Study of system properties in reversed-phase liquid chromatography for binary and ternary solvent mobile phase compositions using the solvation parameter model
Bibliographic record
Abstract
System maps for the individual system constant of the solvation parameter model and five binary solvent containing 20–70% (v/v) acetonitrile, acetone, methanol, 2-propanol, and tetrahydrofuran on a single octadecylsiloxane-bonded silica column (Luna C18) are used to provide insight into the variation of system properties with mobile phase composition and solvent type. Selectivity differences are dominated by variation in solute size and hydrogen-bond basicity with solvent-dependent variation in dipole-type and hydrogen-bond acid interactions. Interactions involving lone pair electrons are important only in the case of the alcohols and to a lesser extent tetrahydrofuran. Selectivity differences are also dependent on solvent strength. To expand the selectivity space four ternary solvent systems (acetonitrile-methanol-water, acetonitrile-2-propanol-water, methanol-tetrahydrofuran-water, and tetrahydrofuran-2-propanol-water) 1:1:2% (v/v) containing 50% (v/v) total organic solvent are compared with the binary solvent systems at the same organic solvent composition. There is no simple model that links the system properties of the ternary solvents to the binary solvent systems, but it is demonstrated that the ternary solvent systems expand the selectivity space available for reversed-phase separations. The solvation properties of the bulk organic solvents provide insufficient information to predict selectivity in RPLC because of the dominant contribution of water and effects related to the selective solvation of the stationary phase and possible changes in the solvent-dependent microstructure of the mobile phase.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".