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Record W2594339151 · doi:10.1002/cjce.22822

Cu submicroparticles catalyzed reduction of 3‐nitro‐4‐methoxyacetanilide to 3‐amino‐4‐methoxyacetanilide in water

2017· article· en· W2594339151 on OpenAlexvenueno aff
Yonghai Feng, Hengbo Yin, Xiaobo Yan, Minjia Meng, Jianli Mi, Aili Wang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsnot available
FundersChina Postdoctoral Science FoundationSenior Talent Foundation of Jiangsu UniversityNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsCatalysisAcetanilideNitroChemistrySelectivityX-ray photoelectron spectroscopyMetalSelective catalytic reductionNuclear chemistryNitro compoundParticle sizeInorganic chemistryOrganic chemistryChemical engineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract 3‐nitro‐4‐methoxy acetanilide (NMA) was selectively reduced to 3‐amino‐4‐methoxy acetanilides (AMA) with metallic Cu submicroparticles as the H‐transfer catalyst in NaBH 4 solution. The metallic Cu submicroparticles were prepared by the wet chemical reduction method and characterized by SEM, XRD, and XPS techniques. The catalytic activity of Cu submicroparticles in the reduction of NMA was significantly affected by the particle sizes of Cu submicroparticles. The catalyst of Cu submicroparticles with an average particle size of 0.36 µm contributes to an AMA selectivity of 96.1 % at the NMA conversion of 100 % after reacting at 303 K for 40 min. The Cu submicroparticles show better catalytic activities than the Cu microparticles in the reduction of NMA to AMA probably due to the low reaction orders and the low activation energy of Cu submicroparticles for the reduction of NMA.

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)0.001
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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