Flexible Embedded Metal Meshes by Nanosphere Lithography for Very Low Sheet Resistance Transparent Electrodes, Joule Heating, and Electromagnetic Interference Shielding
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide We demonstrate the highest transparent electrode performance among metal meshes fabricated via nanosphere lithography (NSL), achieving an order-of-magnitude improvement in the figure of merit FoM (σ DC /σ OP ). Additionally, we present, for the first time, the application of metal meshes fabricated by NSL for transparent electromagnetic interference (EMI) shielding, enabled by exceptional improvements in sheet resistance. Our NSL method produces substrate-embedded metal meshes in PET and glass by etching trenches, yielding high-aspect-ratio features with low sheet resistance. Embedded structures also exhibit superior robustness during bending and tape tests compared to sputtered metallic films on the surface. As a transparent electrode, the flexible Ag meshes exhibit a sheet resistance of 1.52 Ω/sq and transparency of 73.1% as well as a sheet resistance of 0.22 Ω/sq and transparency of 58.1%, corresponding to FoMs of 737 and 2736, respectively. For transparent EMI shielding, the flexible metal meshes achieve a shielding efficiency (SE) of 34.5 dB with 73.1% visible transmission and an EMI SE of 52.8 dB with 58.1% visible transmission. As a flexible heater, the metal meshes can reach a saturation temperature exceeding 70 ◦ C within 60 s under an applied voltage of 1.2 V. These embedded metal meshes hold promise for applications requiring ultralow sheet resistance, including heated windows and defrosting systems, large-area organic light-emitting diode (OLED) lighting and displays, solar cells, and EMI shielding.
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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".