An optoelectrokinetic technique for programmable particle manipulation and bead-based biosignal enhancement
Bibliographic record
Abstract
Technologies that can enable concentration of low-abundance biomarkers are essential for early diagnosis of diseases. In this study, an optoelectrokinetic technique, termed Rapid Electrokinetic Patterning (REP), was used to enable dynamic particle manipulation in bead-based bioassays. Various manipulation capabilities, such as micro/nanoparticle aggregation, translation, sorting and patterning, were developed. The technique allows for versatile multi-parameter (voltage, light intensity and frequency) based modulation and dynamically addressable manipulation with simple device fabrication. Signal enhancement of a bead-based bioassay was demonstrated using dilute biotin-fluorescein isothiocyanate (FITC) solutions mixed with streptavidin-conjugated particles and rapidly concentrated with the technique. As compared with a conventional ELISA reader, the REP-enabled detection achieved a minimal readout of 3.87 nM, which was a 100-fold improvement in sensitivity. The multi-functional platform provides an effective measure to enhance detection levels in more bead-based bioassays.
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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 | 0.001 |
| 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 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".