In spite of extensive studies conducted on carbon nanotubes and silicate layers for their polymer-based nanocomposites, the rise of graphene now provides a more promising candidate due to its exceptionally high mechanical performance and electrical and thermal conductivities. The present study developed a facile approach to fabricate epoxy-graphene nanocomposites by thermally expanding a commercial product followed by ultrasonication and solution-compounding with epoxy, and investigated their morphologies, mechanical properties, electrical conductivity and thermal mechanical behaviour. Graphene platelets (GnPs) of 3.57 AE 0.50 nm in thickness were created after the expanded product was dispersed in tetrahydrofuran using 60 min ultrasonication. Since epoxy resins cured by various hardeners are widely used in industries, we chose two common hardeners: polyoxypropylene (J230) and 4,4 0 -diaminodiphenylsulfone (DDS). DDS-cured nanocomposites showed a better dispersion and exfoliation of GnPs, a higher improvement (573%) in fracture energy release rate and a lower percolation threshold (0.612 vol%) for electrical conductivity, because DDS contains benzene groups which create p-p interactions with GnPs promoting a higher degree of dispersion and exfoliation of GnPs during curing. This research pointed out a potential trend where GnPs would replace carbon nanotubes and silicate layers for many applications of polymer nanocomposites.

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Background Autism spectrum disorders (ASD) imply a spectrum of symptoms rather than a single phenotype. ASD could affect brain connectivity at different degree based on the severity of the symptom. Given their excellent learning capability, graph neural networks (GNN) methods have recently been used to uncover functional connectivity patterns and biological mechanisms in neuropsychiatric disorders, such as ASD. However, there remain challenges to develop an accurate GNN learning model and understand how specific decisions of these graph models are made in brain network analysis. Results In this paper, we propose a graph attention network based learning and interpreting method, namely GAT-LI, which learns to classify functional brain networks of ASD individuals versus healthy controls (HC), and interprets the learned graph model with feature importance. Specifically, GAT-LI includes a graph learning stage and an interpreting stage. First, in the graph learning stage, a new graph attention network model, namely GAT2, uses graph attention layers to learn the node representation, and a novel attention pooling layer to obtain the graph representation for functional brain network classification. We experimentally compared GAT2 model’s performance on the ABIDE I database from 1035 subjects against the classification performances of other well-known models, and the results showed that the GAT2 model achieved the best classification performance. We experimentally compared the influence of different construction methods of brain networks in GAT2 model. We also used a larger synthetic graph dataset with 4000 samples to validate the utility and power of GAT2 model. Second, in the interpreting stage, we used GNNExplainer to interpret learned GAT2 model with feature importance. We experimentally compared GNNExplainer with two well-known interpretation methods including Saliency Map and DeepLIFT to interpret the learned model, and the results showed GNNExplainer achieved the best interpretation performance. We further used the interpretation method to identify the features that contributed most in classifying ASD versus HC. Conclusion We propose a two-stage learning and interpreting method GAT-LI to classify functional brain networks and interpret the feature importance in the graph model. The method should also be useful in the classification and interpretation tasks for graph data from other biomedical scenarios.

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The effects of a streamwise magnetic field on conducting channel flow are studied by analyzing secondary linear perturbations evolving on streamwise streaks and by direct numerical simulations of relaminarization. By means of an optimal perturbation approach, magnetic damping is found to increase the streamwise wavelength of the most amplified secondary perturbations and to reduce their amplification level. Complete suppression of secondary instability is observed at a critical magnetic interaction parameter that depends on the streak amplitude and on the Reynolds number when the transient evolution of the streaky basic flow is taken into account. Relaminarization in the direct numerical simulation occurs at lower values of the interaction parameter than the critical values from the stability computations for the streak amplitudes considered. The dependence of these threshold values of the interaction parameters on the Reynolds number is fairly similar between simulations and stability analysis. Relaminarization thresholds from the simulations are also in good agreement with experiments on pipe flow with streamwise magnetic field.