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Record W2770894404 · doi:10.1002/cjce.23085

NMSBA produced from NMST under the catalysis of supported H<sub>3</sub>PW<sub>12</sub>O<sub>40</sub> and Co/Mn/Br catalytic system

2017· article· en· W2770894404 on OpenAlexvenueno aff
Heng He, Zhou-wen Fang, Chao Zhang, Xiang‐li Long

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPolyoxometalates: Synthesis and Applications
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsCatalysisPhosphotungstic acidOxidizing agentChemistryYield (engineering)Nitric acidCobaltOxygenCalcinationNitrogenInorganic chemistryMolar ratioNuclear chemistryOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract The commercial production of 2‐nitro‐4‐methylsulphonylbenzoic acid (NMSBA) is oxidizing 2‐nitro‐4‐methylsulphonyltoluene (NMST) by nitric acid catalyzed with V2O5. A heterogeneous catalytic system composed of phosphotungstic acid supported on activated carbon, Co, Mn, and Br is used to catalyze the production of NMSBA from NMST by oxygen in this paper. The experiments show that the heterogeneous catalytic system composed of HPW@C, Co, Mn, and Br is capable of speeding up the oxidation of NMST to NMSBA by oxygen and acquiring a higher NMST oxidation rate and NMSBA yield than the homogeneous H3PW12O40/Co/Mn/Br catalytic system. The best HPW@C catalyst is obtained by supporting 0.0125 g/g H3PW12O40 on carbon followed by being calcined at 220 °C for 4 h under nitrogen atmosphere. The best heterogeneous catalytic system consists of 2.38 × 10−3 g/g HPW@C, 925 ppm Br, 236 ppm cobalt, and a Mn/Co molar ratio of 2.5. The usage of phosphotungstic acid in the heterogeneous system is diminished substantially.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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