Strategies for Chemical Sensing Using High Purity Semiconducting Single-Walled Carbon Nanotube Electronic Devices
Bibliographic record
Abstract
Semiconducting single-walled carbon nanotubes (sc-SWCNTs) are attractive in chemical sensing [1] because of their peculiarities: As they present a single atom-thick wall and a quasi 1D form factor, sc-SWCNTs are especially sensitive to their surroundings’ electrostatics. Moreover, their band gap of approximately 1 eV is much greater than room-temperature thermal energy while being in a convenient range for electronic applications. Also, their electronic structure exhibits van Hove singularities (sharp spikes in the density of states) that drastically alters their conductivity and optical properties when charge carriers are injected. Although sc-SWCNT electronic devices are very sensitive, they lack in selectivity and they usually need to be functionalized to implement a lock-and-key detection mechanism. In this talk, we will present various strategies for chemical sensing using enriched sc-SWCNTs. We will discuss the advantages and disadvantages of using sc-SWCNTs in electronic chemical sensing devices. Sub-ppm ammonia sensing will be demonstrated using a sc-SWCNT material wrapped with a decomposable polymer in a chemiresistor configuration. [2] Also using chemiresistors, CO2 detection has been achieved by designing a SWCNT-wrapping polymer with specific interactions. Finally, we will present a strategy to differentiate the response of sensor elements to a variety of volatile analytes by changing the polymer gate dielectrics in a three-terminal bottom gate chemitransistor configuration. [3] This methodology opens the way to the implementation of sc-SWCNT-based chemitransistors in a printed cross-reactive sensor array. References [1] Schroeder, V. et al. Carbon Nanotube Chemical Sensors. Chem. Rev. 119, 599–663 (2019) [2] Li, Z. et al. Decomposable s-Tetrazine Copolymer Enables Single-Walled Carbon Nanotube Thin Film Transistors and Sensors with Improved Sensitivity. Adv. Funct. Mater. 1705568 (2018) [3] Lapointe, F. et al. Carbon Nanotube Transistors as Gas Sensors: Response Differentiation using Polymer Gate Dielectrics. AC S Appl. Polym. Mater. https://doi.org/10.1021/acsapm.9b00707 (2019) – Accepted for publication
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".