Towards sustainable transparent flexible heaters: Integration of a BNNT interlayer using green solvent substitution
Bibliographic record
Abstract
Abstract Processing materials in electronics with non-toxic, green solvents can provide environmental benefits while reducing manufacturing health and safety challenges. Unfortunately, green solvents are often unable to provide comparable solubilizing characteristics and present challenges in printing and film formation compared to conventional organic solvents. Therefore, green materials are often developed in parallel to their processing method for successful implementation. In this study, we report on the use of a polyvinyl butyral (PVB) and ethanol solution as a replacement for poly (3-hexylthiophene-2,5-diyl) (P3HT) and chloroform and its’ first demonstration in boron nitride nanotube (BNNT) thin film interlayers for improved thermal and mechanical performance in silver microgrid transparent heaters. Using PVB/ethanol led to comparable thin films of BNNT, achieving a clear tube network formation across the substrate surface and resulting in near identical optical transparency and surface energy measurements compared to the P3HT/chloroform system. Silver microgrids printed on BNNT-coated polyethylene terephthalate (PET) with PVB as dispersant exhibited a similar conductive performance to the microgrids printed on BNNT-coated PET with P3HT, providing the same level of mechanical endurance and maintaining thermal performance metrics upon applied voltage. The PVB and ethanol system presents an exemplary green material combination for the novel deposition of BNNT thin film interlayers for integration into transparent 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".