Herein, an effective tandem catalysis strategy is developed to improve the selectivity of the CO2RR towards C2H4 by multiple distinct catalytic sites in local vicinity. An earth‐abundant ...elements‐based tandem electrocatalyst PTF(Ni)/Cu is constructed by uniformly dispersing Cu nanoparticles (NPs) on the porphyrinic triazine framework anchored with atomically isolated nickel–nitrogen sites (PTF(Ni)) for the enhanced CO2RR to produce C2H4. The Faradaic efficiency of C2H4 reaches 57.3 % at −1.1 V versus the reversible hydrogen electrode (RHE), which is about 6 times higher than the non‐tandem catalyst PTF/Cu, which produces CH4 as the major carbon product. The operando infrared spectroscopy and theoretic density functional theory (DFT) calculations reveal that the local high concentration of CO generated by PTF(Ni) sites can facilitate the C−C coupling to form C2H4 on the nearby Cu NP sites. The work offers an effective avenue to design electrocatalysts for the highly selective CO2RR to produce multicarbon products via a tandem route.
An effective tandem catalysis strategy is developed to enhance the selectivity of the CO2 electroreduction reaction towards C2H4 with a 6‐fold increase in comparison with that of the non‐tandem catalysts. The local high concentration of CO generated by atomically isolated nickel–nitrogen sites PTF(Ni) sites can facilitate the C−C coupling to form C2H4 on the nearby Cu NP sites, thus switching from CH4 to C2H4 production with a Faradaic efficiency of 57.3 %.
Lacking of adaptation to various array imperfections is an open problem for most high-precision direction-of-arrival (DOA) estimation methods. Machine learning-based methods are data-driven, they do ...not rely on prior assumptions about array geometries, and are expected to adapt better to array imperfections when compared with model-based counterparts. This paper introduces a framework of the deep neural network to address the DOA estimation problem, so as to obtain good adaptation to array imperfections and enhanced generalization to unseen scenarios. The framework consists of a multitask autoencoder and a series of parallel multilayer classifiers. The autoencoder acts like a group of spatial filters, it decomposes the input into multiple components in different spatial subregions. These components thus have more concentrated distributions than the original input, which helps to reduce the burden of generalization for subsequent DOA estimation classifiers. The classifiers follow a one-versus-all classification guideline to determine if there are signal components near preseted directional grids, and the classification results are concatenated to reconstruct a spatial spectrum and estimate signal directions. Simulations are carried out to show that the proposed method performs satisfyingly in both generalization and imperfection adaptation.
The high mortality rate of ovarian cancer is connected with the development of acquired resistance to multiple cancer drugs, especially cisplatin. Activation of cytoprotective autophagy has been ...implicated as a contributing mechanism for acquired cisplatin resistance in ovarian cancer cells. Hexokinase 2 (HK2) phosphorylates glucose to generate glucose-6-phosphate, the rate-limiting step in glycolysis. Higher HK2 expression has been associated with chemoresistance in ovarian cancer. However, whether HK2 functionally contributes to cisplatin resistance in ovarian cancer is unclear. In this study, we investigated the role of HK2 in regulating ovarian cancer cisplatin resistance. Increased HK2 levels were detected in drug-resistant human ovarian cancer cells and tissues. Cisplatin downregulated HK2 in cisplatin-sensitive but not in resistant ovarian cancer cells. HK2 knockdown sensitized resistant ovarian cancer cells to cisplatin-induced cell death and apoptosis. Conversely, HK2 overexpression in cisplatin-sensitive cells induced cisplatin resistance. Mechanistically, cisplatin increased ERK1/2 phosphorylation as well as autophagic activity. Blocking autophagy with the autophagy inhibitor 3-MA sensitized resistant ovarian cancer cells to cisplatin. HK2 overexpression enhanced cisplatin-induced ERK1/2 phosphorylation and autophagy while HK2 knockdown showed the opposite effects. Blocking the MEK/ERK pathway using the MEK inhibitor U0126 prevented cisplatin-induced autophagy enhanced by HK2 overexpression. Furthermore, HK2 knockdown sensitized resistance ovarian tumor xenografts to cisplatin in vivo. In conclusion, our data supported that HK2 promotes cisplatin resistance in ovarian cancer by enhancing drug-induced, ERK-mediated autophagy. Therefore, targeting HK2 may be a new therapeutic strategy to combat chemoresistance in ovarian cancer.
Water pollution caused by organic wastewater has become a serious concern worldwide. Fenton oxidation process is one of the most effective and suitable methods for the abatement of organic ...pollutants. However, the process has three obvious shortcomings: the narrow working pH range, the high costs and risks associated with handling, transportation and storage of reagents (H2O2 and catalyst), the significant iron sludge related second pollution. In order to overcome these shortcomings, various optimized Fenton processes have been widely studied. Therefore, a summary of the study status of Fenton optimization processes is necessary to develop a novel and high efficiency organic wastewater treatment method. Based on the optimization perspective, taking shortcomings of Fenton process as a breakthrough, the fundamentals, advantages and disadvantages of single Fenton optimization processes (heterogeneous Fenton, photo-Fenton and electro-Fenton) for organic wastewater treatment were reviewed and the corresponding reaction mechanism diagrams were drawn in this paper. Then, the feasibility and application of the coupled Fenton optimization processes (photoelectro-Fenton, heterogeneous electro-Fenton, heterogeneous photoelectro-Fenton, three-dimensional electro-Fenton) for organic wastewater treatment were discussed in depth. Additionally, the effect of some important operation parameters (pH and catalyst, H2O2, organic pollutants concentration) on the degradation efficiency of organic pollutants was studied to provide guidance for the optimization of operation parameters. Finally, the possible future research directions for optimized Fenton processes were given. The review aims to assist researchers and engineers to gain fundamental understandings and critical view of Fenton process and its optimization processes, and hopefully with the knowledge it could bring new opportunities for the optimization and future development of Fenton process.
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•Review of single Fenton optimization processes for organic wastewater treatment.•Review of coupled Fenton optimization processes for organic wastewater treatment.•Key operational parameters are discussed.•Insights into future research directions for optimized Fenton processes.
To determine distribution of severe acute respiratory syndrome coronavirus 2 in hospital wards in Wuhan, China, we tested air and surface samples. Contamination was greater in intensive care units ...than general wards. Virus was widely distributed on floors, computer mice, trash cans, and sickbed handrails and was detected in air ≈4 m from patients.
Diabetes-associated affective disorders are of wide concern, and oxidative stress plays a vital role in the pathological process. This study was to investigate the cerebroprotective effects of ...hesperetin against anxious and depressive disorders caused by diabetes, exploring the potential mechanisms related to activation of Nrf2/ARE pathway. Streptozotocin-induced diabetic rats were intragastrically administrated with hesperetin (0, 50, and 150 mg/kg) for 10 weeks. Forced swimming test, open field test, and elevated plus maze were used to evaluate the anxiety and depression-like behaviors of rats. The brain was collected for assays of Nrf2/ARE pathway. Moreover, high glucose-cultured SH-SY5Y cells were used to further examine the neuroprotective effects of hesperetin and underlying mechanisms. Hesperetin showed anxiolytic and antidepressant effects in diabetic rats according to the behavior tests, and increased p-Nrf2 in cytoplasm and Nrf2 in nucleus followed by elevations in mRNA levels and protein expression of glyoxalase 1 (Glo-1) and γ-glutamylcysteine synthetase (γ-GCS) in brain, known target genes of Nrf2/ARE signaling. Moreover, hesperetin attenuated high glucose-induced neuronal damages through activation of the classical Nrf2/ARE pathway in SH-SY5Y cells. Further study indicated that PKC inhibition or GSK-3β activation pretreatment attenuated even abolished the effect of hesperetin on the protein expression of Glo-1 and γ-GCS in high glucose-cultured SH-SY5Y cells. In summary, hesperetin ameliorated diabetes-associated anxiety and depression-like behaviors in rats, which was achieved through activation of the Nrf2/ARE pathway. Furthermore, an increase in nuclear Nrf2 phosphorylation from PKC activation and GSK-3β inhibition contributed to the activation of Nrf2/ARE pathway by hesperetin.
Pulse streams of many emitters have flexible features and complicated patterns. They can hardly be identified or further processed from a statistical perspective. In this paper, we introduce ...recurrent neural networks (RNNs) to mine and exploit long-term temporal patterns in streams and solve problems of sequential pattern classification, denoising, and deinterleaving of pulse streams. RNNs mine temporal patterns from previously collected streams of certain classes via supervised learning. The learned patterns are stored in the trained RNNs, which can then be used to recognize patterns-of-interest in testing streams and categorize them to different classes, and also predict features of upcoming pulses based on features of preceding ones. As predicted features contain sufficient information for distinguishing between pulses-of-interest and noises or interfering pulses, they are then used to solve problems of denoising and deinterleaving of noise-contaminated and aliasing streams. Detailed introductions of the methods, together with explanative simulation results, are presented to describe the procedures and behaviors of the RNNs in solving the aimed problems. Statistical results are provided to show satisfying performances of the proposed methods.
With the developments and applications of the new information technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, a smart manufacturing era is coming. At ...the same time, various national manufacturing development strategies have been put forward, such as Industry 4.0 , Industrial Internet , manufacturing based on Cyber-Physical System , and Made in China 2025 . However, one of specific challenges to achieve smart manufacturing with these strategies is how to converge the manufacturing physical world and the virtual world, so as to realize a series of smart operations in the manufacturing process, including smart interconnection, smart interaction, smart control and management, etc. In this context, as a basic unit of manufacturing, shop-floor is required to reach the interaction and convergence between physical and virtual spaces, which is not only the imperative demand of smart manufacturing, but also the evolving trend of itself. Accordingly, a novel concept of digital twin shop-floor (DTS) based on digital twin is explored and its four key components are discussed, including physical shop-floor, virtual shop-floor, shop-floor service system, and shop-floor digital twin data. What is more, the operation mechanisms and implementing methods for DTS are studied and key technologies as well as challenges ahead are investigated, respectively.
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