Microfluidic Integration of Parallel Solid-Phase Liquid Chromatography
Bibliographic record
Abstract
We report the development of a fully integrated microfluidic chromatography system based on a recently developed column geometry that allows for robust packing of high-performance separation columns in poly(dimethylsiloxane) microfluidic devices having integrated valves made by multilayer soft lithography (MSL). The combination of parallel high-performance separation columns and on-chip plumbing was used to achieve a fully integrated system for on-chip chromatography, including all steps of automated sample loading, programmable gradient generation, separation, fluorescent detection, and sample recovery. We demonstrate this system in the separation of fluorescently labeled DNA and parallel purification of reverse transcription polymerase chain reaction (RT-PCR) amplified variable regions of mouse immunoglobulin genes using a strong anion exchange (AEX) resin. Parallel sample recovery in an immiscible oil stream offers the advantage of low sample dilution and high recovery rates. The ability to perform nucleic acid size selection and recovery on subnanogram samples of DNA holds promise for on-chip genomics applications including sequencing library preparation, cloning, and sample fractionation for diagnostics.
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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 | 0.001 |
| 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 | 0.000 | 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".