Implementing PEDOT:PSS as a Co-Filler for Electrically Conductive Adhesive Applications
Bibliographic record
Abstract
ABSTRACT Electrically conductive adhesives (ECAs) with hybrid fillers have attracted considerable attention due to their lower processing temperature, higher conductivity, simpler processability and finer-pitch capacity. Compared with traditional soldering technology, ECAs offer an environmental-friendly bonding solution in interconnections. In this work, we demonstrated that poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) could be applied as a conductivity enhancing agent in the epoxy and silver micro flakes system to develop a hybrid nanocomposite adhesive. The electrical conductivities of the hybrid ECAs with a constant total amount of silver flakes at various PEDOT:PSS weight concentrations were investigated. It was found that adding a small amount of PEDOT:PSS (0.09 wt%) remarkably improved the electrical conductivity to 289 S/cm, which is 3 times higher than that of the conventional ECA with 60 wt% sliver flakes. The maximum conductivity of 2422 S/cm was achieved at 0.89 wt% PEDOT:PSS concentration. The adhesive strength (or shear strength) was also evaluated for increasing weight loadings to determine whether there was an adverse effect when adding PEDOT:PSS into the composite. It was determined that as the weight loading of PEDOT:PSS was increased, the shear strength variance increased with it. Furthermore, the shear strength also appeared to have slightly decreased with higher weight loading. These results were presumed to come from various sources; however, it is suspected that the main culprit is excess water molecules remaining in the mixture even after evaporation as a result of the large amounts of PEDOT:PSS solution present in the process. Overall, the incorporation of PEDOT:PSS as a co-filler has shown a good potential in ECA applications.
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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) | 0.000 | 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".