Microfluidic platform for selective microparticle parking and paired particle isolation in droplet arrays
Bibliographic record
Abstract
Immobilizing microscale objects (e.g., cells, spheroids, and microparticles) in arrays for direct observation and analysis is a critical step of many biological and chemical assays; however, existing techniques are often limited in their ability to precisely capture, arrange, isolate, and recollect objects of interest. In this work, we present a microfluidic platform that selectively parks microparticles in hydrodynamic traps based on particle physical characteristics (size, stiffness, and internal structure). We present an accompanying scaling analysis for the particle parking process to enable rational design of microfluidic traps and selection of operating conditions for successful parking of desired particles with specific size and elastic modulus. Our platform also enables parking of encoded particle pairs in defined spatial arrangements and subsequent isolation of these pairs in aqueous droplets, creating distinct microenvironments with no cross-contamination. In addition, we demonstrate the ability to recollect objects of interest (i.e., one particle from each pair) after observation within the channel. This integrated device is ideal for multiplexed assays or microenvironment fabrication for controlled biological studies.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".