The resistance of Cu nanowire–nanowire junctions and electro-optical modeling of Cu nanowire networks
Bibliographic record
Abstract
Flexible transparent conductors made from networks of metallic nanowires are a potential replacement for conventional, non-flexible, and transparent conducting materials such as indium tin oxide. Cu nanowires are particularly interesting as cost-effective alternatives to Ag nanowires—the most investigated metallic nanowire to date. To optimize the conductivity of Cu nanowire networks, the resistance contributions from the material and nanowire junctions must be independently known. In this paper, we report the resistivity values (ρ) of individual solution-grown Cu nanowires ⟨ρ⟩ = 20.1 ± 1.3 nΩ m and the junction resistance (Rjxn) between two overlapping Cu nanowires ⟨Rjxn⟩ = 205.7 ± 57.7 Ω. These electrical data are incorporated into an electro-optical model that generates analogs for Cu nanowire networks, which accurately predict without the use of fitting factors the optical transmittance and sheet resistance of the transparent electrode. The model's predictions are validated using experimental data from the literature of Cu nanowire networks composed of a wide range of aspect ratios (nanowire length/diameter). The separation of the material resistance and the junction resistance allows the effectiveness of post-deposition processing methods to be evaluated, aiding research and industry groups in adopting a materials-by-design approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".