Networks of Conjugated Polymer-Wrapped Single-Walled Carbon Nanotubes through Controlled Drop-Dispensing for Thin-Film Transistors
Bibliographic record
Abstract
Despite the simplicity of the drop-casting method to deposit films of single-walled carbon nanotubes (SWNTs) for applications such as printed electronics, this technique presents many challenges and uncertainties that are yet to be addressed. The coffee-ring effect is a known example, which results in an accumulation of solids around the edge of the drop. This can be mitigated by increasing the temperature of the substrate to induce Marangoni flows, but it is unknown what the repercussions of this change would be on dispersions of conjugated polymer-wrapped SWNTs and resulting thin-film transistor devices. In this study, we demonstrate that the drop-casting method benefits greatly from the use of an automated drop-dispenser coupled with a desktop robot, leading to more consistent and improved charge-carrier mobilities (μ) and threshold voltages. We then present the effect of drop size and substrate temperature on the surface coverage, linear density, and appearance of the resulting SWNT networks. A reduction in surface coverage and linear density was noted with increasing temperature above 50 °C, in addition to increased bundling of the SWNTs, as observed in atomic force microscopy images. Raman spectroscopy demonstrated that drops cast at higher temperatures allow for a more even distribution of the SWNTs throughout the drop due to the formation of deposits during transitions between constant contact radius and constant contact angle modes. The devices resulting from drop-casting at higher temperatures also exhibit decreased mobilities (μ) at smaller drop volumes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".