Nanomaterials–plants–microbes interaction: plant growth promotion and stress mitigation
Bibliographic record
Abstract
Soil salinization, extreme climate conditions, and phytopathogens are abiotic and biotic stressors that remarkably reduce agricultural productivity. Recently, nanomaterials have gained attention as effective agents for agricultural applications to mitigate such stresses. This review aims to critically appraise the available literature on interactions involving nanomaterials, plants, and microorganisms. This review explores the role of nanomaterials in enhancing plant growth and mitigating biotic and abiotic stresses. These materials can be synthesized by microbes, plants, and algae, and they can be applied as fertilizers and stress amelioration agents. Nanomaterials facilitate nutrient uptake, improve water retention, and enhance the efficiency of active ingredient delivery. Nanomaterials strengthen plant antioxidant systems, regulate photosynthesis, and stabilize hormonal pathways. Concurrently, their antimicrobial and protective properties provide resilience against biotic stressors, including pathogens and pests, by promoting plant immune responses and optimizing microbial-plant symbiosis. The synergistic interactions of nanomaterials with beneficial microorganisms optimize plant growth under stress conditions. These materials also serve as carriers of nutrients, growth regulators, and pesticides, thus acting like "smart fertilizers. While nanotechnology offers great promise, addressing potential environmental and ecotoxicological risks associated with their use is necessary. This review outlines pathways for leveraging nanotechnology to achieve resilient, sustainable, and climate-smart agricultural systems by integrating molecular insights and practical applications.
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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) | 0.001 | 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 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".