Fully R2R‐Printed Carbon‐Nanotube‐Based Limitless Length of Flexible Active‐Matrix for Electrophoretic Display Application
Bibliographic record
Abstract
Abstract A limitless‐length flexible active‐matrix implies that virtually any surface can be rendered into an interactive medium when laminated with electrophoretic or organic light‐emitting diode sheets. However, performance, cost, and size limitations of current fabrication technologies and semiconducting materials, typically utilized in thin film transistor (TFT) active matrices (TFT‐AMs), have hindered progress, thus preventing the realization of fully printed TFT‐AMs on a plastic roll. A new high‐purity semiconducting single‐walled carbon nanotube (s‐SWCNT) ink is prepared by first isolating 99.9% pure s‐SWCNTs via conjugated polymer extraction, and then utilizing a ligand‐exchange method to formulate a novel hydrophilic gravure‐compatible semiconducting ink. Based on the s‐SWCNT ink, a fully additive manufacturing process using roll‐to‐roll (R2R) gravure printing enables the fabrication of a flexible TFT‐AM, overcoming performance, cost, and size limitations. TFT‐AMs with 10 to 40 PPI resolution where average mobility of 0.23 ± 0.12 cm 2 V −1 s −1 , average on–off ratio of 10 4.1 , and threshold voltage variation of ±13% are attained. As a proof of concept, an inexpensive and flexible electrophoretic display is demonstrated by simply laminating an electrophoretic sheet onto the R2R gravure‐printed s‐SWCNT‐based TFT‐AM.
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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) | 0.002 | 0.001 |
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".