The Electro-Optical Performance of Silver Nanowire Networks
Bibliographic record
Abstract
Networks of metallic nanowires have the potential to meet the needs of next-generation device technologies that require flexible transparent conductors.At present, there does not exist a first principles model capable of predicting the electro-optical performance of a nanowire network.Here we combine an electrical model derived from fundamental material properties and electrical equations with an optical model based on Mie theory scattering of light by small particles.This approach enables the generation of analogues for any nanowire network and then accurately predicts, without the use of fitting factors, the optical transmittance and sheet resistance of the transparent electrode.Predictions are validated using experimental data from the literature of networks comprised of a wide range of aspect ratios (nanowire length/diameter).The separation of the contributions of the material resistance and the junction resistance allows the effectiveness of post-deposition processing methods to be evaluated and provides a benchmark for the minimum attainable sheet resistance.The predictive power of this model enables a material-by-design approach, whereby suitable systems can be prescribed for targeted technology applications.
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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".