Alpha-Ni(OH)(2) nanobelts, nanowires, short nanowires, and beta-Ni(OH)(2) nanoplates have been successfully prepared in high yields and purities by a convenient hydrothermal method under mild conditions from very simple systems composed only of NaOH, NiSO(4), and water. It has been found that the ratio of NaOH to NiSO(4) not only affects the morphology of the Ni(OH)(2) nanostructures, but also determines whether the product is of the alpha- or beta-crystal phase. A notable finding is that porous NiO nanobelts were produced after exposure of the Ni(OH)(2) products to an electron beam for several minutes during transmission electron microscopy (TEM) observations. Another unusual feature is that rectangular nanoplates with many gaps were obtained. Furthermore, porous NiO nanobelts, nanowires, and nanoplates could also be obtained by annealing the as-prepared Ni(OH)(2) products. A sequence of dissolution, recrystallization, and oriented attachment-assisted self-assembly of nanowires into nanobelts is proposed as a plausible mechanistic interpretation for the formation of the observed structures. The method presented here possesses several advantages, including high yields, high purities, low cost, and environmental benignity. It might feasibly be scaled-up for industrial mass production.

Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.

A facile synthetic route under mild conditions to the preparation of gold nanostars (GNSs) with Fe3O4 cores (Fe3O4@GNSs), possessing magnetization and tunable optical properties from the visible to near-infrared (NIR) region, was developed. Additionally, the resulting Fe3O4@GNSs described here show good catalytic activity for the reduction of potassium ferricyanide as a model reaction. Importantly, the catalyst, Fe3O4@GNSs, can be easily recycled with an external magnet and exhibits long-life, good reusability and stability. We also anticipated that Fe3O4@GNSs may provide a platform for broad potential diagnostic and therapeutic biomedical applications due to its magnetization and tunable optical properties from the visible to NIR region.

In this paper, Ag/Cu 2 O heterogeneous nanostructured films (HNFs) were prepared by a one-step hydrothermal method. It involved only two materials, AgNO 3 and Cu foil, in the aqueous solution to form Ag/Cu 2 O HNF. X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), energy dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and high-resolution transmission electron microscopy (HRTEM) were used to characterize the Ag/Cu 2 O films. Ag nanoparticles and Cu 2 O nanocubes were formed by redox reactions and the Ag nanoparticles deposited on the Cu 2 O nanocubes via electrostatic attraction. The obtained Ag/Cu 2 O HNF was found to be a good candidate for SERS application.

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Metal-organic frameworks (MOFs) provide an attractive platform for designing and synthesizing photoactive hybrid materials for photochemical reactions. We report here the utilization of a new visible-light responsive indium MOF for inducing the atom transfer radical polymerization (ATRP) of methacrylate monomers, where well-designed polymers with controlled molecular weights, narrow molecular weight distribution and high retention of chain-end groups have been prepared. The kinetics study reveals that the MOFmediated ATRP shows characteristics of controlled radical polymerization (CRP). Besides, the polymerization can be easily regulated by light. Furthermore, the heterogeneous MOF can be easily recovered from the reaction and recycled for the photopolymerization. This study has involved photoactive MOFs materials into a new photochemical reaction of polymer synthesis.

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