Dielectrics & Electrostatics: Their Effect on Carbon Nanotube Network Field-Effect Transistors and Gas Sensors
Bibliographic record
Abstract
Due to their high surface area, monoatomic wall thickness, electronic structure (band gap, van Hove singularities) and properties (high mobility, ambipolar transport), single-walled carbon nanotubes (SWNTs) stand as a promising active material for chemical sensors. In a sensor context, they however suffer from lack of selectivity. For the same reasons that they are highly sensitive to analytes, they will be affected by their environment: interference from ambient gases (i.e. O2 / H2O redox couple) are to be expected [1,2], and vicinal charges and dipoles [3,4] will change the local electrostatics felt by the SWNTs. The dielectric environment being an integral part of the field-effect transistor (FET) stack, we sought to understand its effect on the transfer characteristics of devices with a carbon nanotube network as the active channel component. References [1] Aguirre, C. M. et al. The Role of the Oxygen/Water Redox Couple in Suppressing Electron Conduction in Field-Effect Transistors. Adv. Mater. 21, 3087–3091 (2009). [2] Lefebvre, J. et al. Hysteresis free carbon nanotube thin film transistors comprising hydrophobic dielectrics. Appl. Phys. Lett. 107, 243301 (2015). [3] Kobayashi, S. et al. Control of carrier density by self-assembled monolayers in organic field-effect transistors. Nat. Mater. 3, 317–322 (2004). [4] Lee, S.-H. et al. Effect of Polymer Gate Dielectrics on Charge Transport in Carbon Nanotube Network Transistors: Low-k Insulator for Favorable Active Interface. ACS Appl. Mater. Interfaces 8, 32421–32431 (2016).
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".