Real-time detection of influenza A virus using semiconductor nanophotonics
Bibliographic record
Abstract
Modern miniaturization and the digitalization of characterization instruments greatly facilitate the diffusion of technological advances in new fields and generate innovative applications. The concept of a portable, inexpensive and semi-automated biosensing platform, or lab-on-a-chip, is a vision shared by many researchers and venture industries. Under this scope, we present a semiconductor monolithic integration approach to conduct surface plasmon resonance studies. This technology is already commonly used for biochemical characterization in pharmaceutical industries, but we have reduced the technological platform to a few nanometers in scale on a semiconductor chip. We evaluate the signal quality of this nanophotonic device using hyperspectral-imaging technology, and we compare its performance with that of a standard prism-based commercial system. Two standard biochemical agents are employed for this characterization study: bovine serum albumin and inactivated influenza A virus. Time resolutions of data acquisition varying between 360 and 2.2 s are presented, yielding 2.7×10−5–1.5×10−6 RIU resolutions, respectively. Scientists in Canada have developed an optoelectronic chip that performs real-time detection of the flu virus. The integrated semiconductor device combines a quantum-well-based light source with a surface plasmon sensor made from a corrugated metal–dielectric (Au–SiO2) interface. Tests performed by Dominic Lepage and co-workers from the Universitié de Sherbrooke show that the nanophotonic chip can detect inactivated influenza A virus and bovine serum albumin with a sensitivity of 1.5 × 10–6 refractive index units and a time resolution as short as 2.2 s. Since the measurements are carried using a microscope, this approach provides biomedical researchers with a convenient and affordable means of studying viral dynamics.
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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 | 0.000 | 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 | 0.001 | 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".