Recent advances in open tubular capillary liquid chromatography
Bibliographic record
Abstract
This review covers advances and applications of open tubular capillary liquid chromatography (OT-LC) over the period 2007-2018. Under the right conditions OT-LC columns have the potential to offer superior column efficiency, higher overall peak capacity, and higher column permeability compared to packed capillary and monolithic columns. However, such advantages are highly dependent upon column format and dimensions, and to date in liquid chromatography the advantages of open tubular format columns have been most widely discussed and applied in the field of proteomics. In this review we have focused on the wider variety of separation mechanisms and applications which can be achieved following the modification of the inner wall of the capillary with a thin-layer stationary phase. In particular the latest advances in stationary phase development and formation, together with new column formats and dimensions are reviewed. Detection options for OT-LC are also discussed and recent advances in this area highlighted. Finally, this review summarises existing applications of OT-LC and illustrates the future potential for this technique.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".