Influence of F <sup>‐</sup> doping on the microstructure, surface morphology and electrochemical properties of the lead dioxide electrode
Bibliographic record
Abstract
The lead dioxide electrode (PbO 2 ) with Ti substrate and SnO 2 ‐Sb 2 O 5 intermediate layer was doped by F ‐ ion through the potentiostatic anode co‐deposition method. The content of F in the coating can be controlled by adjusting deposition potential. The effect of F ‐ doping on the composition, surface morphology and electrochemical properties of the PbO 2 electrode was characterized by X‐ray diffraction, scanning electron microscope, X‐ray photoelectron spectroscopy and electrochemical measurement methods. The results have confirmed that the content of β‐PbO 2 increases with increasing that of F, and the doping can make the β‐PbO 2 grains become fine and the electrode surface become smooth; the specific surface areas and conductivity increase, and the initial potential of oxygen evolution shifts toward positive direction compared with the free‐doped PbO 2 electrode; the oxygen evolution potential increases with the increasing of the F ‐ content in the PbO 2 film electrode. The bulk electrolysis result demonstrated that the performances of the F‐PbO 2 electrode for anodic oxidation aniline have been improved to some extent. Copyright © 2012 John Wiley & Sons, Ltd.
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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) | 0.001 | 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".