Miniaturized opto-fluidic ring resonator for sensitive label-free viral detection
Bibliographic record
Abstract
A rapid, label-free on-line virus detection method has been developed based on opto-fluidic ring resonator (OFRR). The OFRR employs a fused silica capillary with a diameter around 100 μm. The circular cross section of the capillary forms the ring resonator that supports the whispering gallery modes (WGMs). The OFRR wall is only a few micrometers. Thus, the evanescent field of the WGMs extends into the core and interacts with the sample flowing in the core. The WGM spectral position shifts in response to the binding of biomolecules to the OFRR inner surface, providing quantitative and kinetic information about the biomolecule interaction. In this work, M13 filamentous phage and anti-M13 antibody are chosen as a model system to demonstrate the detection and quantification of virus in liquid samples. Anti-M13 antibodies are first covalently attached on the aminosilane coated OFRR surface to provide a bioselective layer. The detection is then performed when the virus concentration varies from 1011 pfu/mL down to 103 pfu/mL. Our experimental results show that the OFRR is capable of detecting M13 at a concentration as low as 1000 pfu/mL. Control experiments are carried out to show the specificity of the detection. A theoretical model is developed to analyze the experimental results. The OFRR are advantageous in virus detection, as it integrates the ring resonator with fluidic channels and provides continuous on-line monitoring capability. It also has great potential for sensitive, rapid, and low-cost micro total analysis devices for biomolecule detection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".