Phenanthroline Additives for Enhanced Semiconducting Carbon Nanotube Dispersion Stability and Transistor Performance
Bibliographic record
Abstract
Dispersions of purely semiconducting single-walled carbon nanotubes (SWCNTs) have enabled solution-processed SWCNT networks as active layers in field-effect transistors (FETs) with high carrier mobilities and excellent on/off current ratios. Although reproducibility has improved in recent years, reaching the level that is required for commercial large-scale processing remains a challenge. A key issue is the tendency of SWCNTs to aggregate over time, resulting in network inhomogeneities that cause large device performance variations. Based on the tailored formulation of colloidal inks by the choice of solvent and use of additives, we demonstrate the strong stabilization effect of phenanthroline additives on polymer-sorted (6,5) SWCNT using time-dependent near-infrared absorption spectroscopy as a fast and simple assessment tool for the aggregation rate. The addition of the N-heteropolycycle 1,10-phenanthroline significantly extends the stability of dispersions of polymer-wrapped nanotubes in toluene and hence improves the morphology of spin-coated networks even after ink storage for several days. Bottom-contact, top-gate FETs based on such networks show much higher charge carrier mobilities and drastically reduced device-to-device variations compared to devices based on SWCNT dispersions without phenanthroline. Nanotube ink formulations with small-molecule additives are an important step toward reproducible device parameters and are crucial for the translation of nanotube FETs from the laboratory to commercial applications.
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 | 0.001 | 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".