Sichuan University

About: Sichuan University is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 107623 authors who have published 102844 publications receiving 1612131 citations. The organization is also known as: Sìchuān Dàxué.

High Ion Conducting Polymer Nanocomposite Electrolytes Using Hybrid Nanofillers

Abstract: There is a growing shift from liquid electrolytes toward solid polymer electrolytes, in energy storage devices, due to the many advantages of the latter such as enhanced safety, flexibility, and manufacturability. The main issue with polymer electrolytes is their lower ionic conductivity compared to that of liquid electrolytes. Nanoscale fillers such as silica and alumina nanoparticles are known to enhance the ionic conductivity of polymer electrolytes. Although carbon nanotubes have been used as fillers for polymers in various applications, they have not yet been used in polymer electrolytes as they are conductive and can pose the risk of electrical shorting. In this study, we show that nanotubes can be packaged within insulating clay layers to form effective 3D nanofillers. We show that such hybrid nanofillers increase the lithium ion conductivity of PEO electrolyte by almost 2 orders of magnitude. Furthermore, significant improvement in mechanical properties were observed where only 5 wt % addition of the filler led to 160% increase in the tensile strength of the polymer. This new approach of embedding conducting-insulating hybrid nanofillers could lead to the development of a new generation of polymer nanocomposite electrolytes with high ion conductivity and improved mechanical properties.

Structural and biochemical basis for induced self-propagation of NLRC4

Abstract: Responding to stimuli, nucleotide-binding domain and leucine-rich repeat-containing proteins (NLRs) oligomerize into multiprotein complexes, termed inflammasomes, mediating innate immunity. Recognition of bacterial pathogens by NLR apoptosis inhibitory proteins (NAIPs) induces NLR family CARD domain-containing protein 4 (NLRC4) activation and formation of NAIP-NLRC4 inflammasomes. The wheel-like structure of a PrgJ-NAIP2-NLRC4 complex determined by cryogenic electron microscopy at 6.6 angstrom reveals that NLRC4 activation involves substantial structural reorganization that creates one oligomerization surface (catalytic surface). Once activated, NLRC4 uses this surface to catalyze the activation of an inactive NLRC4, self-propagating its active conformation to form the wheel-like architecture. NAIP proteins possess a catalytic surface matching the other oligomerization surface (receptor surface) of NLRC4 but not those of their own, ensuring that one NAIP is sufficient to initiate NLRC4 oligomerization.

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)

Abstract: In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.