Friction-enhanced formation of Cu microwire on Si wafer
Bibliographic record
Abstract
Abstract Tribological printing is emerging as a promising technique for micro/nano manufacturing. A significant challenge is enhancing efficiency and minimizing the need for thousands of sliding cycles to create nano- or microstructures (2018 ACS Appl. Mater. Inter. 10 335–47, 2019 Nanotechnology 30 302 ). This study presents a rapid approach for forming Cu microwires on Si wafers through a friction method during the evaporation of an ethanol-based lubricant containing Cu nanoparticles. The preparation time is influenced by the volume of the lubricant added, with optimal conditions reducing the time to 300 s (600 sliding cycles) for producing Cu microwires with a thickness of 200 nm. Key aspects include the lubricating effect of ethanol on the friction pairs and the role of ethanol evaporation in the growth of Cu microwires. Successful formation requires a careful balance between microwire thickening and wear removal. The resulting Cu microwires demonstrate mechanical and electrical properties that make them suitable as micro conductors. This work provides a novel approach for fabricating conductive microstructures on Si surfaces and other curved surfaces, offering potential applications in microelectronics and sensor technologies.
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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".