Functional Mesoporous Material Derived from 3D Net‐Linked SBA‐15
Bibliographic record
Abstract
A novel, versatile functional mesoporous material with the structure of SBA-15 but a specific 3D net-linked morphology has been prepared by using a facile method. This material has been assessed for its ability to reduce the level of nitrosamines in tobacco smoke so that the actual effect of this material in the reduction of carcinogenic environmental pollutants in a complex chemical system can be verified. By adjusting the force field during the hydrolysis and condensation processes of tetramethyl orthosilicate (TMOS) in an aqueous reaction system, the SBA-15 silica, which has a special morphology in that it is long and fiberlike from a microscopic view but large and block-like from a macroscopic angle, has been successfully synthesized. Moreover, the synthetic product is no longer a powder but a block because of its specific 3D net-linked morphology, which enables the material not only to trap particles in smoke but also to potentially be applied as a device. To strengthen the adsorptive capability of this porous material, a dry-impregnation procedure was applied in which zirconia was coated on the silica wall without changing the morphology. As expected, this functional material shows a high activity in the adsorption of N'-nitrosonornicotine (NNN), a bulky tobacco-specific nitrosamine, in solution and of N-nitrosopyrrolidine (NPYR), a volatile nitrosamine, in the gas stream. Furthermore, this 3D net-linked SBA-15 material can efficiently decrease the amount of nitrosamines and total number of particles in mainstream smoke when it is tested as an additive in the filter of a cigarette, thus providing powerful protection for the environment and public health.
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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) | 0.000 | 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".