Engineering Silver Microgrids with a Boron Nitride Nanotube Interlayer for Highly Conductive and Flexible Transparent Heaters
Bibliographic record
Abstract
Abstract The use of flexible transparent conductive electrodes (TCEs) as printed heaters offers unique advantages where transparency is a necessary design feature. Many existing TCE materials however suffer from poor flexibility and require complex fabrication processes and thus are not commercially viable for such applications. The design and process optimization of screen‐printable silver metal microgrids over a layer of boron nitride nanotubes (BNNT) to produce highly conductive and mechanically robust transparent heaters with high transparency and low power requirements is reported. Square and hexagonal geometries are investigated alongside varying line width and pitch combinations to produce 16 different grid designs with optical transparency ranging between 65% and 89% and resistance values as low as 2.90 Ω sq−1. The BNNT thin film coating on polyethylene terephthalate substrates provides mechanical stability to the heater architecture by reducing the effects of deformation by up to 400%. The BNNT interlayer also contributes to an increase in thermal performance, achieving temperatures as high as 74.0 °C by initiating electrical sintering of the microgrids during heater operation which increases the Joule heating capacity of the features. Overall, this physical and material optimization provides a low‐cost, printable microgrid architecture for high performing and stable applications as TCE‐based heaters.
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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".