CE in a Nonuniform Capillary Modulated by a Cylindrical Insert, and Zone-Narrowing Effects during Sample Injection
Bibliographic record
Abstract
The electrophoretic behavior of an analyte in a capillary consisting of two parts of different cross section has been investigated. Modulation of the cross-sectional area of the separation channel has been achieved by inserting a cylindrical fiber different distances into the capillary. It was shown that the zone injected into the capillary part with smaller cross section could be moved using electromigration into the wider part of the capillary with zone compression. As we observed, the zone narrowed longitudinally in the wide part of the capillary in accordance with the ratio of the electric field strength in the two parts of the capillary. The concentration of plug introduced into the capillary by electroinjection can be increased by use of low-conductivity sample buffer. Efficient introduction of extracted analytes desorbed from an SPME fiber to the separation channel was achieved using this approach. Thermoinduced effects caused by temperature increase in the narrow part of the capillary and their influence on sample stacking are analyzed. Possible applications of the effect observed to the sample introduction optimization are also discussed in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".