In-Situ Resistance Characterization During Cure Progression For Electrically Conductive Adhesives
Bibliographic record
Abstract
ABSTRACT Alternatives A new approach has been developed for in-situ characterization of electrical resistance of thermosetting conductive adhesives during the cure process. It has long been known that conductive adhesives have poor conductivity while in their uncured state, and develop conductivity as the polymer cures and forms an increasingly connected network of conductive fillers. In this paper, we will present a novel method for obtaining sheet resistance measurements of a conductive thermosetting composite under controlled heating conditions, during the crosslinking process. In-situ resistance measurements are compared with calorimetry performed under the same heating conditions, allowing direct correlation. As a result, a distinct behavior during the final stages of cure has been observed for the first time: a significant and temporary increase in electrical resistance mid-cure, after the resistance has begun decreasing, contrary to the power-law reduction as cure progresses. The dynamics of this process, and the final adhesive properties, are not yet understood. As such, this new in-situ technique will be instrumental for ongoing work to improve the conductivity of adhesives at lower filler contents and costs.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".