Compact multi-channel surface plasmon resonance sensor for real-time multi-analyte biosensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A compact multi-channel surface plasmon resonance (SPR) biosensor is demonstrated based on a tablet as the measurement platform. The SPR biosensor employs a bundle of fiber-optic SPR sensors as the multiplexed sensing elements that are illuminated by a light-emitting diode (LED) plane light source and detected by a cordless camera. The multi-channel SPR biosensor was based on optical fiber components for precise, label-free and high-throughput detection without the use of complex, specialized or fragile instrumentation that would require optical calibration. The reference and control channels compensated for the fluctuation of the LED light source and the bulk refractive index, increasing the accuracy and reliability of the biosensor. The multi-channel SPR biosensor was applied for multi-analyte biosensing of immunoglobulin G (IgG) and concanavalin A (Con A). The channels functionalized with staphylococcal protein A (SPA) and ribonuclease B (RNase B) only showed relative intensity responses to their corresponding analytes. Moreover, the multi-channel SPR sensors responded to the specific detection of IgG and Con A with an approximately linear relative intensity response to the analyte concentration. Hence, multiple analytes were simultaneously and quantitatively detected with the multi-channel SPR biosensor. This compact, cost-effective multi-channel SPR biosensor is adapted for point-of-care tests, which are important in healthcare and environmental monitoring and for biomolecular interaction analysis.
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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 it