The Joule heating problem in silver nanowire transparent electrodes
Bibliographic record
Abstract
Silver nanowire transparent electrodes have shown considerable potential to replace conventional transparent conductive materials. However, in this report we show that Joule heating is a unique and serious problem with these electrodes. When conducting current densities encountered in organic solar cells, the average surface temperature of indium tin oxide (ITO) and silver nanowire electrodes, both with sheet resistances of 60 ohms/square, remains below 35 °C. However, in contrast to ITO, the temperature in the nanowire electrode is very non-uniform, with some localized points reaching temperatures above 250 °C. These hotspots accelerate nanowire degradation, leading to electrode failure after 5 days of continuous current flow. We show that graphene, a commonly used passivation layer for these electrodes, slows nanowire degradation and creates a more uniform surface temperature under current flow. However, the graphene does not prevent Joule heating in the nanowires and local points of high temperature ultimately shift the failure mechanism from nanowire degradation to melting of the underlying plastic substrate. In this paper, surface temperature mapping, lifetime testing under current flow, post-mortem analysis, and modelling illuminate the behaviour and failure mechanisms of nanowires under extended current flow and provide guidelines for managing Joule heating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".