Capturing H<sub>2</sub>S<sub>(g)</sub>by In Situ-Prepared Ultradispersed Metal Oxide Particles in an Oilsand-Packed Bed Column
Bibliographic record
Abstract
The current oil recovery and upgrading processes contribute directly to air pollution problems. H 2 S (g) is considered one of the major gaseous pollutants in oil recovery and processing. The aim of this study is to investigate the feasibility of methods aimed at the in situ capture of H 2 S (g) and its conversion into an environmentally neutral final product. In this work, we tested the sorption of H 2 S (g) into different in situ-prepared colloidal metal oxides in an oilsand matrix under recovery conditions, namely, ZnO, CuO, NiO, and Al 2 O 3 . In addition, the effect of metal oxide concentration and reaction temperature on H 2 S (g) reactivity was evaluated. Furthermore, commercially available ZnO nanoparticles were tested for comparison. Except for Al 2 O 3, all the considered metal oxides reacted stoichiometrically with H 2 S (g) at the selected temperature and pressure. An increase in the metal oxide concentration favored the removal of H 2 S (g) . The in situ-prepared ZnO ultradispersed particles were found to be more reactive than the commercial nanoparticles, as a result of their dispersion ability and intrinsic reactivity.
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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".