Decoding the Polymer p–n Junction: Controlled Dedoping and Reverse Bias Electroluminescence
Bibliographic record
Abstract
Abstract The polymer light‐emitting electrochemical cell (PLEC) is a unique solid‐state device possessing attractive attributes for low‐cost applications, but also a junction structure that is still poorly understood. In a PLEC, the applied voltage causes in situ electrochemical p‐ and n‐doping of the semiconducting polymer and the formation of a dynamic light‐emitting p–n junction. Once the junction is fixed by cooling or chemical manipulation, the “frozen‐junction” PLEC exhibits a unipolar electroluminescence (EL) and photovoltaic response. Repeated thermal cycling, however, can cause the frozen‐junction PLEC to experience drastically enhanced EL under forward bias and the emergence of reverse bias EL. In this study, a combination of transport measurements and direct imaging is used to elucidate the origin of the mysterious reverse bias EL. A model is developed that explains the reverse bias EL as caused by the tunnel injection of electrons and holes from bandgap states into a dedoped “intrinsic” region between the p‐ and n‐doped regions. The model explains the location, relative intensity, and evolution of EL under both forward and reverse bias. The results hint at a junction that is much narrower than previously resolved.
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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".