Surface plasmon resonance imaging enhanced by dielectrophoresis and AC-electroosmosis
Bibliographic record
Abstract
Surface Plasmon Resonance (SPR) biosensors are standard tools for chemical and biological sensing. They provide sensitive, real-time and label-free detection of biological species in fluids. However, their performance (time and detection threshold) is now close to the theoretical limit. In particular, at low target concentrations, sensitivity is limited by the diffusion of the target analyte to the sensor surface. To overcome the diffusion limit, non-uniform electric fields can be used to induce electrokinetic effects (dielectrophoresis and alternative-current electroosmosis) which attract analytes toward the surface sensing zone. This work proposes to pattern the gold film used for SPR detection and use it as electrodes for the electric field generation. The magnitude of the electrokinetic effects and resulting analyte trapping efficiency of different electrodes designs were studied numerically with COMSOL by modeling the dielectrophoretic and drag forces induced by the AC-electroosmotic flow. A biochip, which consists of a structured gold film on a glass substrate, was mounted in the SPR Kretschmann configuration in contact with a fluidic cell to enable the injection of analyte and rinsing solutions. SPR imaging allowed us to compare the spatial distribution of the SPR response both a planar metal zone similar to a conventional SPR sensor as well as on the electrodes. After microbeads injection into the fluidic cell and application an AC voltage (V=1Vpp, f=1kHz), a strong SPR signal jump was observed due to the analyte’s arrival on the sensing zone. As a result of the electrokinetic effects, the detection threshold of mass transport assisted SPR chips was improved by several orders of magnitude.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".