Micellar affinity gradient focusing in a microfluidic chip with integrated bilinear temperature gradients
Bibliographic record
Abstract
Micellar affinity gradient focusing (MAGF) is a microfluidic counterflow gradient focusing technique that combines the favorable features of MEKC and temperature gradient focusing. MAGF separates analytes on the basis of a combination of electrophoretic mobility and partitioning with the micellar phase. A temperature gradient is produced along the separation channel containing an analyte/micellar system to create a gradient in interaction strength (retention factor) between the analytes and micelles. Combined with a bulk counterflow, species concentrate at a unique point where their total velocity sums to zero. MAGF can be used in scanning mode by varying the bulk flow so that a large number of analytes can be sequentially focused and passed by a single detection point. In this work, we develop a bilinear temperature gradient along the separation channel that improves separation performance over the conventional linear designs. The temperature profile along the channel consists of a very sharp gradient used to preconcentrate the sample followed by a shallow gradient that increases resolution. We fabricated a hybrid PDMS/glass microfluidic chip with integrated micro heaters that generate the bilinear profile. Performance is characterized by separating several different samples including fluorescent dyes using SDS surfactant and pI markers using both SDS and poly-SUS surfactants as the micellar phase. The new design shows a nearly two times improvement in peak capacity and resolution in comparison to the standard linear temperature gradient.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".