Dielectrophoretic cell trapping for improved surface plasmon resonance imaging sensing
Bibliographic record
Abstract
The performance of conventional surface plasmon resonance (SPR) biosensors can be limited by the diffusion of the target analyte to the sensor surface. This work presents an SPR biosensor that incorporates an active mass-transport mechanism based on dielectrophoresis and electroosmotic flow to enhance analyte transport to the sensor surface and reduce the time required for detection. Both these phenomena rely on the generation of AC electric fields that can be tailored by shaping the electrodes that also serve as the SPR sensing areas. Numerical simulations of electric field distribution and microparticle trajectories were performed to choose an optimal electrode design. The proposed design improves on previous work combining SPR with DEP by using face-to-face electrodes, rather than a planar interdigitated design. Two different top-bottom electrode designs were experimentally tested to concentrate firstly latex beads and secondly biological cells onto the SPR sensing area. SPR measurements were then performed by varying the target concentrations. The electrohydrodynamic flow enabled efficient concentration of small objects (3 μm beads, yeasts) onto the SPR sensing area, which resulted in an order of magnitude increased SPR response. Negative dielectrophoresis was also used to concentrate HEK293 cells onto the metal electrodes surrounded by insulating areas, where the SPR response was improved by one order of magnitude.
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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".