Intrinsic Activity of SBA-like Silica in the Catalytic Ozonation of Organic Pollutants
Bibliographic record
Abstract
Hydrothermal synthesis using two gel compositions: i. 1 SiO2: 0.059 P123: 56.130 HCl: 607.638 H2O; ii. 1 SiO2: 0.006 P123: 0.013 F127: 42.439 HCl: 442.876 H2O, resulted in highly crystalline SBA-15 and SBA-16 like silica with intrinsic activity in the catalytic ozonation of oxalic acid (OA), phenol (φ-OH) and Orange G (OG) in water at room temperature. X-ray diffraction, nitrogen adsorption–desorption, transmission and scanning electron microscopies revealed highly ordered mesoporous structure, with large surface area, regular channels and uniform particle size. UV-Vis analysis provided faster, more precise and reliable assessments of the decomposition yield and process selectivity than the conventional and laborious method of Chemical Oxygen Demand (COD). The ozone reactivity was found to be favored by increasing size of the organic substrate. OG and phenol decomposition required shorter ozonation time (20, 15 min, respectively) than OA, involving mainly phenyl ring hydroxylation and cleavage. Measurements through thermal programmed desorption of carbon dioxide and water revealed that the catalytic activity of the SBA samples involves adsorption via both acid-base and hydrophobic interactions. Suitable SBA modifications allow tailoring these interactions for even faster ozonation without persistent traces of short chain derivatives.
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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) | 0.000 | 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 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".