Integrated electrokinetic sample fractionation and solid‐phase extraction in microfluidic devices
Bibliographic record
Abstract
A microfluidic device that performs "in space" sample fractionation, collection, and preconcentration for proteomics is described. Effluents from a 2.75 mm long fractionation channel, focused via sheath flow, were sequentially delivered into an array of 36-collection channels containing monolithic polymer beds for SPE. Optimum conditions for the device design, and simultaneous photolytic fabrication of 36 monolithic columns in the 36 channels, as well as for their proper performance in electrokinetic sample fractionation and collection are described. A hydrophobic butyl methacrylate-based monolithic porous polymer was copolymerized with an ionizable monomer, acryloamido-methyl-propane sulfonate, to form a polymer monolith for SPE that also sustains cathodic electroosmotic flow. The SPE bed was made deep enough to greatly reduce the linear flow rate within the bed, in order to compensate for the lower electroosmotic mobility of the cationically charged SPE bed relative to the glass walled device. Under these conditions, electrokinetic fractionation of a protein sample resulted in tightly focused sample zones delivered into each of the 36-channel polymer beds with no observed crosscontamination. Monolithic columns showed reproducible performance with preconcentration factor of 30 for 2 min loading time. The ability to fractionate, collect, and preconcentrate samples on a microfluidic platform will be especially useful for automated or continuous operation of these devices in proteomics research.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".