Creating Sub-50 Nm Nanofluidic Junctions in PDMS Microfluidic Chip via Self-Assembly Process of Colloidal Particles
Bibliographic record
Abstract
Polydimethylsiloxane (PDMS) is the prevailing building material to make microfluidic devices due to its ease of molding and bonding as well as its transparency. Due to the softness of the PDMS material, however, it is challenging to use PDMS for building nanochannels. The channels tend to collapse easily during plasma bonding. In this paper, we present an evaporation-driven self-assembly method of silica colloidal nanoparticles to create nanofluidic junctions with sub-50 nm pores between two microchannels. The pore size as well as the surface charge of the nanofluidic junction is tunable simply by changing the colloidal silica bead size and surface functionalization outside of the assembled microfluidic device in a vial before the self-assembly process. Using the self-assembly of nanoparticles with a bead size of 300 nm, 500 nm, and 900 nm, it was possible to fabricate a porous membrane with a pore size of ~45 nm, ~75 nm and ~135 nm, respectively. Under electrical potential, this nanoporous membrane initiated ion concentration polarization (ICP) acting as a cation-selective membrane to concentrate DNA by ~1,700 times within 15 min. This non-lithographic nanofabrication process opens up a new opportunity to build a tunable nanofluidic junction for the study of nanoscale transport processes of ions and molecules inside a PDMS microfluidic chip.
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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".