A review of pretreatment techniques for electroless nickel-phosphorus plating on magnesium alloys with enhanced corrosion resistance and mechanical properties
Bibliographic record
Abstract
Magnesium alloys, known for their high hardness-to-density ratio, have significant potential in aerospace, automotive, and other industries but face challenges like high corrosion susceptibility and low hardness. Electroless nickel-phosphorus (Ni-P) plating can address these limitations but requires suitable pretreatment to manage high electrochemical activity, surface heterogeneity, and a loose oxide/hydroxide layer. This article reviews various pretreatment methods for electroless plating of the magnesium alloys, discussing their application procedures and the properties of the final electroless coatings. It emphasizes the significance of pretreatment in overcoming the challenges of magnesium alloys, ensuring uniform, adhesive, and corrosion-resistant Ni-P coatings. Extensive research has focused on various pretreatment methods, including zincating, non-electrical conversion coatings (chromating, phosphating, rare elements, titanate, zirconium, and hydroxide), electrical conversion coatings (anodizing and PEO), etc. The pretreatments aim to enhance the properties of the final electroless coatings, primarily targeting anti-corrosion performance. Electroless Ni-P coatings on pretreated magnesium alloys typically exhibit a nodular, cauliflower-like morphology with phosphorus content ranging from about 3.78–9.6 wt%. The corrosion current of the applied electroless coatings depends on pretreatment, thickness, porosity, and the corrosive medium. Most studies have used 3.5 wt% NaCl as corrosive media, revealing corrosion current densities from 0.0102 to 22.47 μA cm - ². The strategic importance of magnesium alloys and the advancements in pretreatment methods highlight the ongoing efforts to enhance their practical applications while addressing environmental concerns.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".