Comprehensive two dimensional separation based on coupling micellar electrokinetic chromatography with capillary isoelectric focusingPresented at Pittcon 2002.
Bibliographic record
Abstract
Concentrating properties of the Capillary Isoelectric Focusing (CIEF) system with continuous whole-column-imaging detection were investigated for application as a second dimension in a comprehensive two-dimensional (2D) separation process. The concentration/separation/detection was completed within 4 min in a 300 microm inner diameter capillary. As the key to the successful coupling of CIEF to a first dimension separation, a novel interface was developed. A 10-port valve with two conditioning loops was used to perform both comprehensive collection and dialysis desalting of the first dimensional effluent, and as an interface coupling Micellar Electrokinetic Chromatography (MEKC) to CIEF. In the loop, salt and other unwanted first dimension effluent components were eliminated by dialysis and carrier ampholytes (CAs) were added. Peak broadening during the dialysis did not have significant impact on the CIEF separation because of its concentrating effect. Protein digests were first separated by MEKC followed by isoelectric point (pI) using whole-column-imaged CIEF. The dialysis interface allows general coupling of the whole-column-imaged CIEF to microscale separations.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".