Effect of heat treatment on age hardening behaviour of electroless nickel–phosphorus coatings
Bibliographic record
Abstract
Electroless nickel–phosphorus (EN) coatings have been widely used in various industries such as oil, gas, electronic, chemical, automotive, aerospace, and mining. The EN coating process is based on a redox reaction in which a reducing agent is oxidised and Ni2+ ions are reduced on the substrate materials. Once the first layer of Ni is deposited, it acts as a catalyst for the process. Consequently, a linear relation between coating thickness and time usually occurs. If the reducing agent is sodium hypophosphite, the deposit obtained will be a nickel–phosphorus alloy. Also, the actual nickel and phosphorous levels in the EN deposit depend on the composition, temperature, and pH of the plating bath used. In this work, three types of EN coatings have been studied: low, medium, and high phosphorus Ni–P alloys. The techniques used were: differential scanning calorimetry (DSC), SEM, and hardness measurements. Heat treatment resulted in precipitation of nickel phosphides, e.g. Ni3P, and nickel crystallites. Thus, the phosphorus content of the coating was reduced. The results of isochronal age hardening showed that the peak age hardening temperature for the three EN coatings occurred at ∼673 K. However, the time to reach peak hardness during isothermal heat treatment at 593 K varied with phosphorus content. Also, it was found that the temperatures at which peak precipitation reactions occur during DSC scan are influenced by phosphorus content.
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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".