Comparison of Sample Introduction Methods for Continuous Chemical Purification in Two-Dimensional Electro-Fluid-Dynamic Devices
Bibliographic record
Abstract
Two-dimensional electro-fluid-dynamic (2-D EFD) devices, in which both electric field and hydrodynamic pressure are used to drive the analyte and fluid migration, enable chemical separation to proceed in two-dimensional channel networks instead of a one-dimensional column and provide better control on the migration and distribution of analyte in complex channel networks. We have reported the use of a 2-D EFD device to continuously purify multiple components from complex samples ( Liu et al. Anal. Chem. 2010 , 82 , 2182 - 2185 and Liu et al. Anal. Chem. 2011 , 83 , 8208 - 8214 ). A continuous solution stream containing a mixture can be separated into different channels, each containing a pure compound. In previous studies, the sample mixture was introduced into the device by applying an electric field, also known as electrokinetic sample introduction. The initial separation junction requires three separate voltages and one pressure source. In this study, we investigated the mass transfer at the separation junction when the hydrodynamic pressure is used to deliver the sample. The initial separation junction has two voltages and two pressure sources. Continuous chemical purification is demonstrated in EFD devices with different geometries, and the comparison of both sample introduction approaches indicates that hydrodynamic sample introduction is superior to electrokinetic sample introduction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".