The corrosion behavior of electroless Ni-P coatings in concentrated KOH electrolyte
Bibliographic record
Abstract
Abstract Ni-P has been widely used as a protective coating for many substrates. The corrosion resistance of Ni-P in neutral solutions such as NaCl, or acidic electrolytes such as HCl and H2SO4, has been extensively studied. However, the corrosion behavior of Ni-P coatings in caustic media, such as KOH, has received much less attention. Typically, corrosion behavior is studied through the use of electrochemical methods with corrosion rates determined from corrosion currents and potentials measured from Tafel curves. In this work, the corrosion rates of Ni-P coatings, with P concentrations varying from 2 to 11 wt%, in highly alkaline KOH (11 M) are obtained directly through electron microscopy measurements of cross sections and subsequent correlation with electrochemical data. Phosphorus concentration affects the corrosion rate; corrosion rate increases with increasing P content, peaks out at about 6–8 wt% P, and then decreases with any further increase in P content. This behavior is correlated to internal stress levels developed in the coatings.
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 | 0.000 |
| 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) | 0.001 | 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".