Microfluidic devices for electrokinetic sample fractionation
Bibliographic record
Abstract
We present three generations of microchip-based "in-space" sample fractionators and collectors for use in proteomics. The basic chip design consisted of a single channel for CE separation of analytes that then intersects a fractionation zone feed into multiple high aspect ratio microchannels for fractionation of separated components. Achievements of each generation are discussed in relation to important design criteria. CE-separated samples were electrokinetically driven to multiple collection channels in sequence without cross-contamination under the protection of sheath streams. A 36-channel fractionator demonstrated the efficacy of a high-throughput fractionator with no observed cross-contamination. A mixture of IgG and BSA was used to test the efficiency of the fractionator and collector. CE of the fractionated samples was performed on the same device to verify their purity. Our demonstration proved to be efficient and reproducible in obtaining non-contaminated samples over 15 sample injections. Experimental results were found to be in close agreement with PSpice simulation in terms of flow behavior, contamination control and device performance. The design presented here has a great potential to be integrated in proteomic platforms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".