Development of membrane in microfluidic device for sorting and detection for potential heterogeneity investigation
Bibliographic record
Abstract
Membrane fabrication and integration with microfluidic devices has received increased attention for applications including bio-detection (a device providing analytical information in a selective and quantitative manner using a biological recognition element), membrane-based separation, and biological sample purification. The main challenges associated with these applications have been: 1) meeting sensitivity/selectivity requirements, 2) decreasing costs, 3) maintaining the mechanical stability of the membrane, 4) offering high throughput. Therefore, the main goal of this study was to demonstrate size-based membrane separation and bio-detection using double layer channel developed in our lab and to show how the membrane integrated channel can selectively separate rod shape cell separation with various aspect ratio based on size and enhance bio detection rate with flow. Based on an existing double-channel and cross-flow microfluidics platform, we explored various polymeric materials for fabricating porous membranes to use in pore-size-dependent separation. We induced pores via stop-flow lithography, and investigated membrane properties and limitations for pore-size-dependent separation. We investigated potential applications of poly(ethylene glycol) diacrylate (PEGDA)-based membrane integrated platforms in biological molecule detection based on streptavidin and biotin interaction. We demonstrated that flow and concentrations can enhance target detection in this platform.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".