Segregation-controlled self-assembly of silver nanowire networks using a template-free solution-based process
Bibliographic record
Abstract
Metal conductive patterning has been studied as an alternative to the most commonly used indium tin oxide electrodes. Printed electrodes are fabricated by several complicated processes including etching, photolithography, and laser- and template-based techniques. However, these patterning methods have increasingly encountered critical issues of long manufacturing times and high equipment costs that necessitate vacuum and high-temperature conditions. In this study, we present a template-free solution-based patterning method for the fabrication of transparent electronics by inducing segregation-based networks of silver nanowires (SGAgNWs); this is a potential method to fabricate cost effective and scalable optoelectronics. Micro-dimensional fine-patterned segregated networks with conductive cells are created by the self-assembly of one-dimensional nanomaterials under optimal ink conditions wherein different types of solvents and aspect ratios of silver nanowires (AgNWs) are formulated. Photoelectric properties can be controlled by adjusting the size of the cell, which is an empty domain surrounded by the AgNW assembly with microscale cell-to-cell distance dimensions ranging between 4 to 345 μm. The as-obtained AgNW metal grid-formulated on a polyethylene terephthalate film-was identified as a high-performance transparent electrode (TE) device with excellent optoelectronic properties of 87.08% transmittance and 50 Ω □-1 resistance. In addition, the electrical conductivity of the TE film is enhanced with a very low haze of less than 4% because of the intense pulsed light treatment that diminished the sheet resistance to 21.36 Ω □-1, which is attributed to the creation of welded silver networks. The SGAgNW concept for TE technology demonstrates a very promising potential for use in next-generation flexible electronic devices.
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 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".