Bibliographic record
Abstract
A variety of mesoporous tantalum oxide supported catalytic systems were synthesized and investigated for their activities in nitrogen activation, including the Schrauzer-type photocatalytic process and the Haber-Bosch type thermocatalytic process. Mesoporous Ta oxide possesses a high thermal stability, high surface area, tunable wall composition and, most interestingly, variable oxidation states of the transitional metal sites. For this reason it represents a unique support for heterogeneous catalysis. Modification of the surface properties by doping active metal agents and various thermal treatments can further improve the activity. Results obtained in this work showed that for the photocatalytic process pure Ta oxide, with suitable band gap near-UV, exhibited relatively low activity; however, 1 wt% Fe3+ doping increased the activity by a factor of 3. For the Haber process, the Ba-Ru-Ta material was the most active system. Ru3(CO)12 proved to be the best precursor for the active Ru metal component, and Ba(NO 3)2 was the best precursor for the BaO promoter. Remarkably, this system shows a very low activation energy of 9.3 kJ/mol as well as a clear involvement of Ta specie(s) during the catalytic reaction. This suggests a different mechanism than that proposed for standard Ru-based Haber synthesis, which uses alumina, silica and magnesia supports, might be functioning. The results in this thesis clearly show the enormous potential of mesoporous transition metal oxides in catalysis, the first porous support materials offering variable oxidation states. All materials in this work were characterized by a combination of techniques including XRD, TEM, nitrogen adsorption, XPS, EDS, and H 2-TDA. Source: Masters Abstracts International, Volume: 44-03, page: 1373. Thesis (M.Sc.)--University of Windsor (Canada), 2005.
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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".