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Analysis of bubble management performance using dual bubble layer model and electrochemical impedance spectroscopy

2025· article· en· W4407033116 on OpenAlexafffund
Bowen Wang, Minghui Hao, Donald W. Kirk, Daniel Guay, Steven J. Thorpe

Bibliographic record

VenueElectrochimica Acta · 2025
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBubbleDielectric spectroscopyElectrical impedanceMaterials scienceDual (grammatical number)ElectrochemistryAnalytical Chemistry (journal)Chemical engineeringChemistryElectrodeEngineeringMechanicsPhysicsElectrical engineeringChromatographyPhysical chemistry

Abstract

fetched live from OpenAlex

This study applies a dual bubble layer model and the Hydrogen Evolution Reaction (HER) test protocol originally developed by Kitajima et al., to both polished flat and 3D microporous Dynamic Hydrogen Bubble Template (DHBT) nickel surfaces. The results showed that bubble-induced diffusion resistance was present on both surface types, supporting the development of a universal, non-destructive protocol for assessing bubble management performance. By focusing on bubble diffusion impedance as a primary performance indicator, the study isolated bubble dynamics from electrochemically active surface area (ECSA) effects, which is often overlooked in traditional assessments. Time-domain transformations of impedance data helped identify the components of an equivalent circuit model, with porous DHBT surfaces exhibiting an additional charge transfer process compared to flat surfaces. Using photographic evidence and Nyquist plot observations, we define three distinct stages in the HER process: (1) Initial discrete bubble formation with charge transfer dominance, (2) emergence of hydrogen oxidation product (H OPD ) and bubble accumulation [17], and (3) formation of a dense bubble layer. Circuit fitting revealed that bubble diffusion resistance was highly dependent on surface morphology, with polished surfaces exhibiting greater resistance than porous ones. Diffusion resistance correlated with bubble layer thickness and scales with the square root of applied current density.

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 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 score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.008
GPT teacher head0.245
Teacher spread0.236 · 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

Citations12
Published2025
Admission routes2
Has abstractyes

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