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Record W4413103336 · doi:10.1016/j.cej.2025.167107

Balanced adsorbate interactions in CeVO4 nanosheets: A highly efficient and stable electrocatalyst for water splitting

2025· article· en· W4413103336 on OpenAlexafffund
Youness El Issmaeli, Amina Lahrichi, Bruno G. Pollet

Bibliographic record

VenueChemical Engineering Journal · 2025
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrocatalystWater splittingChemistryMaterials scienceChemical engineeringCatalysisNanotechnologyElectrochemistryEngineeringPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

This study focusses on the synthesis of cerium vanadate (CeVO 4 ) nanosheets on nickel foam (NF) via a single-step hydrothermal method and their bifunctional performance as electrocatalysts for overall water splitting under alkaline conditions. The CeVO 4 /NF catalyst exhibited excellent oxygen evolution reaction (OER) activity with a low overpotential of 255 mV at 10 mA cm −2 and outstanding long-term durability, retaining 94.3 % of its activity after 75 h. It also showed competitive performance for the hydrogen evolution reaction (HER), achieving an overpotential of 184 mV. Density functional theory (DFT) calculations revealed that vanadium serves as the primary active site, offering balanced adsorbate interactions, strong enough to promote adsorption of key intermediates while still allowing efficient desorption. This balance explains the favorable energy profile and the rate-determining step (OOH* desorption), contributing to enhanced OER efficiency. When coupled with Pt/C as the cathode in a two-electrode alkaline electrolyser, CeVO 4 /NF delivered a low cell voltage of 1.58 V and maintained excellent performance for over 120 h, outperforming the IrO 2 ||Pt/C benchmark. These findings underscore the potential of CeVO 4 /NF as a highly efficient and durable electrocatalyst for sustainable water splitting.

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 score0.058
Threshold uncertainty score0.636

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 head0.004
GPT teacher head0.208
Teacher spread0.204 · 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

Citations10
Published2025
Admission routes2
Has abstractyes

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