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Record W4281724756 · doi:10.1002/chem.202201574

Carbon‐Supported Potassium Hydride for Efficient Low‐Temperature Desulfurization

2022· article· en· W4281724756 on OpenAlexfundno aff
Fei Chang, Alexey Fedorov

Bibliographic record

VenueChemistry - A European Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsnot available
FundersEidgenössische Technische Hochschule ZürichMcGill University
KeywordsOrganosulfur compoundsFlue-gas desulfurizationChemistryMesityleneSulfurDeoxygenationCatalysisHydridePotassiumCarbon fibersAlkylInorganic chemistryOrganic chemistryHydrogenMaterials science

Abstract

fetched live from OpenAlex

Abstract The industrial removal of organosulfur impurities from fossil fuels relies on transition‐metal‐based catalysts in harsh conditions (ca. 400 °C, up to 100 bar H 2 ), yet desulfurization (DS) of refractory alkyl dibenzothiophenes (DBTs) remains challenging. Here, we report that carbon‐supported potassium hydride (KH/C) enables efficient DS of DBTs in mild conditions, viz. >97 % conversion of DBTs is achieved at 165 °C in 3–6 h while the yields of respective biphenyls are 84–95 % by using only 15 % excess of KH per a C−S bond. In addition, KH/C allows to lower the concentration of 4,6‐Me 2 DBT in the mesitylene solution from 1000 ppm to <3 ppm (165 °C, 20 h) and provides deoxygenation, denitrogenation and catalytic aromatic hydrogenation reactions. DS of various sulfur heterocycles by using KH/C, a transition‐metal‐free material based on earth abundant elements, is viable at low temperature and has prospects for the further development towards decentralized removal of organosulfur species from fossil fuels.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.187
Teacher spread0.180 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

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