Microchip‐based capillary electrochromatography using packed beds
Bibliographic record
Abstract
Integration of a packed column onto a microchip for performance of capillary electrochromatography (CEC) is described. The quartz device incorporated a cross-injector, and a double weir trapping design for formation of 1, 2 and 5 mm long CEC columns. Three fluorescent dyes were baseline-resolved with plate numbers of 330,(330,000 plates/m; height equivalent to a theoretical plate, H = 3.0 microm) for BODIPY 493/503, 360 (360,000 plates/m; H = 2.8 microm) for rhodamine 123 and 244 (244,000 plates/m; H = 4.1 microm) for acridine orange (AO) with 500 V applied on a 1 mm long column. The 2 mm column yielded approximately 1.8 times more theoretical plates than did the 1 mm column, when operated at the same flow rate. Van Deemter plots were obtained for the three column lengths, showing increased plate height for the 5 mm length. A 2 mm column gave peak height and area relative standard deviation (RSD) values of 2.5 and 3.3%, respectively, as averages for the three dyes (n = 15). The RSD for the dye retention times was 1% (n = 6) over one day, and 3% (n = 30) over five days. Indirect fluorescence detection of thiourea and of amino acids was possible using a neutral indicator dye (BODIPY 493/503), with a detection limit of 10 microM for amino acids.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".