Effect of Surfactant Concentration on the Behavior of Single Oxygen Bubble in Photoelectrochemical Water Splitting
Bibliographic record
Abstract
The control of bubble growth and departure during the process of photoelectrochemical water splitting is crucial for enhancing gas production efficiency. By introducing varying concentrations of surfactants into the electrolyte and using a synchronous measurement system, we examined how the change in surfactant concentration affects bubble nucleation and growth characteristics. The results indicate that bubbles’ critical nucleation potentials initially decrease and subsequently increase with increasing surfactant concentration, reaching a minimum value when the concentration is 1 × 10 –7 M. In contrast, bubble growth periods and departure diameters reach their maximum values. Further analysis shows that the gas production rate and the average gas evolution efficiency reach their maximum when the surfactant concentration is 1 × 10 –7 M, indicating that adding an appropriate amount of surfactant is advantageous for gas production. In addition, a force equilibrium model incorporating the Marangoni force at various surfactant concentrations was developed to predict oxygen bubbles’ departure diameter. The predicted values exhibit excellent concurrence with the experimental data. The solutal Marangoni force is the primary determinant that affects bubble departure diameter when the surfactant concentrations are below approximately 4 × 10 –6 M, while the thermal Marangoni force assumes precedence when concentrations exceed about 4 × 10 –6 M.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".