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Record W4403638359 · doi:10.1021/acsenergylett.4c02344

Regulating Reconstruction Activity of Cobalt Electrode for Optimized Water Oxidation

2024· article· en· W4403638359 on OpenAlexaff
Miao Wang, Ziyi Wang, Yunze Zhang, Yan Shi, Ting‐Shan Chan, Shu‐Chih Haw, Jian Wang, Hongsheng Wang, Siyuan Wang, Hao Fei, Ruoqi Liu, Tong Liu, Chang-Feng Yan

Bibliographic record

VenueACS Energy Letters · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersDepartment of Science and Technology of Sichuan ProvinceResearch Grants Council, University Grants CommitteeNational Natural Science Foundation of ChinaGuangdong Science and Technology DepartmentCity University of Hong Kong
KeywordsCobaltElectrodeMaterials scienceChemistryChemical engineeringInorganic chemistryEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Elucidating and regulating dynamic catalyst reconstruction are crucial for various electrocatalytic reactions. Here, we applied model fittings with operando characterizations to quantify the reconstruction activity (i.e., ability to reconstruct) of layered Co(OH) 2 toward the oxygen evolution reaction (OER). By modulating the intercalation species into Co(OH) 2, we governed distinct reconstruction thermodynamics and kinetics, with diverse mass changes and heterogeneous Co oxidation during the OER. We further established a volcano-type relationship between reconstruction activity and OER activity and identified that a moderate reconstruction activity, achieved by dual-anion intercalation, favored a high OER activity. This might result from its proper basal spacing that regulated the coupled ion (de)intercalation–electron transfer for reconstruction, leading to optimal Co for binding OER intermediates. The optimally reconstructed Co(OH) 1.26 Cl 0.08 (CO 3 ) 0.33 ·0.84 H 2 O delivered 1 A cm –2 at 1.78 V for anion-exchange membrane water electrolysis. This work laid the foundation for modulating reconstruction activities to benefit electrocatalysis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.054
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

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 head0.008
GPT teacher head0.214
Teacher spread0.207 · 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.

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

Citations20
Published2024
Admission routes1
Has abstractyes

Explore more

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