Silver-Polyaniline-Epoxy Electrical Conductive Adhesives - A Percolation Threshold Analysis
Bibliographic record
Abstract
ABSTRACT Electrically conductive adhesives (ECA) find extensive applications in electronic manufacturing and packaging industries. Compared to the soldering technology, adhesive joining offers numerous advantages including mild processing conditions, fewer processing steps (reducing process cost), and especially, the fine pitch capability due to the availability of small size conductive fillers. ECA’s function is based on the conductive fillers, typically 1-10μ in size, supported in a polymeric matrix. To be conductive, the loading of fillers needs to be above its percolation threshold - the minimum fraction of fillers needed to establish the conducting network. In this paper, we report a systematic study of percolation thresholds of silver-epoxy (Ag-epoxy) and silver-polyaniline-epoxy (Ag-PANI-epoxy) system. The polyaniline (PANI) has a moderate conductivity in between the silver and epoxy; it was used to dope the epoxy matrix to minimize two problems in the ECAs: (1) localization of charge carriers because of the aggregation of the silver fillers, and (2) the interfacial polarization arising from the intrinsic difference in polarities and surface energies of fillers and epoxy. Two different methods were used to prepare the Ag-PANI-epoxy adhesives. The first method was to add silver fillers into liquid epoxy resin, add PANI, and then the amine hardener. The second method was to add PANI into liquid epoxy resin, add silver fillers, and then the amine hardener. Our experiments showed that silver fillers were well dispersed in cured adhesives prepared in the first method while silver fillers formed aggregates in cured adhesives prepared in second method. We also propose a mechanism of conduction based on surface properties and interactions between fillers and the epoxy matrix. Our results and analysis may help to explain the reduced percolation threshold and enhanced conductivity.
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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 | 0.001 | 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".