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- All Subjects: multiaxial fatigue
- Creators: Jiao, Yang
Description
Layered chalcogenides are a diverse class of crystalline materials that consist of covalently bound building blocks held together by van der Waals forces, including the transition metal dichalcogenides (TMDCs) and the pnictogen chalcogenides (PCs) among all. These materials, in particular, MoS2 which is the most widely studied TMDC material, have attracted significant attention in recent years due to their unique physical, electronic, optical, and chemical properties that depend on the number of layers. Due to their high aspect ratios and extreme thinness, 2D materials are sensitive to modifications via chemistry on their surfaces. For instance, covalent functionalization can be used to robustly modify the electronic properties of 2D materials, and can also be used to attach other materials or structures. Metal adsorption on the surfaces of 2D materials can also tune their electronic structures, and can be used as a strategy for removing metal contaminants from water. Thus, there are many opportunities for studying the fundamental surface interactions of 2D materials and in particular the TMDCs and PCs.
The work reported in this dissertation represents detailed fundamental studies of the covalent functionalization and metal adsorption behavior of layered chalcogenides, which are two significant aspects of the surface interactions of 2D materials. First, we demonstrate that both the Freundlich and Temkin isotherm models, and the pseudo-second-order reaction kinetics model are good descriptors of the reaction due to the energetically inhomogeneous surface MoS2 and the indirect adsorbate-adsorbate interactions from previously attached nitrophenyl (NP) groups. Second, the covalent functionalization using aryl diazonium salts is extended to nanosheets of other representative TMDC materials MoSe2, WS2, and WSe2, and of the representative PC materials Bi2S3 and Sb2S3, demonstrated using atomic force microscopy (AFM) imaging and Fourier transform infrared spectroscopy (FTIR). Finally, using AFM and X-ray photoelectron spectroscopy (XPS), it is shown that Pb, Cd Zn and Co form nanoclusters on the MoS2 surface without affecting the structure of the MoS2 itself. The metals can also be thermally desorbed from MoS2, thus suggesting a potential application as a reusable water purification technology.
The work reported in this dissertation represents detailed fundamental studies of the covalent functionalization and metal adsorption behavior of layered chalcogenides, which are two significant aspects of the surface interactions of 2D materials. First, we demonstrate that both the Freundlich and Temkin isotherm models, and the pseudo-second-order reaction kinetics model are good descriptors of the reaction due to the energetically inhomogeneous surface MoS2 and the indirect adsorbate-adsorbate interactions from previously attached nitrophenyl (NP) groups. Second, the covalent functionalization using aryl diazonium salts is extended to nanosheets of other representative TMDC materials MoSe2, WS2, and WSe2, and of the representative PC materials Bi2S3 and Sb2S3, demonstrated using atomic force microscopy (AFM) imaging and Fourier transform infrared spectroscopy (FTIR). Finally, using AFM and X-ray photoelectron spectroscopy (XPS), it is shown that Pb, Cd Zn and Co form nanoclusters on the MoS2 surface without affecting the structure of the MoS2 itself. The metals can also be thermally desorbed from MoS2, thus suggesting a potential application as a reusable water purification technology.
ContributorsLi, Duo, Ph.D (Author) / Wang, Qing Hua (Thesis advisor) / Green, Alexander A. (Committee member) / Chan, Candace K. (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2019
Description
Two fatigue life prediction methods using the energy-based approach have been proposed. A number of approaches have been developed in the past five decades. This study reviews some common models and discusses the model that is most suitable for each different condition, no matter whether the model is designed to solve uniaxial, multiaxial, or biaxial loading paths in fatigue prediction. In addition, different loading cases such as various loading and constant loading are also discussed. These models are suitable for one or two conditions in fatigue prediction. While most of the existing models can only solve single cases, the proposed new energy-based approach not only can deal with different loading paths but is applicable for various loading cases. The first energy-based model using the linear cumulative rule is developed to calculate random loading cases. The method is developed by combining Miner’s rule and the rainflow-counting algorithm. For the second energy-based method, I propose an alternative method and develop an approach to avert the rainflow-counting algorithm. Specifically, I propose to use an energy-based model by directly using the time integration concept. In this study, first, the equivalent energy concept that can transform three-dimensional loading into an equivalent loading will be discussed. Second, the new damage propagation method modified by fatigue crack growth will be introduced to deal with cycle-based fatigue prediction. Third, the time-based concept will be implemented to determine fatigue damage under every cycle in the random loading case. The formulation will also be explained in detail. Through this new model, the fatigue life can be calculated properly in different loading cases. In addition, the proposed model is verified with experimental datasets from several published studies. The data include both uniaxial and multiaxial loading paths under constant loading and random loading cases. Finally, the discussion and conclusion based on the results, are included. Additional loading cases such as the spectrum including both elastic and plastic regions will be explored in future research.
ContributorsTien, Shih-Chuan (Author) / Liu, Yongming (Thesis advisor) / Nian, Qiong (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Extensive efforts have been devoted to understanding material failure in the last several decades. A suitable numerical method and specific failure criteria are required for failure simulation. The finite element method (FEM) is the most widely used approach for material mechanical modelling. Since FEM is based on partial differential equations, it is hard to solve problems involving spatial discontinuities, such as fracture and material interface. Due to their intrinsic characteristics of integro-differential governing equations, discontinuous approaches are more suitable for problems involving spatial discontinuities, such as lattice spring method, discrete element method, and peridynamics. A recently proposed lattice particle method is shown to have no restriction of Poisson’s ratio, which is very common in discontinuous methods. In this study, the lattice particle method is adopted to study failure problems. In addition of numerical method, failure criterion is essential for failure simulations. In this study, multiaxial fatigue failure is investigated and then applied to the adopted method. Another critical issue of failure simulation is that the simulation process is time-consuming. To reduce computational cost, the lattice particle method can be partly replaced by neural network model.First, the development of a nonlocal maximum distortion energy criterion in the framework of a Lattice Particle Model (LPM) is presented for modeling of elastoplastic materials. The basic idea is to decompose the energy of a discrete material point into dilatational and distortional components, and plastic yielding of bonds associated with this material point is assumed to occur only when the distortional component reaches a critical value. Then, two multiaxial fatigue models are proposed for random loading and biaxial tension-tension loading, respectively. Following this, fatigue cracking in homogeneous and composite materials is studied using the lattice particle method and the proposed multiaxial fatigue model. Bi-phase material fatigue crack simulation is performed. Next, an integration of an efficient deep learning model and the lattice particle method is presented to predict fracture pattern for arbitrary microstructure and loading conditions. With this integration, computational accuracy and efficiency are both considered. Finally, some conclusion and discussion based on this study are drawn.
ContributorsWei, Haoyang (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Jiang, Hanqing (Committee member) / Jiao, Yang (Committee member) / Oswald, Jay (Committee member) / Arizona State University (Publisher)
Created2021