The Use of Graphene to Replace Silver in Electricaly Conductive Adhesives - A Study on Electrical Conductivity and Mechanical Properties
Bibliographic record
Abstract
ABSTRACT In this talk, we will present the research progress on the use of graphene to reduce the amount of silver in electrically conductive adhesives. The graphene nanosheets were first modified using sodium dodecyl sulfate (SDS) and used as auxiliary fillers inside the conventional electrically conductive adhesive (ECA) composite. Using the SDS modification approach, we were able to facilitate the dispersion of graphene inside the composite, which resulted in a significant electrical conductivity improvement of ECAs at noticeably low filler content. Addition of 1.5 wt% SDS-modified graphene into the conventional ECA with 10 wt% silver flakes led to a relatively low electrical resistivity of 35 Ω.cm, while at least 40 wt% of silver flakes was required for the conventional and the hybrid ECAs with non-modified graphene to be electrically conductive. A highly conductive ECA with very low bulk resistivity of 1.6 × 10 -5 Ω.cm was prepared by adding 1.5 wt% of SDS-modified graphene into the conventional ECA with 80 wt% silver flakes. The mechanical properties of the ECA were investigated using the Hertzian indentation method and found that SDS-stabilized graphene nanosheets increased the modulus of the nanocomposites at much lower weight percentages. However, once it passes a certain weight percent, the modulus begins to quickly decrease.
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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) | 0.000 | 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".