TAILIEUCHUNG - Effect of bulk and nano-Fe2O3 particles on peanut plant leaves studied by Fourier transform infrared spectral studies

The interaction of metal oxide nanoparticles with plants has not been extensively studied. An attempt has been made to examine the potential variation in peanut plant leaves due to the application of Fe2O3 nanoparticle by pre-sowing technique and to compare with its bulk counterpart. Fe2O3 nanoparticle was synthesized by chemical route and characterized using X-ray diffraction, atomic force and scanning electron microscopy. The Fe2O3 nanoparticle and its bulk counterpart are applied to the peanut seeds by pre-soaking method at two different concentrations: 500 and 4000 ppm. A total of three replicates were chosen for each morphological and physiological measurement (at an average of three plants per replica). The Fourier transform infrared spectral analysis shows the most prominent peaks at 2923 and 1636 cm 1 , and other peaks vary due to Fe2O3 stress, which was confirmed by the calculated mean ratio of the peak intensities for various frequency regions. All leaf samples show considerable increase in glycoprotein, with 500 ppm bulk and 4000 ppm nano-Fe2O3 samples exhibiting a maximum increase of and , respectively. The total amide I and II protein content of leaf sample soaked in 500 ppm bulk Fe2O3 suspension decreased to a greater extent compared with other leaf samples. The leaf samples soaked in 500 ppm concentration of both bulk and nano-Fe2O3 suspension exhibited lower lipid content with total band area of ± and ± , respectively. | Effect of bulk and nano-Fe2O3 particles on peanut plant leaves studied by Fourier transform infrared spectral studies

• Hoá học
• Multivariate analyses
• Pre sowing method
• Peanut plant leaves
• Nano Fe2O3 particles
• Peanut plant leaves studied
• Fourier transform
• Multivariate statistical analyses
• Salt stress
• Principal component analysis
• Multiple linear regression
• Soils irrigated
• Plant Biology
• Ionomic traits
• Agronomic traits
• Multivariate GWAS
• Multivariate methods
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• Needle variation
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