Enhancing the Performance of Surface-based Biosensors by AC Electrokinetic Effects - a Review
Bibliographic record
Abstract
Miniaturized surface based biosensors are a cost effective and portable means for the sensing of biologically active compounds. With advents in micro-and nanotechnology, the design of surface based biosensors can be adapted for various detection goals and for integration with multiple detection techniques. In particular, the issue of pathogen detection is an important challenge with applications in defence, health care, food safety, diagnostics and clinical research. The research of micro-fluidic analytical systems, such as surface based biosensors or "lab-on-a-chip" designs, have gained increasing popularity, not only due to the enhancement of the analytical performance, but also due to their reduced size, decreased consumption of reagents and the ability to integrate multiple technologies within a single device. Although conventional pathogen detection methods are well established, they are greatly restricted by the assay time. For pathogens that typically occur at low concentrations, the mass transfer required for detection is diffusion limited and incubation is often needed in order to enhance the concentration of the target analyte. AC electrokinetic effects provide a means for biosensors to detect pathogens quickly and at lower concentrations, thus overcoming these bottlenecks.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".