In addition, the mercury temperature-programmed desorption results suggest that there are two forms of mercury present on O2-/NO-codoped porous carbon, including a carbon-bonded Hg atom and HgO.Combination therapy such as photodynamic therapy (PDT)-enhanced chemotherapy is regarded as a promising strategy for cancer treatment. Boron-dipyrromethene (BODIPY), as close relatives of porphyrins, was widely used in PDT. However, poor water solubility, rapid metabolism by the body and lack of targeting limits its clinical application. Lenvatinib, as the first-line drug for molecular-targeted therapy of liver cancer, restricted its clinical application for its side effects. Herein, to achieve the synergy between PDT and chemotherapy, we synthesized two halogenated BODIPY, BDPBr2 and BDPCl2, which were prepared into self-assembly nanoparticles with lenvatinib, and were encapsulated with Pluronic F127 through the nanoprecipitation method, namely, LBPNPs (LBBr2 NPs and LBCl2 NPs). The fluorescence quantum yields of LBPNPs were 0.73 and 0.71, respectively. The calculated loading rates of lenvatinib for LBBr2 NPs and LBCl2 NPs were 11.8 and 10.2%, respectively. LBPNPs can be hydrolyzed under weakly acidic conditions (pH 5.0) to generate reactive oxygen species (ROS), and the release rate of lenvatinib reached 88.5 and 82.4%. Additionally, LBPNPs can be effectively taken up by Hep3B and Huh7 liver cancer cells, releasing halogenated BODIPY and lenvatinib in the acidic environment of tumor cells to enhance the targeting performance of chemotherapeutics. Compared with free lenvatinib and separate halogenated BODIPY, LBPNPs can inhibit tumor growth more effectively through pH-responsive chemo/photodynamic synergistic therapy and significantly promote the cascade of caspase apoptotic protease. This study shows that LBPNPs can be a promising nanotheranostic agent for synergetic chemo/photodynamic liver cancer therapy.Two types of NiO-based composites (NiO@diatomite and Ni/NiO@diatomite) were synthesized as modified products of enhanced catalytic performances during the transesterification reactions of waste cooking oil. The influence of the diatomite substrate and the integration of metallic Ni0 in inducing the catalytic activity were evaluated in a series of transesterification reactions. The experimental conditions were adjusted according to the response surface methodology and the central composite statistical design. Experimentally, the diatomite substrate and the Ni0 metal induced the efficiency of the reaction to achieve a yield of 73.4% (NiO@diatomite) and 91% (Ni/NiO@diatomite), respectively, as compared to 66% for the pure phase (NiO). This was obtained under experimental conditions of 80 °C temperature, 100 min time, 121 methanol/oil molar ratio, and 3.75 wt % loading. The theoretical optimization functions of the designs suggested enhancement to the experimental conditions to achieve a yield of 76.3% by NiO@diatomite and 93.2% by Ni/NiO@diatomite. This reflected the role of the diatomite substrate in enhancing the surface area, the adsorption of fatty acids, and the exposure of the catalytic sites in addition to the effect of the Ni0 metal in enhancing the catalytic reactivity of the final product. Finally, the biodiesel produced over Ni/NiO@diatomite as the best product was of acceptable properties according to the international standards.Toxicity prediction using quantitative structure-activity relationship has achieved significant progress in recent years. However, most existing machine learning methods in toxicity prediction utilize only one type of feature representation and one type of neural network, which essentially restricts their performance. Moreover, methods that use more than one type of feature representation struggle with the aggregation of information captured within the features since they use predetermined aggregation formulas. In this paper, we propose a deep learning framework for quantitative toxicity prediction using five individual base deep learning models and their own base feature representations. We then propose to adopt a meta ensemble approach using another separate deep learning model to perform aggregation of the outputs of the individual base deep learning models. We train our deep learning models in a weighted multitask fashion combining four quantitative toxicity data sets of LD50, IGC50, LC50, and LC50-DM and minimizing the root-mean-square errors. Compared to the current state-of-the-art toxicity prediction method TopTox on LD50, IGC50, and LC50-DM, that is, three out of four data sets, our method, respectively, obtains 5.46, 16.67, and 6.34% better root-mean-square errors, 6.41, 11.80, and 12.16% better mean absolute errors, and 5.21, 7.36, and 2.54% better coefficients of determination. We named our method QuantitativeTox, and our implementation is available from the GitHub repository https//github.com/Abdulk084/QuantitativeTox.Despite silicon being a promising candidate for next-generation lithium-ion battery anodes, self-pulverization and the formation of an unstable solid electrolyte interface, caused by the large volume expansion during lithiation/delithiation, have slowed its commercialization. In this work, we expand on a controllable approach to wrap silicon nanoparticles in a crumpled graphene shell by sealing this shell with a polydopamine-based coating. This provides improved structural stability to buffer the volume change of Si, as demonstrated by a remarkable cycle life, with anodes exhibiting a capacity of 1038 mA h/g after 200 cycles at 1 A/g. The resulting composite displays a high capacity of 1672 mA h/g at 0.1 A/g and can still retain 58% when the current density increases to 4 A/g. A systematic investigation of the impact of spray-drying parameters on the crumpled graphene morphology and its impact on battery performance is also provided.Hypochlorous acid (HClO), a reactive oxygen species, plays an essential role in the processes of physiology and pathology via reacting with most biological molecules. The abnormal level of HClO may cause inflammation, especially arthritis. To further understand its key role in inflammation, in situ detection of HClO is necessary. Herein, a water-soluble small molecule fluorescent probe (HDI-HClO) is employed to monitor and identify trace amounts of HClO in the biological system. https://www.selleckchem.com/products/pik-iii.html In the presence of HClO, the probe releases a hydroxyl group emitting strong fluorescence because of the restoration of the intramolecular charge transfer process. Furthermore, this probe displays a 150-fold fluorescence enhancement accompanied by a large Stokes shift and a lower detection limit (8.3 nM). Moreover, the probe can make a rapid response to HClO within 8 s, which provides the possibility of real-time monitoring of intracellular HClO. Based on the advantages of rapid dynamics, good water solubility, and excellent biocompatibility, this probe could effectively monitor the fluctuations of exogenous and endogenous HClO in living cells.