Adsorptive Removal of Nitrogen and Sulfur Containing Compounds by SBA15 Supported Nickel (II) and Tungsten Phosphides and the Adsorption Mechanisms
Bibliographic record
Abstract
Abstract SBA15 supported transition metal phosphides Ni 2 P/SBA15 and WP/SBA15 have been identified as promising adsorbents especially for removing neutral nitrogen-containing compounds. Adsorption of model nitrogen- and sulfur-containing compounds as well as light cycled oil (LCO) was performed and applied for the evaluation of kinetics and isotherms. The pseudo second-order kinetic model was well fitted to both nitrogen and sulfur adsorption data. Molecular size of the adsorbates plays an important role in the adsorption. Despite of higher initial adsorption rates, the adsorption capacities for carbazole and DBT were lower than those for indole and quinoline due to their larger molecular size. Monolayer adsorption was observed for quinoline due to the acid-base interaction between the basic nitrogen adsorbate and the weak acidic support. The Freundlich model was suitable in describing the adsorption of nitrogen- and sulfur-containing compounds from LCO. Cooperative adsorption took place when replacing the model compound DBT by the sulfur-containing compounds in LCO.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".