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Record W4410486325 · doi:10.1016/j.gee.2025.05.007

Advancements in catalytic hydrogenation of nitrocyclohexane to cyclohexanone oxime

2025· article· en· W4410486325 on OpenAlexaff
Jinzhi Lu, Tongxin Song, Weiping Ding, Yan Zhu

Bibliographic record

VenueGreen Energy & Environment · 2025
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsMinistry of Education and Child Care
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsCyclohexanone oximeCyclohexanoneCatalysisOximeChemistryOrganic chemistryCatalytic hydrogenationCombinatorial chemistry

Abstract

fetched live from OpenAlex

Cyclohexanone oxime serves as a crucial intermediate in the synthesis of caprolactam, which is an essential precursor for manufacturing nylon fibers, high-performance engineering plastics, and specialized plastic films. Catalytic hydrogenation of nitrocyclohexane to cyclohexanone oxime has been documented to be an atom-economical, green and environmentally friendly process. In this review, we first introduce the current design rules of catalysts for catalytic hydrogenation of nitrocyclohexane in terms of both active metals and supports. Secondly, we discuss the influence of solvent effects on the cyclohexanone oxime from the nitrocyclohexane conversion. In addition, we concisely discuss typically proposed reaction pathways for the hydrogenation of nitrocyclohexane to produce cyclohexanone oxime. Finally, we provide our perspectives on some issues for catalytic conversion of nitrocyclohexane to cyclohexanone oxime in the future.

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)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.005
GPT teacher head0.209
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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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