Performance improvement of plasmonic sensors using a combination of AC electrokinetic effects for (bio)target capture
Bibliographic record
Abstract
Analytes concentration techniques are being developed with the appealing expectation to boost the performance of biosensors. One promising method lies in the use of electrokinetic forces. We present hereafter a new design for a microstructured plasmonic sensor which is obtained by conventional microfabrication techniques, and which can easily be adapted on a classical surface plasmon resonance imaging (SPRI) system without further significant modification. Dielectrophoretic trapping and electro-osmotic displacement of the targets in the scanned fluid are performed through interdigitated 200 μm wide gold electrodes that also act as the SPR-sensing substrate. We demonstrate the efficiency of our device's collection capabilities for objects of different sizes (200 nm and 1 μm PS beads, as well as 5-10 μm yeast cells). SPRI is relevant for the spatial analysis of the mass accumulation at the electrode surface. We demonstrate that our device overcomes the diffusion limit encountered in classical SPR sensors thanks to rapid collection capabilities (<1 min) and we show a consequent improvement of the detection limit, by a factor >300. This study of an original device combining SPRI and electrokinetic forces paves the way to the development of fully integrated active plasmonic sensors with direct applications in life sciences, electrochemistry, environmental monitoring and agri-food industry.
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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".