Electrotuneable Radical Polymers for Thin-Film Electronic Device Applications
Bibliographic record
Abstract
Polyradicals, organic polymers in which each repeating sub-unit contains a stable radical with an unpaired spin, are establishing themselves as unique alternatives to state-of-the-art conducting polymers for a variety of applications. A decisive advantage of polyradicals rests on their charge tunability and switchable transport properties. Current interests in polyradicals have been focused on batteries and resistive memory devices. Polyradical doping has remained a challenge, however, notwithstanding recent developments. Due to doping issues, single-component polyradical organic field-effect transistors (OFETs), a vital milestone in polyradical electronics, have not yet been achieved. Here, we demonstrate that an OFET architecture with source-drain contacts sandwiching the polyradical active layer led to on/off ratios >103 in what are the first p-type polyradical-FETs (PR-FETs) reported so far. These OFETs are based on a 6-oxoverdazyl polyradical with no impurity doping, in which hole injection occurs due to tunable charge states at the polyradical/source interface. By introducing the new concept of contact-doping, our work provides a route for translating single-component polyradical thin films into OFETs, the fundamental building blocks of organic electronics. Because of the switchable transport properties of 6-oxoverdazyl polyradicals, our PR-FETs could be used to implement the first organic mem-transistors reported to date.
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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) | 0.003 | 0.001 |
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".