Polymorphous Supercapacitors Constructed from Flexible Three-Dimensional Carbon Network/Polyaniline/MnO2 Composite Textiles.

Polymorphous supercapacitors were constructed from flexible three-dimensional carbon network/polyaniline (PANI)/MnO2 composite textile electrodes. The flexible textile electrodes were fabricated through a layer-by-layer construction strategy: PANI, carbon nanotubes (CNTs), and MnO2 were deposited on activated carbon fiber cloth (ACFC) in turn through an electropolymerization process, "dipping and drying" method, and in situ chemical reaction, respectively. In the fabricated ACFC/PANI/CNTs/MnO2 textile electrodes, the ACFC/CNT hybrid framework serves as a porous and electrically conductive 3D network for the rapid transmission of electrons and electrolyte ions, where ACFC, PANI, and MnO2 are high-performance supercapacitor electrode materials. In the electrolyte of H2SO4 solution, the textile electrode-based symmetric supercapacitor delivers superior areal capacitance, energy density, and power density of 4615 mF cm-2 (for single electrode), 157 µW h cm-2, and 10372 µW cm-2, respectively, whereas asymmetric supercapacitor assembled with the prepared composite textile as the positive electrode and ACFC as the negative electrode exhibits an improved energy density of 413 µW h cm-2 and a power density of 16120 µW cm-2. On the basis of the ACFC/PANI/CNTs/MnO2 textile electrodes, symmetric and asymmetric solid-state textile supercapacitors with a PVA/H2SO4 gel electrolyte were also produced. These solid-state textile supercapacitors exhibit good electrochemical performance and high flexibility. Furthermore, flexible solid-state fiber-like supercapacitors were prepared with fiber bundle electrodes dismantled from the above composite textiles. Overall, this work makes a meaningful exploration of the versatile applications of textile electrodes to produce polymorphous supercapacitors.

Influence of heavy metal ions on the spectra and charge characteristics of DOM of municipal sewage secondary effluent.

The spectral and charged characteristics of dissolved organic matter (DOM) in municipal sewage secondary effluent influenced by heavy metal ions were studied by three-dimensional fluorescence spectroscopy, ultraviolet spectrometry, etc. As the Cu(II) concentration increased, the fluorescence intensity of the DOM's fluorescence peak reduced. When the Cu(II) concentration was 100 µmol/L, the fluorescence intensity of the humic-like acid in the DOM reduced by 19.5%-27.6%. However, there was no obvious change in the fluorescence intensity when Cd(II) was added. According to the modified Stern-Volmer equation, the conditional stability constant lgK and the proportion of the fluorescent functional groups of the DOM binding with Cu(II) were 2.19-2.69 and 16%-48%, respectively. The UV253/UV203 value changed little with the additional dosage of metal ions below 10 µmol/L, which increased as the concentration of Cu(II) or Pb(II) increased from 10-100 µmol/L. As the metal concentrations increase, the absolute values of the Zeta potential and the particle sizes of the DOM basically increased. The results showed that Cu(II) had the strongest binding ability to the groups that emitted fluorescence in the secondary effluent DOM compared to Pb(II) and Cd(II).

Brain response pattern identification of fMRI data using a particle swarm optimization-based approach.

Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a relatively effective approach using machine learning techniques in the recent literature. MVPA can be considered as a multi-objective pattern classification problem with the aim to optimize response patterns, in which informative voxels interacting with each other are selected, achieving high classification accuracy associated with cognitive stimulus conditions. To solve the problem, we propose a feature interaction detection framework, integrating hierarchical heterogeneous particle swarm optimization and support vector machines, for voxel selection in MVPA. In the proposed approach, we first select the most informative voxels and then identify a response pattern based on the connectivity of the selected voxels. The effectiveness of the proposed approach was examined for the Haxby's dataset of object-level representations. The computational results demonstrated higher classification accuracy by the extracted response patterns, compared to state-of-the-art feature selection algorithms, such as forward selection and backward selection.

Mental disorder recovery correlated with centralities and interactions on an online social network.

Recent research has established both a theoretical basis and strong empirical evidence that effective social behavior plays a beneficial role in the maintenance of physical and psychological well-being of people. To test whether social behavior and well-being are also associated in online communities, we studied the correlations between the recovery of patients with mental disorders and their behaviors in online social media. As the source of the data related to the social behavior and progress of mental recovery, we used PatientsLikeMe (PLM), the world's first open-participation research platform for the development of patient-centered health outcome measures. We first constructed an online social network structure based on patient-to-patient ties among 200 patients obtained from PLM. We then characterized patients' online social activities by measuring the numbers of "posts and views" and "helpful marks" each patient obtained. The patients' recovery data were obtained from their self-reported status information that was also available on PLM. We found that some node properties (in-degree, eigenvector centrality and PageRank) and the two online social activity measures were significantly correlated with patients' recovery. Furthermore, we re-collected the patients' recovery data two months after the first data collection. We found significant correlations between the patients' social behaviors and the second recovery data, which were collected two months apart. Our results indicated that social interactions in online communities such as PLM were significantly associated with the current and future recoveries of patients with mental disorders.

Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood.

We investigated the development of spontaneous (resting state) cerebral electric fields and their network organization from early to late childhood in a large community sample of children. Critically, we examined electrocortical maturation across one-year windows rather than creating aggregate averages that can miss subtle maturational trends. We implemented several novel methodological approaches including a more fine grained examination of spectral features across multiple electrodes, the use of phase-lagged functional connectivity to control for the confounding effects of volume conduction and applying topological network analyses to weighted cortical adjacency matrices. Overall, there were major decreases in absolute EEG spectral density (particularly in the slow wave range) across cortical lobes as a function of age. Moreover, the peak of the alpha frequency increased with chronological age and there was a redistribution of relative spectral density toward the higher frequency ranges, consistent with much of the previous literature. There were age differences in long range functional brain connectivity, particularly in the alpha frequency band, culminating in the most dense and spatially variable networks in the oldest children. We discovered age-related reductions in characteristic path lengths, modularity and homogeneity of alpha-band cortical networks from early to late childhood. In summary, there is evidence of large scale reorganization in endogenous brain electric fields from early to late childhood, suggesting reduced signal amplitudes in the presence of more functionally integrated and band limited coordination of neuronal activity across the cerebral cortex.

[Machinable property of a novel dental mica glass-ceramic].

OBJECTIVE: To investigate the machinability of a novel dental mica glass-ceramic and analyze the effect of heat-treatment on its ductile machinable behavior. METHODS: The drilling and turning experiment were used to measure the machinabilities of the control group (feldspar ceramic: Vita Mark II) and 7 experiment groups treated with different crystallization techniques. The microstructures were analyzed using scanning electron microscopy (SEM) and X-ray diffraction (XRD). RESULTS: The average drilling depths in 30 s of the experimental groups ranged from (0.5 +/- 0.1) mm to (7.1 +/- 0.8) mm. There were significant differences between the control [(0.8 +/- 0.1) mm] and the experimental groups (P < 0.05) except the group crystallized at 740 degrees C for 60 min. When crystallized at 650 degrees C (60 min), continuous band chips could formed in machining at a high velocity and cut depth. The crystal portion of this group is only about 40%. CONCLUSIONS: This material has a satisfactory machinability. The mechanism could be attributed to a combination of the interlocked structure of mica crystals and the low viscosity of glassy phase.