Boron Nitride Nanotube Coatings for Thermal Management of Printed Silver Inks on Temperature Sensitive Substrates
Bibliographic record
Abstract
Abstract Printed electronics provide inexpensive and light weight electrical components to fuel emerging applications. One major challenge is the high temperature required to sinter conductive metal inks, which leads to thermal degradation of the substrate and subsequently poor performance. A boron nitride nanotube (BNNT) interfacial film is reported for thermal management in rapid processing of a printable silver molecular ink platform using intense pulsed light (IPL) sintering techniques. The inclusion of BNNT thin films of varying surface concentrations deposited between the substrate and the printed features reduces thermal damage to the substrate during sintering while simultaneously improving electrical performance, achieving a sheet resistance value as low as 140 mΩ sq−1. A wide range of sintering energies ranging from 2.0 and 3.2 J cm−2 are investigated along with printed trace widths ranging from 5 mil (0.127 mm) to 20 mil (0.508 mm). Increases in the rate of cooling and in the current carrying capacity are confirmed with the inclusion of the BNNTs. Overall the thin coating of BNNTs presents no drawbacks while significantly improving the electrical properties of IPL sintered conductive traces and thus represents a simple approach that will advance the adoption of IPL for fabricating printed electronic components.
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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".