Bilinear Temperature Gradient Focusing in a Hybrid PDMS/Glass Microfluidic Chip Integrated with Planar Heaters for Generating Temperature Gradients
Bibliographic record
Abstract
Temperature gradient focusing (TGF) is a counterflow gradient focusing technique, which utilizes a temperature gradient across a microchannel or capillary to separate analytes. With an appropriate buffer, the temperature gradient creates a gradient in both the electric field and electrophoretic velocity. Combined with a bulk counter flow, ionic species concentrate at a unique point where the total velocity sums to zero and separate from each other. Scanning TGF uses varying bulk flow so that a large number of analytes that have large differences in electrophoretic mobility can be sequentially focused and passed by a single detection point. Up to now, scanning TGF examples have been performed using a linear temperature gradient which has limitations in improving peak capacity and resolution at the same time. In this work, we develop a bilinear temperature gradient along the separation channel that improves both peak capacity and separation resolution simultaneously. The temperature profile along the channel consists of a very sharp gradient used to preconcentrate the sample followed by a shallow gradient that increases separation resolution. A specialized design is developed for the heaters to achieve the bilinear profile using both analytical and numerical modeling. The heaters are integrated onto a hybrid PDMS/glass chip fabricated using conventional sputtering and soft-lithography techniques. Separation performance is characterized by separating several different dyes and amino acids that have close electrophoretic mobilities. Experiments show a dramatic 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".