Mesoporous tantalum oxide photocatalysts for Schrauzer-type conversion of dinitrogen to ammonia
Bibliographic record
Abstract
Mesoporous tantalum oxide, Fe3+-doped mesoporous tantalum oxide, and bis(toluene) titanium reduced mesoporous tantalum oxide were used for the first time as Schrauzer-type photocatalysts for the conversion of dinitrogen to ammonia. The materials were characterized by XRD, TEM, XPS, and nitrogen absorption before and after catalytic runs. The results showed low to moderate activities depending on the composition. In contrast to previously studied Ti catalysts, Fe doping and heat pretreatment were not prerequisites for photocatalytic activity, but did improve the turnover rates by up to a factor of two. The optimal Fe loading for the tantalum oxides was found to be 1 wt% and the optimal heating condition at 300 °C for 3 h. Increased surface area and heat treatment were also found to improve activities. Contrary to our expectations, reduction of the mesostructure with bis(toluene) titanium had little effect on the catalytic activity. In spite of the dramatically higher surface areas of the mesoporous tantalum oxides as compared with bulk titanias used previously in this process, the overall catalytic activities were still less than those obtained in the Schrauzer system. This suggests that the increase in diffusion and surface area offered by the mesoporous structure is offset by the smaller crystalline domain sizes in the walls of the structure, leading to poor electron-hole separation and a reduction in catalytic efficiency. Key words: mesoporous, Schrauzer, ammonia, photocatalysis, tantalum oxide.
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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".