A Modular Solder System with Hierarchical Morphology and Backward Compatibility
Bibliographic record
Abstract
Abstract A modular solder system with hierarchical morphology and micro/nanofeatures in which solder nanoparticles are distributed on the surface of template micropowders is reported. A core–shell structure of subsidiary nanostructures, which improved the intended properties of the modular solder is also presented. In addition, polymer additives can be used not only as an adhesive (like epoxy resin) but also to impart other functions. By combining all of these, it is determined that the modular solder system is able to increase reflowability on a heat‐sensitive plastic substrate, oxidation resistance, and electrical conductivity. In this respect, the system could be readily modified by changing the structure and composition of each constituent and adopting backward compatibility with which the knowledge and information attained from a previously designed solder can offer feedback toward further improving the properties of a newly designed one. In practice, In–Sn–Bi nanoparticles engineered on the surface of Sn–Zn micropowders result in pronounced reflowing on a flexible Au‐coated polyethylene terephthalate (PET) substrate even at the low temperature of 110 °C. Depending on their respective concentrations, the incorporation of CuO@CeO 2 nanostructures and poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) polymers increases oxidation resistance and electrical conductivity of the modular solder.
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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) | 0.001 | 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 | 0.001 |
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".