Dependence of the Electrochemical and Passive Behavior of the Lead-Acid Battery Positive Grid on Electrode Surface Roughness
Bibliographic record
Abstract
The corrosion resistance of the positive grid alloy plays an important role on the performance and service life of lead-acid batteries. There are many parameters influencing the corrosion behavior of the grid alloy. Grid surface roughness, which is of importance from a manufacturing point of view, is a parameter that has received rather less attention. In the present study, the dependence of the electrochemical and the passive behavior of the positive grid alloy on electrode surface roughness was investigated. Cyclic voltammetry, potentiodynamic polarization, galvanostatic polarization, electrochemical impedance spectroscopy, and Mott-Schottky analysis were used to evaluate the influence of surface roughness on the electrochemical properties of the grid alloy. The results indicated that increasing the surface roughness hindered formation of the passive film and increased the corrosion resistance of the lead alloy electrode. Moreover, it was found that the donor density of the passive film is proportional to the surface roughness.
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) | 0.000 | 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".