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Record W4210813986 · doi:10.1002/ange.202003654

Enhancing Oxygen Reduction Activity of Pt‐based Electrocatalysts: From Theoretical Mechanisms to Practical Methods

2020· article· en· W4210813986 on OpenAlexaff
Zhong Ma, Zachary P. Cano, Aiping Yu, Zhongwei Chen, Gaopeng Jiang, Xiaogang Fu, Lin Yang, Tianpin Wu, Zhengyu Bai, Jun Lü

Bibliographic record

VenueAngewandte Chemie · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOxygen reductionOxygen reduction reactionViewpointsPlatinumChemistryNanotechnologyCatalysisReduction (mathematics)Fuel cellsCombinatorial chemistryComputer scienceBiochemical engineeringMaterials scienceChemical engineeringElectrochemistryElectrodePhysical chemistryEngineeringOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract Pt‐based electrocatalysts are considered as one of the most promising choices to facilitate the oxygen reduction reaction (ORR), and the key factor enabling their success is to reduce the required amount of platinum. Herein, we focus on illuminating both the theoretical mechanisms which enable enhanced and sustained ORR activity and the practical methods to achieve them in catalysts. The various multi‐step pathways of ORR are firstly reviewed and the rate‐determining steps based on the reaction intermediates and their binding energies are analyzed. We then explain the critical aspects of Pt‐based electrocatalysts to tune oxygen reduction properties from the viewpoints of active sites exposure and altering the surface electronic structure, and further summarize representative research progress towards practically achieving these activity enhancements with a focus on platinum size reduction, shape control and core Pt elimination methods. We finally outline the remaining challenges and provide our perspectives with regard to further enhancing their activities.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.479
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.297
Teacher spread0.276 · 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

Citations54
Published2020
Admission routes1
Has abstractyes

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