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Record W4297199023 · doi:10.1002/cjce.24678

Supported <scp> TiO <sub>2</sub> </scp> /silica gel as an efficient and inexpensive catalyst for nonsolvent liquid‐phase oxidation of cyclohexylamine to cyclohexanone oxime

2022· article· en· W4297199023 on OpenAlexvenueno aff
Shuilin Liu, Ning Liu, Aiyang Li, Su‐Yun Wu, Jianghuan Luo, Xinde Tang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysis and Oxidation Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsCyclohexylamineCyclohexanone oximeCatalysisCyclohexanoneX-ray photoelectron spectroscopySilica gelChemistryFourier transform infrared spectroscopyCyclohexanolSelectivityMaterials scienceNuclear chemistryInorganic chemistryChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A benign approach is proposed for the highly efficient synthesis of cyclohexanone oxime through nonsolvent liquid‐phase oxidation of cyclohexylamine with dioxygen over supported TiO 2 /silica gel. The as‐prepared catalyst was characterized by Brunauer–Emmett–Teller (BET), Fourier‐transform infrared spectroscopy (FT‐IR), scanning electron microscope (SEM), X‐ray photoelectron spectroscopy (XPS), and X‐ray powder diffraction (XRD) analyses. The results indicate that titanium active sites could be grafted on silica gel supports as catalytic centres. Various parameters such as reaction time and reaction temperature were systematically optimized; the results showed that supported 20%TiO 2 /silica gel exhibited the best catalytic effect with 63.2% of amine conversion and 86.3% of selectivity to CHO. A possible pathway is proposed for cyclohexylamine oxidation over supported TiO 2 /silica gel. The method developed in this study using dioxygen as the oxidant and inexpensive TiO 2 /silica gel as an efficient catalyst has incredible industrial application potential for green synthesis of cyclohexanone oxime.

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)0.001
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 score0.080
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.222
Teacher spread0.214 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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