SE-ECL on CMOS: a miniaturized electrochemiluminescence biosensor
Bibliographic record
Abstract
Biosensors exhibit high potential for the detection of analytes of interest at the point-of-need. Over the past two decades, the combination of novel biosensing systems - such as electrochemiluminescence (ECL) biosensors - and advances in microfluidic techniques has allowed the development of lab-on-a-chip devices with enhanced overall performance and simplified sample handling. However, recording data with conventional platforms requires advanced and complicated instruments, such as sensitive photodetectors coupled to microscopes, to capture the photons from the chemiluminescent reaction. In this work, we integrated microfluidic and luminol/hydrogen peroxide ECL systems on a complementary metal-oxide-semiconductor (CMOS) chip for sample handling and data collection on the same platform. This was achieved by the adaptation of a single electrode as an electrochemical transducer and a CMOS chip as a built-in detector. We demonstrated the application of this platform for the detection of uric acid (UA), a biomarker of gout disease. A linear detection range was observed from 25 to 300 μM, with a detection limit (LOD) as low as 26.09 μM. The device showed high reusability and reproducibility within the linear detection range while maintaining high selectivity for UA detection. The analytical performance has also been evaluated in simulated saliva and urine samples, demonstrating the potential utility in medical diagnosis at the point-of-need. Compared to other ECL imaging platforms, this device showed an eightfold increase in photon collection efficiency. Overall, this approach has promising potential as an inexpensive, portable, and efficient ECL platform for measuring analytes at the point-of-need.
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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) | 0.000 | 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".