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Record W4390812703 · doi:10.3390/catal14010060

Platinum Alloys for Methanol Oxidation Electrocatalysis: Reaction Mechanism and Rational Design of Catalysts with Exceptional Activity and Stability

2024· article· en· W4390812703 on OpenAlexaff
Renqin Yu, Yifan Zhang, Sixu Deng, Rongying Zhu, Shiming Zhang, Jiujun Zhang, Yufeng Zhao, Zhonghong Xia

Bibliographic record

VenueCatalysts · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsConcordia University
Fundersnot available
KeywordsCatalysisPlatinumElectrocatalystRational designMethanolMaterials scienceIntermetallicNanotechnologyFuel cellsCarbon monoxideAlloyChemical engineeringChemistryElectrochemistryPhysical chemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Direct methanol fuel cells have emerged as highly promising energy conversion devices in the past few decades. However, some challenges, such as carbon monoxide (CO) poisoning and unsatisfactory long-term stability, remain for platinum (Pt) as a methanol oxidation reaction (MOR) catalyst. This review covers recent advances in Pt alloy MOR catalysts and provides some insights. This review presents MOR catalytic mechanisms based on CO or non-CO pathways. Typical dimension-based designs of MOR catalysts, such as anisotropic nanowires, metallene, nanoframes, and corresponding rationales for performance enhancements, are introduced. More importantly, some key tuning strategies are elaborated, including intermetallic compound synthesis, interface engineering, and surface facet engineering. High-entropy alloys as an intriguing class of MOR catalysts with favorable prospects are also discussed. Finally, future directions and opportunities are outlined.

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)0.000
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 score0.094
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.237
Teacher spread0.219 · 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

Citations27
Published2024
Admission routes1
Has abstractyes

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