Effect of Impregnation on the Structure of Niobium Oxide/Alumina Catalysts Studied by Multinuclear Solid-State NMR, FTIR, and Quantum Chemical Calculations
Bibliographic record
Abstract
Multinuclear solid-state 1 H, 27 Al, and 93 Nb NMR experiments and DFT calculations were carried out for structural characterization of alumina-supported niobium oxide catalysts with high niobium content following an every stage in the catalyst preparation. It was found that the first stage of the impregnation procedure plays a key role in determining the catalyst structure and acidity. In order to monitor the presence in catalysts of aluminum niobate phase, AlNbO 4, a series of 27 Al and 93 Nb NMR experiments was performed for several different individual AlNbO 4 samples. Aluminum and niobium NMR parameters were determined for AlNbO 4, which crystal structure contains two different crystallographic sites for each element. The compound was investigated through a combination of experimental 93 Nb and 27 Al NMR spectroscopy methods at several magnetic field strengths (9.4, 11.7, 19.4, and 21.1 T) and complemented by ab initio quantum chemical calculations of NMR parameters for these nuclei. The chemical shielding and the quadrupole coupling tensor parameters were determined for both 93 Nb and 27 Al.
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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 | 0.001 |
| 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 | 0.001 | 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".