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Record W4321503520 · doi:10.1021/acs.est.2c09606

In Situ Reconstruction of Active Catalysis Sites Triggered by Chromium Immobilization for Sulfite Oxidation

2023· article· en· W4321503520 on OpenAlexaff
Tieyue Qi, Shuo Zhang, Jingzhao Zhang, Tong Li, Lei Xing, Zhimo Fang, Shanlong An, Zhongfei Xu, Huining Xiao, Lidong Wang

Bibliographic record

VenueEnvironmental Science & Technology · 2023
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of New Brunswick
FundersNatural Science Foundation of Hebei ProvinceCentral University Basic Research Fund of ChinaNational Natural Science Foundation of China
KeywordsFlue-gas desulfurizationSulfiteHexavalent chromiumChromiumCatalysisChemistryAdsorptionWastewaterBiocharSulfurWaste managementInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hexavalent chromium (Cr(VI)) is a highly toxic substance in wastewater, triggering grievous detriment to aquatic life and human health. Magnesium sulfite is spawned along with the desulfurization process in coal-fired power plants, which is usually disposed of as solid waste. Here, a “waste control by waste” method was proposed upon the redox of Cr(VI)–sulfite, in which highly toxic Cr(VI) is detoxicated and sequent enriched on a novel biochar-induced cobalt-based silica composite (BISC) due to the forced electron transfer from chromium to surface hydroxyl. The immobilized Cr on BISC gave rise to the reconstruction of catalytic active sites “Cr–O–Co”, which further enhance its performance in sulfite oxidation by elevating O 2 adsorption. As a result, the sulfite oxidation rate increased by 10 times compared with the non-catalysis benchmark together with the maximum chromium adsorption capacity being 120.3 mg/g. Therefore, this study provides a promising strategy to simultaneously control highly toxic Cr(VI) and sulfite, achieving high-grade sulfur resource recovery for wet magnesia desulfurization.

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)0.000
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 score0.038
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.252
Teacher spread0.245 · 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

Citations26
Published2023
Admission routes1
Has abstractyes

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