Improving Printed and Thermoformed Conductors on Polycarbonate with a Thin-Film BNNT Interlayer for Next-Generation In-Mold Electronics
Bibliographic record
Abstract
The processes of thermoforming 2D-printed electronics into 3D structures can introduce defects that impact the electrical performance of conductors, making them more susceptible to thermal failure during high electrical power/current applications on temperature-sensitive substrates. We therefore report the use of a thin-film boron nitride nanotube (BNNT) interlayer to directly reduce heat stress on linear and serpentine metallic traces on polycarbonate substrates thermoformed to 3D spherocylindrical geometries at varying elongation percentages. We demonstrate that the BNNT interlayer helps to improve the electrical conductivity of highly elongated thermoformed 3D traces in comparison to traces on bare polycarbonate. Further, we correlate localized substrate thinning at high elongation areas with increases in the local trace resistance. These resistance increases create localized "hot spots" in the traces when high voltages and currents are applied to them. BNNT interlayers provide thermal protection to the underlying substrate and enable them to endure localized temperatures 1.5 times higher than those on bare substrates, as high currents are applied to the silver traces. Overall, this study demonstrates the use of BNNT interlayers as valuable thermal management materials to facilitate the development of more reliable and higher-performing conductive metal traces for use in 3D electronics and in-mold electronics applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 teacher head, 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".