Composites of Shellac and Silver Nanowires as Flexible, Biobased, and Corrosion-Resistant Transparent Conductive Electrodes
Bibliographic record
Abstract
Silver nanowires (AgNWs) are one of the most promising materials to replace indium tin oxide as transparent conductive electrodes (TCEs) in next-generation flexible optoelectronic devices. AgNWs are more environmentally friendly than indium tin oxide, and additionally offer solution processability, high conductivity, and high optical transparency. However, a barrier to practical use is the tendency of AgNWs to corrode in the ambient environment, which damages the AgNW network and reduces life span. This study presents the use of shellac, and eco-friendly natural biopolymer, as a planarizing and protective matrix for AgNWs. Shellac has not been significantly explored in flexible electronics but has a long history as a protective coating due to its high gas barrier properties. We report the first example of a shellac-based TCE comprising a AgNW network embedded at the surface of a shellac matrix. Shellac-AgNW TCEs provide high conductivity, optical transparency, and mechanical stability. The shellac matrix protects AgNWs from corrosion in challenging environments of humidified air and corrosive acid vapors. As concerns about the environmental persistence of synthetic polymers intensify, we demonstrate that shellac is a promising material for next generation flexible electronics.
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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".