Patterned, Flexible, and Stretchable Silver Nanowire/Polymer Composite Films as Transparent Conductive Electrodes
Bibliographic record
Abstract
The emergence of flexible and stretchable optoelectronics has motivated the development of new transparent conductive electrodes (TCEs) to replace conventional brittle indium tin oxide. For modern optoelectronics, these new TCEs should possess six key characteristics: low cost, solution-based processing; high transparency; high electrical conductivity; a smooth surface; mechanical flexibility or stretchability; and scalable, low-cost patterning methods. Among many materials currently being studied, silver nanowires (AgNWs) are one of the most promising, with studies demonstrating AgNW films and composites that exhibit each of the key requirements. However, AgNW-based TCEs reported to date typically fulfill two or three requirements at the same time, and rare are examples of TCEs that fulfill all six requirements simultaneously. Here, we present a straightforward method to fabricate AgNW/polymer composite films that meet all six requirements simultaneously. Our fabrication process embeds a AgNW network patterned using a solution-based wetting-dewetting protocol into a flexible or stretchable polymer, which is then adhered to an elastomeric poly(dimethylsiloxane) substrate. The resulting patterned AgNW/polymer films exhibit ∼85% transmittance with an average sheet resistance of ∼15 Ω/sq, a smooth surface (a root-mean-square surface roughness value of ∼22 nm), and also withstand up to 71% bending strain or 70% stretching strain. We demonstrate the use of these new TCEs in flexible and stretchable alternating current electroluminescent devices that emit light to 20% bending strain and 60% stretching strain.
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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".