Hydrophilic interaction liquid chromatography for the separation of acidic agricultural compounds
Bibliographic record
Abstract
Organic acids with very low pKa require extremely low pH conditions to achieve adequate retention in reversed-phase liquid chromatography, but an extremely low pH mobile phase can cause instrument reliability problems and limit the choice of columns. Hydrophilic interaction chromatography is a potential alternative to reversed-phase liquid chromatography for the separation of organic acids using more moderate conditions. However, the hydrophilic interaction chromatography separation mechanism is known to be very complex and involves multiple competing mechanisms. In the present study, a hydrophilic interaction chromatography column packed with bare silica core-shell particles was used as the separation column and six agricultural organic acids were used as model analytes to evaluate the effects of buffer concentration, buffer pH, and temperature on sample loading capacity, selectivity, retention, and repeatability. It was found that using a higher concentration of buffer can lead to a significant improvement in the overall performance and reproducibility of the separation. Investigation of column equilibration time revealed that a very long equilibration time is needed when changing mobile phase conditions in between runs. This limitation needs to be acknowledged in hydrophilic interaction chromatography method development and sufficient equilibration time needs to be allowed in method scouting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".