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Record W4225410146 · doi:10.1038/s41467-022-30155-4

Modulating Pt-O-Pt atomic clusters with isolated cobalt atoms for enhanced hydrogen evolution catalysis

2022· article· en· W4225410146 on OpenAlexfundno aff
Yufei Zhao, Priyank V. Kumar, Xin Tan, Xinxin Lu, Xiaofeng Zhu, Junjie Jiang, Jian Pan, Shibo Xi, Hui Ying Yang, Zhipeng Ma, Tao Wan, Dewei Chu, Wenjie Jiang, Sean C. Smith, Rose Amal, Zhaojun Han, Xunyu Lu

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsnot available
FundersNational Cancer InstituteUniversity of TorontoUniversity of WollongongAustralian Research CouncilAustralian Renewable Energy AgencyUniversity of New South WalesAustralian National UniversityAustralian Government
KeywordsCatalysisCobaltMaterials scienceHydrogenNanotechnologyChemistryCrystallographyInorganic chemistryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Platinum is the most efficient catalyst for hydrogen evolution reaction in acidic conditions, but its widespread use has been impeded by scarcity and high cost. Herein, Pt atomic clusters (Pt ACs) containing Pt-O-Pt units were prepared using Co/N co-doped carbon (CoNC) as support. Pt ACs are anchored to single Co atoms on CoNC by forming strong interactions. Pt-ACs/CoNC exhibits only 24 mV overpotential at 10 mA cm −2 and a high mass activity of 28.6 A mg −1 at 50 mV, which is more than 6 times higher than commercial Pt/C with any Pt loadings. Spectroscopic measurements and computational modeling reveal the enhanced hydrogen generation activity attributes to the charge redistribution between Pt and O atoms in Pt-O-Pt units, making Pt atoms the main active sites and O linkers the assistants, thus optimizing the proton adsorption and hydrogen desorption. This work opens an avenue to fabricate noble-metal-based ACs stabilized by single-atom catalysts with desired properties for electrocatalysis.

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), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
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
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.008
GPT teacher head0.243
Teacher spread0.235 · 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 designSimulation or modeling
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

Citations297
Published2022
Admission routes1
Has abstractyes

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