Ultrafast VCSEL-based plasmonic polymerase chain reaction with real-time label-free amplicon detection for point-of-care diagnostics
Bibliographic record
Abstract
Recent progress in microfluidics and optical systems has made enormous impact in the advancement of nucleic acid amplification and detection. However, commercial and currently reported microfluidic PCR devices have not yet found their utilization in point-of-care (POC) applications. This is due to long amplification time, high power requirement, and bulky size of commercial PCR machines or cost-inefficiency, complex fabrication and operation of microfluidic chips. In this work, we present a compact PCR device in which fast amplification is accomplished by photothermal heating of gold nanorods evenly dispersed in PCR reaction by a vertical-cavity surface-emitting laser (VCSEL). This thermocycler offers sub-ten-minute amplification time for 30 thermal cycles with high temperature stability and PCR products comparable to conventional bench-top machines. The proposed device is approximately 100mm×50mm×50mm in size, and its small footprint is obtained by hardware miniaturization. Retaining conventional sample volumes (20μL) makes our device more user-friendly in terms of sample loading and capable of more sensitive amplicon detection for on-site assays. Also, its cost-effectiveness due to disposable AuNRs and inexpensive light source outweigh surface plasmon heating methods utilizing embedded Au films with limited lifetimes and other previously presented plasmonic thermocyclers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".