Nano-enabled pesticides for sustainable agriculture and global food security

Abstract

Achieving sustainable agricultural productivity and global food security are two of the biggest challenges of the new millennium. Addressing these challenges requires innovative technologies that can uplift global food production, while minimizing collateral environmental damage and preserving the resilience of agroecosystems against a rapidly changing climate. Nanomaterials with the ability to encapsulate and deliver pesticidal active ingredients (AIs) in a responsive (for example, controlled, targeted and synchronized) manner offer new opportunities to increase pesticidal efficacy and efficiency when compared with conventional pesticides. Here, we provide a comprehensive analysis of the key properties of nanopesticides in controlling agricultural pests for crop enhancement compared with their non-nanoscale analogues. Our analysis shows that when compared with non-nanoscale pesticides, the overall efficacy of nanopesticides against target organisms is 31.5% higher, including an 18.9% increased efficacy in field trials. Notably, the toxicity of nanopesticides toward non-target organisms is 43.1% lower, highlighting a decrease in collateral damage to the environment. The premature loss of AIs prior to reaching target organisms is reduced by 41.4%, paired with a 22.1% lower leaching potential of AIs in soils. Nanopesticides also render other benefits, including enhanced foliar adhesion, improved crop yield and quality, and a responsive nanoscale delivery platform of AIs to mitigate various pressing biotic and abiotic stresses (for example, heat, drought and salinity). Nonetheless, the uncertainties associated with the adverse effects of some nanopesticides are not well-understood, requiring further investigations. Overall, our findings show that nanopesticides are potentially more efficient, sustainable and resilient with lower adverse environmental impacts than their conventional analogues. These benefits, if harnessed appropriately, can promote higher crop yields and thus contribute towards sustainable agriculture and global food security.

Fig. 1 |. Classification of nanopesticides.

Nanopesticides were classified by analysis of 36,658 patents (among which 1,163 are nanopesticides of interest; see Methods section). a, In Type 1 nanopesticides, NMs are used directly as AIs. Metal-based NMs are the most widely applied Type 1 nanopesticides and they include Ag-based NMs (as nanobactericides, nanofungicides and nanoinsecticides), Ti-based NMs (as nanobactericides and nanofungicides), Cu-based NMs (as nanofungicides and nanobactericides) and Zn-, Fe- and Al-based NMs. b, In Type 2 nanopesticides, NMs serve as nanocarriers to encapsulate AIs to achieve controlled, targeted and synchronized release of AIs at the right target, time and dose (that is, through the RNDP). The AIs in Type 2 nanopesticides are mainly conventional pesticides, such as atrazine, avermectin and glyphosate. The common nanocarrier types include polymers (b1–b4) such as chitosan, cellulose and polyethylene existing in the forms of nanocapsules (b1), nanospheres (b2), nano(hydro)gels (b3) and nanomicelles (b4), clay NMs (for example, silica, montmorillonite and kaolinite; b5), nanocomposites (b6), carbon nanotubes (CNTs; b7), 2D NMs (for example, graphene; b8), nanoliposomes (b9), dendrimers (b10), nanozeolites (b11), solid lipid NPs (b12), layered double hydroxides (LDHs; b13), zein NPs (b14) and polymersomes (b15). The numbers in parentheses indicate the number of patents indexed by Google Patents (https://patents.google.com/), which showed 305 Type 1 nanopesticide patents and 858 Type 2 patents, some of which are projected to hit the market very soon or are already on the market (for example, Nu-Clo silvercide (EPA registration number 7124–101, approved in 2007) and DuPont Kocide 3000 (EPA registration number 352–662, approved in 2007) in which nano-Ag and nano-Cu(OH)₂ are the AIs, respectively).

Fig. 2 |. Characterization of Ag- and Cu-based nanopesticides against Pseudomonas aeruginosa.

The antimicrobial properties of Ag- and Cu-based nanopesticides (AgNPs and CuNPs) against bacteria (P. aeruginosa) are revealed by high-resolution TEM (HRTEM) and scanning TEM high-angle annular dark-field (STEM-HAADF) measurements. a,b, TEM images of spherical 10–30 nm AgNPs (a) and semi-spherical 60 nm CuNPs (b). The insets show the selected area electron diffraction patterns. c,d, TEM images of cross-sections of P. aeruginosa interacting with AgNPs (c) and CuNPs (d). AgNPs and CuNPs cluster around the cell walls, penetrate (shown by red arrows) and are internalized into the cells. The CuNPs in d are not semi-spherical and exhibit a mean size of 30 nm (compare with semi-spherical 60 nm CuNPs in b). This suggests the transformation of CuNPs in liquid cell media (for example, partial dissolution to form Cu²⁺). e,f, STEM-HAADF images and elemental mapping of Ag, Cu, P and S in P. aeruginosa after interacting with AgNPs (e) and CuNPs (f). Integrating HRTEM and STEM-HAADF observations enables direct visualization of the morphological and structural changes in both bacterial cells and NPs (CuNP transformation; d). g, Proposed antimicrobial mechanisms of action of nanopesticides. Type 1 nanopesticides follow steps 1–4, whereas Type 2 nanopesticides follow steps 1–5. Step 1: NP adhesion causes cell wall and membrane damage due to lipid peroxidation, altered structure and permeability of the membrane and leakage of the cellular components. Step 2: the penetration of NPs and metallic ions (for example, Ag⁺ and Cu²⁺) into cells damages intracellular organelles and biomolecules (for example, protein denaturation, DNA damage, ribosome destabilization and mitochondrial dysfunction). Step 3: cytotoxicity and oxidative stress due to the generation of ROS (for example, ·OH, ¹O₂ and O₂^•−) and other radical-related agents (H₂O₂ and HOCl). Step 4: genotoxicity by modulation of microbial signal transduction pathways, ultimately causing cell death. Step 5: possible antimicrobial actions of nanopesticides due to interior metal NPs, exterior nanocarriers and transformed species from metal NPs and nanocarriers within the cells. Panels a–f adapted with permission from ref. , Bentham Science Publishers.

Fig. 3 |. Comparison of the particle sizes of nanopesticides.

Comparison of the sizes of the most common Type 1 (Ag- and Cu-based) and Type 2 (polymer- and clay-based) nanopesticides measured by TEM, scanning electron microscopy (SEM) and DLS. In total, 900 comparisons were made. Colour scale: lightest shading, TEM; medium shading, SEM; darkest shading, DLS. The bottom and top ends of the boxplots indicate the 25th and 75th percentiles, the bottom and top whiskers indicate the 10th and 90th percentiles, and the solid line and circle within each boxplot mark the median and mean values, respectively. The numbers below the boxplots indicate the number of nanopesticides analysed by TEM, SEM and DLS. The two dashed horizontal lines indicate thresholds of 100 and 500 nm. The datasets were extracted from 500 published papers (Supplementary Tables 1 and 2).

Fig. 4 |. Comparison of the overall efficacy of nanopesticides and non-nanoscale pesticides.

Efficacy comparison (586 comparisons) between non-nanoscale pesticides (left boxplot) and nanopesticides (right boxplot). The non-nanoscale pesticides are metal-based bulk materials (primary particle size above 1,000 nm) and conventional pesticides (for example, atrazine, avermectin and glyphosate). a, Normalized efficacy variation of non-nanoscale pesticides and nanopesticides includes their efficacy against target organisms (314 results, including 47 field trials) and non-target organisms (for example, toxicity; 59 comparisons). b, Efficacy variation of non-nanoscale pesticides (conventional formulations) and Type 2 nanopesticides includes temperature-controlled (T-ctrl) release of AIs, premature loss of AIs, leaching loss of AIs and foliar contact angle of AIs. The lower and higher ends of the boxplots indicate the 25th and 75th percentiles, the lower and higher whiskers indicate the 10th and 90th percentiles, and the solid line and circle within the boxplots mark the median and mean values, respectively. Δ indicates the efficacy variation of mean values between non-nanoscale pesticides and nanopesticides. N indicates the number of comparisons. The datasets were extracted from 500 published papers (Supplementary Tables 1 and 2). The independent samples t-test results are shown in Supplementary Tables 3–8.

Fig. 5 |. Uptake, translocation and transformation of nanopesticides in a typical plant–soil system, and their impacts on the rhizosphere microbiome.

a, Type 2 nanopesticides. b, Nanopesticides can enter plants through openings in the above-ground shoots (for example, cuticle, epidermis and stomata) and below-ground roots (for example, cortex and lateral root junction). Soil invertebrates (earthworm), rhizosphere microbes (bacteria and fungi), symbiotic microorganisms (mycorrhiza), soil particles (clay), organic matter and rhizosphere deposits (root exudates) can affect nanopesticide uptake and translocation in plants. c, Nanopesticides can translocate vertically and radially across plant tissues through apoplastic and symplastic pathways. In b and c, a smart release of AIs from the RNDP can alter the fate and transport of nanopesticides. d, The internalization pathways of nanopesticides into cells include endocytosis and facilitated transport through pore formation, carrier proteins and plasmodesmata. e, The accumulation, translocation and transformation of nanopesticides in a plant can be differentiated by integrating multiscale techniques such as micro-/nano-X-ray computed tomography (i,ii), micro-X-ray fluorescence (iii) and micro-X-ray absorption near-edge structure (μ-XANES; iv) techniques. The images in (i,ii) and (iii) show wheat roots exposed to non-nanoscale (AgNO₃) and nanopesticides (AgNPs), respectively. The red arrows in (iii) show the preferential accumulation locations of AgNPs. The associated μ-XANES spectra in (iv) were collected from root tissues and show Ag speciation in different root tissues and the transformation of AgNPs (Ag⁰, Ag₂S NPs, Ag-thiol and AgNO₃) in plants. Numbers in parentheses in (iv) indicate the nodes from operational taxonomic units analysis. Ep, epidermis; Cx, cortex; Ed, endodermis; Xy, xylem; Pe, pericycle; Max, maximum; Min, minimum. f, Nanopesticide exposure to soil affects the rhizosphere microbiome (archaea, bacteria and fungi): relative abundance (i) and co-occurrence network (ii) analyses of the bacterial community at the phylum level under different scenarios (agricultural practice and nanopesticide exposure). In image (ii), the nodes indicate major phyla (for example, proteobacteria, actinobacteria and bacteroidetes), and the red and blue edges indicate positive and negative correlations, respectively, between two nodes. Panels adapted with permission from: b–d, ref. under a Creative Commons license CC BY 4.0; e, ref. , American Chemical Society; f, ref. , Elsevier.

Fig. 6 |. Multiomics strategies to unveil stress responses, tolerance pathways and modes of action of biota upon nanopesticide exposure.

a, Multiomics strategies integrate genomics (DNA level), transcriptomics (messenger RNA), proteomics (protein) and metabolomics (metabolite) to unveil stress responses, tolerance pathways and modes of action of biota upon nanopesticide exposure at molecular, genetic, cellular and organismal levels. b, Venn network diagrams can group genes with structural, functional and responsive similarities upon stimuli. The numbers in the Venn diagram in b indicate the number of similarly (reside at the intersection) and uniquely (outside the intersection) regulated proteins of a model plant (A. thaliana) exposed to Type 1 metal-based nanopesticides. c, Heatmaps can also group genes based on their expression patterns and identify up- and downregulated genes and biological signatures upon nanopesticide exposure. Shown in c is the multicoloured hierarchical gene clustering heatmap of A. thaliana exposed to biotic stress (fungus and bacterium), abiotic stress (salinity, drought and wounding), nanopesticide exposure (10, 20, 40 and 80 nm AgNPs, 10, 20 and 40 nm TiO₂ NPs, CNTs and CNTs with abscisic acid (CNT_ABA)) and others (AgNO₃ and TiO₂ bulk materials). d, Gene correlation network analysis identifying shared biological responses to metal-based nanopesticides. Highly redundant (over-expressed) gene sets are grouped together as clusters to highlight enriched metabolic processes upon stimuli, including antioxidant response, biosynthesis of secondary metabolites, glutathione metabolism and glycolysis. e, A comprehensive illustration of cellular pathways, metabolic processes and modes of action mediated by metal-based nanopesticides. These include interference with cell organelles (for example, chloroplasts and mitochondria), oxidative stress (ROS generation) and protein/DNA damage. The primary response relates to ROS generation, which is negatively correlated with the activity of flavonoids, glutaredoxin proteins, peroxidase proteins, superoxide dismutases and glutathione S-transferases (shown by blue arrows). Up- and downregulated genes upon nanopesticide exposure are indicated by red and green arrows, respectively. Notably, gene ontology (GO) analysis indicates that chloroplasts and mitochondria share similar, conservative GO classes involved in metal-based nanopesticide responses (pink box). Panels adapted with permission from: c, ref. under a Creative Commons license CC BY 4.0; d, ref. , American Chemical Society.