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Record W4400000753 · doi:10.1007/s43939-024-00087-5

Amorphous multimetal based catalyst for oxygen evolution reaction

2024· article· en· W4400000753 on OpenAlexafffund
Zishuai Zhang, Daniela Vieira, Jake E. Barralet, Géraldine Merle

Bibliographic record

VenueDiscover Materials · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsPolytechnique MontréalMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsOverpotentialOxygen evolutionTafel equationElectrocatalystWater splittingHydrogen productionMaterials scienceCatalysisChemical engineeringElectrolysis of waterMetallurgyChemistryElectrodeElectrochemistryElectrolysisElectrolyteEngineering

Abstract

fetched live from OpenAlex

Abstract The development of efficient, low-cost water splitting electrocatalysts is needed to store energy by generating sustainable hydrogen from low power clean but intermittent energy sources such as solar and wind. Here, we report a highly sustained low overpotential for oxygen evolution reached by the unique combination of three metals (NiCoV) prepared from a simple low temperature auto-combustion process. The amorphous multimetal oxygen evolving catalyst could be stably coated on a stainless-steel support using a tribochemical particle blasting method to create an oxygen evolution reaction (OER) electrode with a low overpotential of 230 mV at 10 mA cm −2 and a low Tafel slope of 40 mV dec −1 . In addition to their low overpotential, this oxygen evolving electrocatalyst preserved performance demonstrating a stability after 10 h at the technologically relevant current density and without any surface morphology alteration. Given the importance of sustainable hydrogen production, the development of this new OER catalyst points the way to removing a key technical bottleneck for the water splitting reaction and could offer a route to cost reduction and lowering hurdles to more widespread adaptation of electrolyser technologies for 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)0.000
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 score0.041
Threshold uncertainty score0.827

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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