Field-Flow Fractionation and Hydrodynamic Chromatography on a Microfluidic Chip
Bibliographic record
Abstract
We present gravitational field-flow fractionation and hydrodynamic chromatography of colloids eluting through 18 μm microchannels. Using video microscopy and mesoscopic simulations, we investigate the average retention ratio of colloids with both a large specific weight and neutral buoyancy. We consider the entire range of colloid sizes, including particles that barely fit in the microchannel and nanoscopic particles. Ideal theory predicts four operational modes, from hydrodynamic chromatography to Faxén-mode field-flow fractionation. We experimentally demonstrate, for the first time, the existence of the Faxén-mode field-flow fractionation and the transition from hydrodynamic chromatography to normal-mode field-flow fractionation. Furthermore, video microscopy and simulations show that the retention ratios are largely reduced above the steric-inversion point, causing the variation of the retention ratio in the steric- and Faxén-mode regimes to be suppressed due to increased drag. We demonstrate that theory can accurately predict retention ratios if hydrodynamic interactions with the microchannel walls (wall drag) are added to the ideal theory. Rather than limiting the applicability, these effects allow the microfluidic channel size to be tuned to ensure high selectivity. Our findings indicate that particle velocimetry methods must account for the wall-induced lag when determining flow rates in highly confining systems.
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.
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) | 0.001 | 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".