A quantitative study of continuous flow‐counterbalanced capillary electrophoresis for sample purification
Bibliographic record
Abstract
A systematic evaluation of continuous flow-counterbalanced capillary electrophoresis for microscale preparative applications is presented. The success of the technique was found to depend on the specific nature of the analyte and background electrolyte and was most heavily influenced by factors such as solution conductivity and rate of buffer depletion. The performance of the technique for the system studied was ultimately limited by contamination arising from changes in analyte mobilities during extended run times. Marked improvements in both purification rate and yield were observed using the zwitterionic buffer 2-(N-cyclohexylamino)ethanesulfonic acid when compared to similar runs carried out using a borate background electrolyte. A quantitative evaluation of the technique was performed for the analytes studied and the performance of the technique has been compared to fraction collection methods. The highest recovery achieved was 5.2% of the fastest migrating component present in the original sample with a resolution of 9 to the adjacent peak and required 100 min under optimized conditions. Recovery could be further increased to 9.0% in 120 min for the same analyte using pressure-ramped flow-counterbalanced capillary electrophoresis.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".