Mesoporous Silicate−Surfactant Composites with Hydrophobic Surfaces and Tailored Pore Sizes
Bibliographic record
Abstract
Mesoporous silicate−surfactant composites were prepared via washing of as-synthesized silicate−surfactant−expander materials in appropriate solvents. The expander can be generated in situ from the surfactant decomposition under high-temperature conditions, or supplied externally at the stage of the synthesis or postsynthesis hydrothermal treatment. The washing removed the expander as well as a minor part of the surfactant ions present and thus opened the mesopores whose walls were covered by a relatively dense layer of surfactant ions. Depending on the synthesis method for silicate−surfactant−expander composites, both ordered and disordered mesoporous silicate−surfactant composites were obtained with narrow pore size distributions and a wide range of adjustable pore sizes (4−12 nm). Despite the fact that these materials contained 25−35 wt % of surfactant, they exhibited large BET specific surface areas (360−550 m 2 g -1 ) and total pore volumes (from 0.4 to as large as 1.8 cm 3 g -1 ). It is postulated that the surfactant retained in the silicate−surfactant composites is bonded to the silicate walls via electrostatic interactions, which are originally responsible for the formation of silicate−surfactant or silicate−surfactant−expander mesophases. The mesoporosity of the composites is mostly constituted by the space initially occupied by expander, which was later removed upon washing. The synthesis approach described herein is the first successful method for synthesis of ordered and disordered mesoporous silicate−surfactant composites with hydrophobic surfaces, the latter being predominantly formed by long alkyl chains of surfactant ions.
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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) | 0.000 | 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 | 0.000 |
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".