Metallic zinc is a promising anode candidate of aqueous zinc‐ion batteries owing to its high theoretical capacity and low redox potential. However, Zn anodes usually suffer from dendrite and side ...reactions, which will degrade their cycle stability and reversibility. Herein, we developed an in situ spontaneously reducing/assembling strategy to assemble a ultrathin and uniform MXene layer on the surface of Zn anodes. The MXene layer endows the Zn anode with a lower Zn nucleation energy barrier and a more uniformly distributed electric field through the favorable charge redistribution effect in comparison with pure Zn. Therefore, MXene‐integrated Zn anode exhibits obviously low voltage hysteresis and excellent cycling stability with dendrite‐free behaviors, ensuring the high capacity retention and low polarization potential in zinc‐ion batteries.
A MXene‐coated Zn (MZn) anode was fabricated by an in situ spontaneously reducing/assembling strategy, which effectively decreases the Zn nucleation energy barrier and uniformizes electric field distribution. The dendrite‐free zinc anode not only exhibits prolonged cycle life and lower voltage hysteresis, but also endows Zn/MnO2 batteries with the excellent electrochemical performance.
CXCL13 is a B-cell chemokine produced mainly by mesenchymal lymphoid tissue organizer cells, follicular dendritic cells, and human T follicular helper cells. By binding to its receptor, CXCR5, CXCL13 ...plays an important role in lymphoid neogenesis, lymphoid organization, and immune responses. Recent studies have found that CXCL13 and its receptor CXCR5 are implicated in the pathogenesis of several autoimmune diseases, such as rheumatoid arthritis, multiple sclerosis, systemic lupus erythematosus, primary Sjögren's syndrome, myasthenia gravis, and inflammatory bowel disease. In this review, we discuss the biological features of CXCL13 and CXCR5 and the recent findings on the pathogenic roles of the CXCL13/CXCR5 axis in autoimmune diseases. Furthermore, we discuss the potential role of CXCL13 as a disease biomarker and therapeutic target in autoimmune diseases.
The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research ...hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis.
The pursuit of harmonic combination of technology and fashion intrinsically points to the development of smart garments. Herein, we present an all-solid tailorable energy textile possessing ...integrated function of simultaneous solar energy harvesting and storage, and we call it tailorable textile device. Our technique makes it possible to tailor the multifunctional textile into any designed shape without impairing its performance and produce stylish smart energy garments for wearable self-powering system with enhanced user experience and more room for fashion design. The “threads” (fiber electrodes) featuring tailorability and knittability can be large-scale fabricated and then woven into energy textiles. The fiber supercapacitor with merits of tailorability, ultrafast charging capability, and ultrahigh bending-resistance is used as the energy storage module, while an all-solid dye-sensitized solar cell textile is used as the solar energy harvesting module. Our textile sample can be fully charged to 1.2 V in 17 s by self-harvesting solar energy and fully discharged in 78 s at a discharge current density of 0.1 mA.
Heart failure with preserved ejection fraction (HFpEF) is a difficult disease with high morbidity and mortality rates and lacks an effective treatment. Here, we report the therapeutic effect of ...dapagliflozin, a sodium-glucose cotransporter 2 inhibitor (SGLT2i), on hypertension + hyperlipidemia-induced HFpEF in a pig model.
HFpEF pigs were established by infusing a combination of deoxycorticosterone acetate (DOCA) and angiotensin II (Ang II), and Western diet (WD) feeding for 18 weeks. In the 9th week, half of the HFpEF pigs were randomly assigned to receive additional dapagliflozin treatment (10 mg/day) by oral gavage daily for the next 9 weeks. Blood pressure, lipid levels, echocardiography and cardiac hemodynamics for cardiac structural and functional changes, as well as epinephrine and norepinephrine concentrations in the plasma and tissues were measured. After sacrifice, cardiac fibrosis, the distribution of tyrosine hydroxylase (TH), inflammatory factors (IL-6 and TNF-α) and NO-cGMP-PKG pathway activity in the cardiovascular system were also determined.
Blood pressure, total cholesterol (TC), triglyceride (TG) and low-density lipoprotein (LDL) were markedly increased in HFpEF pigs, but only blood pressure was significantly decreased after 9 weeks of dapagliflozin treatment. By echocardiographic and hemodynamic assessment, dapagliflozin significantly attenuated heart concentric remodeling in HFpEF pigs, but failed to improve diastolic function and compliance with the left ventricle (LV). In the dapagliflozin treatment group, TH expression and norepinephrine concentration in the aorta were strongly mitigated compared to that in the HFpEF group. Moreover, inflammatory cytokines such as IL-6 and TNF-α in aortic tissue were markedly elevated in HFpEF pigs and inhibited by dapagliflozin. Furthermore, the reduced expression of eNOS and the PKG-1 protein and the cGMP content in the aortas of HFpEF pigs were significantly restored after 9 weeks of dapagliflozin treatment.
9 weeks of dapagliflozin treatment decreases hypertension and reverses LV concentric remodeling in HFpEF pigs partly by restraining sympathetic tone in the aorta, leading to inhibition of the inflammatory response and NO-cGMP-PKG pathway activation.
While the printed circuit board (PCB) has been widely considered as the building block of integrated electronics, the world is switching to pursue new ways of merging integrated electronic circuits ...with textiles to create flexible and wearable devices. Herein, as an alternative for PCB, we described a non-printed integrated-circuit textile (NIT) for biomedical and theranostic application via a weaving method. All the devices are built as fibers or interlaced nodes and woven into a deformable textile integrated circuit. Built on an electrochemical gating principle, the fiber-woven-type transistors exhibit superior bending or stretching robustness, and were woven as a textile logical computing module to distinguish different emergencies. A fiber-type sweat sensor was woven with strain and light sensors fibers for simultaneously monitoring body health and the environment. With a photo-rechargeable energy textile based on a detailed power consumption analysis, the woven circuit textile is completely self-powered and capable of both wireless biomedical monitoring and early warning. The NIT could be used as a 24/7 private AI "nurse" for routine healthcare, diabetes monitoring, or emergencies such as hypoglycemia, metabolic alkalosis, and even COVID-19 patient care, a potential future on-body AI hardware and possibly a forerunner to fabric-like computers.
As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which ...indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas's oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing the emissions of waste gas and pollutants. This study proposes a prediction model for the oxygen content in the flue gas that combines the whale optimization algorithm (WOA) and long short-term memory (LSTM) networks. Among them, the whale optimization algorithm (WOA) was used to optimize the learning rate, the number of hidden layers, and the regularization coefficients of the long short-term memory (LSTM). The data used in this study were obtained from a 350 MW power generation unit in a coal-fired power plant to validate the practicality and effectiveness of the proposed hybrid model. The simulation results demonstrated that the whale optimization algorithm-long short-term memory (WOA-LSTM) model achieved an
of 0.16493, an
of 0.12712, an
of 2.2254%, and an R2 value of 0.98664. The whale optimization algorithm-long short-term memory (WOA-LSTM) model demonstrated enhancements in accuracy compared with the least squares support vector machine (LSSVM), long short-term memory (LSTM), particle swarm optimization-least squares support vector machine (PSO-LSSVM), and particle swarm optimization-long short-term memory (PSO-LSTM), with improvements of 4.93%, 4.03%, 1.35%, and 0.49%, respectively. These results indicated that the proposed soft sensor model exhibited more accurate performance, which can meet practical requirements of coal-fired power plants.
Recently, high‐entropy alloys (HEAs) have been extensively investigated due to their unique structural design, superior stability, excellent functional feature and superior mechanical performance. ...However, most of the reported HEAs focus on studying the compositional design and microstructure and mechanical properties of materials. There are relatively few studies on electrochemical performance and theoretical studies of HEAs. In addition, the potential applications of HEAs as energy storage materials for electrocatalysts have attracted widely attention in the development and application aspects of electrocatalysis. It can be attributed to their high conductivity, excellent structural stability and superior electrocatalytic activities with small overpotential and abundant active sites, which is comparable to the commercial noble metal catalysts. In this review, firstly, we briefly discuss the concept and structure characteristics of high entropy alloys. Then, the research progress of high‐entropy alloys as electrocatalysis are also summarized, including hydrogen evolution reaction (HER), oxygen evolution reaction (OER) and oxygen reduction reaction (ORR), respectively. Finally, the future development trend of HEAs is also prospected for energy conversion fields.
The potential applications of HEAs as energy storage materials for electrocatalysts have attracted widely attention in the development and application aspects of electrocatalysis. This article summarizes some research progress of high‐entropy alloys as electrocatalysis, and the future development trend of HEAs is also prospected for energy conversion fields.
ε-Polylysine (ε-PL) was studied for the growth inhibition of
(
). The minimal inhibitory concentration (MIC) of ε-PL against
was measured by the broth dilution method, while the membrane permeability ...and metabolism of
were assessed after ε-PL treatment. Additionally, growth curves, the content of alkaline phosphatase (AKP), the electrical conductivity (EC), the UV absorbance and scanning electron microscope (SEM) data were used to study cellular morphology. The impact of ε-PL on cell metabolism was also investigated by different methods, such as enzyme activity (peroxidase POD, catalase CAT, succinodehydrogenase SDH and malic dehydrogenase MDH) and cell metabolic activity. The results showed that the MIC of ε-PL against
was 1.0 mg/mL. When
was treated with ε-PL, the growth of the bacteria was inhibited and the AKP content, electrical conductivity and UV absorbance were increased, which demonstrated that ε-PL could damage the cell structure. The enzyme activities of POD, CAT, SDH, and MDH in the bacterial solution with ε-PL were decreased compared to those in the ordinary bacterial solution. As the concentration of ε-PL was increased, the enzyme activity decreased further. The respiratory activity of
was also inhibited by ε-PL. The results suggest that ε-PL acts on the cell membrane of
, thereby increasing membrane permeability and inhibiting enzyme activity in relation to respiratory metabolism and cell metabolism. This leads to inhibition of cell growth, and eventually cell death.
This paper proposes a fuzzy cognitive map scheme for real-time online data prediction. The fuzzy cognitive maps (FCMs) are constructed on the basis of abstracting a numerical time series into a ...limited number of nodes (concepts), and used as a modeling tool for predicting time series. By representing time series in terms of information granules constructed in the space of amplitude and change of amplitude of the time series, a fuzzy cognitive map is dynamically constructed by using the set of information granule, where the particle swarm optimization (PSO) is utilized to study the parameters. In order to find better weights in the global search process, each parameter of the particle swarm algorithm (PSO) is not set to a fixed value but adaptively changes. In this paper, a dynamic fuzzy C-means clustering algorithm is used to online adjust the cluster center and weight according to the impact of the incoming data at the current moment such that the model can capture real-time changes in the data information. The proposed approach is illustrated in detail by a series of experiments using a collection of publicly available data.
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