Arabinogalactan and glycyrrhizin based nanopesticides as novel delivery systems for plant protection.
Olga Yu SelyutinaSalavat S KhalikovNikolay E PolyakovPublished in: Environmental science and pollution research international (2019)
During the past decade, nanotechnologies opened a new era in delivery of plant protection products through the development of nanosized controlled release systems, such as polymer nanoparticles, micelles, and so on using a wide variety of materials. To increase the pesticides penetration into the grain under the presowing seed treatment, a new approach based on non-covalent associate preparation with natural polysaccharides and oligosaccharides as delivery systems (DSs) was applied. Earlier, this approach was tested on antidote 1,8-naphthalic anhydride (NA). Enhancement of the NA solubility and penetration into the barley and wheat seeds had been demonstrated. In the present study, these DSs were used to prepare nanocomposites of pesticides (tebuconazole, imidacloprid, imazalil, prochloraz). The composite formation of the pesticides with poly- and oligosaccharides was proved by NMR relaxation method. Enhancement of the pesticides solubility and improvement of its penetration into the seeds of corn and rapeseeds has been detected. The strongest enhancement of penetration ability was observed for arabinogalactan nanocomposites: 5-folds for tebuconazole and imidacloprid, and more than 10-folds for imazalil and prochloraz. Our data show that the effect of polysaccharides and oligosaccharides on the nanopesticide penetration might be associated with the solubility enhancement, affinity of DSs to the surface of grains, and the modification of cell membranes by poly- and oligosaccharides.
Keyphrases
- risk assessment
- gas chromatography
- drug delivery
- mass spectrometry
- magnetic resonance
- reduced graphene oxide
- single cell
- stem cells
- high resolution
- cell therapy
- big data
- carbon nanotubes
- electronic health record
- mesenchymal stem cells
- hyaluronic acid
- cell wall
- bone marrow
- machine learning
- replacement therapy
- capillary electrophoresis