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Amphiphilic molecules such as cetyl trimethylammonium bromide (CTAB) and minerals such as graphene oxide (GO) self-assemble to form lyotropic liquid crystal (LLC) systems in water. Here we describe the preparation, structures and mechanical properties of the hybrid CTAB and GO LLC system. We present a series of phase diagrams for the CTAB/GO/water ternary system with independently varying CTAB and GO loadings, covering various combinations of the isotropic and the different lyotropic phases of each that occur as a function of concentration and temperature. The corresponding LLC microstructures are identified through polarised optical microscopy (POM) in a confined environment. We find that GO either promotes or suppresses the formation of the different LLC phases of CTAB depending on the concentration of GO and whether the GO itself is in its isotropic or lyotropic phase, resulting in the formation of a complex hybrid system. GO also significantly depresses the melting point of CTAB at moderate loadings, but the melting point recovers for higher GO loading. Rheology of the CTAB/GO/water system reveals a reinforcement effect of the GO sheets through the formation of CTAB/GO complexes that alter the hexagonal phase nanostructure. Our work reveals the diverse effects on molecular LC phases by an interpenetrating mineral LC phase, and the potential to design a wide range of novel nanostructured hybrid LLC materials. Image 1 [ABSTRACT FROM AUTHOR]

Modern integrated electronics are in great demand for high-performance electromagnetic interference (EMI) shielding materials with exceptional mechanical properties. Liquid metal (LM) has demonstrated great potential in EMI shielding by its superior electrical conductivity. However, its real-world EMI application is limited by the poor compatibility, insulating oxide shells, and unpredictable leakage. Here, graphene oxide (GO) is used to encapsulate LM to form LM@GO microdroplets dispersion, and bacterial cellulose (BC) is applied to construct a biocompatible fabric network. Moreover, GO is in-situ reduced by hydrazine vapor, which generates synergistic LM/reduced graphene oxide (rGO) conductive pathways with the aid of roll-in process, obtaining flexible LM/rGO/BC (LGB) composite film with outstanding electrical conductivity of 4.5 × 104 S/m and exceptional shielding effectiveness of 64.0 dB. The rGO sheets and BC network demonstrate layered structure after roll-in process, effectively impeding the leakage and oxidation of LM and achieving a tensile strength up to 62.9 MPa of LGB films. Meanwhile, the LGB films exhibit exceptional Joule heating performance, and the stable surface temperature reaches 110 °C with high stability and reliability when the applied voltage is 4 V. This work provides a feasible engineering approach to prepare LM-based films for applications in EMI shielding and wearable electronics. [Display omitted] [ABSTRACT FROM AUTHOR]

• New CNN design with complex-valued operations. • Practical complex-valued CNN for image denoising. • Proposed CNN is effective for handling unseen noises. • State-of-the-art results are achieved. While complex-valued transforms have been widely used in image processing and have their deep connections to biological vision systems, complex-valued convolutional neural networks (CNNs) have not seen their applications in image recovery. This paper aims at investigating the potentials of complex-valued CNNs for image denoising. A CNN is developed for image denoising with its key mathematical operations defined in the complex number field to exploit the merits of complex-valued operations, including the compactness of convolution given by the tensor product of 1D complex-valued filters, the nonlinear activation on phase, and the noise robustness of residual blocks. The experimental results show that, the proposed complex-valued denoising CNN performs competitively against existing state-of-the-art real-valued denoising CNNs, with better robustness to possible inconsistencies of noise models between training samples and test images. The results also suggest that complex-valued CNNs provide another promising deep-learning-based approach to image denoising and other image recovery tasks. [ABSTRACT FROM AUTHOR]