An automated materials screening approach for the development of sol–gel derived monolithic silica enzyme reactor columns
Bibliographic record
Abstract
Fabrication of monolithic protein-doped capillary columns was reported almost 10 years ago. These columns were derived from a diglycerylsilane precursor, however this material is not commercially available, is difficult to produce in large quantities and has very short phase separation and gelation times, which leads to issues with column reproducibility. Herein, we investigate the use of sodium silicate (SS), a commercially available biofriendly sol–gel precursor, for the fabrication of bimodal meso/macroporous protein-doped monolithic silica columns that are suitable for immobilized enzyme reactor (IMER) assays. Using an automated liquid handler and platereader, a hierarchical materials screening approach was applied to ∼1400 formulations, from which we have identified materials with long gelation times that can form robust bimodal meso/macroporous materials suitable for fabrication of monolithic silica columns. A subset of these materials was observed to have good chromatographic behavior (appropriate backpressure and good stability). A secondary screen around lead materials was performed to identify optimal materials for fabrication of IMER columns. These materials were tested for leaching and activity of immobilized acetylcholine esterase to identify an optimal material for IMER column fabrication. The optimal material was formed from 2% (w/v) silica which was combined with 1.25% PEG 600 at pH 6.4 in 100 mM TRIS buffer. Such columns showed reproducible IMER performance and were able to quantitatively measure the inhibition of immobilized AChE by galanthamine with an inhibition constant of 175 ± 5 nM, which is in excellent agreement with the literature value.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".