Dielectric resonating microspheres for biosensing: An optical approach to a biological problem
Bibliographic record
Abstract
Detecting and identifying biomolecules or microorganisms in aqueous solutions are often a complex task requiring precious amounts of time. Decreasing this time while reducing costs and minimizing complexity is crucial for several applications in the life sciences and other fields and is the subject of extensive work by biologists and biomedical engineers around the world. Optical sensors, more specifically dielectric microspheres, have been proposed as suitable sensors for viruses, bacteria, and other biological analytes. This paper reviews initial key publications as well as the latest progress regarding such microspheres and their potential use as biological sensors. We cover recent work on fluorescent microspheres and their integration in microfluidic devices, while addressing the limitations and practical requirements of such biodiagnostics. Our aim in this paper is to appeal to both biologists and physicists, even if new to this field. We conclude by briefly suggesting ways of integrating dielectric microspheres and biosensing into college and university courses in both physics and in biology.
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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".