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Record W4397023328 · doi:10.1126/sciadv.adn7012

Efficient and durable seawater electrolysis with a V <sub>2</sub> O <sub>3</sub> -protected catalyst

2024· article· en· W4397023328 on OpenAlexaff
Huashuai Hu, Zhaorui Zhang, Lijia Liu, Xiangli Che, Jiacheng Wang, Ye Zhu, J. Paul Attfield, Minghui Yang

Bibliographic record

VenueScience Advances · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsWestern University
FundersFundamental Research Funds for the Central UniversitiesEngineering and Physical Sciences Research CouncilScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of China
KeywordsSeawaterCatalysisElectrolysisEnvironmental scienceChemistryOceanographyGeologyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The ocean, a vast hydrogen reservoir, holds potential for sustainable energy and water development. Developing high-performance electrocatalysts for hydrogen production under harsh seawater conditions is challenging. Here, we propose incorporating a protective V 2 O 3 layer to modulate the microcatalytic environment and create in situ dual-active sites consisting of low-loaded Pt and Ni 3 N. This catalyst demonstrates an ultralow overpotential of 80 mV at 500 mA cm −2 , a mass activity 30.86 times higher than Pt-C and maintains at least 500 hours in seawater. Moreover, the assembled anion exchange membrane water electrolyzers (AEMWE) demonstrate superior activity and durability even under demanding industrial conditions. In situ localized pH analysis elucidates the microcatalytic environmental regulation mechanism of the V 2 O 3 layer. Its role as a Lewis acid layer enables the sequestration of excess OH − ions, mitigate Cl − corrosion, and alkaline earth salt precipitation. Our catalyst protection strategy by using V 2 O 3 presents a promising and cost-effective approach for large-scale sustainable green 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.203
Teacher spread0.199 · 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

Citations131
Published2024
Admission routes1
Has abstractyes

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