Bibliographic record
Abstract
In the past few decades, the applications of organic or polymeric semiconductors in photonics, electronics and optoelectronics have advanced significantly. This has been primarily because of improvements in the quality organic/polymeric materials after processing, as well as the processing techniques and technologies. For example, roll‐to‐roll, sheet‐to‐sheet or printing technologies are being proposed as suitable manufacturing candidates because they can be carried out at room temperature, do not require the kind of clean room environment needed for traditional semiconductor manufacturing, and are very suitable for very low‐cost, high volume production. In this article, we concentrated on four types of organic semiconductor devices. Because of their huge commercial potential, a significant part of the article is devoted to light‐emitting diodes (LEDs) made either with polymers or organic semiconductor materials. To improve the linewidth and enhance the efficiency of the LEDs, microcavities are discussed And for active display driver transistors and other electronic applications, thin‐film transistors with organic semiconductors and all‐organic transistors are described. Finally, photovoltaic cells, photodiodes, and metal–organic semiconductor junctions are discussed.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.015 |
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".