Microfluidic-based high-throughput optical trapping of nanoparticles
Bibliographic record
Abstract
Optical tweezers have emerged as a powerful tool for multiparametric analysis of individual nanoparticles with single-molecule sensitivity. However, its inherent low-throughput characteristic remains a major obstacle to its applications within and beyond the laboratory. This limitation is further exacerbated when working with low concentration nanoparticle samples. Here, we present a microfluidic-based optical tweezers system that can 'actively' deliver nanoparticles to a designated microfluidic region for optical trapping and analysis. The active microfluidic delivery of nanoparticles results in significantly improved throughput and efficiency for optical trapping of nanoparticles. We observed a more than tenfold increase in optical trapping throughput for nanoparticles as compared to conventional systems at the same nanoparticle concentration. To demonstrate the utility of this microfluidic-based optical tweezers system, we further used back-focal plane interferometry coupled with a trapping laser for the precise quantitation of nanoparticle size without prior knowledge of the refractive index of nanoparticles. The development of this microfluidic-based active optical tweezers system thus opens the door to high-throughput multiparametric analysis of nanoparticles using precision optical traps in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".