Bibliographic record
Abstract
Sulfur in gases causes many problems associated with environmental pollution, and the loss of industrial equipment.Selective adsorption of sulfur compounds is one of the most widely used methods, the most important advantages of this method are the desulphurisation reaction at low temperature and pressure, which reduces the cost of refining operations.In this study, ZnO nanoparticles was made based on Silica mesoporous (SBA-15), different methods such as: ICP, BET, XRD were used to check the physical and chemical properties and the absorbent action to remove gas-Iso¬ propyl-mercaptan (IPM).Various experiments were carried out to remove the mercaptan gas by adsorbent discontinuously.Then, the effect of effective absorption parameters such as temperature, loading rate, contact time, and initial gas concentration were studied.The maximum absorption was achieved in condition, contact time is 50 minutes, and temperatyre is 298 k, With a loading rate of 5% nanoparticles, initial concentration of 500 ppm.For determining the most suitable absorption isotherm, Temkin, Langmuir, Freundlich adsorption isotherm have been studied.The relationships between Langmuir Temkin, Langmuir isotherms for adsorbents were investigated.The result of this study showed that isotherm temperature with Freundlich is more consistent with experimental data (R 2 = 0.998).The results showed that the adsorbents have a good ability to remove Iso propyl mercaptan gas.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.911 | 0.895 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".