X-ray Structural Modeling and Gas Adsorption Analysis of Cagelike SBA-16 Silica Mesophases Prepared in a F127/Butanol/H<sub>2</sub>O System
Bibliographic record
Abstract
A detailed characterization of large-pore cagelike mesoporous SBA-16 silica materials with tailored pore dimensions is reported. The materials were synthesized in a EO 106 PO 70 EO 106 (F127)−butanol−H 2 O system under mildly acidic conditions, and the pore diameters were tailored by varying the hydrothermal treatment temperature. Structural information was acquired by full-profile analysis of powder X-ray diffraction (XRD) patterns. High-resolution diffraction data were obtained for all the materials using synchrotron radiation as the X-ray source, enabling a comprehensive XRD modeling supplemented with the generation of electron density distribution maps. The structural parameters derived from the XRD modeling were compared with data obtained from nitrogen and argon physisorption experiments performed at −196 °C. An excellent agreement was found between the XRD modeling results and those obtained by a new nonlocal density functional theory (NLDFT) kernel developed for pore size analysis based on gas adsorption in spherical pores, while NLDFT analysis based on a cylindrical pore model was shown to systematically underestimate the pore dimensions by about 30% which exceeds previous expectations. Furthermore, the Barrett−Joyner−Halenda model was shown to give errors up to about 45% in the pore size range above 4 nm. The structure of the surfactant−silica hybrid materials was also analyzed by XRD, which shed more light on the structural changes accompanying the thermal surfactant removal process. The present study is expected to provide a reference source for the accurate characterization of large cagelike mesoporous silica materials, on the basis of a direct comparison of suitable data collected independently by gas physisorption and comprehensive XRD modeling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".