Organic field-effect transistor-based flexible sensors
Bibliographic record
Abstract
Flexible electronic devices have attracted a great deal of attention in recent years due to their flexibility, reduced complexity and lightweight. Such devices can conformably attach themselves to any bendable surface and can possess diverse transduction mechanisms. Consequently, with continued emphasis on innovation and development, major technological breakthroughs have been achieved in this area. This review focuses on the advancements of using organic field-effect transistors (OFETs) in flexible electronic applications in the past 10 years. In addition, to the above mentioned features, OFETs have multiple advantages such as low-cost, readout integration, large-area coverage, and power efficiency, which yield synergy. To begin with, we have introduced organic semiconductors (OSCs), followed by their applications in various device configurations and their mechanisms. Later, the use of OFETs in flexible sensor applications is detailed with multiple examples. Special attention is paid to discussing the effects induced on physical parameters of OFETs with respect to variations in external stimuli. The final section provides an outlook on the mechanical aspects of OSCs, activation and revival processes of sensory layers, small area analysis, and pattern recognition techniques for electronic devices.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".