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Record W2981909388 · doi:10.1021/acscatal.9b02872

Active Sulfur Sites in Semimetallic Titanium Disulfide Enable CO<sub>2</sub> Electroreduction

2019· article· en· W2981909388 on OpenAlexafffund
Abdalaziz Aljabour, Halime Coskun, X. R. Zheng, Md. Golam Kibria, Moritz Strobel, Sabine Hild, Matthias Kehrer, David Stifter, Edward H. Sargent, Philipp Stadler

Bibliographic record

VenueACS Catalysis · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersEuropean Regional Development FundUniversity of TorontoAustrian Science Fund
KeywordsSulfurCatalysisChemistryTitaniumElectrocatalystInorganic chemistryDisulfide bondElectrochemistryChemical engineeringNanotechnologyMaterials scienceElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Electrocatalytic CO 2 -to-CO conversion represents one pathway to upgrade CO 2 to a feedstock for both fuels and chemicals (CO, deployed in ensuing Fischer–Tropsch or bioupgrading). It necessitates selective and energy-efficient electrocatalysts—a requirement met today only using noble metals such as gold and silver. Here, we show that the two-dimensional sulfur planes in semimetallic titanium disulfide (TiS 2 ) provide an earth-abundant alternative. In situ Fourier transform infrared mechanistic studies reveal that CO 2 binds to conductive disulfide planes as intermediate monothiocarbonate. The sulfur–CO 2 intermediate state steers the reduction kinetics toward mainly CO. Using TiS 2 thin films, we reach cathodic energy efficiencies up to 64% at 5 mA cm 2 . We conclude with directions for the further synthesis and study of semimetallic disulfides developing CO-selective electrocatalysts.

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 score0.004

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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

Citations39
Published2019
Admission routes2
Has abstractyes

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