effect of different metal phases of mixed carbides on the catalyst performance and raise the prospect of optimizing catalyst design for a hydrodeoxygenation processing of lignin depolymerization products.

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 score0.235
Threshold uncertainty score0.535

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 head0.212
Teacher spread0.204 · 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

Citations11
Published2024
Admission routes2
Has abstractyes

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