Excess Polymer in Single-Walled Carbon Nanotube Thin-Film Transistors: Its Removal Prior to Fabrication Is Unnecessary
Bibliographic record
Abstract
Ultrapure semiconducting single-walled carbon nanotube (sc-SWNT) dispersions produced through conjugated polymer sorting are ideal candidates for the fabrication of solution-processed organic electronic devices on a commercial scale. Protocols for sorting and dispersing ultrapure sc-SWNTs with conjugated polymers for thin-film transistor (TFT) applications have been well refined. Conventional wisdom dictates that removal of excess unbound polymer through filtration or centrifugation is necessary to produce high-performance TFTs. However, this is time-consuming, wasteful, and resource-intensive. In this report, we challenge this paradigm and demonstrate that excess unbound polymer during semiconductor film fabrication is not necessarily detrimental to device performance. Over 1200 TFT devices were fabricated from 30 unique polymer-sorted SWNT dispersions, prepared using two different alternating copolymers. Detailed Raman spectroscopy, x-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM) studies of the random-network semiconductor films demonstrated that a simple solvent rinse during TFT fabrication was sufficient to remove unbound polymer from the sc-SWNT films, thus eliminating laborious polymer removal before TFT fabrication. Furthermore, below a threshold polymer concentration, the presence of excess polymer during fabrication did not significantly impede TFT performance. Preeminent performance was achieved for devices prepared from native polymer-sorted SWNT dispersions containing the "original" amount of excess unbound polymer (immediately following enrichment). Lastly, we developed an open-source Machine Learning algorithm to quantitatively analyze AFM images of SWNT films for surface coverage, number of tubes, and tube alignment.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".