Adsorptive Denitrogenation and Desulfurization of Diesel Fractions by Mesoporous SBA15-Supported Nickel(II) Phosphide Synthesized through a Novel Approach of Urea Matrix Combustion
Bibliographic record
Abstract
Stringent environmental regulations on the sulfur content in transportation fuels have made ultradeep desulfurization of diesel an important research topic. One of the approaches to promote the effectiveness of the conventional hydrodesulfurization (HDS) process is to remove the organic nitrogen-containing compounds from the feed before HDS. Adsorptive removal of nitrogen compounds at room temperature and pressure without the presence of hydrogen was studied using a high-capacity adsorbent, Ni 2 P/SBA15, which was prepared by the urea matrix combustion (UMxC) method. A metal loading of 7 wt % Ni was observed to be optimum among the loadings tested. The nitrogen adsorption capacity reached 9.1 mg/g of adsorbent, which is higher than the capacities of most of the reported adsorbents. Ni 2 P/SBA15 was characterized by Brunauer–Emmett–Teller analysis, X-ray diffraction, Fourier transform IR spectroscopy, and transmission electron microscopy (TEM). The mesoporous nature of the adsorbent was confirmed by nitrogen adsorption/desorption analysis as well as TEM analysis. Uniform dispersion of Ni 2 P was observed in TEM images. Solvent-washing regeneration was studied, and four adsorption-and-regeneration cycles were carried out. Approximately 95% of the adsorptive capacity of the sorbent was recovered after four cycles.
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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".