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Record W3024506717 · doi:10.1149/ma2020-01282057mtgabs

Strategies for Chemical Sensing Using High Purity Semiconducting Single-Walled Carbon Nanotube Electronic Devices

2020· article· en· W3024506717 on OpenAlexaff
François Lapointe

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsChemiresistorCarbon nanotubeMaterials scienceNanotechnologyPolymerBand gapOptoelectronics

Abstract

fetched live from OpenAlex

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, CO 2 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.262
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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