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Heterogeneous advanced oxidation processes (AOPs) based on non-radical reactive species are considered as a powerful technology for wastewater purification due to their long half-lives and high adaptation in a wide pH range. Herein, we fabricate surface Co defect-rich spinel ZnCo2O4 porous nanosheets, which can generate ≡CoIV=O and 1O2 over a wide pH range of 3.81–10.96 by the formation of amphoteric ≡Zn(OH)2 in peroxymonosulfate (PMS) activation process. Density functional theory (DFT) calculations show Co defect-rich ZnCo2O4 possesses much stronger adsorption ability and more electron transfer to PMS. Moreover, the adsorption mode changes from terminal oxygen Co–O–Co to Co–O, accelerating the polarization of adjacent oxygen, which is beneficial to the generation of ≡CoIV=O and 1O2. Co defect-rich ZnCo2O4 porous nanosheets exhibit highly active PMS activation activity and stability in p-nitrophenol (PNP) degradation, whose toxicity of degradation intermediates is significant reduction. The Co defect-rich ZnCo2O4 nanosheet catalyst sponge/PMS system achieved stable and efficient removal of PNP with a removal efficiency higher than 93% over 10 h. This work highlights the development of functional catalyst and provides an atomic-level understanding into non-radical PMS activation process in wastewater treatment. [ABSTRACT FROM AUTHOR]

Metal–organic frameworks (MOFs) have received widespread attention for their large specific surface area, porous structure, and tunable particle size. MOFs can be utilized as precursors to prepare structurally diverse bimetallic oxides, which exhibit outstanding electrochemical properties as anode materials for lithium-ion batteries (LIBs). This paper introduces the study of MOF-derived bimetallic oxides as anode materials for LIBs, focusing on the application of MOF-derived manganese-, cobalt-, iron-, and vanadium-based bimetallic oxides in LIBs. MOF-derived bimetallic oxides, with the advantages of tunable compositions and nano-structures, are capable of further contributing to the enhancement of electrochemical performances. Finally, the problems of these derived materials in the application of LIBs are prospected, and reliable solutions and future development prospects are proposed. [ABSTRACT FROM AUTHOR]

Data-driven individualized decision making has recently received increasing research interest. However, most existing methods rely on the assumption of no unmeasured confounding, which cannot be ensured in practice especially in observational studies. Motivated by the recently proposed proximal causal inference, we develop several proximal learning methods to estimate optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. Explicitly, in terms of two types of proxy variables, we are able to establish several identification results for different classes of ITRs respectively, exhibiting the tradeoff between the risk of making untestable assumptions and the potential improvement of the value function in decision making. Based on these identification results, we propose several classification-based approaches to finding a variety of restricted in-class optimal ITRs and establish their theoretical properties. The appealing numerical performance of our proposed methods is demonstrated via extensive simulation experiments and a real data application. for this article are available online. [ABSTRACT FROM AUTHOR]

The combat domain of modern warfare is becoming increasingly multidimensional. It is important to evaluate the resilience of the air-ground cooperative network for defending against attack threats and recovery performance. First, a resilience analysis model was proposed to effectively analyze and evaluate the resilience of the air-ground cooperative network. Then, considering the available resources, three dynamic reconfiguration strategies were given from the global perspective to help the air-ground cooperative network quickly recover performance and enhance combat capabilities. Finally, a typical 50-node network was taken as an example to prove the effectiveness and feasibility of the proposed model. The proposed method can provide scientific guidance for improving the air-ground cooperative network combat capabilities. [ABSTRACT FROM AUTHOR]