Surface plasmon resonance imaging enhanced by dielectrophoresis and AC-electroosmosis for rapid and label-free bacteria detection (Conference Presentation)
Bibliographic record
Abstract
Surface plasmon resonance imaging (SPRI) biosensors allow sensitive, real-time and label-free detection of biological species in fluids when they bound to the sensing surface. However, their sensitivity is now close to the theoretical limit. In particular, at ultra-low target concentration, the main limit is the diffusion of the biological target (protein, DNA, bacteria…) to the gold film surface. To locally increase the target concentration on the sensitive surface and overcome such diffusion limit, active mass transport of analytes can be induced by non-uniform electric fields using dielectrophoresis (DEP) and alternative-current electroosmosis (ACEO) flow. Depending on the frequency of the electric field applied and the conductivity of the suspension medium, DEP and ACEO can concentrate biological objects on electrodes. This work focuses on the trapping and the detection of bacteria. The gold film used for SPR imaging is also used as electrode for particle collection, after photolithography and wet etching. To obtain the most efficient electrode design, numerical simulations were performed to estimate the trapping force applied on bacteria in the fluidic chamber volume depending on the geometry of the electrodes. SPR biochips obtained were mounted in the Kretschmann configuration. Then, a DI water solution containing E.coli bacteria was injected in the fluidic chamber of the chip. AC voltage (10Vpp, 1 kHz) was applied. The arrival of bacteria on the sensing zone is monitored by a strong jump of the SPR signal when no signal was observed without mass transport. The easy integration of such DEP/ACEO-assisted SPR chips on commercial SPR benches makes them suitable fur ultralow detection of a wide range of biological species, from biomolecules to pathogens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".