MétaCan
Menu
Back to cohort
Record W3084475586 · doi:10.1149/2162-8777/abb796

Geometrical Optimization of Organic Electrochemical Transistor for High Transconductance

2020· article· en· W3084475586 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueECS Journal of Solid State Science and Technology · 2020
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsTransconductancePEDOT:PSSMaterials scienceTransistorCapacitanceElectrochemistryOptoelectronicsBiosensorNanotechnologyVoltageElectrodeLayer (electronics)Electrical engineeringChemistry

Abstract

fetched live from OpenAlex

Organic electrochemical transistors (OECTs) are used widely as active devices for biosensing applications. OECT geometry can also be optimized by modifying the parameters like channel thickness and width to length ratio of the device to achieve optimum performance. The geometrical optimization of the OECT was conducted in this work using poly(3,4-ethylenedioxythiophene) poly(styrenesulfonate) (PEDOT:PSS) as the channel material. The fabricated OECTs demonstrate a ON/OFF current ratio (1.1 × 10 3 ) and μC * of 219.1 ± 27.5 F cm −1 V −1 s −1 . The devices with a higher width/length (W/L) ratio and a thicker channel of 260 nm demonstrate a transconductance of 2.17 mS. Pulse measurements were conducted on 260 nm thick PEDOT:PSS demonstrate high volumetric capacitance and hole mobility of 57.11 ± 1.92 F cm −3 and ∼4 cm 2 V −1 s −1 respectively.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.080
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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