Capillary Isoelectric Focusing of Proteins with Liquid Core Waveguide Laser-Induced Fluorescence Whole Column Imaging Detection
Bibliographic record
Abstract
A capillary isoelectric focusing (CIEF) system with liquid core waveguide (LCW) laser-induced fluorescence whole column imaging detection was developed in this study. A Teflon AF 2400 capillary was used as both the separation channel and the axially illuminated LCW. The excitation light was introduced at one end of the capillary, and propagated forward within the capillary. As the Teflon AF 2400 capillary has a refractive index (n = 1.29-1.31) lower than that of water (n = 1.33), total internal reflection was very apparent The employment of the Teflon AF 2400 capillary avoided the use of high refractive index additives such as glycerol, accommodating the system to wider applications. Due to its inert chemical properties, the capillary exhibited limited protein adsorption and electroosmotic flow; thus, the need for capillary preconditioning with polymeric solution and the addition of polymeric additives into the sample mixture can be avoided. Three types of proteins, naturally fluorescent proteins, covalently labeled proteins, and noncovalently labeled proteins, were examined using this method. CIEF under denaturing conditions was also explored, and several advantages over the native mode were found. When compared to a commercially available instrument with UV detection, the separation efficiency and peak capacity were similar while the detection sensitivity was enhanced by 3-5 orders of magnitude.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".