Influence of silver content and MnSO<sub>4</sub>addition on performance of different lead–silver alloys during polarisation and decay periods
Bibliographic record
Abstract
The electrochemical activity and the corrosion properties of five commercial lead anodes were examined by the conventional polarisation methods and the electrochemical impedance spectroscopy method. The results show that the anode Pb–0·62Ag alloy had a lower overpotential for anode reaction than the others. The corrosion resistance after polarisation of the anode Pb–0·62Ag alloy was higher than that of the Pb–0·29Ag–0·1Ca alloy, Pb–0·58Ag alloy and Pb–0·67Ag alloy, but lower than that of the anode Pb–0·72Ag alloy. However, the anode Pb–0·72Ag alloy had the highest overpotential. When anodes Pb–0·72Ag alloy and Pb–0·62Ag alloy were used, the cathode current efficiencies were higher, while anodes Pb–0·29Ag–0·1Ca alloy, Pb–0·58Ag alloy and Pb–0·67Ag alloy gave lower current efficiency. Regarding these several factors, the anode Pb–0·62Ag alloy is the best choice.On a examiné l’activité électrochimique et les propriétés de corrosion de cinq anodes commerciales en plomb, au moyen des méthodes conventionnelles de polarisation et de la méthode de spectroscopie d’impédance électrochimique. Les résultats montrent que l’anode de Pb–0·62Ag avait une surtension plus basse que celle des autres pour la réaction de l’anode. La résistance à la corrosion après la polarisation de l’anode de Pb–0·62Ag était plus élevée que pour l’anode de Pb–0·29Ag–0·1Ca, l’anode de Pb–0·58Ag ou l’anode de Pb–0·67Ag, mais plus basse que celle de l’anode de Pb–0·72Ag. Cependant, l’anode de Pb–0·72Ag avait la surtension la plus élevée. Lorsqu’on utilisait les anodes de Pb–0·72Ag ou de Pb–0·62Ag, le rendement du courant de la cathode était plus élevé, alors que les anodes de Pb–0·29Ag–0·1Ca, de Pb–0·58Ag et de Pb–0·67Ag donnaient un rendement de courant plus bas. Par rapport aux divers facteurs, l’anode de Pb–0·62Ag constitue le meilleur choix.
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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 | 0.000 | 0.000 |
| 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 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".