Bundled capillary electrophoresis using microstructured fibres
Bibliographic record
Abstract
Joule heating, arising from the electric current passing through the capillary, causes many undesired effects in CE that ultimately result in band broadening. The use of narrow-bore capillaries helps to solve this problem as smaller cross-sectional area results in decreased Joule heating and the rate of heat dissipation is increased by the larger surface-to-volume ratio. Issues arising from such small capillaries, such as poor detection sensitivity, low loading capacity and high flow-induced backpressure (complicating capillary loading) can be avoided by using a bundle of small capillaries operating simultaneously that share buffer reservoirs. Microstructured fibres, originally designed as waveguides in the telecommunication industry, are essentially a bundle of parallel ∼5 μm id channels that extend the length of a fibre having otherwise similar dimensions to conventional CE capillaries. This work presents the use of microstructured fibres for CZE, taking advantage of their relatively high surface-to-volume ratio and the small individual size of each channel to effect highly efficient separations, particularly for dye-labelled peptides.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".