Bimodal phase separated block copolymer/homopolymer blends self-assembly for hierarchical porous metal nanomesh electrodes
Bibliographic record
Abstract
Transparent conducting electrodes (TCEs) are essential components in various optoelectronic devices. Nanostructured metallic thin film is one of the promising candidates to complement current metal oxide films, such as ITO, where high cost rare earth elements have been a longstanding issue. Herein, we present that multiscale porous metal nanomesh thin films prepared by bimodal self-assembly of block copolymer (BCP)/homopolymer blends may offer a new opportunity for TCE. This hierarchical concurrent self-assembly consists of macrophase separation between BCP and homopolymer as well as microphase separation of BCP, and thus provides a straightforward spontaneous production of a highly porous multiscale pattern over an arbitrary large area. Employing a conventional pattern transfer process, we successfully demonstrated a multiscale highly porous metallic thin film with reasonable optical transparency, electro-conductance, and large-area uniformity, taking advantage of low loss light penetration through microscale pores and significant suppression of light reflection at the nanoporous structures. This well-defined controllable bimodal self-assembly can offer valuable opportunities for many different applications, including optoelectronics, energy harvesting, and membranes.
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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".