4.8 Article

Nanoparticle Charge and Size Control Foliar Delivery Efficiency to Plant Cells and Organelles

Journal

ACS NANO
Volume 14, Issue 7, Pages 7970-7986

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsnano.9b09178

Keywords

carbon dots; cerium oxide nanoparticles; silica nanoparticles; surfactant; crops; agriculture

Funding

  1. National Science Foundation under the Center for Sustainable Nanotechnology [CHE1503408]
  2. Chinese Scholarship Council Fellowship

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Fundamental and quantitative understanding of the interactions between nanoparticles and plant leaves is crucial for advancing the field of nanoenabled agriculture. Herein, we systematically investigated and modeled how zeta potential (-52.3 mV to +36.6 mV) and hydrodynamic size (1.7-18 nm) of hydrophilic nanoparticles influence delivery efficiency and pathways to specific leaf cells and organelles. We studied interactions of nanoparticles of agricultural interest including carbon dots (CDs, 0.5 and 5 mg/mL), cerium oxide (CeO2, 0.5 mg/mL), and silica (SiO2, 0.5 mg/mL) nanoparticles with leaves of two major crop species having contrasting leaf anatomies: cotton (dicotyledon) and maize (monocotyledon). Biocompatible CDs allowed real-time tracking of nanoparticle translocation and distribution in planta by confocal fluorescence microscopy at high spatial (similar to 200 nm) and temporal (2-5 min) resolution. Nanoparticle formulations with surfactants (Silwet L-77) that reduced surface tension to 22 mN/m were found to be crucial for enabling rapid uptake (<10 min) of nanoparticles through the leaf stomata and cuticle pathways. Nanoparticle-leaf interaction (NLI) empirical models based on hydrodynamic size and zeta potential indicate that hydrophilic nanoparticles with <20 and 11 nm for cotton and maize, respectively, and positive charge (>15 mV), exhibit the highest foliar delivery efficiencies into guard cells (100%), extracellular space (90.3%), and chloroplasts (55.8%). Systematic assessments of nanoparticle-plant interactions would lead to the development of NLI models that predict the translocation and distribution of nanomaterials in plants based on their chemical and physical properties.

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