Numerical simulation of hydrogen bubble growth at an electrode surface
Bibliographic record
Abstract
Abstract A single hydrogen bubble generated at an electrode surface during water electrolysis is simulated via the volume of fluid (VOF) multiphase flow model to capture the details of the interface evolution and the mass transfer that occurs at the interface. The hydrogen bubble that grows at the electrode is driven by supersaturation of the dissolved hydrogen in the liquid. Two models are used to calculate the gas‐liquid interface mass transfer coefficient. The bubble growth from experimental results agrees closely with theoretical predictions. In addition, the mass transfer of dissolved hydrogen from the electrode surface to the bulk liquid is evaluated during the bubble nucleation and growth stages. During the nucleation stage, the mass transfer coefficient is < 5.1 × 10−5 m/s. Once the bubble embryo is formed, the mass transfer greatly increases. Before the bubble releases, the mass transfer coefficient reaches 2.8 × 10−4 m/s. More detailed information about the bubble growth is presented, including bubble‐induced convection and the concentration distribution of dissolved hydrogen around the growing bubble. The results indicate that the VOF method is suitable and reliable for simulating bubble behaviour during electrolysis or other electrochemical reactions that involve gas bubble desorption.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".