Chemodynamic Metal-Phenolic Nanopesticide Performs In Situ Hydrogen Peroxide Self-Supply against Plant Pathogens for Food Sustainability
Bibliographic record
Abstract
The growing reliance on pesticides for food sustainability has led to environmental pollution and food safety concerns. Herein, we present a chemodynamic strategy using a Fenton-type nanopesticide, referred to as metal-phenolic ROS-nanogenerator (nanoRSG), to enhance the control of two widely spreading plant pathogens ( Pseudomonas syringae and Fusarium oxysporum ). The nanoRSG is constructed by the supramolecular self-assembly of natural polyphenols and Cu 2+ ions, followed by an in situ transition into phenolic-stabilized CuO 2 nanoclusters with the aid of hydroxide ions in the presence of H 2 O 2 . Subsequently, the nanoRSG decomposes in the pathogenic-relevant microenvironment into Fenton-catalyzed H 2 O 2 and Cu 2+ ions, followed by the highly efficient Fenton reactions for generating •O 2 – to damage pathogenic cell membranes. Regarding curative effects on tomato leaves against P. syringae and F. oxysporum, nanoRSG outperforms the commercial Kocide 3000 formulations with 94.7 and 86.9% increasing efficacy, respectively. Moreover, for curative activity on tomato roots, nanoRSG also has a better performance (87.8 and 78.9%) than Kocide 3000 (31.3 and 43.9%). Besides, the biosafety of nanoRSG is confirmed by toxicity tests in zebrafish and lettuce cultivation in a field test of hydroponics. Our findings demonstrate that the metal-phenolic nanoenabled strategy offers a promising formulation for innovating conventional pesticides and enhancing food sustainability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".