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Record W4366256786 · doi:10.1002/admi.202300079

N‐Type Single Walled Carbon Nanotube Thin Film Transistors Using Green Tri‐Layer Polymer Dielectric

2023· article· en· W4366256786 on OpenAlexafffund
Mathieu N. Tousignant, Bahar Ronnasi, Vanessa Tischler, Benoît H. Lessard

Bibliographic record

VenueAdvanced Materials Interfaces · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsMaterials scienceDielectricThin-film transistorCarbon nanotubeFlexible electronicsComposite materialNanotechnologyLayer (electronics)Optoelectronics

Abstract

fetched live from OpenAlex

Abstract The proliferation of disposable, wearable, and implantable printable electronics requires the development of high‐performance biodegradable, and sustainable electronic components. Often green materials don't have the necessary properties for high‐performance electronics, therefore obtaining the ideal properties requires a combination of multiple green materials. A tri‐layer dielectric is reported using poly(lactic acid) (PLA), poly(vinyl alcohol)/cellulose nanocrystals (PVAc), and toluene diisocyanate terminated poly(caprolactone) (TPCL), which is integrated into semiconducting single‐walled carbon nanotube (sc‐SWCNT) based thin film transistors (TFTs) in a top gate bottom contact architecture. The PVA provides a high dielectric constant due to the hydroxy groups, the cellulose is used to optimize the viscosity, the TPCL layer provides a robust hydrophobic surface, and the PLA eliminates the interfacial charge traps present in the PVAc and improves the adhesion between PVAc and the substrate. This leads to a decrease in leakage currents and reduces the polarity at the dielectric/semiconductor interface. The TFTs fabricated using tri‐layer dielectrics led to air‐stable n‐type devices with higher overall performance when compared against the PVAc/TPCL bilayer devices.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.028
GPT teacher head0.248
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2023
Admission routes2
Has abstractyes

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