Self-generated Local Heating Induced Nanojoining for Room Temperature Pressureless Flexible Electronic Packaging
Bibliographic record
Abstract
Metallic bonding at an interface is determined by the application of heat and/or pressure. The means by which these are applied are the most critical for joining nanoscale structures. The present study considers the feasibility of room-temperature pressureless joining of copper wires using water-based silver nanowire paste. A novel mechanism of self-generated local heating within the silver nanowire paste and copper substrate system promotes the joining of silver-to-silver and silver-to-copper without any external energy input. The localized heat energy was delivered in-situ to the interfaces to promote atomic diffusion and metallic bond formation with the bulk component temperature stays near room-temperature. This local heating effect has been detected experimentally and confirmed by calculation. The joints formed at room-temperature without pressure achieve a tensile strength of 5.7 MPa and exhibit ultra-low resistivity in the range of 101.3 nOhm · m. The good conductivity of the joint is attributed to the removal of organic compounds in the paste and metallic bonding of silver-to-copper and silver-to-silver. The water-based silver nanowire paste filler material is successfully applied to various flexible substrates for room temperature bonding. The use of chemically generated local heating may become a potential method for energy in-situ delivery at micro/nanoscale.
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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".