In the last ten years, the research of solid oxide fuel cells (SOFCs) or ceramic fuel cells (CFC) had focused on reducing the working temperature through the development of novel materials, ...especially the high ionic conductive electrolyte materials. Many progresses on single-phase electrolyte materials with the enhanced ionic conductivity have been made, but they are still far from the criteria of commercialization. The studies of ceria oxide based composite electrolytes give an alternative solution to these problems because of their impressive ionic conductivities and tunable ionic conduction behaviors. Significant advances in the understanding the ceria based composite material and construction of efficient fuel cell systems have been achieved within a short period. This report reviews recent developments of ceria-based composite from different aspects: materials, fundamentals, technologies, fabrication/construction parameters, electrochemistry and theoretical studies. Particular attention is given to ceria-carbonate (nano)composite, including its fuel cell performance, multi-ionic transport properties, advanced applications, corresponding electrode material and stability concerning. Besides, several novel fuel cell (FC) concepts like nanowire FC, all-nanocomposite FC and single-component/electrolyte-free fuel cell (SC-EFFC) are presented. This mini-review emphasizes the promise of ceria-based composites for advanced FC application and highlights the breakthrough of SC-EFFC research for high efficient energy conversion.
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► Development of Ceria–salt composites with impressive ionic conductivity and tunable conduction behaviors is reviewed. ► Particular attention is given to ceria–carbonate composite, a promising electrolyte for LTCFC. ► Parameters affecting the electrical properties of ceria–carbonate composite are analyzed. ► Advanced applications, electrode materials and stability measurements development are presented. ► Novel fuel cell concepts and demonstrations such as EFFC are highlighted.
Accumulating evidence has highlighted the importance of microglial and astrocyte responses in the pathological development of postoperative cognitive dysfunction (POCD). However, the mechanisms ...involved are not well understood.
A perioperative neurocognitive disorders (PND) mouse model was generated by administering etomidate, and cognitive function was assessed using the Morris water maze and novel object recognition tests. Excitatory and inhibitory postsynaptic currents were recorded to analyze neuronal activity. In addition, microglia and astrocytes were isolated by magnetic-activated cell sorting, and genes that were activated in these cells were identified using quantitative polymerase chain reaction.
We observed dramatic cognitive impairment at 1 and 3 weeks after etomidate was administered to 18 month-old mice. Microglia and astrocytes isolated from the hippocampus showed significant microglial activation during the early pathological stage (i.e., 1 week after etomidate injection) and an A1-specific astrocyte response during the late pathological stage (i.e., 3 weeks after etomidate injection). Furthermore, when microglia were eliminated before etomidate was injected, the A1-specific astrocyte activation response was significantly reduced, and cognitive function improved. However, when microglia were eliminated after etomidate application, astrocyte activation and cognitive function were not significantly altered. In addition, activating microglia immediately after a sedative dose of etomidate was injected markedly increased A1-specific astrocyte activation and cognitive dysfunction.
A1-specific astrocyte activation is triggered by activated microglia during the initial pathological stage of PND and induces long-term synaptic inhibition and cognitive deficiencies. These results improve our understanding of how PND develops and may suggest therapeutic targets.
In this study, activated carbon (AC)-Fe3O4 nanoparticles asymmetric supercapacitor cells have been assembled and characterized in 6 M KOH aqueous electrolyte for the first time. The nanostructure ...Fe3O4 was prepared by the microwave method. It only cost several minutes to prepare magnetite nanoparticles with average particle size of 35 nm. The electrochemical performances of the hybrid AC-Fe3O4 supercapacitor were tested by cyclic voltammetry, electrochemical impedance spectroscopy, and galvanostatic charge−discharge tests. The results show that the asymmetric supercapacitor has electrochemical capacitance performance within potential range 0−1.2 V. The supercapacitor delivered a specific capacitance of 37.9 F/g at a current density of 0.5 mA/cm2. The result of cyclic characteristic test showed that it also can keep 82% of initial capacity over 500 cycles.
With the advantages of high stickiness and stretchability of the hydrogel electrolyte as well as the resilient properties of film electrodes, the facile “prestrain-stick-release” strategy can be ...utilized for the assembly of a stretchable supercapacitor. Two major issues of concern are the relatively low mechanical strength of the hydrogel electrolyte and the low energy density of the assembled device. Herein, vinyl group grafted silica (CH2CHSiO2) nanoparticles were used as a nanoparticle cross-linker for polyacrylamide (PAAM), enhancing the tensile strength of 844 kPa at the strain of 3400% for the KClCH2CHSiO2/PAAM hydrogel electrolyte. Besides, carbon nanotube supported polypyrrole (CNT@PPy) and manganese dioxide (CNT@MnO2) film electrodes are prepared to assemble the stretchable asymmetric CNT@MnO2//KClCH2CHSiO2/PAAM//CNT@PPy supercapacitor, significantly enhancing the potential window to 0–2.0 V and achieving a high energy density of 40 Wh kg–1 at the power density of 519 kW kg–1 with the strain of 100%, which is the best known for the reported stretchable supercapacitors.
•The security level of our encryption framework can be significantly improved by increasing the number of Shearlet coefficient sub-images.•The proposed encryption framework can flexibly select ...scrambling algorithm to permutation and diffusion the pixels in sub-images.•The proposed encryption framework can flexibly select reversible synthesize methods to combined the scrambled sub-images into one.
The security of medical image data for transmission and storage is a critical issue. Texture information of medical images is important for diagnosis. Since Shearlets is particularly effective in characterizing texture information, in this paper, we propose a generalized optical encryption framework based on Shearlets and double random phase encoding (DRPE) especially for medical images. In the proposed encryption framework, the secret medical image is first decomposed into n sub-images with shearlet transform. Then, the sub-images or pixel positions are shuffled by scrambling algorithm. Subsequently, the shuffled sub-images are synthesized into one image. Finally, the synthesized image is encoded with DRPE. The security level of the proposed encryption framework can be improved by changing the parameters of shearlet transform and selecting different methods for scrambling pixels and synthesizing multiple images to single image. Extensive simulation results have shown the performance of the proposed optical encryption framework. The proposed encryption framework can be improved along with novelty scrambling method and reversible synthesize methods.
In this paper, we first discuss the definition of modularity (Q) used as a metric for community quality and then we review the modularity maximization approaches which were used for community ...detection in the last decade. Then, we discuss two opposite yet coexisting problems of modularity optimization; in some cases, it tends to favor small communities over large ones while in others, large communities over small ones (so-called the resolution limit problem). Next, we overview several community quality metrics proposed to solve the resolution limit problem and discuss Modularity Density (Q ds ), which simultaneously avoids the two problems of modularity. Finally, we introduce two novel fine-tuned community detection algorithms that iteratively attempt to improve the community quality measurements by splitting and merging the given network community structure. The first one, referred to as Fine-tuned , is based on modularity (Q), while the second is based on Modularity Density (Q ds ) and denoted as Fine-tuned . Then, we compare the greedy algorithm of modularity maximization (denoted as Greedy ), Fine-tuned , and Fine-tuned on four real networks, and also on the classical clique network and the LFR benchmark networks, each of which is instantiated by a wide range of parameters. The results indicate that Fine-tuned Q ds is the most effective among the three algorithms discussed. Moreover, we show that Fine-tuned Q ds can be applied to the communities detected by other algorithms to significantly improve their results.
Spontaneous functional recovery occurs during the acute phase after stroke onset, but this intrinsic recovery remains limited. Therefore, exploring the mechanism underlying spontaneous recovery and ...identifying potential strategies to promote functional rehabilitation after stroke are very important. The CD200/CD200R signaling pathway plays an important role in neurological recovery by modulating synaptic plasticity during multiple brain disorders. However, the effect and mechanism of action of the CD200/CD200R pathway in spontaneous functional recovery after stroke are unclear.
In this study, we used a transient middle cerebral artery occlusion (MCAO) model in rats to investigate the function of CD200/CD200R signaling in spontaneous functional recovery after stroke. We performed a battery of behavioral tests (Longa test, adhesive removal test, limb-use asymmetry test, and the modified grip-traction test) to evaluate sensorimotor function after intracerebroventricular (i.c.v.) injection with CD200 fusion protein (CD200Fc) or CD200R blocking antibody (CD200R Ab) post-stroke. Density and morphology of dendritic spines were analyzed by Golgi staining. Microglia activation was evaluated by immunofluorescence staining. Western blot was used to detect the levels of protein and the levels of mRNA were measured by qPCR.
Our study demonstrated that sensorimotor function, synaptic proteins, and structures were gradually recovered and CD200R was transiently upregulated in ipsilateral cortex after stroke. Synapse-related proteins and dendritic spines were preserved, accompanied by sensorimotor functional recovery, after stereotaxic CD200Fc injection post-stroke. In addition, CD200Fc restrained microglia activation and pro-inflammatory factor release (such as Il-1, Tnf-α, and Il-6) after MCAO. On the contrary, CD200R Ab aggravated sensory function recovery in adhesive removal test and further promoted microglia activation and pro-inflammatory factor release (such as Il-1) after MCAO. The immune-modulatory effect of CD200/CD200R signaling might be exerted partly by its inhibition of the MAPK pathway.
This study provides evidence that the CD200/CD200R signaling pathway contributes to spontaneous functional recovery by enhancing synaptic plasticity via inhibition of microglia activation and inflammatory factor release.
With the development of economic integration, Beijing has become more closely connected with surrounding areas, which gradually formed the Beijing metropolitan area (BMA). The authors define the ...scope of BMA from two dimensions of space and time. BMA is determined to be the built-up area of Beijing and its surrounding 10 districts. Designed questionnaire survey the personal characteristics, family characteristics, and travel characteristics of residents from 10 districts in the surrounding BMA. The statistical analysis of questionnaires shows that the supply of public transportation is insufficient and cannot meet traffic demand. Further, the travel mode prediction model of Softmax regression machine learning algorithm for BMA (SRBM) is established. To further verify the prediction performance of the proposed model, the Multinomial Logit Model (MNL) and Support Vector Machine (SVM), model are introduced to compare the prediction accuracy. The results show that the constructed SRBM model exhibits high prediction accuracy, with an average accuracy of 88.35%, which is 2.83% and 18.11% higher than the SVM and MNL models, respectively. This article provides new ideas for the prediction of travel modes in the Beijing metropolitan area.
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