Potential of Two-Dimensional Electro-Fluid-Dynamic Devices for Continuous Purification of Multiple Components from Complex Samples
Bibliographic record
Abstract
Two-dimensional electro-fluid-dynamic (EFD) devices, in which both electric field and hydrodynamic pressure are used to drive the analyte and fluid migration, enable two-dimensional channel networks to be used for chemical separation instead of one-dimensional column separation systems. Investigation of the theory of mass transfer in symmetrical Y-shaped EFD devices shows that the magnitude of pressure-induced velocity in lateral channels at critical boundary conditions between different steady state migration paths is independent of the channel cross-sectional area ratio. Therefore, the analyte has four possible mass transfer pathways according to the electric field and pressure setup in all symmetrical Y-shaped 2-D EFD devices, and such devices with any cross-sectional area ratio have the capacity to continuously purify two analytes from a mixture simultaneously. In addition, a new format of multiple-branched 2-D EFD devices is introduced to process multiple analytes. A "proof-reading" mechanism based on the infinite resolution conditions ensures the purity of the components collected. The separation processes are simulated by COMSOL Multiphysics, and the migration behavior of the analytes was monitored using fluorescent dyes to verify the flow behavior of different analytes in individual channels. These 2-D EFD devices offer the potential of continuous fractionation and purification of analytes from complex sample mixtures.
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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 | 0.001 |
| 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 | 0.000 | 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".