Gradient Elution in Microchannel Electrochromatography
Bibliographic record
Abstract
There is great interest in using microfluidic channels packed with a stationary phase for chemical separations of complex mixtures. A key advantage of such techniques is the use of electroosmotic flow (EOF), controlled simply by applying electrical potentials between reservoirs. A disadvantage for this technique, however, is a lack of compatibility with gradient elution separations. This limitation arises from the dependence of EOF velocity on run buffer content (including the concentration of organic modifier). Here, we introduce a method for implementing gradient elution in electrochromatography in which multiple run buffers are velocity-matched, such that the elution profile resembles that found in conventional HPLC. This method is driven entirely with EOF, meaning that pumps, valves, and pressure fittings are not required. The method was validated by application to separations of peptide standards and protein digests. These results suggest that microfluidic electrochromatography may be compatible with a wide range of applications that have previously been unexplored.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".