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 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".