A nanofilter for fluidic devices by pillar-assisted self-assembly microparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it