Label‐Free, Sensitive, and Direct Detection of Cardiac Troponin Biomarkers Using Frequency‐Locked Microring Resonators
Bibliographic record
Abstract
Abstract Silicon photonic microring resonators have emerged as promising sensors for Point‐of‐Care applications, where the readout of one or many biomarkers at once is required. In the context of rapid heart attack detection, a limit of detection reaching an ultra‐low concentration of biomarkers is needed, however, such sensors are prone to fundamental noise influence in optical systems which can potentially jeopardize sensor readings. While noise reduction has previously been explored with the Pound–Drever–Hall (PDH) technique, its full implementation in microring biosensors has not been realized due to the complexity of the setup. Recent innovations in photonic integration and compatibility with MEMS structures have sparked new interest in validating PDH's potential to be used with chip‐scale sensors. By enabling the signal readout of microrings through their phase response, instead of power transmission, the impact of optical noise can be greatly reduced. This study explores a proof‐of‐concept for this system against the cardiac troponin biomarker, demonstrating the sensor's capacity for selective measurement down to a limit of 10 ng mL −1 while using frequency locking. An improved limit of detection for this system is achieved, down to 5.03 × 10 −7 RIU, which is two orders of magnitude improved compared to the equivalent sensing based on intensity alone.
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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 | 0.001 |
| 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".