Characterization of zinc oxide nanoparticle (nZnO) alginate beads in reducing gaseous emission from swine manure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it