Silicate Polymerization for the Preparation of Bed-Retention Frits in Capillary Electrochromatography
Bibliographic record
Abstract
Bubble formation, which is associated with bed-retention frits, is a critical experimental problem in capillary electrochromatography systems. In this investigation, porous silica frits were prepared via spot-heating of a silicate solution, and the effects of several experimental parameters on their performance were studied. The optimal sodium silicate concentrations were 10.8% and 5.4% (w/v) for outlet and inlet frits, respectively. The heating times were 5-6 s for outlet frits and < 1 s for inlet frits. Under optimized conditions, outlet frits were 75 microns (+/- 12 microns) and the heat treatment did not make the capillary fragile at the frit location. Bubble formation was affected by frit length, density, and silanization of the frits with trimethylchlorosilane. Packed capillaries with optimized frits were used successfully in a commercial CE instrument over a normal working day without pressurization, at relatively high ionic strengths (10 mM), and over a wide range of acetonitrile compositions (20%-80%). Currents were also stable for > or = 3 h under very high current (27 microA) conditions. As part of this study, the efficiency and reproducibility of packed capillaries were also briefly evaluated.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".