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Sono-electrolysis: A look into the distinguished effects of direct and indirect sonication at high frequency ultrasounds (490 kHz)

2025· article· en· W4417178278 on OpenAlexaff
Kaouther Kerboua, Md Hujjatul Islam, Nour Hane Merabet, Henrik Erring Hansen, Frode Seland, Odne Stokke Burheim, Bruno G. Pollet

Bibliographic record

VenueUltrasonics Sonochemistry · 2025
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsSonicationElectrolysisTafel equationElectrochemistryHydrogenCavitationDirect currentSonochemistryBubble

Abstract

fetched live from OpenAlex

This study investigates the impact of direct and indirect high-frequency (490 kHz) ultrasonication on alkaline water electrolysis for hydrogen production. Calorimetric analysis reveal that direct sonication transmits 54.17 W of acoustic power, corresponding to an efficiency of 77.39 %, whereas indirect configurations achieves substantially lower efficiencies. Hydrogen quantification showed that conventional electrolysis alone produced 0.288 mM H 2 in 60 min (0.08 mM/s), indirect sono-electrolysis improves yields modestly by 3–6 %, while direct sono-electrolysis reaches 0.777 mM in 60 min, representing a 170 % enhancement. This improvement stems from both sonolytic hydrogen generation (0.448 mM) and electrochemical synergy (0.034 mM). Electrochemical characterizations demonstrate enhanced current densities, reduced Tafel slopes, and improved kinetics for both HER and OER under direct ultrasound exposure, attributed to localized cavitation, bubble detachment, and intensified mass transport. Modeling of cavitation dynamics confirm that passing from indirect to direct sonication increases bubble compression ratios up to 8.64, resulting in order-of-magnitude increases in collapse temperature and hydrogen yields. These findings highlight the strong dependence of sono-electrolysis performance on acoustic coupling geometry, with direct configurations offering a promising pathway toward energy-efficient 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)0.002
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.002
GPT teacher head0.205
Teacher spread0.203 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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