Harnessing the potential of zinc oxide nanoparticles and their derivatives as nanofertilizers: Trends and perspectives
Bibliographic record
Abstract
The agriculture sector is currently facing a decline in plant productivity and yield. Different technologies are being developed to combat these risks. However, innovation in existing technologies is required. Nanotechnology has the potential to solve these difficulties by modifying traditional farming practices and approaches. Nanoparticles (NPs) can bind and transport various substances, such as plant nutrients, and govern their slow release over a longer period, which can reduce the danger of nutrient losses while maintaining environmental safety. In this aspect, the role of zinc oxide nanoparticles (ZnO NPs) and their derivatives in agriculture has recently sparked a lot of interest. ZnO NPs can be coated with different compounds which enhance their biocompatibility within the plant cells. The unique nanostructures and nano-characteristics of ZnO NPs and their derivatives have resulted in the development of a novel approach for boosting plant development and productivity as well as improved stress tolerance via targeted delivery and slow-release mechanism, resulting in enhanced nutrient use efficiency, regulating phytohormone levels, enhancing root morphology, and increasing enzyme activity, leading to their application as nanofertilizers. There are important knowledge gaps regarding the long-term environmental consequences and the specific biochemical pathways influenced by ZnO NPs. This review aims to provide an overview of the most recent advancements in the use of ZnO NPs in agriculture, identify areas where more research is needed, and suggest potential future research directions. • Zinc oxide nanoparticles play a remarkable role as nanofertilizers. • Zinc oxide nanoparticles applied under in vitro, in vivo , and field conditions significantly alter plant biology. • Coating zinc oxide nanoparticles with other molecules enhances their capability.
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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 | 0.000 | 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 | 0.001 |
| 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".