Although carbon nanotubes (CNTs) exhibit excellent performance, they are prone to agglomeration because of their high surface energy and large specific surface area. Moreover, CNTs are hardly compatible with polymers due to their nonpolar properties, as manifested by the less stable interface between these two components. This study was aimed at improving the compatibility between multi-walled carbon nanotubes (MWNTs) and polypropylene (PP). Herein, a practical strategy for the modification of MWNTs and the subsequent fabrication of polypropylene-grafted multi-walled carbon nanotubes (PP-g-MWNTs) are reported. The morphology of the as-obtained PP-g-MWNTs was observed using a scanning electron microscope (SEM), a transmission electron microscope (TEM) and a polarizing microscope, whereas their elemental composition and bond structures were characterized by Fourier transform infrared (FT-IR) spectroscopy, energy dispersive spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS). X-ray diffraction (XRD) was used for crystallographic analyses. A performance comparison between the PP-g-MWNT samples and the undecorated samples was conducted based on the results obtained via dynamic mechanical analysis (DMA), tensile testing, differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Serial characterizations proved the successful grafting of PP molecular chains onto the MWNT surfaces. Thus, the MWNTs, the filler phase, could be included into the PP matrix covalently and thus existed as an integrated component of the composite system. As a consequence, the specific design of PP-g-MWNTs remarkably improved both thermal and mechanical properties of the PP composites.
Abstract As an important hydrological parameter in the upper ocean, the depth of the thermocline (DOT) in the Okinawa Trough (OT) is closely linked to air‐sea interactions and Kuroshio Current (KC) intrusions. However, high‐resolution DOT records in the OT since the last deglaciation have not been well reconstructed. In this study, we generated surface ( ) and shallow subsurface ( ) temperature records from core M063‐05, obtained from the middle OT, and reconstructed annual mean DOT variations since the last deglaciation based on vertical temperature gradients (ΔT ). The DOT variations in the OT are interpreted to be mainly driven by the KC, with a stronger KC resulting in a deeper DOT and a smaller ΔT ( ). In our reconstruction, the KC intensity increased gradually during the last deglaciation, with a reduction during the Younger Dryas, reached a maximum in the early Holocene, and then gradually declined during the middle to late Holocene. We also investigated the responses of the KC to high‐ and low‐latitude climate systems. On orbital timescales, the evolution of the KC occurred in parallel to precession‐induced El Niño‐Southern Oscillation (ENSO) dynamics. On millennial timescales, fluctuations in the intensity of the KC during the last deglaciation suggest a dynamic link to the North Atlantic climate via the East Asian Monsoon. We propose that the ΔT between ‐ and ‐based temperatures is a reliable indicator for high‐resolution studies on the hydrographic structure of the OT and paleoceanographic evolution in the East China Sea.
IV–VI group compounds are excellent candidates for optoelectronics. Here, a solvothermal controlled synthesis of SnS 2 nanosheets with different thickness is reported. The bandgap of nanosheets with thickness of 15 nm was estimated as 2.0 eV which is quite suitable for visible light harvesting. The nanosheets show about 180 times photoinduced current compare with the 35 nm nanosheets and fast response time of 4.9 ms, indicating that the SnS 2 nanosheets are potential materials for high‐performance photodetectors.
Since the early 1950s, the development of human settlements and over-exploitation of agriculture in the China side of the Amur River Basin (CARB) have had a major impact on the water environment of the surrounding lakes, resulting in a decrease of aquatic vegetation. According to the United Nations Sustainable Development Goals, a comprehensive understanding of the extent and variability of aquatic vegetation is crucial for preserving the structure and functionality of stable aquatic ecosystems. Currently, there is a deficiency in the CARB long-sequence dataset of aquatic vegetation distribution in China. This shortage hampers effective support for actual management. Therefore, the development of a fast, robust, and automatic method for accurate extraction of aquatic vegetation becomes crucial for large-scale applications. Our objective is to gather information on the spatial and temporal distribution as well as changes in aquatic vegetation within the CARB. Utilizing a hybrid approach that combines the maximum spectral index composite and Otsu algorithm, along with the integration of convolutional neural networks (CNN) and random forest, we applied this methodology to obtain an annual dataset of aquatic vegetation spanning from 1985 to 2020 using Landsat series imagery. The accuracy of this method was validated through both field investigations and Google Images. Upon assessing the confusion matrix spanning from 1985 to 2020, the producer accuracy for aquatic vegetation classification consistently exceeded 87%. Further quantitative analysis unveiled a discernible decreasing trend in both the water and vegetation areas of lakes larger than 20 km2 within the CARB over the past 36 years. Specifically, the total water area decreased from 3575 km2 to 3412 km2, while the vegetation area decreased from 745 km2 to 687 km2. These changes may be attributed to a combination of climate change and human activities. These quantitative data hold significant practical implications for establishing a scientific restoration path for lake aquatic vegetation. They are particularly valuable for constructing the historical background and reference indices of aquatic vegetation.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)