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Record W4390495641 · doi:10.1007/s12274-023-6328-0

Lattice-distorted Pt wrinkled nanoparticles for highly effective hydrogen electrocatalysis

2024· article· en· W4390495641 on OpenAlexaff
Xue Li, Xiang Han, Zhenrui Yang, Shun Wang, Yun Yang, Juan Wang, Jiadong Chen, Zhongwei Chen, Huile Jin

Bibliographic record

VenueNano Research · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOverpotentialElectrocatalystDensity functional theoryMaterials scienceHydrogenNanoparticleChemical physicsNanotechnologyChemistryChemical engineeringComputational chemistryPhysical chemistryElectrodeElectrochemistry

Abstract

fetched live from OpenAlex

Modulating Pt surfaces through the introduction of lattice distortion emerges as immensely effective strategy that enhances the kinetics of alkaline hydrogen evolution and oxidation processes. In this study, we fabricated lattice-distorted Pt wrinkled nanoparticles (LD-Pt WNPs) for efficient hydrogen electrocatalysis. The LD-Pt WNPs not only outperform the Pt/C benchmark in hydrogen oxidation reaction, achieving an excellent mass-specific current of 968.5 mA·mg Pt −1 (9 times that of Pt/C), but also demonstrate outstanding hydrogen evolution reaction activity with a small overpotential of 58.0 mV. Comprehensive experiments and density functional theory calculations reveal that lattice defects introduce an abundance of unsaturated coordination atoms while modifying the d-band center of Pt. This dual effect optimizes the binding strength of crucial H and OH intermediates, leading to a significant reduction in the energy barrier of the reaction bottleneck, commonly known as the Volmer step. This work unveils a fresh viewpoint on projecting and developing high efficiency electrocatalysts through defect engineering.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.137
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.328
Teacher spread0.301 · 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

Citations21
Published2024
Admission routes1
Has abstractyes

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