Controlling morphology and porosity to improve performance of molecularly imprinted sol–gel silica
Bibliographic record
Abstract
The wealth of molecular precursors for organic and inorganic polymers has resulted in an incredible volume of molecular imprinting literature. The vast majority of reports deal with organic polymer systems, and molecular imprinting in silica can still be considered a small niche in the field. In this review, we present key concepts of molecular imprinting, sol-gel processing, and the synthesis of templated mesoporous silica. We take a small fraction of the literature and use it to understand the ways in which molecular imprinting in siliceous materials of controlled morphology has achieved success in the past fifteen years. Using selected case studies rather than a comprehensive review of the entire field, our goal is to illustrate the key aspects of imprinted silica-based materials as demonstrated by judiciously controlled systems, looking first at control on the micrometre scale in bulk phase materials, and then on the nanometre scale in templated mesoporous materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".