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Spinel Li4Ti5O12 (LTO) is a promising candidate for lithium-ion battery anodes because of its exceptional stability and safety. However, its extensive application is limited by a high comprehensive cost, poor electronic conductivity, and other inherent defects. This work presents a novel synthesis procedure to synthesize carbon-coated Fe-doped LTO composites through carbon reduction, in the presence of Fe-containing industrial H2TiO3 as the titanium source, and glucose as the carbon source. The presence of the Fe-dopant is confirmed through XRD, with Rietveld refinement and EDS experiments. Results show that Fe2+ replaces a portion of Ti4+ after doping, leading to an increase in the LTO cell parameters and the corresponding cell volume. FLTO/C, presents a capacity of 153.79 mAh g−1 at 10 C, and the capacity decay per cycle is only 0.0074% after 1000 cycles at 5 C. Moreover, EIS experiments indicate that the incorporation of Fe and carbon lowers the charge transfer resistance and improves the diffusion and migration of Li+. Notably, since this preparation process requires no additional Fe source as a raw material, it is simple, cost-effective, and suitable for large-scale production and further application. [ABSTRACT FROM AUTHOR]

Semantic segmentation plays a crucial role in interpreting remote sensing images, especially in high-resolution scenarios where finer object details, complex spatial information and texture structures exist. To address the challenge of better extracting semantic information and ad-dressing class imbalance in multiclass segmentation, we propose utilizing diffusion models for remote sensing image semantic segmentation, along with a lightweight classification module based on a spatial-channel attention mechanism. Our approach incorporates unsupervised pretrained components with a classification module to accelerate model convergence. The diffusion model component, built on the UNet architecture, effectively captures multiscale features with rich contextual and edge information from images. The lightweight classification module, which leverages spatial-channel attention, focuses more efficiently on spatial-channel regions with significant feature information. We evaluated our approach using three publicly available datasets: Postdam, GID, and Five Billion Pixels. In the test of three datasets, our method achieved the best results. On the GID dataset, the overall accuracy was 96.99%, the mean IoU was 92.17%, and the mean F1 score was 95.83%. In the training phase, our model achieved good performance after only 30 training cycles. Compared with other models, our method reduces the number of parameters, improves the training speed, and has obvious performance advantages., (© 2024. The Author(s).)

Hepatitis C virus (HCV), dengue virus (DENV) and Japanese encephalitis virus (JEV) belong to the family Flaviviridae. Their viral particles have the envelope composed of viral proteins and a lipid bilayer acquired from budding through the endoplasmic reticulum (ER). The phospholipid content of the ER membrane differs from that of the plasma membrane (PM). The phospholipase A 2 (PLA 2 ) superfamily consists of a large number of members that specifically catalyse the hydrolysis of phospholipids at a particular position. Here we show that the CM-II isoform of secreted PLA 2 obtained from Naja mossambica mossambica snake venom (CM-II-sPLA 2 ) possesses potent virucidal (neutralising) activity against HCV, DENV and JEV, with 50% inhibitory concentrations (IC 50 ) of 0.036, 0.31 and 1.34 ng/ml, respectively. In contrast, the IC 50 values of CM-II-sPLA 2 against viruses that bud through the PM (Sindbis virus, influenza virus and Sendai virus) or trans-Golgi network (TGN) (herpes simplex virus) were >10,000 ng/ml. Moreover, the 50% cytotoxic (CC 50 ) and haemolytic (HC 50 ) concentrations of CM-II-sPLA 2 were >10,000 ng/ml, implying that CM-II-sPLA 2 did not significantly damage the PM. These results suggest that CM-II-sPLA 2 and its derivatives are good candidates for the development of broad-spectrum antiviral drugs that target viral envelope lipid bilayers derived from the ER membrane.