Hybrid Copper–Silver Conductive Tracks for Enhanced Oxidation Resistance under Flash Light Sintering
Bibliographic record
Abstract
We developed a simple method to prepare hybrid copper-silver conductive tracks under flash light sintering. The developed metal nanoparticle-based ink is convenient because its preparation process is free of any tedious washing steps. The inks were composed of commercially available copper nanoparticles which were mixed with formic acid, silver nitrate, and diethylene glycol. The role of formic acid is to remove the native copper oxide layer on the surface of the copper nanoparticles. In this way, it facilitates the formation of a silver outer shell on the surface of the copper nanoparticles through a galvanic replacement. In the presence of formic acid, the copper nanoparticles formed copper formate, which was present in the unsintered tracks. However, under illumination by a xenon flash light, the copper formate was then converted to copper. Moreover, the resistance of the copper-only films increased by 6 orders of magnitude when oxidized at high temperatures (∼220 °C). However, addition of silver nitrate to the inks suppressed the oxidation of the hybrid copper-silver films, and the resistance changes in these inks at high temperatures were greatly reduced. In addition, the hybrid inks proved to be advantageous for use in electrical circuits as they demonstrated a stable electrical conductivity after exposure to ambient air at 180 °C.
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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".