Temperature Sensors Based on Organic Field-Effect Transistors
Bibliographic record
Abstract
The rapid growth of wearable electronics, Internet of Things, smart packaging, and advanced healthcare technologies demand a large number of flexible, thin, lightweight, and ultralow-cost sensors. The accurate and precise determination of temperature in a narrow range (~0–50 °C) around ambient temperatures and near-body temperatures is critical for most of these applications. Temperature sensors based on organic field-effect transistors (OFETs) have the advantages of low manufacturing cost, excellent mechanical flexibility, easy integration with other devices, low cross-sensitivity, and multi-stimuli detectability and, therefore, are very suitable for the above applications. This article provides a timely overview of research progress in the development of OFET-based temperature sensors. First, the working mechanism of OFETs, the fundamental theories of charge transport in organic semiconductors, and common types of OFET temperature sensors based on the sensing element are briefly introduced. Next, notable advances in the development of OFET temperature sensors using small-molecule and polymer semiconductors are discussed separately. Finally, the progress of OFET temperature sensors is summarized, and the challenges associated with OFET temperature sensors and the perspectives of research directions in this field are presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".