A comprehensive insight into metal oxide nanoparticle synthesis, associated regulations, and application in the agri‐food sector
Bibliographic record
Abstract
Abstract To achieve global sustainability goals, it is necessary to ensure food safety and security by adopting sustainable agricultural practices. However, the current agri‐food sector is not only in its most vulnerable state, but it is also becoming a threat to the environment due to the combination of industrial and human activities that are detrimental. These activities, including the use of highly toxic agrochemicals, have deteriorated the quality of arable soil, thereby impacting food security. One area of research emerging in recent years as a promising avenue to combat concerns around agricultural soil quality and productivity is that of the application of metal and metal oxide nanoparticles (MONPs). In this review, we comprehensively examine the synthesis processes, application, environmental impact, and regulations associated with MONPs in the agri‐food sector. In agriculture, these nanoparticles have been demonstrated to enhance crop yields by acting as delivery systems for nutrients, preventing soil degradation, and reducing the need for pesticides and fertilizers. In relation to ensuring food quality and security, these nanoparticles are used as additives to enhance nutritional content, improve texture, and extend shelf life. Alternatively, the antimicrobial properties of metal and metal oxide nanoparticles can be used to modify packaging materials and make them more effective at preserving food, reducing food waste by inhibiting the growth of harmful bacteria, and reducing the risk of foodborne illnesses. Overall, using nanoparticles in the agriculture and food industry offers exciting opportunities to promote sustainability, reduce environmental pollution, and improve food quality and safety.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".