A novel switchable water stationary phase for supercritical fluid chromatography
Bibliographic record
Abstract
A novel pH switchable water stationary phase is presented for use in supercritical fluid chromatography (SFC). By adding NH 4 OH to the water coating and system hydration , changes in CO 2 pressure and temperature allow a wide range of stationary phase pH conditions (∼3–9) to be achieved, which impact analyte retention properties. For example, 100 atm and 50 °C produces an acidic water stationary phase (pH near 4.0) where octanoic acid readily elutes while the base caffeine does not. Conversely, at 80 atm and 120 °C a basic water stationary phase (pH near 8.0) is obtained and the opposite occurs. Further, under constant pressure and temperature conditions, simply adding or removing NH 4 OH from the system is also found to readily allow switching between the basic and acidic water stationary phase modes and demonstrates control over ionizable analyte elution. For instance, hexanoic acid elution is near 40 times more delayed on a basic water stationary phase and, as such, it can be eluted at later points in time as desired by removing the NH 4 OH and switching to an acidic stationary phase. Experiments indicate that stationary phase pH switching occurs uniformly across the 15 m column length within about 18 s and that analyte retention times are very reproducible upon performing a switch (1.4% RSD; n = 3). Results demonstrate the selectivity factor between acidic and neutral analytes can be reversed and increased about 35 times, while in other trials resolution also similarly increased near 40-fold. By rapidly switching the stationary phase pH back and forth between acidic and basic modes, the selectivity between ionizable analytes could also be increased as desired. Various applications with the system show that it can vastly increase the separation between target analytes and matrix components as required by the dynamics of a particular separation.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".