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Record W4417102764 · doi:10.1002/admt.202500627

Fully Printed Organic Electrochemical Transistors With Low‐Resistance Electrodes on Planarized 3D‐Printed Substrates

2025· article· en· W4417102764 on OpenAlexafffund
Mohamad Kannan Idris, Ali Eskandari, Saumik Dey Shovan, Gerd Grau

Bibliographic record

VenueAdvanced Materials Technologies · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsPrinted electronicsTransistorElectrodeBioelectronicsFlexible electronicsElectronicsInkwellOrganic electronics

Abstract

fetched live from OpenAlex

ABSTRACT This work presents a novel methodology for fabricating organic electrochemical transistors (OECTs) integrated with planarized 3D‐printed substrates, offering a scalable approach to integrate functional electronics into 3D‐printed systems. By combining fused deposition modeling (FDM) with in situ surface ironing, dispense printing for low‐resistance silver electrodes, and inkjet printing for high‐quality poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) channels, we achieve high‐performance transistors with an average transconductance (g m ) of 78.2 mS, one of the highest values reported in the literature. OECTs integrated with planarized 3D printed substrates are compared to devices fabricated on glass substrates and achieved comparable performance. The thick, low‐resistance electrodes produced via dispense printing are critical for this performance, demonstrating the technique's suitability for OECT manufacturing. This study establishes a process for integrating OECTs with 3D‐printed structures, paving the way for customizable, application‐specific electronic devices. By addressing key challenges in material compatibility and integrated fabrication, this work contributes to advancing the fields of bioelectronics, wearables, and additive manufacturing.

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)0.000
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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
Published2025
Admission routes2
Has abstractyes

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