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Record W4402171333 · doi:10.1021/acs.jpclett.4c02297

Substrate-Assisted Atomic Dispersion of Cobalt for Alkaline Water Electrolysis

2024· article· en· W4402171333 on OpenAlexafffund
Jonathan Quintal, Cameron McGuire, Tony Shi, Wei‐Hsiang Huang, Darren Chow, Chi-Kai Hung, De-Tong Jiang, Bing‐Joe Hwang, Aicheng Chen

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsOverpotentialCatalysisDispersion (optics)Materials scienceCobaltElectrolysisSubstrate (aquarium)Electrolysis of waterAlkali metalWater splittingElectrochemistryInorganic chemistryAnalytical Chemistry (journal)Chemical engineeringChemistryPhysical chemistryMetallurgyElectrodePhotocatalysis

Abstract

fetched live from OpenAlex

Atomically dispersed single-atom catalysts have recently attracted broad research interest due to their high atom efficiency and unique catalytic performance. In this study, atomic dispersion of cobalt is achieved using a chemical bath deposition method on a highly stable alkali titanate film (Ti/KTiO). These films were characterized using a variety of techniques, with atomic dispersion confirmed via grazing incidence X-ray absorption spectroscopy and ab initio modeling of single-atom systems. This modeling indicated that the alkali ion incorporated into the film facilitates atomic dispersion. Experimentally, the Ti/KTiO-supported Co(OH) 2 catalysts exhibited remarkable electrochemical performance, with an overpotential of 163 mV to achieve a current density of 10 mA cm –2 with a catalyst loading of ∼0.1 mg cm –2 and high stability. These results show the potential of Ti/KTiO/Co(OH) 2 catalysts for atomically efficient hydrogen production.

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 score0.001
Threshold uncertainty score0.003

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 integrity0.0000.001
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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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