Improved electrical and thermo-mechanical properties of a MWCNT/In–Sn–Bi composite solder reflowing on a flexible PET substrate
Bibliographic record
Abstract
Multi-walled carbon nanotube (MWCNT)/indium-tin-bismuth (In-Sn-Bi) composite nanostructures in which In-Sn-Bi nanoparticles have been penetrated by the MWCNT arrays were synthesized using a chemical reduction method. The incorporation of 0.6 wt% MWCNTs with high electrical conductivity into the In-based solder resulted in low minimum electrical resistivity (19.9 ± 1.0 µΩ·cm). Despite being reflowed at the relatively low temperature of 110 °C, the composite solder nanostructures were able to form mechanically stable solder bumps on a flexible polyethylene terephthalate (PET) substrate due to the MWCNT arrays with a high thermal conductivity of 3000 W/(m·K) and In-Sn-Bi nanoparticles with a low melting temperature of 98.2 °C. Notably, the composite solder bumps exhibited high flexibility (17.7% resistance increase over 1000 cycles of operation in a bending test) and strong adhesion strength (0.9 N average shear strength in a scratch test) on the plastic substrate because of the presence of mechanically flexible and strong MWCNTs dispersed within the solder matrix materials. These overall properties are due to the improved diffusivity of the composite solder nanostructures by the cover of the In-Sn-Bi nanoparticles along the MWCNT arrays and the network structure formation of the composite solder bumps.
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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".