Self-Assembled Colloidal Arrays as Three-Dimensional Nanofluidic Sieves for Separation of Biomolecules on Microchips
Bibliographic record
Abstract
We report on a biomolecular sieving system based on the use of ordered colloidal arrays to define the sieve structure within a microfluidic device. A facile microfluidic colloidal self-assembly strategy has been developed to create ordered, robust, three-dimensional nanofluidic sieves within microfluidic devices, with which fast separation of DNA and proteins of a wide size range was achieved. Compared to conventional colloidal deposition procedures, such as vertical deposition, this approach features much faster assembling speed, the absence of drying-caused cracks that may jeopardize the separation performance, and better flexibility to couple with current microfabrication techniques. The flexibility of pore size enabled by this methodology provides separation of biomolecules with a wide size distribution, ranging from proteins (20-200 kDa) to dsDNA (0.05-50 kbp). Under moderate electric fields, complete separation can be finished in minutes, with separation efficiency comparable to gel/polymer-filled or micro-/nanofabricated microsystems. To our knowledge, this is the first demonstration of size separation of biomolecules within self-assembled ordered colloidal lattices embedded within a microfluidic system.
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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 | 0.000 | 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 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".