25 Years of Light‐Emitting Electrochemical Cells: A Flexible and Stretchable Perspective
Bibliographic record
Abstract
Light-emitting electrochemical cells (LECs) are simple electroluminescent devices comprising an emissive material containing mobile ions sandwiched between two electrodes. The operating mechanism of the LEC involves both ionic and electronic transport, distinguishing it from its more well-known cousin, the organic light-emitting diode (OLED). While OLEDs have become a leading player in commercial displays, LECs have flourished in academic research due to the simple device architecture and unique features of its operating mechanism, inviting exploration of new materials and fabrication strategies. These explorations have brought LECs to an exciting frontier in advanced optoelectronics: flexible and stretchable light-emitting devices. Flexible and stretchable LECs are discussed herein, presenting the LEC system as a robust and fault-tolerant development platform. The engineering of emissive composites is highlighted to control mechanical properties, and how the tolerance of LECs to electrode work function and roughness has enabled the incorporation of new electrode materials to achieve flexibility and stretchability. As part of this story, the solution processability of LECs has led to exciting demonstrations of flexible and printed LECs. An outlook is provided for LECs that builds on these strengths, potentially leading to flexible, stretchable, low-cost devices such as illuminated tags, smart packaging, flexible signage, and wearable illumination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".