Preparing titania aerogel monolithic chromatography columns using supercritical carbon dioxide
Bibliographic record
Abstract
The search for a method to fabricate monolithic inorganic columns has attracted significant recent attention due to their unique ability in separation applications of various biomolecules. Silica and polymer based monolithic columns have been prepared, but titania and other metal oxide monoliths have been elusive, primarily due to their fragility. This article describes a new approach for preparing nanostructured titania based columns, which offer better performance over conventional particle packed columns for separating a wide variety of biomolecules including phosphopeptides. TiO(2) monolithic aerogels were synthesized in separation columns using in situ sol-gel reactions in supercritical carbon dioxide (scCO(2)) followed by calcination, and compared to those prepared in heptanes. The characterization results show that scCO(2) is a better solvent for the sol-gel reactions, providing lower shrinkage with the anatase TiO(2) monolith composed of nanofibers with very high surface areas. The monolithic columns show the ability to isolate phosphopeptides with little flow resistance compared to conventional titania particle based microcolumns.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".