Study of the microstructure, morphology, micro- and nanoindentation hardness, and corrosion behaviour of Ni-SiO <sub>2</sub> composites electrodeposited from a deep eutectic solvent
Bibliographic record
Abstract
Composite coatings containing Ni and small amounts of SiO 2 nanoparticles (NPs) were electrodeposited from an environmentally friendly deep eutectic solvent (DES) containing 0, 15, and 30 g/L of SiO 2 . The effects of the presence of SiO 2 in the Ni coatings were investigated in relation to their microstructure, as well as their mechanical and corrosion properties. The average crystallite size of Ni decreased by approximately 9% with the incorporation of 30 g/L SiO 2 . At this SiO 2 concentration, the Si content reached 0.56 wt.% in the bulk and 4.1 wt.% at the surface of the coating based on the results obtained by EDX and X-ray photoelectron spectroscopy, respectively. Microstructure studies of all coatings identified a granular growth with a broad Ni cluster size distribution. With the addition of 30 g/L of SiO 2 , the surface roughness decreased, and the normality of the surface texture improved. Maximum micro- and nanoindentation hardness values of 4.9 GPa 0.0981 N (500 Hv 0.0981 N ) and 6.7 GPa 20 mN , respectively, were achieved with the addition of 30 g/L of silica nanopowder, representing increases of 18% and 8% compared to pure Ni. Electrochemical results showed that the incorporation of 15 and 30 g/L SiO 2 , along with reduced surface defects, significantly improved polarization behaviour. This enhancement is attributed to improved passive film formation and the role of SiO 2 as a corrosion barrier. Corrosion current density decreased by factors of 11 and 7, respectively, compared to unreinforced Ni.
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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) | 0.000 | 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 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".