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Ether-based electrolyte is beneficial to obtaining good low-temperature performance and high ionic conductivity in potassium ion batteries. However, the dilute ether-based electrolytes usually result in ion-solvent co-intercalation of graphite, poor cycling stability, and hard to withstand high voltage cathodes above 4.0â V. To address the aforementioned issues, an electron-withdrawing group (chloro-substitution) was introduced to regulate the solid-electrolyte interphase (SEI) and enhance the oxidative stability of ether-based electrolytes. The dilute (~0.91â M) chloro-functionalized ether-based electrolyte not only facilitates the formation of homogeneous dual halides-based SEI, but also effectively suppress aluminum corrosion at high voltage. Using this functionalized electrolyte, the K||graphite cell exhibits a stability of 700â cycles, the K||Prussian blue (PB) cell (4.3â V) delivers a stability of 500â cycles, and the PB||graphite full-cell reveals a long stability of 6000â cycles with a high average Coulombic efficiency of 99.98 %. Additionally, the PB||graphite full-cell can operate under a wide temperature range from -5 °C to 45 °C. This work highlights the positive impact of electrolyte functionalization on the electrochemical performance, providing a bright future of ether-based electrolytes application for long-lasting, wide-temperature, and high Coulombic efficiency PIBs and beyond.
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This journal retracts the article, 'An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network' [...].
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The interdependence of financial institutions is primarily responsible for creating a systemic hierarchy in the industry. In this paper, an Adaptive Hierarchical Network Model is proposed to study the problem of hierarchical relationships arising from different individuals in the economic domain. In the presented dynamically evolving network model, new directed edges are generated depending on the existing nodes and the hierarchical structures among the network, and these edges decay over time. When the preference of nodes in the network for higher ranks exceeds a certain threshold value, the equality state in the network becomes unstable and rank states emerge. Meanwhile, we select four real data sets for model evaluation and observe the resilience in the network hierarchy evolution and the differences formed by different patterns of hierarchy preference mechanisms, which help us better understand data science and network dynamics evolution.
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Background: Clear cell renal cell carcinoma (ccRCC) remains a common malignancy in the urinary system. Although dramatic progress was made in multimodal therapies, the improvement of its prognosis continues to be unsatisfactory. The antibody-binding crystallizable fragment (Fc) γ receptors (FcγRs) are expressed on the surface of leukocytes, to mediate antibody-induced cell-mediated anti-tumor responses when tumor-reactive antibodies are present. FcγRs have been studied extensively in immune cells, but rarely in cancer cells. Methods: ONCOMINE, UALCAN, GEPIA, TIMER, TISIDB, Kaplan-Meier Plotter, SurvivalMeth, and STRING databases were utilized in this study. Results: Transcriptional levels of FcγRs were upregulated in patients with ccRCC. There was a noticeable correlation between the over expressions of FCGR1A/B/C, FCGR2A, and clinical cancer stages/tumor grade in ccRCC patients. Besides, higher transcription levels of FcγRs were found to be associated with poor overall survival (OS) in ccRCC patients. Further, high DNA methylation levels of FcγRs were also observed in ccRCC patients, and higher DNA methylation levels of FcγRs were associated with shorter OS. Moreover, we also found that the expression of FcγRs was significantly correlated with immune infiltrates, namely, immune cells (NK, macrophages, Treg, cells) and immunoinhibitor (IL-10, TGFB1, and CTLA-4). Conclusions: Our study demonstrated that high DNA methylation levels of FcγRs lead to their low mRNA, protein levels, and poor prognosis in ccRCC patients, which may provide new insights into the choice of immunotherapy targets and prognostic biomarkers.