Electrodeposition of Novel Nano-Composite Coatings of Niw Containing SiC and CeO<sub>2</sub> with Outstanding Tribological Properties and Corrosion Performance
Bibliographic record
Abstract
A special electrodeposition process utilizing a well-designed reversed pulse (RP) current waveform and specially formulated bath chemistry were used to produce nanostructured composite coating of nickel-tungsten (NiW) containing silicon carbide (SiC) and cerium oxide (CeO2). In this bath formulation, a specific type of propargyl derivative compound was used as a grain refiner due to its acetylene-type of bonding (i.e. -C≡C-H) at the end of its alkyl chain. This typical bond had a tendency to be adsorbed at high current density locations on the substrate being plated resulting in better control of nickel ion diffusion towards the cathode. Therefore, a uniform and defect-free deposit with mirror-finish surface was obtained. As well, this compound increased the nucleation sites for initiating of metal deposition on the surface of substrate resulting in the decrease in grain size of nickel. The resulting material demonstrated outstanding corrosion and tribological performances. It was found that the addition of SiC and CeO2 improved the corrosion performance of NiW. It was also found that the composite of NiW-SiC obtained by RP waveform, performed better in corrosion resistance compared to the same coatings deposited by DC waveform. The electrochemical corrosion tests including potentiodynamic polarization (PP) and cyclic potentiodynamic polarization (CPP) tests were used to evaluate the corrosion behaviors of the deposits. Following the PP tests, Time-of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS) was used to investigate the various corrosion products on deposits. As well, tribological properties including hardness, and wear resistance of the deposits were investigated
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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".