Precursor Mediated Synthesis of Nanostructured Silicas: From Precursor-Surfactant Ion Pairs to Structured Materials
Bibliographic record
Abstract
The synthesis of nanostructured anionic-surfactant-templated mesoporous silica (AMS) recently appeared as a new strategy for the formation of nanostructured silica based materials. This method is based on the use of anionic surfactants together with a co-structure-directing agent (CSDA), mostly a silylated ammonium precursor. The presence of this CSDA is necessary in order to create ionic interactions between template and silica forming phases and to ensure sufficient affinity between the two phases. This synthetic strategy was for the first time applied in view of the synthesis of surface functionalized silica bearing ammonium groups and was then extended on the formation of materials functionalized with anionic carboxylate and bifunctional amine-carboxylate groups. In the field of silica hybrid materials, the "anionic templating" strategy has recently been applied for the synthesis of silica hybrid materials from cationic precursors. Starting from di- or oligosilylated imidazolium and ammonium precursors, only template directed hydrolysis-polycondensation reactions involving complementary anionic surfactants allowed accessing structured ionosilica hybrid materials. The mechanistic particularity of this approach resides in the formation of precursor-surfactant ion pairs in the hydrolysis-polycondensation mixture. This review gives a systematic overview over the various types of materials accessed from this cooperative ionic templating approach and highlights the high potential of this original strategy for the formation of nanostructured silica based materials which appears as a complementary strategy to conventional soft templating approaches.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".