Coaxial electrospinning is a novel technique for producing core-shell nanofibers that provide a robust structure and deliver hydrophilic bioactive agents. Optimization of parameters used in the coaxial electrospinning process allows for the fabrication of uniform and bead-free polyvinyl alcohol (PVA)/poly (l-lactic acid) (PLLA) core-shell nanofibers. Herein, a cold atmospheric plasma (CAP) process was used to enhance the surface features of electrospun core-shell nanofibers for increased surface pore size and altered surface hydrophilicity. After CAP treatment, the scaffolds' water contact angle was reduced from 110° to 50° and its protein and water adsorption were significantly elevated. The changes in hydrophilicity and improved scaffold surface area dramatically enhanced cell attachment and proliferation of fibroblasts and osteoblasts. Also, the increased levels of alkaline phosphatase (ALP) activity, total protein content and calcium deposition from mesenchymal stem cells (MSCs) indicated a higher osteoinductivity of the CAP-modified nanofibrous scaffold. Most importantly, the increased nanofiber surface pore size induced by the CAP treatment further contributed to significant variations in drug release profiles. The CAP-treated scaffolds showed more rapid release kinetics compared to untreated scaffolds, which eventually led to complete drug release. These results indicated that the CAP-treated and bioactive protein-loaded core-shell nanofibers could be a valuable regenerative medicine and drug delivery system for improved bone tissue engineering.
The morphology of tetrahexahedral nanocrystals could be understood on the basis of a hypothesis that the atoms or molecules on or near spherical surfaces can migrate till reaching their equilibrium positions. Such migration of atoms/molecules is shown to be closely related to the formation of high-index surfaces in nanopolyhedrons. On account of this hypothesis, a theoretical calculation about the indices of the surfaces in tetrahexahedrons is found in good agreement with the empirical results. A group of high-index surfaces for nanocrystals that can be formed under certain environments are thus predicted. This study may provide a novel idea for preparing the catalysts at nanoscale.
Rice is one of the world's most important staple foods. Accurately and timely detecting paddy rice planting extent is critical to ensure food security. China, with its vast territory ranging from tropical to subarctic regions, has significant differences in rice cultivation across regions. Traditional methods generally rely on field surveys to collect real sample points and use multi-temporal remote sensing images combined with machine learning to extract rice planting areas in a single climate zone, resulting in high accuracy. However, this approach requires a large number of high-quality samples, and is limited in terms of research area, which is not conducive to large-scale. In this study, we proposed a novel method for extracting rice using high spatio-temporal resolution of Sentinel-1/2 remote sensing images and transfer learning algorithm on the Google Earth Engine (GEE) platform. Firstly we use sentinel-1 and sentinel-2 remote sensing image, with pre-procession and computation, obtaining the dense time series normalized difference vegetation index (NDVI) and backscattering data (VH) datasets. Using the above datasets, we designed a rice extraction feature suitable for multiple climate zones, and multiple planting rhythms based on the characteristics of rice in flooding periods, multi-season growth, and the characteristics of different varieties of rice combined with rice phenological characteristics. And then, according to the rice phenological characteristics and rice planting rhythm, we select the several suitable time windows and growth time such as start of the growing season (SOS) and length of the growing season (LOS) for rice extraction to integrate the rice extraction feature set. To achieve high-precision extraction of rice in a large scale, we proposed a transfer learning-based method. Specifically, we first fuse a small amount of field data and a large number of supplementary samples from high precision rice extraction results we have had, resulting a big-data sample set. Then we use a transfer learning domain adaption method-TrAdaboost to calculate sample weights according to their adaptation to the characteristics of rice in large regions. Finally, we train the random forest model with weighted sample set, and obtain the large-scale rice extraction results. The results showed that our method outperformed traditional methods in terms of both accuracy and efficiency in a large area, with an overall accuracy of 90% This method provides a reference for extracting rice planting areas, ensuring agricultural production and food security.
This paper uses ENVI to convert TM data of the study area in different times and gains 6 first-classes and 20 second-classes combining with unsupervised classification and supervised classification, and then creates land use vector maps of 1995 and 2000. And according to land use data and land use changes matrix gained from RS images, it discusses arable changes and relevant driving forces in the study area in urbanization. This study area is Shijiazhuang City as a whole, including 17 counties, 1 mining district and 5 urban districts Shijiazhuang City lies in the north-east of China and borders on the Taihang Mountains in the west side and on the wide Huabei Plain on the other three sides. As a transportation center, Shijiazhuang City not only develops quickly in urbanization in recent years, but also changes obviously in land use, especially in cultivated land. Therefore, arable changes in urbanization should receive more attention in Shijiazhuang City. Based on RS data of land use, with methods in RS, GIS and statistics, this paper analyzes land use changes in urbanization from 1995 to 2000 in Shijiazhuang City and makes sure of directions of arable changes. At the same time, in term of social economy indexes, it confirms driving forces of arable changes and discusses relationship of changes between urbanization and cultivated land. The study shows, form 1995 to 2000, cultivated land was the core of land use changes in Shijiazhuang City. It decreased in this period. And construction use is the main way of arable decrease which accounted for 52.3%. Agricultural structure adjustment and disaster destroying accounted for 38% and 9.7% respectively. And in construction use, single industry or mining land made up 40%. It was a token of lagging urbanization influence on arable changes.
Electrically driven ultraviolet lasing behavior from p-ZnO:P nanonail array/n-Si heterojunction was demonstrated. Phosphorus-doped ZnO nanonail arrays were grown by chemical vapor deposition method. The constructed heterojunction with indium tin oxide films as the contacted electrodes demonstrated clear rectifying behavior, and the turn-on voltage was about 2.5 V. The p-n junction lowered the excitation threshold effectively and the electrically driven ultraviolet lasing behavior exhibited high monochromaticity: when the applied forward current reached 24 mA, distinct ultraviolet laser emission peaks were obtained at room temperature, and the full width at half maxims were 0.7, 0.9, and 0.5 nm, respectively. The three sharp peaks represented different lasing modes.
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