Interactions of metal-based nanoparticles (MBNPs) and metal-oxide nanoparticles (MONPs) with crop plants: a critical review of research progress and prospects
Bibliographic record
Abstract
Over the past decade, the production and applications of metal-based and metal-oxide nanoparticles (MBNPs and MONPs, respectively) have increased significantly due to their enhanced physicochemical properties and biological activities when compared with their bulk parent materials. Once MBNPs and MONPs enter agricultural soil via direct or indirect pathways, they can interact with crop plants and thus pose a threat to both animal and human health through food chain pathways. Although many review articles on engineered nanoparticles have been published, few have focused on the interactions of MBNPs and MONPs with crop plants and their current applications. Therefore, we reviewed the sources and behaviors of MBNPs and MONPs in agricultural soil, physiological and biochemical effects of MBNPs and MONPs on plants, uptake, translocation of MBNPs and MONPs in crop plants, factors affecting the interaction between MBNPs and MONPs and plants, and the applications of MBNPs and MONPs. Lastly, we propose where the future research priorities should be focused to provide a better understanding of MBNPs and MONPs. This review will help to promote scientific research regarding MBNPs and MONPs and to understand the risks and benefits of their association with plants and will contribute to the advancement of nanotechnology.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 teacher head, 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".