Self-generated exclusion zone in a dead-end pore microfluidic channel
Bibliographic record
Abstract
Particles can be manipulated by gradients of concentration (diffusiophoresis) and electric potential (electrophoresis) to transport them to desired locations. To establish these gradients, external stimuli are usually required. In this work, we manipulate particles through a self-generated concentration gradient within a PDMS-based microfluidic platform, without directly applying an external field. The interfacial chemistry of the PDMS results in a local increase of hydronium ions, leading to a concentration and electrical potential gradient in the system, which in turn generate a temporary exclusion zone at the pore entrance, extending up to half of the main channel, or 150 μm. With time, this exclusion zone diminishes as equilibrium in the ion concentration is reached. We study the dynamics of the exclusion zone thickness and find that the Sherwood number determines the size and stability of the exclusion zone. Our work shows, that even without introducing external ionic gradients, particle diffusiophoresis is significant in lab-on-a-chip systems. The interfacial chemistry of the microfluidic platform can have a significant influence on particle movement and this should be considered when designing experiments on diffusiophoresis. The observed phenomenon can be employed to design lab-on-a-chip-based sorting of colloidal particles.
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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".