Enhanced Durability and Antireflective Performance of Ag-Based Transparent Conductors Achieved via Controlled N-Doping
Bibliographic record
Abstract
Ag-based transparent conductors (TCs) are often proposed as an alternative to ITO coatings. However, while their performance has been widely demonstrated, their environmental durability is frequently overlooked or addressed with the use of highly specific encapsulating layers. In this work, the durability and antireflective performance of Ag-based TCs are simultaneously enhanced. To do so, a transfer matrix modeling approach is used to determine the general requirements for high performance antireflective properties as a function of Ag thickness and dielectric refractive indices, offering more widely applicable insight into stack optimization. Coating durability is investigated as a function of the Ag microstructure, which is modified by altering the N 2 concentration used for doping of the Ag layer and the selection of the seed layer. Increasing N 2 concentration during Ag deposition was found to decrease grain size and durability of Ag coatings deposited on Si 3 N 4 whereas all coatings on ZnO (Al) showed higher stability. Significantly higher durability is found when specifically combining intermediate N 2 concentrations in the sputtering gas mixture (Ag (N):5%, compared to 0% and 50%) and a ZnO (Al) seed layer, and a mechanism accounting for this increased durability is proposed. The addition of NiCrN x protective coatings increases the system durability without altering these trends. These findings are combined to fabricate a highly performant Ag-based TC ( T V = 89.2%, R V FS = 0.23%, 21.4 Ω), which shows minimal property changes following corrosion testing by immersion in a heated and highly concentrated aqueous NaCl solution (200 g/L, 50 °C).
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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) | 0.001 | 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".