Characterization of zinc oxide nanoparticle (nZnO) alginate beads in reducing gaseous emission from swine manure
Bibliographic record
Abstract
Hydrogen sulfide (H2S) and greenhouse gases’ emission from livestock production facilities are of concern to human welfare and the environment. Application of nanoparticles (NPs) has emerged as a potential option for minimizing these gaseous emissions. Application of bare NPs, however, could have an adverse effect on plants, soil, human health, and the environment. To minimize NPs’ exposure to the environment by recovering them, NPs were entrapped in polymeric beads for treating livestock manure. The objectives of the research were to understand the mechanism of gaseous reduction in swine manure treated for 33 days with zinc oxide nanoparticles (nZnO) or nZnO-entrapped alginate (alginate-nZnO) beads by different characterization techniques. Headspace gases from treated manure flasks were collected in 2–6-day intervals during the experimental period and were analyzed for methane (CH4), carbon dioxide (CO2), and H2S concentrations. The microbial analysis of manure was carried out using bacterial plate counts and Real-Time Polymerase Chain Reaction methods. Morphology and chemical composition of alginate-nZnO beads were analyzed by Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), and X-ray Photoelectron Spectroscopy (XPS). Alginate-nZnO beads or bare nZnO proved to be an effective NP in reducing H2S (up to 99%), CH4 (49–72%), and CO2 (46–62%) from manure stored under anaerobic conditions and these reductions are likely due to the microbial inhibitory effect from nZnO, as well as chemical conversion. Both SEM-EDS and XPS analysis confirmed the presence of zinc sulfide (ZnS) in the beads, which is likely formed by reacting nZnO with H2S.
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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) | 0.000 | 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".