Perovskite Photodetector Integrated with Microfluidics for Low-Level Fluorescence Detection: Toward Self-Powered Biomarker Sensing
Bibliographic record
Abstract
Inorganic (cesium) metal halide perovskites have been of extensive interest to the broader scientific community owing to their higher stability and breakthrough performance in photoelectric conversion. While substantial progress has been made in perovskite-based devices, they are increasingly attracting interest as potential candidates for fluorescent-based sensors in biological marker detection and quantification. Herein, a self-driven perovskite photodetector for fluorescence detection is reported, elucidating controlled charge carrier dynamics under the light matter interaction. The light-induced doping phenomenon, resulting from the migration of optically activated ions, generates an electric field that enables device operation without external power. However, the uncontrolled migration of those ions increases the dark current and reduces the stability of the output current. To address this, we fabricate a vertically stacked FTO/PEDOT: PSS/CsPbBr 2 I/PCBM/Ag photodetector with nonsymmetrical electrode design to trigger controlled ion migration upon light illumination, thereby improving the device performance and output stability. The photodetector, driven by induced electric field due to the directional polarization, achieves an exceptionally low dark current (∼298 pA), a high on/off ratio on the order of 10, 5 a responsivity of 202 mA/W, a high detectivity of 2.5 × 10 11 Jones, and a fast rise and decay time (190 and 100 μs), all are measured at 0 V, surpassing the performance of many similar state-of-the-art works. These insights are crucial for practical applications where weak light detection is required, and we demonstrate the integration of this detector with a microfluidic chip for fluorescence detection from quantum dot conjugated beads. The photodetector showcased sufficient sensitivity to detect signals from quantum dot solutions as low as ∼23 nM in a microfluidic channel, highlighting the potential for future self-powered integrated platforms for biomarker sensing applications.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".