Advances in Flexible Organic Photodetectors: Materials and Applications
Bibliographic record
Abstract
Future electronics will need to be mechanically flexible and stretchable in order to enable the development of lightweight and conformal applications. In contrast, photodetectors, an integral component of electronic devices, remain rigid, which prevents their integration into everyday life applications. In recent years, significant efforts have been made to overcome the limitations of conventional rigid photodetectors, particularly their low mechanical deformability. One of the most promising routes toward facilitating the fabrication of flexible photodetectors is to replace conventional optoelectronic materials with nanomaterials or organic materials that are intrinsically flexible. Compared with other functional materials, organic polymers and molecules have attracted more attention for photodetection applications due to their excellent photodetection performance, cost-effective solution-fabrication capability, flexible design, and adaptable manufacturing processes. This article comprehensively discusses recent advances in flexible organic photodetectors in terms of optoelectronic, mechanical properties, and hybridization with other material classes. Furthermore, flexible organic photodetector applications in health-monitoring sensors, X-ray detection, and imager devices have been surveyed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".