Perspective in the industrial applications of sonoelectrochemical hydrogen production
Bibliographic record
Abstract
Sonoelectrochemistry is the incorporation of power ultrasound in electrochemistry. The use of ultrasound in electrochemical processes such as water electrolysis can lead to an energy efficiency enhancement in the range of 2–25% in low-temperature water electrolysers (LT-WE). However, this improvement greatly depends upon several factors such as the cell reactor design, the ultrasonic frequency, the transmitted acoustic power, and the distance between the ultrasonic transducer and the electrode. The main objectives of this review are to highlight recent advancements in using power ultrasound in water electrolysis and shed some light on possible commercial development by addressing the fundamental obstacles that lie in this technology. Several research works have highlighted that the efficiency improvement in ultrasound-aided water electrolysis is principally due to the gas bubble removal from the electrode surface, which ultimately reduces the ohmic resistance of the electrolytic cell. However, even with the observed higher efficiencies from the sonoelectrolysers for hydrogen production in R&D labs, this technology still faces challenges for further development due to the efficiency in competing with commercial LT-WEs, which are already in the range of 60–70%. If sonoelectrolysers are to succeed for commercial development and large-scale industrial applications, they would need to achieve overall efficiency much higher than current commercial LT-WEs. • Sonoelectrochemical hydrogen production is reviewed. • Power ultrasound enhances electrochemical hydrogen production by 2–25%. • The energy efficiency gain depends on the ultrasonic parameters and the cell design. • Power ultrasound cannot be used in zero-gap cells due to their compact designs. • Direct sonication by high-frequency transducers is suitable for industrial use.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".