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Zeolitic Imidazolate Frameworks-8 (ZIF-8) is commonly used as an ideal precursor for non-noble metal catalysts because of its high specific surface area, ultra-high porosity, and N-rich content. Upon pyrolyzing ZIF-8 at 900 °C in Ar, the resulting material, referred to as Z8, displayed good activity toward the oxygen reduction reaction (ORR). Then the ZIF-8 was mixed with various conductive carbon materials, such as multiwall carbon nanotubes (MWCNTs), Acetylene black (ACET), Vulcan XC-72R (XC-72R), and Ketjenblack EC-600JD (EC-600JD), to form Z8 composites. The Z8/MWCNTs composite exhibited enhanced ORR activity owing to its network structure, meso-/microporous hierarchical porous structure, improved electrical conductivity, and graphitization. Subsequently, iron and nitrogen co-doping is achieved through the pyrolysis of a mixture comprising Fe, N precursor, and ZIF-8/MWCNTs, which is denoted as FeN-Z8/MWCNTs. The intrinsically high electrical conductivity of MWCNTs facilitated efficient electron transfer during the ORR, while the meso-/microporous hierarchical porous structure and network structure of Fe, N co-doped ZIF-8/MWCNTs promoted oxygen transport. The presence of Fe-containing species in the catalyst acted as activity centers for ORR. This strategy of preparing Z8 composites and modifying them with Fe, N co-doping offers an insightful approach to designing cost-effective electrocatalysts. [ABSTRACT FROM AUTHOR]

Oil spill detection plays an important role in marine environment protection. The technique of global navigation satellite system-reflectometry (GNSS-R) has the advantage of a short revisit time, which could help with timely cleanup of marine oil pollution. The conventional GNSS-R oil spill detection algorithm can resolve only the dielectric constant of oil based on power ratio measurements, while that of water cannot be realized. This is because the dielectric constant of water is much larger than that of oil such that the range of the equation used in the conventional algorithm is inadequate. To resolve this problem, we proposed a new algorithm containing a new equation with a larger scope, which has never been applied previously to GNSS-R oil spill detection. We derived a lookup method to resolve the dielectric constant of both oil and water. To validate our method, a drone-borne GNSS-R experiment based on dual-frequency QZSS reflection signals was conducted on 17 July 2023 using experimental pools simulating oil spills. Raw IF data in the L1 and L5 bands, collected using dual antennas and a data recorder, were processed using a software-defined receiver to deduce the power ratios and SNR of the GNSS signals. Results showed that the proposed algorithm is capable of resolving the dielectric constants of the reflected surface. In addition, the L5 signal was found to provide more detail and better contrast than the L1 C/A signal. [ABSTRACT FROM AUTHOR]

Monitoring marine oil pollution holds both practical and scientific significance. Synthetic Aperture Radar (SAR) is an active microwave remote sensing technique capable of all-weather and all-day with fine spatial resolution. However, under low wind conditions, rain cells and young ice are examples of look-alikes affect the accuracy of oil spill detection. Polarimetric SAR assumes a crucial role in this context, as it can extract abundant polarimetric features by polarimetric target decomposition. Drawing inspiration from this advancement, an improved polarimetric feature named relative feature based on Cloude-Pottier target decomposition was proposed. The Jeffries-Matusita distance indicates the substantial potential of the relative feature in detecting oil spills. The improved polarimetric feature within U-Net, FCN-8s, and DeepLabv3+ResNet-18 for oil spill detection using polarization SAR images. Experiment results demonstrated that the relative feature has superior performance compared to other polarimetric features and obtained the highest accuracy and dice within U-Net compared to the other two models. These findings introduce promising concepts for achieving rapid and precise detection of oil spills in future applications. [ABSTRACT FROM AUTHOR]