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Record W3204336702 · doi:10.1021/acsami.1c12054

Two-Dimensional Graphdiyne-Confined Platinum Catalyst for Hydrogen Evolution and Oxygen Reduction Reactions

2021· article· en· W3204336702 on OpenAlexafffund
Li Xin Chen, Ming Jiang, Zhuole Lu, Chan Gao, Zhiwen Chen, Chandra Veer Singh

Bibliographic record

VenueACS Applied Materials & Interfaces · 2021
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsOverpotentialCatalysisPlatinumMaterials scienceDensity functional theoryAdsorptionHydrogenChemical engineeringOxygen reduction reactionNanotechnologyPhysical chemistryChemistryComputational chemistryOrganic chemistryElectrochemistryElectrode

Abstract

fetched live from OpenAlex

Pt-based materials are the state-of-the-art catalysts for hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR); however, there is still much room to improve the catalytic activity and enhance the stability of Pt-based catalysts. In this work, two-dimensional (2D) graphdiyne (GDY) with uniform distributed pores was applied to cover the Pt surface for catalyzing HER and ORR through density functional theory (DFT) calculations. The 2D confinement induced by GDY was found to improve the catalytic performance of the Pt catalyst from three aspects: (1) the 2D covering layer increases the stability of the Pt catalyst through forming the heterogeneous interface of GDY/Pt(111); (2) GDY/Pt(111) shows better catalytic activities of HER and ORR, with the smaller average overpotential values of 0.26 and 0.51 V, respectively, compared with those (0.29 V for HER, 0.62 V for ORR) on the Pt catalyst; (3) the confinement effect of GDY weakens the adsorption energy of CO to -1.81 eV (average value) from -2.14 eV on Pt(111), inhibiting CO poisoning. This work sheds new light on 2D confinement effects for HER and ORR, which opens up a new strategy for improving the catalytic performance of Pt-based catalysts.

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 categoriesMeta-epidemiology (narrow)
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 score0.017
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.009
GPT teacher head0.226
Teacher spread0.216 · 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.

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

Citations25
Published2021
Admission routes2
Has abstractyes

Explore more

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