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Record W4313422376 · doi:10.1139/cjc-2022-0167

Enhancing catalytic activity of V<sub>2</sub>O<sub>5</sub>/TiO<sub>2</sub> in H<sub>2</sub>S selective oxidation by modulating oxygen adsorption conditions

2022· article· en· W4313422376 on OpenAlexvenueno aff
Hyun Soo Lee, Jae Hwan Yang

Bibliographic record

VenueCanadian Journal of Chemistry · 2022
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsnot available
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsCatalysisChemistryOxygenX-ray photoelectron spectroscopyAdsorptionYield (engineering)SulfurCatalytic oxidationInorganic chemistryPhysical chemistryChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

This paper aimed to reveal the effects of chemisorbed oxygen species on selective catalytic oxidation applied for H 2 S removal. A sequence of thermal treatments were conducted on V 2 O 5 /TiO 2 samples to prepare catalysts that possess different distributions of oxygen species. Characterization by X-ray photoelectron spectroscopy enabled a distinction among three different chemisorbed oxygen species: O – , O 2 2– , and O 2 – . In addition, the dominance of the O – species, which is a very reactive oxygen species for catalytic oxidation reactions, was observed when the catalyst was exposed to 5% O 2 /N 2 gas at the low temperature of 30 °C. It was also found that the N400A30 with the highest ratio of O – species was superior to others at converting H 2 S into elemental sulfur over the whole range of temperatures: the best S yield of N400A30 was 85% at 130 °C. These results are promising, as thermal treatments for oxygen adsorption were adopted for the first time to improve the catalytic activity of H 2 S removal by modulating the proportion of highly reactive O – species.

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 categoriesMeta-epidemiology (narrow), Research integrity
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 score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.195
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 teacher head, not a consensus.

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
Published2022
Admission routes1
Has abstractyes

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