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Record W4318196196 · doi:10.1021/acsaem.2c03339

A Comparison of Photodeposited RuO <sub> <i>x</i> </sub> for Alkaline Water Electrolysis

2023· article· en· W4318196196 on OpenAlexafffund
Katelynn Daly, Santiago Jimenez-Villegas, Benjamin Godwin, Martin Schoen, Oliver Calderon, Ning Chen, Simon Trudel

Bibliographic record

VenueACS Applied Energy Materials · 2023
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsCanadian Light Source (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundAlexander von Humboldt-Stiftung
KeywordsElectrolysisNanocrystalline materialOxygen evolutionCatalysisAlkaline water electrolysisX-ray photoelectron spectroscopyRenewable energyElectrochemistryWater splittingElectrolysis of waterMaterials scienceChemical engineeringAmorphous solidHydrogen economyHydrogen productionChemistryInorganic chemistryNanotechnologyPhotocatalysisElectrode

Abstract

fetched live from OpenAlex

The storage of renewable energy is a pressing challenge to overcome in the transition toward a power grid based on plentiful, yet intermittent energy supplies. The renewables-driven electrolysis of water to form hydrogen fuel is an attractive avenue, but requires better oxygen-evolution reaction (OER) catalysts to be feasible at scale. RuO 2 is touted as one of the superior OER catalysts but only under acidic conditions; RuO 2 electrocatalysts suffer from poor stability under alkaline conditions. In this work, we evaluate three photodeposited RuO x OER electrocatalysts, all prepared via a scalable photodeposition method. Based on electrochemical and spectroscopic studies (X-ray photoelectron spectroscopy and X-ray absorption spectroscopy), our main findings are that nanocrystalline RuO x catalysts outperform their amorphous counterpart and are more stable under alkaline (0.1 M KOH) conditions. This works thus lifts a major hurdle toward the use of RuO x for alkaline water electrolysis.

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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.242
Teacher spread0.231 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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