Impact of Noncompensating Ions on the Electrochemical Performance of n-Type Polymeric Mixed Conductors
Bibliographic record
Abstract
Organic mixed ionic-electronic conductors (OMIECs) have emerged as essential materials for applications in bioelectronics, neuromorphics, and energy storage, owing to their ability to transport both ions and electrons. While significant progress has been made in understanding their operation, the role of noncompensating ions in polymer redox processes remains underexplored, particularly in the context of their impact on charge compensation and device performance. In this study, we systematically investigate the influence of noncompensating ions on the performance of n-type OMIECs with and without polar side chains, focusing on their interactions with electrolytes containing anions from the Hofmeister series. Our findings reveal a stark contrast in charging behavior and organic electrochemical transistor (OECT) performance based on side-chain chemistry. Polar oligoether side chains promote interactions with anions, resulting in significant performance variations. We demonstrate the critical role of polymer side-chain interactions with the different anions, where polyatomic anions capable of infiltrating the film degrade device performance, particularly in terms of transconductance and operational stability. In contrast, OMIECs without side chains exhibit performance independent of the noncompensating ion nature. Through electrochemical analysis, spectroscopic techniques, and molecular dynamics simulations, we provide a comprehensive understanding of how ion incorporation and polymer-electrolyte interactions shape device behavior. This study highlights the transformative role of side-chain functionality in tailoring the properties of the OMIEC and offers a design framework for high-performance OECTs, enabling advancements in biosensing, neuromorphic computing, and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".