Oxynitride-Encapsulated Silver Nanowire Transparent Electrode with Enhanced Thermal, Electrical, and Chemical Stability
Bibliographic record
Abstract
Silver nanowire (AgNW) networks have been explored as a promising technology for transparent electrodes due to their solution-processability, low-cost implementation, and excellent trade-off between sheet resistance and transparency. However, their large-scale implementation in applications such as solar cells, transparent heaters, and display applications has been hindered by their poor thermal, electrical, and chemical stability. In this work, we present reactive sputtering as a method for fast deposition of metal oxynitrides as an encapsulant layer on AgNWs. Because O 2 cannot be used as a reactive gas in the presence of oxidation-sensitive materials such as Ag, N 2 is used under moderate sputtering base pressures to leverage residual H 2 O on the sample and chamber to deposit Al, Ti, and Zr oxynitrides (AlO x N y, TiO x N y, and ZrO x N y ) on Ag nanowires on glass and polymer substrates. All encapsulants improve AgNW networks’ electrical, thermal, and chemical stability. In particular, AlO x N y -encapsulated networks present exceptional chemical stability (negligible increase in resistance over 7 days at 80% relative humidity and 80 °C) and transparency (96% for 20 nm films on AgNWs), while TiO x N y demonstrates exceptional thermal and electrical stability (stability up to over temperatures 100 °C more than that of bare AgNW networks, with a maximum areal power density of 1.72 W/cm 2, and no resistance divergence at up to 20 V), and ZrO x N y presents intermediate properties in all metrics. In summary, a novel method of oxynitride deposition, leveraging moderate base pressure reactive sputtering, is demonstrated for AgNW encapsulant deposition, which is compatible with roll-to-roll processes that are operated at commercial scales, and this technique can be extended to arbitrary, vacuum-compatible substrates and device architectures.
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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".