Browsing by Author "Liu, Sheng"

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    Ion transport and storage of ionic liquids in ionic polymer conductor network composites
    Liu, Yang; Liu, Sheng; Lin, Junhong; Wang, Dong; Jain, Vaibhav; Montazami, Reza; Heflin, James R.; Li, Jing; Madsen, Louis A.; Zhang, Q. M. (AIP Publishing, 2010-05-01)
    We investigate ion transport and storage of ionic liquids in ionic polymer conductor network composite electroactive devices. Specifically, we show that by combining the time domain electric and electromechanical responses, one can gain quantitative information on transport behavior of the two mobile ions in ionic liquids (i.e., cation and anion) in these electroactive devices. By employing a two carrier model, the total excess ions stored and strains generated by the cations and anions, and their transport times in the nanocomposites can be determined, which all depend critically on the morphologies of the conductor network nanocomposites. (C) 2010 American Institute of Physics. [doi:10.1063/1.3432664]
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    Optimization and Supervised Machine Learning Methods for Inverse Design of Cellular Mechanical Metamaterials
    Liu, Sheng (Virginia Tech, 2024-05-22)
    Cellular mechanical metamaterials (CMMs) are a special class of materials that consist of microstructural architectures of macroscopic hierarchical frameworks that can have extraordinary properties. These properties largely depend on the topology and arrangement of the unit cells constituting the microstructure. The material hierarchy facilitates the synthesis and design of CMMs on the micro-scale to achieve enhanced properties (i.e., improved strength, toughness, low density) on the component (macro)-scale. However, designing on-demand cellular metamaterials usually requires solving a challenging inverse problem to explore the complex structure-property relations. The first part of this study (Ch. 3) proposes an experience-free and systematic design methodology for microstructures of CMMs using an advanced stochastic searching algorithm called micro-genetic algorithm (μGA). Locally, this algorithm minimizes the computational expense of the genetic algorithm (GA) with a small population size and a conditionally reduced parameter space. Globally, the algorithm employs a new search strategy to avoid local convergence induced by the small population size and the complexity of the parameter space. What's more, inspired by natural evolution in the GA, this study applies the inverse design method with the standard GA (sGA) as a sampling algorithm for intuitively mapping material-property spaces of CMMs, which requires the selection of objective properties and stochastic search of property points within the property space. The mapping methodology utilizing the sGA is proposed in the second part of the study (Ch. 4). This methodology involves a robust strategy that is shown to identify more comprehensive property spaces than traditional mapping approaches. The resulting property space allows designers to acknowledge the limitations of material performance, and select an appropriate class of CMMs based on the difficulty of the realization and fabrication of their microstructures. During the fabrication process, manufacturing defects cause uncertainty in the microstructures, and thus the structural properties. The third part of the study (Ch. 5) investigates the effects of the uncertainty stemming from manufacturing defects on the material property space. To accelerate the uncertainty quantification (UQ) via the Monte Carlo method, this study utilizes a machine learning technique to bypass the expensive simulations to compute properties. In addition to reducing the computational expense of the simulations, the deep learning method has been proven to be practical to accomplish non-intuitive design tasks. Due to the numerous combinations of properties and complex underlying geometries of metamaterials, it is numerically intractable to obtain optimal material designs that satisfy multiple user-defined performance criteria at the same time. Nevertheless, a deep learning method called conditional generative adversarial networks (CGANs) is capable of solving this many-to-many inverse problem. The fourth part of the study (Ch. 6) proposes a new inverse design framework using CGANs to overcome this challenge. Given combinations of target properties, the framework can generate a group of geometric patterns providing these target properties. Therefore, the proposed strategy provides alternative solutions to satisfy on-demand requirements while increasing the freedom in the fabrication process. Besides, with the advances in additive manufacturing (AM), the design space of an engineering material can be further enlarged by multi-scale topology optimization. As the interplay between microstructure and macrostructure drives the overall mechanical performance of engineering materials, it is necessary to develop a multi-scale design framework to optimize structural features in these two scales simultaneously. The final part of the study (Ch. 7) presents a concurrent multi-scale topology optimization method of CMMs. Structures in micro and macro scales are optimized concurrently by utilizing sequential quadratic programming (SQP) with the Solid Isotropic Material with Penalization (SIMP) method and a numerical homogenization approach.
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    Thickness dependence of curvature, strain, and response time in ionic electroactive polymer actuators fabricated via layer-by-layer assembly
    Montazami, Reza; Liu, Sheng; Liu, Yang; Wang, Dong; Zhang, Qiming; Heflin, James R. (American Institute of Physics, 2011-05-15)
    Ionic electroactive polymer (IEAP) actuators containing porous conductive network composites (CNCs) and ionic liquids can result in high strain and fast response times. Incorporation of spherical gold nanoparticles in the CNC enhances conductivity and porosity, while maintaining relatively small thickness. This leads to improved mechanical strain and bending curvature of the actuators. We have employed the layer-by-layer self-assembly technique to fabricate a CNC with enhanced curvature (0.43 mm(-1)) and large net intrinsic strain (6.1%). The results demonstrate that curvature and net strain of IEAP actuators due to motion of the anions increase linearly with the thickness of the CNC as a result of the increased volume in which the anions can be stored. In addition, after subtracting the curvature of a bare Nafion actuator without a CNC, it is found that the net intrinsic strain of the CNC layer is independent of thickness for the range of 20-80 nm, indicating that the entire CNC volume contributes equivalently to the actuator motion. Furthermore, the response time of the actuator due to anion motion is independent of CNC thickness, suggesting that traversal through the Nafion membrane is the limiting factor in the anion motion. (C) 2011 American Institute of Physics. [doi:10.1063/1.3590166]