The First Review on Nano‐Agricultural Applications of MXene and MBene‐Based Materials for Plant‐Immunoengineering, Controlled Protection, and Inducing Biostimulation Mechanisms
Bibliographic record
Abstract
Producing quality food crops with a focus on climate and environmental improvement policies has become central to modern farming and sustainability strategies. However, the rising world population and food demand have region-dependently pushed these boundaries to the overuse of agrochemical inputs. These include plant antimicrobials, pesticides, and soil fertilizers, applied to boost crop yields, reaching a critical juncture. The reliance on agrochemicals has been proven effective in priming, plant growth, and enhancing defense/resistance to biotic stressors, such as phytopathogens and invasive organisms, as well as abiotic pressures, including heat, drought, salinity, and light stress, by increasing nutrient absorption and innate immunity or adaptive stress resistance. However, increasing concerns about the safety, cost, and environmental impact of agrochemicals have intensified the necessity for applying sustainable precision technologies. Nano-agriculture has introduced emerging possibilities for utilizing low-dimensional biomaterials for plant protection/stimulation applications, once these technologies are proven safe. Among them, carbon-based MXenes and derivatives (MBenes) show potential due to their high surface-to-volume area, biocompatibility at controlled doses, and tunable physicochemical/biological properties. These unique specifications support targeted delivery and sustained release, while also enhancing plant growth and stress tolerance. This comprehensive review covers their effect on seed germination, seedling maturation, plant-immunoengineering, priming, eliciting, stomatal closure, antimicrobial actions, and gene or phytohormone regulation. It also discusses their role as sustainable carriers for the delivery and release of agrochemicals and plant protection by nano-design, aiming to reduce agrochemical consumption. Lastly, we discuss the current environmental regulations for nanomaterials and recommend rational outlooks for future work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.008 | 0.002 |
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".