A nanofilter for fluidic devices by pillar-assisted self-assembly microparticles
Bibliographic record
Abstract
We present a nanofilter based on pillar-assisted self-assembly microparticles for efficient capture of bacteria. Under an optimized condition, we simply fill the arrays of microscale pillars with submicron scale polystyrene particles to create a filter with nanoscale pore diameter in the range of 308 nm. The design parameters such as the pillar diameter and the inter-pillar spacing in the range of 5 μm-40 μm are optimized using a multi-physics finite element analysis and computational study based on bi-directionally coupled laminar flow and particle tracking solvers. The underlying dynamics of microparticles accumulation in the pillar array region are thoroughly investigated by studying the pillar wall shear stress and the filter pore diameter. The impact of design parameters on the device characteristics such as microparticles entrapment efficiency, pressure drop, and inter-pillar flow velocity is studied. We confirm a bell-curve trend in the capture efficiency versus inter-pillar spacing. Accordingly, the 10 μm inter-pillar spacing offers the highest capture capability (58.8%), with a decreasing entrapping trend for devices with larger inter-pillar spacing. This is the case that the 5 μm inter-pillar spacing demonstrates the highest pillar wall shear stress limiting its entrapping efficiency. As a proof of concept, fluorescently labeled Escherichia coli bacteria (E. coli) were captured using the proposed device. This device provides a simple design, robust operation, and ease of use. All of which are essential attributes for point of care devices.
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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 | 0.000 |
| Research integrity | 0.001 | 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".