Control of Gas and Odor Levels in Swine Facilities Using Filters with Zinc Oxide Nanoparticles
Bibliographic record
Abstract
Abstract. The effectiveness of zinc oxide (ZnO) nanoparticles as filtering media for controlling the levels of hydrogen sulfide (H2S), ammonia (NH3), and odor in swine facilities was evaluated in this study. Semi-pilot scale tests were done to determine basic operational factors, the results of which showed that the fluidized bed air filtration system (FBAFS), loaded with ZnO nanoparticles at a rate of 0.28 g cm-2 of filter area, and a gas flow rate equivalent to 0.5 m s-1 face velocity achieved significant reduction in target gas levels. The performance of this filter system was further investigated in a room-scale environmental chamber representative of normal swine production conditions. When installed as part of the ventilation air recirculation system of the room, the FBAFS with ZnO nanoparticles achieved about 65% H2S and 42% NH3 reductions in the human-occupied zones but had no significant impact on pig performance as well as odor levels in the chamber. Keywords: Ammonia, Face velocity, Filtration, Hydrogen sulfide, Nanoparticles, Odor, Swine, Ventilation, Zinc oxide.
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".