Synthesis and Characterization of γ-Fe<sub>2</sub>O<sub>3</sub> for H<sub>2</sub>S Removal at Low Temperature
Bibliographic record
Abstract
The performance of γ-Fe 2 O 3 as sorbent for H 2 S removal at low temperatures (20–80 °C) was investigated. First, γ-Fe 2 O 3 /SiO 2 sorbents with a three-dimensionally ordered macropores (3DOM) structure were successfully prepared by a colloidal crystal templating method. Then, the performance of the γ-Fe 2 O 3 -based material, e.g., reference γ-Fe 2 O 3 and 3DOM γ-Fe 2 O 3 /SiO 2 sorbents, for H 2 S capture was compared with that of α-Fe 2 O 3 and the commercial sorbent HXT-1 (amorphous hydrated iron oxide). The results show that γ-Fe 2 O 3 has an enhanced activity compared to that of HXT-1 for H 2 S capture at temperatures over 60 °C, whereas α-Fe 2 O 3 has little activity. Because of the large surface area, high porosity, and nanosized active particles, 3DOM γ-Fe 2 O 3 /SiO 2 sorbent shows the best performance in terms of sulfur capacity and utilization. Moreover, it was found that moist conditions favor H 2 S removal. Furthermore, it was found that the conventional regeneration method with air at high temperature was not ideal for the composite regeneration because of the transmission of some amount of γ-Fe 2 O 3 to α-Fe 2 O 3 . However, simultaneous regeneration by adding oxygen in the feed stream allowed the breakthrough sulfur capacity of FS-8 to increase up to 79.1%, which was two times the value when there was no O 2 in the feed stream.
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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".