Application of ring down measurement approach to micro-cavities for bio-sensing applications
Bibliographic record
Abstract
Optical biosensors can detect biomarkers in the blood serum caused by either infections or exposure to toxins. Until now, most work on the micro-cavity biosensors has been based on measurement of the resonant frequency shift induced by binding of biomarkers to a cavity. However, frequency domain measurements are not precise for such high Q micro-cavities. We hypothesize that more accurate measurements and better noise tolerance can be achieved by the application of the ring down measurement approach to the micro-cavity in a biosensor. To test our hypothesis, we have developed a full vectorial finite element model of a silica toroidal micro-cavity immersed in water. Our modeling results show that a toroidal cavity with a major diameter of 70μm and a minor diameter of 6μm can achieve a sensitivity of 28.6μs/RIU refractive index units (RIU) at 580nm. Therefore, our sensor would achieve the resolution of 5 x 10-8 RIU by employing a detector with picosecond resolution. Hence we propose a micro-cavity ring down biosensor with high sensitivity which will find wide applications in real time and label free bio-sensing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".