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Record W2901875107 · doi:10.1021/acs.est.8b03565

Nitrogen-Doped Carbon Materials as Metal-Free Catalyst for the Dechlorination of Trichloroethylene by Sulfide

2018· article· en· W2901875107 on OpenAlexaff
Longzhen Ding, Pengpeng Zhang, Hong Qun Luo, Yongfeng Hu, Mohammad Norouzi Banis, Xiaoling Yuan, Na Liu

Bibliographic record

VenueEnvironmental Science & Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsWestern UniversityCanadian Light Source (Canada)
FundersState Administration of Foreign Experts AffairsNational Natural Science Foundation of ChinaMinistry of Education of the People's Republic of ChinaNational Science Foundation
KeywordsChemistrySulfideTrichloroethyleneReagentNucleophileSulfurInorganic chemistryCatalysisReductive dechlorinationNucleophilic substitutionCarbon fibersOrganic chemistryBiodegradationMaterials scienceComposite number

Abstract

fetched live from OpenAlex

A new method for trichloroethylene (TCE) dechlorination is proposed using sulfide (HS– and S2–) as reductant under the mediation of nitrogen-doped carbon materials (NCMs). About 99% of the TCE was converted to acetylene after 200 h using this method. Dechlorination of TCE in the NCMs–sulfide system (NCSS) followed pseudo-first-order kinetics. Pyridinic N (N6) on surface of the NCMs appeared to play a critical role in NCSS as shown by the good linear relationship between the surface content of N6 and kobs. Nucleophilic substitution was suggested as the first step in TCE dechlorination, and the nucleophilic reagent was identified as a sulfur intermediate with C–S–S–H as the functional group. The generation of C–S–S–H could be ascribed to the interaction between positively charged carbon atoms in N6 and negative charged sulfide. This work is the first to demonstrate that sulfide combined with NCMs can produce active substances that are effective in TCE dechlorination and the findings will assist in the development of strategies that use natural sulfide as reductant for detoxicating organic chloroethene contaminants.

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 score0.003
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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

Citations64
Published2018
Admission routes1
Has abstractyes

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