Integration of Organic Light Emitting Diodes and Organic Photodetectors for Lab-on-a-Chip Bio-Detection Systems
Bibliographic record
Abstract
The rapid development of microfluidics and lab-on-a-chip (LoC) technologies have allowed for the efficient separation and manipulation of various biomaterials, including many diagnostically relevant species. Organic electronics have similarly enjoyed a great deal of research, resulting in tiny, highly efficient, wavelength-selective organic light-emitting diodes (OLEDs) and organic photodetectors (OPDs). We consider the blend of these technologies for rapid detection and diagnosis of biological species. In the ideal system, optically active or fluorescently labelled biological species can be probed via light emission from OLEDs, and their subsequent light emission can be detected with OPDs. The relatively low cost and simple fabrication of the organic electronic devices suggests the possibility of disposable test arrays. Further, with full integration, the finalized system can be miniaturized and made simple to use. In this review, we consider the design constraints of OLEDs and OPDs required to achieve fully organic electronic optical bio-detection systems. Current approaches to integrated LoC optical sensing are first discussed. Fully realized OLED- and OPD-specific photoluminescence detection systems from literature are then examined, with a specific focus on their ultimate limits of detection. The review highlights the enormous potential in OLEDs and OPDs for integrated optical sensing, and notes the key avenues of research for cheap and powerful LoC bio-detection systems.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".