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Record W4415617813 · doi:10.1002/cjce.70116

Selective hydrogenation of methyl benzoate to benzaldehyde over a manganese‐based catalyst with weak acidity centres

2025· article· en· W4415617813 on OpenAlexvenueno aff
Guofeng Wang, Yin Zhang, Chuanzhi Xu, Fang Wang, Kuai Yu, Mei Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsnot available
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsBenzaldehydeMethyl benzoateCatalysisSelectivityReaction mechanismHydrogenSpace velocity

Abstract

fetched live from OpenAlex

Abstract Benzaldehyde is a prominent product resulting from the catalytic hydrogenation of methyl benzoate, which is commonly utilized as a key intermediate in the chemical industry. In this work, Mn/Al 2 O 3 catalysts with different loading contents were synthesized through the equal volume impregnation method. A systematic investigation of the selective hydrogenation of methyl benzoate was conducted to delve into the influence of loading capacity and support type. The optimized catalyst demonstrated outstanding catalytic performance, achieving a conversion rate of 97.2% and a benzaldehyde selectivity of 77.2% under the reaction conditions of 430°C, atmospheric pressure, a hydrogen and methyl benzoate molar ratio of 36:1, and a GHSV of 0.2 h −1 . The reaction mechanism of methyl benzoate to benzaldehyde was investigated by NH 3 ‐TPD and EPR experiments. The findings indicated that the abundant weak acidity of Al 2 O 3 in the Mn/Al 2 O 3 catalyst facilitated reactant adsorption, while an appropriate level of oxygen vacancy in the active centre MnO significantly enhanced benzaldehyde selectivity.

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 score0.191
Threshold uncertainty score0.494

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 head0.004
GPT teacher head0.182
Teacher spread0.178 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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