A promising capillary electrophoresis–electrospray ionization–mass spectrometry method for carbohydrate analysis
Bibliographic record
Abstract
A method for adapting widely used CE conditions for the separation of fluorescently labeled carbohydrates to permit online ESI-MS detection is presented. Reverse polarity separations were performed in bare fused-silica capillaries with an acidic BGE. Under these conditions, negatively charged 8-aminopyrene 1,3,6-trisulfonate-labeled carbohydrates migrate forward against the EOF, which is towards the capillary inlet. Therefore, the CE-MS interface must simultaneously back-fill the capillary, in order to maintain the CE circuit, and provide a stable forward flow at the sprayer tip to support the electrospray process. This was achieved using a junction-at-the-tip interface, which provides a flow of solution to the junction formed by the capillary terminus and the inner wall of the emitter needle tip. Because the flow rate required for this arrangement is much less than in conventional sheath flow interfaces, dilution of the analytes is minimized. Optimized separation conditions permit baseline resolution of glucose oligomers containing up to 15 glucose units, while longer oligomers, up to 33 glucose units, were observed as resolved peaks in the negative ion mode mass spectrum.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".