Coating properties of a novel water stationary phase in capillary supercritical fluid chromatography
Bibliographic record
Abstract
The coating properties of a novel water stationary phase used in capillary supercritical fluid chromatography were investigated. The findings confirm that increasing the length or internal diameter of the type 316 stainless-steel column used provides a linear increase in the volume of stationary phase present. Under normal operating conditions, results indicate that about 4.9 ± 0.5 μL/m of water phase is deposited uniformly inside of a typical 250 μm internal diameter 316 stainless-steel column, which translates to an area coverage of about 6.3 ± 0.5 nL/mm(2) regardless of dimension. Efforts to increase the stationary phase volume present showed that etching the stainless-steel capillary wall using hydrofluoric acid was very effective for this. For instance, after five etching cycles, this volume doubled inside of both the type 304 and the type 316 stainless-steel columns examined. This in turn doubled analyte retention, while maintaining good peak shape and column efficiency. Overall, 316 stainless-steel columns were more resistant to etching than 304 stainless-steel columns. Results indicate that this approach could be useful to employ as a means of controlling the volume of water stationary phase that can be established inside of the stainless-steel columns used with this supercritical fluid chromatography 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".