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In this work, an innovative seedless and surfactant-free chemical bath deposition (CBD) method at low temperature was applied to obtain flower-shaped ZnO nanostructures (FZONSs) on glass and p-type silicon substrates for the first time. Structural properties of these FZONSs were examined. The NSs were produced from zinc nitrate hexahydrate and hexamethylenetetramine, HMTA solution without any catalyst, template, or seed layer. An electric soldering iron pen was used to simultaneously heat the substrate and aqueous mixture of the constituents to grow the FZONSs. Field emission scanning electron microscopy images of the samples showed the presence of three-dimensional (3D) flower-shaped nanomorphology. Energy-dispersive X-ray spectroscopy detected the right trace elements in the FZONSs. X-ray diffraction analysis of the as-grown samples confirmed the existence of high purity nanocrystalline hexagonal phase of ZnO with preferred growth along (002) lattice planer orientation. The growth of ZnO nanorods into unified flower-like morphology was interpreted using a nucleation dissolution-mediated recrystallization mechanism. The fabricated FZONSs may provide potential in various applications including advanced catalysts, sensing devices, and solar cells. [ABSTRACT FROM AUTHOR]

In this study, the spectral responses to drought stress of different parts in the leaf of oil palm seedlings named base, middle, and tip were investigated. The ability to detect stress even before symptoms emerge requires knowledge of which part of oil palm leaves responds more quickly to the stress. The analysis of the reflectance spectra in region 650–1050 nm was conducted on respective sections of the leaves of the oil palm seedlings. The analysis revealed that the stress affects the tip of the leaf, followed by the middle and then the base. It was noticed that the greatest loss of water and chlorophyll content was at the tip of the leaf. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for clustering, while support vector machine (SVM) and linear discriminant analysis (LDA) were applied for categorization purposes. The outcomes of the PCA and HCA showed that the separation between the samples was based on the day and stress levels at respective sections of the leaf. With this, the possession of distinct morphological and physiological features by each part of the leaf can be concluded. From the PCA loadings, it was found that the regions 699–756 nm, 833–877 nm, and 933–958 nm showed noticeable bands and can be used to distinguish between the oil palm seedlings under stress. In addition, LDA and SVM classifiers demonstrated that the prediction accuracy of the stress level in oil palm seedlings was not influenced by the location in the leaf where the spectra were acquired. [ABSTRACT FROM AUTHOR]