Multiplexed electrokinetic sample fractionation, preconcentration and elution for proteomics
Bibliographic record
Abstract
Both 6 and 8-channel integrated microfluidic sample pretreatment devices capable of performing "in space" sample fractionation, collection, preconcentration and elution of captured analytes via sheath flow assisted electrokinetic pumping are described. Coatings and monolithic polymer beds were developed for the glass devices to provide cationic surface charge and anodal electroosmotic flow for delivery to an electrospray emitter tip. A mixed cationic ([2-(methacryloyloxy)ethyl] trimethylammonium chloride) (META) and hydrophobic butyl methacrylate-based monolithic porous polymer, photopolymerized in the 6- or 8-fractionation channels, was used to capture and preconcentrate samples. A 0.45 wt% META loaded bed generated comparable anodic electroosmotic flow to the cationic polymer PolyE-323 coated channel segments in the device. The balanced electroosmotic flow allowed stable electrokinetic sheath flow to prevent cross contamination of separated protein fractions, while reducing protein/peptide adsorption on the channel walls. Sequential elution of analytes trapped in the SPE beds revealed that the monolithic columns could be efficiently used to provide sheath flow during elution of analytes, as demonstrated for neutral carboxy SNARF (residual signal, 0.08% RSD, n = 40) and charged fluorescein (residual signal, 2.5% n = 40). Elution from monolithic columns showed reproducible performance with peak area reproducibility of ~8% (n = 6 columns) in a single sequential elution and the run-to-run reproducibility was 2.4-6.7% RSD (n = 4) for elution from the same bed. The demonstrated ability of this device design and operation to elute from multiple fractionation beds into a single exit channel for sample analysis by fluorescence or electrospray mass spectrometry is a crucial component of an integrated fractionation and assay system for proteomics.
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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".