Power ultrasound as performance enhancer for alkaline water electrolysis: A review
Bibliographic record
Abstract
The industry is advancing decarbonization in hydrogen production through water splitting technologies like water electrolysis, which involves the hydrogen evolution reaction (HER) at the cathode and oxygen evolution reaction (OER) at the anode. Alkaline water electrolyser (AWE) is particularly suited for industrial applications due to its use of cost-effective and abundant nickel-based electrodes. However, AWE faces significant challenges, including energy losses from gas bubble coverage and poor detachment, known as “bubble resistance”. Recent research highlights the role of power ultrasound in mitigating these issues by leveraging Bjerknes forces . These forces facilitate the ejection of larger bubbles and the coalescence of smaller ones, enhancing gas removal. Additionally, ultrasound improves mass transfer from the electrolyte to electrodes and boosts heat transfer via acoustic streaming and acoustic cavitation, which the latter also enhances electrocatalytic properties for both HER and OER. However, employing ultrasonic fields presents both benefits and challenges for scaling the system. • Impact of gas bubble formation on the overpotential in alkaline water electrolysis. • Stirring effects of power ultrasound for enhancing heat and mass transfers. • Enhancing nickel as electrocatalyst in alkaline media through acoustic cavitation. • Bjerknes forces from power ultrasound facilitate gas bubble removal. • Modification of gas bubble evolution under ultrasonication.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".