Understanding Oxygen-Induced Reactions and Their Impact on n-Type Polymeric Mixed Conductor-Based Devices
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Electron transporting (n-type) polymeric mixed conductors are an exciting class of materials for devices with aqueous electrolyte interfaces, such as bioelectronic sensors, actuators, and soft charge storage systems. However, their charge transport performance falls short of their p-type counterparts, primarily due to electrochemical side reactions such as the oxygen reduction reaction (ORR). To mitigate ORR, a common strategy in n-type organic semiconductor design focuses on lowering the lowest unoccupied molecular orbital (LUMO) level. Despite empirical observations suggesting a correlation between deep LUMO levels, low ORR, and enhanced electrochemical cycling stability in water, this relationship lacks robust evidence. In this work, we delve into the electrochemical reactions of n-type polymeric mixed conductors with varying LUMO levels and assess the impact of ORR on charge storage performance and organic electrochemical transistor (OECT) operation. Our results reveal a limited correlation between LUMO levels and ORR currents, as well as the electrochemical operational stability of the films. While ORR currents minimally contribute to OECT channel currents under fixed biasing conditions, n-type films self-discharge rapidly at floating potentials in a capacitor-like configuration. The density functional theory analysis, complemented by X-ray photoelectron spectroscopy, underscores the critical role of backbone chemistry in controlling O 2 -related degradation pathways and device performance losses. These findings highlight the persistent challenge posed by ORR in n-type semiconductor design and advocate for shifting the focus toward exploring chemical moieties with limited O 2 interactions to enhance operational stability and performance at n-type film/water interfaces.
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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 | 0.001 |
| 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".