1wileyonlinelibrary.com an extremely high graphene loading is required. For example, the fi rst graphene based EMI shielding composite exhibited an EMI SE of ≈21 dB with a graphene loading of 15 wt%. [ 5 ] Ling et al. reduced the graphene loading of a polyetherimide/ graphene composite to 10 wt%, while keeping EMI SE at 20.0 dB. [ 6 ] On the other hand, an improved EMI SE, 29.3 dB, of graphene/polystyrene (PS) composite was obtained at the cost of extremely high graphene loading of 30 wt%. [ 7 ] Graphene or reduced graphene oxide (rGO) composites based on poly (methyl methacrylate), [ 8 ] water-borne polyurethane, [ 9 ] phenolic [ 10 ] were also reported in published papers, nevertheless, satisfactory EMI SE always requires abundant nanofi llers due to the homogenous dispersion structure of these composites. High nanofi ller concentrations result in high production costs and poor composite processability. Preparing CPC materials with superior EMI SE at low nanofi ller loading remains a challenge.The formation of segregated architectures can reduce the electrical percolation threshold, and improve electrical conductivity. [11][12][13][14][15] In such architectures, electrical nanofi llers are distributed only at the interfaces of polymer granules not homogeneously distributed in the whole volume of the polymer matrix. Graphene was fi rst utilized to construct segregated conductive networks in ultrahigh molecular weight polyethylene (UHMWPE) matrix, exhibiting an electrical conductivity of 0.04 S m −1 at a rather low content of 0.6 vol%. 12] A comparative study of segregated and homogeneous graphene/polycarbonate composites showed that the percolation threshold of the former composite was one third of that for the latter, and electrical conductivity was also higher by 220% at the same graphene loading of 4 wt%. [ 13 ] Segregated architectures also provide enhanced EMI SE, for example, when Cu nanowires were used as an electrical nanofi ller in PS, the segregated composites exhibited EMI SE levels of 26 and 42 dB at 10 and 13 wt% Cu loading, respectively. [ 15 ] Very recently, our group reported an in situ thermally reduced graphene/ultrahigh molecular weight polyethylene composite with a segregated structure, revealing the EMI SE of 28.3-32.4 dB at an ultralow graphene loading. [ 16 ] Although the formation of such segregated architectures could improve electrical and EMI shielding performance, one major issue of segregated architectures is that the existence of nanofi ller agglomerates at polymer granule interfaces restricts molecular diffusion between granules, leading to poor Ding-Xiang Yan , Huan Pang , Bo Li , Robert Vajtai , Ling Xu , Peng-Gang Ren , Jian-Hua Wang , and Zhong-Ming Li * A high-performance electromagnetic interference shielding composite based on reduced graphene oxide (rGO) and polystyrene (PS) is realized via highpressure solid-phase compression molding. Superior shielding effectiveness of 45.1 dB, the highest value among rGO based polymer composite, is achieved with only 3.47 vol% rGO lo...

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Graphene oxide (GO) was successfully prepared by a modified Hummer's method. The reduction effect and mechanism of the as-prepared GO reduced with hydrazine hydrate at different temperatures and time were characterized by x-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), elemental analysis (EA), x-ray diffractions (XRD), Raman spectroscopy and thermo-gravimetric analysis (TGA). The results showed that the reduction effect of GO mainly depended on treatment temperature instead of treatment time. Desirable reduction of GO can only be obtained at high treatment temperature. Reduced at 95 °C for 3 h, the C/O atomic ratio of GO increased from 3.1 to 15.1, which was impossible to obtain at low temperatures, such as 80, 60 or 15 °C, even for longer reduction time. XPS, 13C NMR and FTIR results show that most of the epoxide groups bonded to graphite during the oxidation were removed from GO and form the sp(2) structure after being reduced by hydrazine hydrate at high temperature (>60 °C), leading to the electric conductivity of GO increasing from 1.5 × 10(-6) to 5 S cm(-1), while the hydroxyls on the surface of GO were not removed by hydrazine hydrate even at high temperature. Additionally, the FTIR, XRD and Raman spectrum indicate that the GO reduced by hydrazine hydrate can not be entirely restored to the pristine graphite structures. XPS and FTIR data also suggest that carbonyl and carboxyl groups can be reduced by hydrazine hydrate and possibly form hydrazone, but not a C = C structure.

Neurophysiological field-potential signals consist of both arrhythmic and rhythmic patterns indicative of the fractal and oscillatory dynamics arising from likely distinct mechanisms. Here, we present a new method, namely the Irregular-Resampling Auto-Spectral Analysis (IRASA), to separate fractal and oscillatory components in the power spectrum of neurophysiological signal according to their distinct temporal and spectral characteristics. In this method, we irregularly resampled the neural signal by a set of non-integer factors, and statistically summarized the auto-power spectra of the resampled signals to separate the fractal component from the oscillatory component in the frequency domain. We tested this method on simulated data and demonstrated that IRASA could robustly separate the fractal component from the oscillatory component. In addition, applications of IRASA to macaque electrocorticography (ECoG) and human magnetoencephalography (MEG) data revealed a greater power-law exponent of fractal dynamics during sleep compared to wakefulness. The temporal fluctuation in the broadband power of the fractal component revealed characteristic dynamics within and across the eyes-closed, eyes-open and sleep states. These results demonstrate the efficacy and potential applications of this method in analyzing electrophysiological signatures of large-scale neural circuit activity. We expect that the proposed method or its future variations would potentially allow for more specific characterization of the differential contributions of oscillatory and fractal dynamics to distributed neural processes underlying various brain functions.

A combination of high-pressure compression molding plus saltleaching was first proposed to prepare porous graphene/polystyrene composites. The specific shielding effectiveness of the lightweight composite was as high as 64.4 dB cm 3 g À1 , the highest value ever reported for polymer based EMI shielding materials at such a low thickness (2.5 mm).

Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships between the CNN and the brain. Through the encoding models, the CNN-predicted areas covered not only the ventral stream, but also the dorsal stream, albeit to a lesser degree; single-voxel response was visualized as the specific pixel pattern that drove the response, revealing the distinct representation of individual cortical location; cortical activation was synthesized from natural images with high-throughput to map category representation, contrast, and selectivity. Through the decoding models, fMRI signals were directly decoded to estimate the feature representations in both visual and semantic spaces, for direct visual reconstruction and semantic categorization, respectively. These results corroborate, generalize, and extend previous findings, and highlight the value of using deep learning, as an all-in-one model of the visual cortex, to understand and decode natural vision.

Low-dimensional nanoparticles have a strong ability to induce the crystallization of polymer matrices. One-dimensional carbon nanotubes (CNTs) and two-dimensional graphene nanosheets (GNSs), both of which are both carbon-based nanoparticles, provide a good opportunity to investigate the effects of differently dimensional nanoparticles on the crystallization behavior of a polymer. For this purpose, respective nanocomposites of CNTs and GNSs with poly(l-lactide) (PLLA) as matrix were prepared by solution coagulation. Time-resolved Fourier-transform infrared spectroscopy (FTIR) and synchrotron wide-angle X-ray diffraction (WAXD) were performed to probe chain conformational changes and to determine the crystallization kinetics during the isothermal crystallization of the PLLA nanocomposites and neat PLLA, especially in the early stages. Both CNTs and GNSs could serve as nucleating agents in accelerating the crystallization kinetics of PLLA; however, the ability of CNTs to induce crystallization was stronger than that of GNSs. On increasing the content of CNTs from 0.05 to 0.1 wt %, the induction period was shortened and the crystallization rate was enhanced, but the reverse situation was found for GNSs nanocomposites. In the case of neat PLLA, −CH3 interchain interactions preceded −(COC + CH3) interchain interactions during the crystallization. Conversely, in the CNTs and GNSs nanocomposites, the conformational ordering began with −(COC + CH3) interchain interactions, which resulted directly in a reduced induction period. Interchain interactions of this type could be explained in terms of surface-induced conformational order (SICO). Finally, the effect of the dimensionality of the nanoparticles on the crystallization behavior of PLLA is discussed.

Recent resting-state fMRI studies have shown that the apparent functional connectivity (FC) between brain regions may undergo changes on time-scales of seconds to minutes, the basis and importance of which are largely unknown. Here, we examine the electrophysiological correlates of within-scan FC variations during a condition of eyes-closed rest. A sliding window analysis of simultaneous EEG-fMRI data was performed to examine whether temporal variations in coupling between three major networks (default mode; DMN, dorsal attention; DAN, and salience network; SN) are associated with temporal variations in mental state, as assessed from the amplitude of alpha and theta oscillations in the EEG. In our dataset, alpha power showed a significant inverse relationship with the strength of connectivity between DMN and DAN. In addition, alpha power covaried with the spatial extent of anticorrelation between DMN and DAN, with higher alpha power associated with larger anticorrelation extent. Results suggest an electrical signature of the time-varying FC between the DAN and DMN, potentially reflecting neural and state-dependent variations.