Graphene oxide (GO) is an excellent bacteria-killing nanomaterial. In this work, macroscopic applications of this promising nanomaterial by fixing GO sheets onto cotton fabrics, which possess strong antibacterial property and great laundering durability, are reported. The GO-based antibacterial cotton fabrics are prepared in three ways: direct adsorption, radiation-induced crosslinking, and chemical crosslinking. Antibacterial tests show that all these GO-containing fabrics possess strong antibacterial property and could inactivate 98% of bacteria. Most significantly, these fabrics can still kill >90% bacteria even after being washed for 100 times. Also importantly, animal tests show that GO-modified cotton fabrics cause no irritation to rabbit skin. Hence, it is believed that these flexible, foldable, and re-usable GO-based antibacterial cotton fabrics have high promise as a type of new nano-engineered antibacterial materials for a wide range of applications.

Holey reduced graphene oxide (RGO) sheets were formed by one-pot hydrothermal treatment of graphene oxide (GO) at a dilute concentration (0.25 mg ml À1 ). The holes were generated through a carbon gasification reaction with the steam created in the hydrothermal system. The perforated structure provides not only sufficient active sites for molecule adsorption, but also provides channels for gas diffusion. Thus, the sensor based on the networks, which were constructed from the perforated RGO nanosheets, exhibited an enhanced sensitivity to toxic NO 2 gas at ppb level, surpassing the performance of the annealed RGO. Interestingly, the polarity of the holey RGO transformed from p-mode to n-mode after exposure to NH 3 and a repeatable sensing to NH 3 within-mode nanoporous RGO was observed.The results demonstrate that structure modification of RGO through perforation is a promising approach for improving the sensing performance. Furthermore, the perforated RGO obtained through a one-pot hydrothermal process can be used for high performance gas sensing at room temperature, demonstrating its practical application in flexible and wearable gas sensors based on the good mechanical flexibility of RGO.

Yield evaluation of breeding lines is the key to successful release of cultivars, which is becoming a serious issue due to soil heterogeneity in enlarged field tests. This study aimed at establishing plot-yield prediction models using unmanned aerial vehicle (UAV)-based hyperspectral remote sensing for yield-selection in large-scale soybean breeding programs. Three sets of soybean breeding lines (1103 in total) were tested in blocks-in-replication experiments for plot yield and canopy spectral reflectance on 454~950 nm bands at different growth stages using a UAV-based hyperspectral spectrometer (Cubert UHD185 Firefly). The four elements for plot-yield prediction model construction were studied respectively and concluded as: the suitable reflectance-sampling unit-size in a plot was its 20%–80% central part; normalized difference vegetation index (NDVI) and ration vegetation index (RVI) were the best combination of vegetation indices; the initial seed-filling stage (R5) was the best for a single stage prediction, while another was the best combination for a two growth-stage prediction; and multi-variate linear regression was suitable for plot-yield prediction. In model establishment for each material-set, a random half was used for modelling and another half for verification. Twenty-one two growth-stage two vegetation-index prediction models were established and compared for their modelling coefficient of determination (RM2) and root mean square error of the model (RMSEM), verification RV2 and RMSEV, and their sum RS2 and RMSES. Integrated with the coincidence rate between the model predicted and the practical yield-selection results, the models, MA1-2, MA4-2 and MA6-2, with coincidence rates of 56.8%, 58.5% and 52.4%, respectively, were chosen for yield-prediction in yield-test nurseries. The established model construction elements and methods can be used as local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries. Furthermore, multiple models can be used jointly for plot-yield prediction in soybean breeding programs.

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Abstract:Although optical remote sensing can intuitively detect algal bloom, it is limited by the weather conditions. Synthetic aperture radar (SAR) is not affected by inadequate weather conditions. According to visual interpretation of SAR images and comparisons of quasi-synchronized optical images, the gathering areas of algal bloom present as "dark regions" on SAR images. It is shown that using SAR to monitor the water surface is workable. However, dark regions may also be caused by other factors, such as low wind speeds. This challenges with SAR monitoring of algal bloom on the water surface. In this study, an improved K-means algorithm, combined with multi-Otsu thresholding algorithm, was proposed to segment the dark regions. After feature analysis and extraction of Sentinel-1A images, an algal bloom recognition model with a support vector machine (SVM) was applied to discriminate the algal bloom dark regions from the low wind dark regions. According the experimental results, the overall accuracy achieved 74.00% in Taihu Lake. Additionally, this method was also validated in Chaohu Lake and Danjiangkou Reservoir. Therefore, it can be concluded that SAR can provide a new technical means for monitoring algal bloom of inland lakes, particularly when it is cloudy and unsuitable for optical remote sensing. To obtain more information about algal bloom, multi-band and multi-polarization SAR images can be considered for future.

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