Tribological, mechanical and electrochemical properties of nanocrystalline copper deposits produced by pulse electrodeposition
Bibliographic record
Abstract
Nanocrystalline metals and alloys with grain sizes smaller than 100 nm have attracted extensive interest due to their improved mechanical, physical and chemical properties. Although electrodeposition has been one of the methods for synthesizing nanocrystalline materials, properties of nanocrystalline electrodeposits are less evaluated, especially for tribological applications or potential applications in nanoscale devices such as MEMS and NEMS. In this work, nanocrystalline and microcrystalline copper deposits were produced by pulse and direct current electrodeposition processes respectively. Effects of deposition parameters, such as the peak density, frequency, current-on time and current-off time of the pulse current (PC), on the grain size were investigated for the purpose of process optimization. The grain size of nanocrystalline coatings was determined using x-ray diffraction and atomic force microscopy (AFM). Mechanical and tribological properties of the deposits were investigated using nanoindentation, nanoscratch and microscratch techniques. It was demonstrated that the nanocrystalline film was markedly superior to regularly grained film made by direct current (DC) plating; the nanocrystalline deposit shows higher hardness, lower friction coefficient and lower wear rate. The surface electron stability and chemical reactivity of the deposits were also evaluated by measuring their electron work function (EWF). Results indicate that the nanocrystalline surface is more electrochemically stable than the DC-plated one. This increased stability result is attributed to the formation of a stronger and more adherent passive film on the nanocrystalline copper, confirmed by potentiodynamic polarization and electrical contact resistance measurements.
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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) | 0.001 | 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".