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Record W4386888855 · doi:10.1021/acscatal.3c03113

Basal Plane Activation via Grain Boundaries in Monolayer MoS<sub>2</sub> for Carbon Dioxide Reduction

2023· article· en· W4386888855 on OpenAlexafffund
Ying Zhao, Yiqing Chen, Pengfei Ou, Jun Song

Bibliographic record

VenueACS Catalysis · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsMolybdenum disulfideMonolayerDensity functional theoryRedoxTransition metalElectrochemical reduction of carbon dioxideCatalysisMaterials scienceElectrochemistryCarbon dioxideGrain boundaryNanotechnologyBasal planeChemistryInorganic chemistryChemical engineeringComputational chemistryMetallurgyCrystallographyPhysical chemistryOrganic chemistryElectrodeCarbon monoxide

Abstract

fetched live from OpenAlex

With the electrochemical carbon dioxide reduction reaction (CO 2 RR) being a promising method to reduce atmospheric carbon dioxide (CO 2 ), transition metal dichalcogenides (TMDCs), such as molybdenum disulfide (MoS 2 ), have recently risen as potential catalysts for CO 2 RR. However, pristine TMDCs are bottlenecked by the insufficiency of active sites in the basal plane. In this study, focusing on polycrystalline MoS 2, we perform systematic density functional theory calculations to investigate the role of grain boundaries (GBs) on the catalytic performance of MoS 2 for CO 2 RR. Our results show that most GBs contribute to lowering the reaction energy of the potential-limiting step in CO 2 RR. This effect can be further amplified with the introduction of S vacancies. In addition, the introduction of GBs with vacancies is shown to act as an effective method to break the scaling relations between reaction intermediates, which is crucial in improving catalytic efficiencies. Our findings demonstrate that defect engineering holds great potential to activate the basal plane of TMDCs for CO 2 RR, providing valuable insights into engineering TMDCs for high-performing CO 2 RR 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 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 score0.048
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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

Citations45
Published2023
Admission routes2
Has abstractyes

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