Miniaturized Quantum Semiconductor Surface Plasmon Resonance Platform for Detection of Biological Molecules
Bibliographic record
Abstract
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 have investigated the application of optical emission from quantum well (QW) microstructures for monitoring surface phenomena on gold layers remaining in proximity (<300 nm) with QW microstructures. The uncollimated QW radiation excites surface plasmons (SP) and through the surface plasmon resonance (SPR) effect allows for detection of small perturbation in the density surface adsorbates. The SPR technology is already commonly used for biochemical characterization in pharmaceutical industries, but the reduction of the distance between the SP exciting source and the biosensing platform to a few hundreds of nanometers is an innovative approach enabling us to achieve an ultimate miniaturization of the device. We evaluate the signal quality of this nanophotonic QW-SPR 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. With an innovative conical method of SPR data collection, we demonstrate that individually collected SPR scan, each in less than 2.2 s, yield a resolution of the detection at 1.5 × 10-6 RIU.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".