Synthesis, Acid Properties and Catalysis by Niobium Oxide Nanostructured Materials
Bibliographic record
Abstract
Several forms of niobium oxide were prepared, including nanostructured mesoporous materials, and their acidity properties were comprehensively investigated and compared with commercially available materials. The composites were characterized by a variety of techniques, including XRD, TEM, N2 adsorption and Hammett acid indicator studies. The acidity of the niobium oxide derivatives was also investigated by the ability of the materials to successfully promote the halochromic ring-opening of an oxazine-coumarin probe that was specifically designed for use in fluorescence imaging studies. The ring-opening reaction was easily monitored using UV-visible, fluorescence and NMR spectroscopy. Single molecule microscopy was employed to gain a more in-depth understanding of the niobium oxide acid catalysis pathway. Using this technique, the rate of niobium oxide mediated protonation was estimated to be 1.8 × 10−13 mol m−2 s−1. Single molecule analysis was also used to obtain a detailed map of Brønsted acid sites on the niobium oxide surface. The active sites, located by multiple blinking events, do not seem to be localized on any area of the material, but rather randomly distributed throughout the solid state surface. As the reaction proceeds, the sites with the highest acidity and accessibility are gradually consumed, making the next tier of acid sites available for reaction. The phenomenon was more closely characterized by using time lapsed reactivity maps.
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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".