Sono-electrolysis: A look into the distinguished effects of direct and indirect sonication at high frequency ultrasounds (490 kHz)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".