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Record W4366606337 · doi:10.1021/acsaem.2c02443

In Situ Sonoactivation of Polycrystalline Ni for the Hydrogen Evolution Reaction in Alkaline Media

2023· article· en· W4366606337 on OpenAlexafffund
Faranak Foroughi, Marina Tintor, Alaa Y. Faid, Svein Sunde, Gregory Jerkiewicz, Christophe Coutanceau, Bruno G. Pollet

Bibliographic record

VenueACS Applied Energy Materials · 2023
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversité du Québec à Trois-RivièresQueen's University
FundersCentre National de la Recherche ScientifiqueMitacsNatural Sciences and Engineering Research Council of CanadaUniversité de PoitiersQueen's UniversityNorges Teknisk-Naturvitenskapelige UniversitetU.S. Department of Energy
KeywordsOverpotentialCrystalliteSonicationNickelElectrolyteMaterials scienceElectrochemistryHydrogenAqueous solutionUltrasonic sensorSonochemistryMetalInorganic chemistryElectrodeChemical engineeringChemistryMetallurgyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide In this investigation, we report on the development of a method for activating polycrystalline metallic nickel (Ni(poly)) surfaces toward the hydrogen evolution reaction (HER) in N 2 -saturated 1.0 M KOH aqueous electrolyte through continuous and pulsed ultrasonication (24 kHz, 44 ± 1.40 W, 60% acoustic amplitude, ultrasonic horn). It is found that ultrasonically activated Ni shows an improved HER activity with a much lower overpotential of −275 mV vs RHE at −10.0 mA cm –2 when compared to nonultrasonically activated Ni. It was observed that the ultrasonic pretreatment is a time-dependent process that gradually changes the oxidation state of Ni and longer ultrasonication times result in higher HER activity as compared to untreated Ni. This study highlights a straightforward strategy for activating nickel-based materials by ultrasonic treatment for the electrochemical water splitting reaction.

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 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 score0.093
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.225
Teacher spread0.213 · 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

Citations12
Published2023
Admission routes2
Has abstractyes

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