How interface compatibility affects conductivity evolution of silver nanobelts-filled electrically conductive composites during cure and post-treatments
Bibliographic record
Abstract
Using silver nanobelts and silver microflakes in a DGEBA/TETA epoxy matrix, we sought to investigate the relationship between the evolving electrical resistivity of formulations of hybrid nanocomposites during the curing process. This was characterized using three methods: (i) in situ four-wire electrical resistance measurements; (ii) differential scanning calorimetry, and (iii) dilatometry. In a previous work we reported that the resistivity of microcomposites was strongly affected by partial vitrification during curing. In this study, the reported vitrification effect is observed again, further validating the concern of far-ranging implications on the industry practices. The addition of silver nanobelts greatly improved conductivity of the composites, though it was observed that the improvements are often lost during subsequent heating and cooling cycles. Resistivity observations indicate that the sensitivity may be due to insufficient nanobelt-nanobelt contacts in the composite, and thus further increasing the nanobelt fraction of the filler content can maximize 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 | 0.000 | 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 | 0.000 |
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".