Coordinating the pore size of paper substrates and aspect ratio of silver nanowires to improve printed electronics
Bibliographic record
Abstract
Abstract The internet of things is advancing toward a world of ubiquitous electronic devices, composed in large part of low-cost printed electronics (PE) such as sensors. PE typically use plastic substrates, such as polyethylene terephthalate (PET), but these materials are not biodegradable. The proliferation of PE devices and their degradation to form micro- and nanoplastics pose significant environmental hazards. Paper is a promising substrate to replace PET for greener PE due to its recyclability, affordability, and compatibility with many printing processes. However, the porous cellulosic structure of paper can be an obstacle when trying to print active inks due to wicking of the ink into the paper pores, which disperses the functional ink and negatively impacts electronic performance. Filling the pores of paper with a polymer to planarize the surface is a commonly used remedy, although this approach can compromise recyclability. Here, we present an approach to manage the dispersion of silver nanowires, a widely used and printable 1D nanomaterial ink, in paper substrates. We deposit solutions of short (20–30 μms) and long (100–200 μms) silver nanowires onto various graded filter papers that differ in pore size and examine the trends in wicking distance, wicking speed, and electrical properties. We show that with careful selection of AgNW length and the pore size of the paper, it is possible to control the lateral spreading of the ink and minimize the concentration of the AgNWs needed to achieve a specific electrical performance.
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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 | 0.001 |
| 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 | 0.000 | 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".