Analysis of bubble management performance using dual bubble layer model and electrochemical impedance spectroscopy
Bibliographic record
Abstract
This study applies a dual bubble layer model and the Hydrogen Evolution Reaction (HER) test protocol originally developed by Kitajima et al., to both polished flat and 3D microporous Dynamic Hydrogen Bubble Template (DHBT) nickel surfaces. The results showed that bubble-induced diffusion resistance was present on both surface types, supporting the development of a universal, non-destructive protocol for assessing bubble management performance. By focusing on bubble diffusion impedance as a primary performance indicator, the study isolated bubble dynamics from electrochemically active surface area (ECSA) effects, which is often overlooked in traditional assessments. Time-domain transformations of impedance data helped identify the components of an equivalent circuit model, with porous DHBT surfaces exhibiting an additional charge transfer process compared to flat surfaces. Using photographic evidence and Nyquist plot observations, we define three distinct stages in the HER process: (1) Initial discrete bubble formation with charge transfer dominance, (2) emergence of hydrogen oxidation product (H OPD ) and bubble accumulation [17], and (3) formation of a dense bubble layer. Circuit fitting revealed that bubble diffusion resistance was highly dependent on surface morphology, with polished surfaces exhibiting greater resistance than porous ones. Diffusion resistance correlated with bubble layer thickness and scales with the square root of applied current density.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".