(Invited) Alteration of the Electrical Transport in Carbon Nanotube Network Field-Effect Transistors Using Polymer Encapsulants and Gate Dielectrics
Bibliographic record
Abstract
Enriched semiconducting single-walled carbon nanotubes (SWCNTs) inks of high purity opens a wealth of possibilities for device fabrication using printing techniques and solution processes. [1] Logics, displays, sensors and large-scale integrated circuits [2] appear to be within reach. For those promises to concretize, a better control over parameters such as carrier type and mobility, operation power and device-to-device variability is needed. Beside the ambient effect (notably water and oxygen) on the transport of carbon nanotube network field-effect transistors (CNN-FETs), the nature of the surrounding materials (as a passivation layer or as a gate dielectric) appears to play a crucial role in determining the device's characteristics. Firstly, we have tested a large number of polymeric materials as the bottom gate in three-terminal CNN-FETs. We have found that the transport characteristics parameters such as the threshold voltage and the hysteresis are affected by the chemical nature of the polymer. [3] Subjecting those CNN-FETs with various gate dielectrics to a series of volatile analytes, we observed drastically different responses which suggests the usefulness of polymer gate dielectric variations in cross-reactive sensors arrays. Secondly, polymeric materials were used as an encapsulant over bottom gate CNN-FETs. [4] A smooth and continuous variation of the threshold voltage has been achieved by using a series of poly(styrene–co–2-vinyl pyridine) copolymers with different monomer ratios. A surface charge density measurement methodology has been developed to rationalize the threshold voltage variations upon encapsulation with various polymers. Finally, we explored the use of n-doping molecules blended with polymer encapsulants as an efficient way to obtain n-type transport characteristics. This strategy is further used in the implementation of simple p-n junctions showing a modest rectification. References [1] Bati, A. S. R. et al. Recent Advances in Applications of Sorted Single‐Walled Carbon Nanotubes. Adv. Funct. Mater. 29 1902273 (2019) [2] Hills, G. et al. Modern Microprocessor Built from Complementary Carbon Nanotube Transistors. Nature 572 595–602 (2019) [3] Lapointe, F. et al. Carbon Nanotube Transistors as Gas Sensors: Response Differentiation Using Polymer Gate Dielectrics. ACS Appl. Polym. Mater. 1 3269-3278 (2019) [4] Lapointe, F. et al. Polymer Encapsulants for Threshold Voltage Control in Carbon Nanotube Transistors. ACS Appl. Mater. Interfaces 11 36027-36034 (2019)
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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) | 0.006 | 0.002 |
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".