MétaCan
Menu
Back to cohort
Record W2793080310 · doi:10.1002/adfm.201705568

Decomposable <i>s</i>‐Tetrazine Copolymer Enables Single‐Walled Carbon Nanotube Thin Film Transistors and Sensors with Improved Sensitivity

2018· article· en· W2793080310 on OpenAlexaff
Zhao Li, Jianfu Ding, Chang Guo, J. Lefebvre, Patrick R. L. Malenfant

Bibliographic record

VenueAdvanced Functional Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceCarbon nanotubePolymerTransistorConjugated systemCopolymerNanotechnologyChemical engineeringOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

Abstract Semiconducting single‐walled carbon nanotubes (sc‐SWCNTs) enriched by a conjugated polymer extraction process have been actively studied for various applications in both electronics and optoelectronics. Although the resulting tube samples usually have high sc‐purity and concentration, SWCNT networks from such dispersions typically contain residual conjugated polymer that may degrade device performance and its removal remains a challenge while maintaining uniform, dense SWCNT thin film networks. In this study, a novel polymer–SWCNT combination based on an alternating bisfuran‐ s ‐tetrazine and benzo[1,2‐ b :4,5‐ b′ ]dithiophene copolymer abbreviated as PBDTFTz is proposed. This polymer decomposes at &gt;250 °C or under UV irradiation. In situ transistor characterization under laser irradiation confirms the polymer decomposition. The study of the tube network in the transistor channel at various channel lengths reveals significantly reduced contact resistance attributed to removal of the wrapping PBDTFTz polymer. In ammonia sensing experiments, sc‐SWCNT networks demonstrate rapid and reversible responses, while the unwrapped nanotube networks prove superior in terms of signal to noise ratio and a detection limit of 2.5 ppb is calculated, almost four times better than polymer wrapped nanotubes.

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)0.000
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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

Citations43
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Functional MaterialsSame topicCarbon Nanotubes in CompositesFrench-language works237,207