Fast and sensitive virus detection using a plasmonic sensor that integrates electrokinetically assisted sampling and surface-enhanced Raman scattering
Bibliographic record
Abstract
• We integrate electrokinetic effects with SERS to achieve sensitive virus detection. • Accelerated sampling and electric field-mediated virus capture on SERS-active substrates. • Addition of silver nanoparticles increases SERS hotspot density. • Simulations provide insights into the mechanisms behind concentration amplification and signal enhancement. We present a novel plasmonic biosensing method for on-chip detection on viral particles featuring a microelectrode platform that integrates accelerated sampling of virus with surface-enhanced Raman scattering (SERS). We show experimentally that our approach can produce spectacular results owing to the unique incorporation of two key features: (1) Concentration amplification of virus on the SERS-active substrate; (2) Local plasmonic activity enhancement due to the targeted superimposition of silver nanoparticles on the captured virus sites. When tested for the detection of the M13 bacteriophage our “sandwich” assay yielded excellent reproducibility (signal variation <6 %) and a very low limit of detection (1.13 × 10 2 pfu/ml). Compared with the performance of our standard SERS substrates, SERS signals stronger by at least one order of magnitude are typically achieved. In addition to experimental results, our work also includes finite element (COMSOL Multiphysics) and finite-difference time-domain (FDTD) simulations that provide insights into the mechanisms of concentration amplification and plasmonic activity enhancement, respectively.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".