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Record W2907988105 · doi:10.1016/j.isci.2018.12.029

Ternary Heterostructural Pt/CNx/Ni as a Supercatalyst for Oxygen Reduction

2019· article· en· W2907988105 on OpenAlexaff
Teng Chen, Yida Xu, Siqi Guo, Dali Wei, Luming Peng, Xuefeng Guo, Nianhua Xue, Yan Zhu, Zhao‐Xu Chen, Bin Zhao, Weiping Ding

Bibliographic record

VenueiScience · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of ChinaMinistry of Science and Technology of the People's Republic of ChinaNational Science Foundation
KeywordsCatalysisTernary operationCyclic voltammetryOxygenChemistryCarbon fibersElectron transferChemical engineeringOxygen reduction reactionInorganic chemistryMaterials scienceElectrochemistryNanotechnologyPhotochemistryOrganic chemistryElectrodePhysical chemistryComposite number

Abstract

fetched live from OpenAlex

We report here a supercatalyst for oxygen reduction of Pt/CN x /Ni in a unique ternary heterostructure, in which the Pt and the underlying Ni nanoparticles are separated by two to three layers of nitrogen-doped carbon (CN x ), which mediates the transfer of electrons from the inner Ni to the outer Pt and protects the Ni against corrosion at the same time. The well-engineered low-Pt catalyst shows ∼780% enhanced specific mass activity or 490% enhanced specific surface activity compared with a commercial Pt/C catalyst toward oxygen reduction. More importantly, the exceptionally strong tune on the Pt by the unique structure makes the catalyst superbly stable, and its mass activity of 0.72 A/mg Pt at 0.90 V (well above the US Department of Energy's 2020 target of 0.44 A/mg Pt at 0.90 V) after 50,000 cyclic voltammetry cycles under acidic conditions is still better than that of the fresh commercial catalyst.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 head0.235
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations44
Published2019
Admission routes1
Has abstractyes

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