Bio‐Inspired Adaptive Sensing through Electropolymerization of Organic Electrochemical Transistors
Bibliographic record
Abstract
Abstract Organic electrochemical transistors are considered today as a key technology to interact with a biological medium through their intrinsic ionic‐electronic coupling. In this paper, the authors show how this coupling can be finely tuned (in operando) post‐microfabrication via the electropolymerization technique. This strategy exploits the concept of adaptive sensing where both transconductance and impedance are tunable and can be modified on‐demand to match different sensing requirements. Material investigation through Raman spectroscopy, atomic force microscopy, and scanning electron microscopy reveals that electropolymerization can lead to a fine control of poly(3,4‐ethylenedioxythiophene) (PEDOT) microdomains organization, which directly affects the iono‐electronic properties of organic electrochemical transistors (OECTs). They further highlight how volumetric capacitance and effective mobility of PEDOT:polystyrene sulfonate influence distinctively the transconductance and impedance of OECTs. This approach shows to improve the transconductance by 150% while reducing their variability by 60% in comparison with standard spin‐coated OECTs. Finally, they show how the technique can influence voltage spike rate hardware classification with direct interest in bio‐signals sorting applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".