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Record W1821760212 · doi:10.1002/cjce.22349

Preparation of Pt‐B/Al<sub>2</sub>O<sub>3</sub> amorphous alloy catalysts via microemulsion methods and application into hydrogenation of <i>m</i>‐chloronitrobenzene

2015· article· en· W1821760212 on OpenAlexvenueno aff
Feng Li, Bo Cao, Rui Ma, Hualin Song, Hua Song

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsnot available
FundersDepartment of Education, Heilongjiang ProvinceNational Natural Science Foundation of China
KeywordsMicroemulsionCatalysisAmmonium bromideSelected area diffractionCyclohexaneInorganic chemistryAqueous solutionMaterials scienceAmorphous solidSelectivityBorohydridePlatinumNuclear chemistrySodium borohydrideTransmission electron microscopyChemistryOrganic chemistryPulmonary surfactantNanotechnology

Abstract

fetched live from OpenAlex

Abstract Pt‐B/Al 2 O 3 catalysts were prepared through chemical reduction of platinum ions with borohydride in a water/oil (W/O) microemulsion system comprising cetyltrimethyl ammonium bromide (CTAB), n ‐butanol, cyclohexane, and H 2 PtCl 6 solution. Transmission electron microscope (TEM) and selected‐area electron diffraction (SAED) analyses show that the active Pt particles on the catalysts are uniformly distributed as a Pt‐B amorphous alloy. We studied the effects of microemulsion composition, reduction conditions, and preparation methods on the catalysts applied into m ‐chloronitrobenzene ( m ‐CNB) hydrogenation. We also investigated the kinetics of m ‐CNB hydrogenation. The catalyst prepared via a single‐microemulsion method exhibited much higher activity and even higher selectivity than that prepared with the aqueous solution method. Finally, we propose the mechanism for Pt‐B effects on the catalytic performance of Pt‐B/Al 2 O 3 in m ‐CNB hydrogenation.

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)0.001
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 score0.004
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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