High Surface Area, Thermally Stable, Hydrophobic, Microporous, Rigid Gels Generated at Ambient from MeSi(OEt)<sub>3</sub>/(EtO)<sub>3</sub>SiCH<sub>2</sub>CH<sub>2</sub>Si(OEt)<sub>3</sub> Mixtures by F<sup>−</sup>‐Catalyzed Hydrolysis
Bibliographic record
Abstract
Abstract High surface area materials are of considerable interest for gas storage/capture, molecular sieving, catalyst supports, as well as for slow‐release drug‐delivery systems. We report here a very simple and fast route to very high surface area, mechanically robust, hydrophobic polymer gels prepared by fluoride‐catalyzed hydrolysis of mixtures of MeSi(OEt)3 and bis‐triethoxysilylethane (BTSE) at room temperature. These materials offer specific surface areas up to 1300 m2 g−1, peak pore sizes of 0.8 nm and thermal stabilities above 200 °C. The gelation times and surface areas can be controlled by adjusting the solvent volume (dichloromethane), percent fluoride (as nBu4NF or TBAF) and the BTSE contents. Polymers with other corners and linkers were also explored. These materials will further expand the materials databank for use in vacuum insulation panels and as thermally stable release and capture media.
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) | 0.000 | 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".