Matching Items (30)
Filtering by

Clear all filters

156283-Thumbnail Image.png
Description
In this dissertation, three complex material systems including a novel class of hyperuniform composite materials, cellularized collagen gel and low melting point alloy (LMPA) composite are investigated, using statistical pattern characterization, stochastic microstructure reconstruction and micromechanical analysis. In Chapter 1, an introduction of this report is provided, in which a

In this dissertation, three complex material systems including a novel class of hyperuniform composite materials, cellularized collagen gel and low melting point alloy (LMPA) composite are investigated, using statistical pattern characterization, stochastic microstructure reconstruction and micromechanical analysis. In Chapter 1, an introduction of this report is provided, in which a brief review is made about these three material systems. In Chapter 2, detailed discussion of the statistical morphological descriptors and a stochastic optimization approach for microstructure reconstruction is presented. In Chapter 3, the lattice particle method for micromechanical analysis of complex heterogeneous materials is introduced. In Chapter 4, a new class of hyperuniform heterogeneous material with superior mechanical properties is investigated. In Chapter 5, a bio-material system, i.e., cellularized collagen gel is modeled using correlation functions and stochastic reconstruction to study the collective dynamic behavior of the embed tumor cells. In chapter 6, LMPA soft robotic system is generated by generalizing the correlation functions and the rigidity tunability of this smart composite is discussed. In Chapter 7, a future work plan is presented.
ContributorsXu, Yaopengxiao (Author) / Jiao, Yang (Thesis advisor) / Liu, Yongming (Committee member) / Wang, Qing Hua (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2018
156130-Thumbnail Image.png
Description
Two-dimensional transition metal dichalcogenides (TMDCs) such as

molybdenum disulfide (MoS2), tungsten disulfide (WS2), molybdenum diselenide (MoSe2) and tungsten diselenide (WSe2) are attractive for use in biotechnology, optical and electronics devices due to their promising and tunable electrical, optical and chemical properties. To fulfill the variety of requirements for different applications, chemical

Two-dimensional transition metal dichalcogenides (TMDCs) such as

molybdenum disulfide (MoS2), tungsten disulfide (WS2), molybdenum diselenide (MoSe2) and tungsten diselenide (WSe2) are attractive for use in biotechnology, optical and electronics devices due to their promising and tunable electrical, optical and chemical properties. To fulfill the variety of requirements for different applications, chemical treatment methods are developed to tune their properties. In this dissertation, plasma treatment, chemical doping and functionalization methods have been applied to tune the properties of TMDCs. First, plasma treatment of TMDCs results in doping and generation of defects, as well as the synthesis of transition metal oxides (TMOs) with rolled layers that have increased surface-to-volume ratio and are promising for electrochemical applications. Second, chemical functionalization is another powerful approach for tuning the properties of TMDCs for use in many applications. To covalently functionalize the basal planes of TMDCs, previous reports begin with harsh treatments like lithium intercalation that disrupt the structure and lead to a phase transformation from semiconducting to metallic. Instead, this work demonstrates the direct covalent functionalization of semiconducting MoS2 using aryl diazonium salts without lithium treatments. It preserves the structure and semiconducting nature of MoS2, results in covalent C-S bonds on basal planes and enables different functional groups to be tethered to the MoS2 surface via the diazonium salts. The attachment of fluorescent proteins has been used as a demonstration and it suggests future applications in biology and biosensing. The effects of the covalent functionalization on the electronic transport properties of MoS2 were then studied using field effect transistor (FET) devices.
ContributorsChu, Ximo (Author) / Wang, Qing Hua (Thesis advisor) / Sieradzki, Karl (Committee member) / Green, Alexander (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2018
156687-Thumbnail Image.png
Description
Additive manufacturing (AM) describes an array of methods used to create a 3D object layer by layer. The increasing popularity of AM in the past decade has been due to its demonstrated potential to increase design flexibility, produce rapid prototypes, and decrease material waste. Temporary supports are an

Additive manufacturing (AM) describes an array of methods used to create a 3D object layer by layer. The increasing popularity of AM in the past decade has been due to its demonstrated potential to increase design flexibility, produce rapid prototypes, and decrease material waste. Temporary supports are an inconvenient necessity in many metal AM parts. These sacrificial structures are used to fabricate large overhangs, anchor the part to the build substrate, and provide a heat pathway to avoid warping. Polymers AM has addressed this issue by using support material that is soluble in an electrolyte that the base material is not. In contrast, metals AM has traditionally approached support removal using time consuming, costly methods such as electrical discharge machining or a dremel.

This work introduces dissolvable supports to single- and multi-material metals AM. The multi-material approach uses material choice to design a functionally graded material where corrosion is the functionality being varied. The single-material approach is the primary focus of this thesis, leveraging already common post-print heat treatments to locally alter the microstructure near the surface. By including a sensitizing agent in the ageing heat treatment, carbon is diffused into the part decreasing the corrosion resistance to a depth equal to at least half the support thickness. In a properly chosen electrolyte, this layer is easily chemically, or electrochemically removed. Stainless steel 316 (SS316) and Inconel 718 are both investigated to study this process using two popular alloys. The microstructure evolution and corrosion properties are investigated for both. For SS316, the effect of applied electrochemical potential is investigated to describe the varying corrosion phenomena induced, and the effect of potential choice on resultant roughness. In summary, a new approach to remove supports from metal AM parts is introduced to decrease costs and further the field of metals AM by expanding the design space.
ContributorsLefky, Christopher (Author) / Hildreth, Owen (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Azeredo, Bruno (Committee member) / Rykaczewski, Konrad (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2018
156705-Thumbnail Image.png
Description
Flame retardants (FRs) are applied to variety of consumer products such as textiles and polymers for fire prevention and fire safety. Substantial research is ongoing to replace traditional FRs with alternative materials that are less toxic, present higher flame retardancy and result in lower overall exposure as there are potential

Flame retardants (FRs) are applied to variety of consumer products such as textiles and polymers for fire prevention and fire safety. Substantial research is ongoing to replace traditional FRs with alternative materials that are less toxic, present higher flame retardancy and result in lower overall exposure as there are potential health concerns in case of exposure to popular FRs. Carbonaceous nanomaterials (CNMs) such as carbon nanotubes (CNTs) and graphene oxide (GO) have been studied and applied to polymer composites and electronics extensively due to their remarkable properties. Hence CNMs are considered as potential alternative materials that present high flame retardancy. In this research, different kinds of CNMs coatings on polyester fabric are produced and evaluated for their use as flame retardants. To monitor the mass loading of CNMs coated on the fabric, a two-step analytical method for quantifying CNMs embedded in polymer composites was developed. This method consisted of polymer dissolution process using organic solvents followed by subsequent programmed thermal analysis (PTA). This quantification technique was applicable to CNTs with and without high metal impurities in a broad range of polymers. Various types of CNMs were coated on polyester fabric and the efficacy of coatings as flame retardant was evaluated. The oxygen content of CNMs emerged as a critical parameter impacting flame retardancy with higher oxygen content resulting in less FR efficacy. The most performant nanomaterials, multi-walled carbon nanotubes (MWCNTs) and amine functionalized multi-walled carbon nantoubes (NH2-MWCNT) showed similar FR properties to current flame retardants with low mass loading (0.18 g/m2) and hence are promising alternatives that warrant further investigation. Chemical/physical modification of MWCNTs was conducted to produce well-dispersed MWCNT solutions without involving oxygen for uniform FR coating. The MWCNTs coating was studied to evaluate the durability of the coating and the impact on the efficacy during use phase by conducting mechanical abrasion and washing test. Approximately 50% and 40% of MWCNTs were released from 1 set of mechanical abrasion and washing test respectively. The losses during simulated usage impacted the flame retardancy negatively.
ContributorsNosaka, Takayuki (Author) / Herckes, Pierre (Thesis advisor) / Westerhoff, Paul (Committee member) / Wang, Qing Hua (Committee member) / Arizona State University (Publisher)
Created2018
156953-Thumbnail Image.png
Description
Advanced material systems refer to materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to their superior properties over conventional materials. This dissertation is motivated by the grand challenge in accelerating the design of advanced material systems through systematic optimization with respect to material microstructures

Advanced material systems refer to materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to their superior properties over conventional materials. This dissertation is motivated by the grand challenge in accelerating the design of advanced material systems through systematic optimization with respect to material microstructures or processing settings. While optimization techniques have mature applications to a large range of engineering systems, their application to material design meets unique challenges due to the high dimensionality of microstructures and the high costs in computing process-structure-property (PSP) mappings. The key to addressing these challenges is the learning of material representations and predictive PSP mappings while managing a small data acquisition budget. This dissertation thus focuses on developing learning mechanisms that leverage context-specific meta-data and physics-based theories. Two research tasks will be conducted: In the first, we develop a statistical generative model that learns to characterize high-dimensional microstructure samples using low-dimensional features. We improve the data efficiency of a variational autoencoder by introducing a morphology loss to the training. We demonstrate that the resultant microstructure generator is morphology-aware when trained on a small set of material samples, and can effectively constrain the microstructure space during material design. In the second task, we investigate an active learning mechanism where new samples are acquired based on their violation to a theory-driven constraint on the physics-based model. We demonstrate using a topology optimization case that while data acquisition through the physics-based model is often expensive (e.g., obtaining microstructures through simulation or optimization processes), the evaluation of the constraint can be far more affordable (e.g., checking whether a solution is optimal or equilibrium). We show that this theory-driven learning algorithm can lead to much improved learning efficiency and generalization performance when such constraints can be derived. The outcomes of this research is a better understanding of how physics knowledge about material systems can be integrated into machine learning frameworks, in order to achieve more cost-effective and reliable learning of material representations and predictive models, which are essential to accelerate computational material design.
ContributorsCang, Ruijin (Author) / Ren, Yi (Thesis advisor) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2018
156984-Thumbnail Image.png
Description
Polymer fibers have broad applications in wearable electronics, bulletproof vests, batteries, fuel cells, filters, electrodes, conductive wires, and biomedical materials. Polymer fibers display light density and flexibility but are mostly weak and compliant. The ceramic, metallic, and carbon nanoparticles have been frequently included in polymers for fabricating continuous, durable, and

Polymer fibers have broad applications in wearable electronics, bulletproof vests, batteries, fuel cells, filters, electrodes, conductive wires, and biomedical materials. Polymer fibers display light density and flexibility but are mostly weak and compliant. The ceramic, metallic, and carbon nanoparticles have been frequently included in polymers for fabricating continuous, durable, and functional composite fibers. Nanoparticles display large specific areas, low defect density and can transfer their superior properties to polymer matrices. The main focus of this thesis is to design, fabricate and characterize the polymer
anocarbon composite fibers with unique microstructures and improved mechanical/thermal performance. The dispersions and morphologies of graphene nanoplatelets (GNPs), the interactions with polyvinyl alcohol (PVA) molecules and their influences on fiber properties are studied. The fibers were fabricated using a dry-jet wet spinning method with engineered spinneret design. Three different structured fibers were fabricated, namely, one-phase polymer fiber (1-phase), two-phase core-shell composite fiber (2-phase), and three-phase co-axial composite fiber (3-phase). These polymer or composite fibers were processed at three stages with drawing temperatures of 100˚C, 150˚C, and 200˚C. Different techniques including the mechanical tester, wide-angle X-Ray diffraction (WAXD), scanning electron microscope (SEM), thermogravimetric analysis (TGA), and differential scanning calorimeter (DSC) have been used to characterize the fiber microstructures and properties.
ContributorsVerma, Rahul (Author) / Song, Kenan (Thesis advisor) / Jiang, Hanqing (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2018
168312-Thumbnail Image.png
Description
Stiffness and flexibility are essential in many fields, including robotics, aerospace, bioengineering, etc. In recent years, origami-based mechanical metamaterials were designed for better mechanical properties including tunable stiffness and tunable collapsibility. However, in existing studies, the tunable stiffness is only with limited range and limited controllability. To overcome these challenges,

Stiffness and flexibility are essential in many fields, including robotics, aerospace, bioengineering, etc. In recent years, origami-based mechanical metamaterials were designed for better mechanical properties including tunable stiffness and tunable collapsibility. However, in existing studies, the tunable stiffness is only with limited range and limited controllability. To overcome these challenges, two objectives were proposed and achieved in this dissertation: first, to design mechanical metamaterials with metamaterials with selective stiffness and collapsibility; second, to design mechanical metamaterials with in-situ tunable stiffness among positive, zero, and negative.In the first part, triangulated cylinder origami was employed to build deployable mechanical metamaterials through folding and unfolding along the crease lines. These deployable structures are flexible in the deploy direction so that it can be easily collapsed along the same way as it was deployed. An origami-inspired mechanical metamaterial was designed for on-demand deployability and selective collapsibility: autonomous deployability from the collapsed state and selective collapsibility along two different paths, with low stiffness for one path and substantially high stiffness for another path. The created mechanical metamaterial yields unprecedented load bearing capability in the deploy direction while possessing great deployability and collapsibility. The principle in this prospectus can be utilized to design and create versatile origami-inspired mechanical metamaterials that can find many applications. In the second part, curved origami patterns were designed to accomplish in situ stiffness manipulation covering positive, zero, and negative stiffness by activating predefined creases on one curved origami pattern. This elegant design enables in situ stiffness switching in lightweight and space-saving applications, as demonstrated through three robotic-related components. Under a uniform load, the curved origami can provide universal gripping, controlled force transmissibility, and multistage stiffness response. This work illustrates an unexplored and unprecedented capability of curved origami, which opens new applications in robotics for this particular family of origami patterns.
ContributorsZhai, Zirui (Author) / Nian, Qiong (Thesis advisor) / Zhuang, Houlong (Committee member) / Huang, Huei-Ping (Committee member) / Zhang, Wenlong (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2021
168364-Thumbnail Image.png
Description
Laser powder bed fusion (LPBF) additive manufacturing (AM) has received widespread attention due to its ability to produce parts with complicated design and better surface finish compared to other additive techniques. LPBF uses a laser heat source to melt layers of powder particles and manufactures a part based on the

Laser powder bed fusion (LPBF) additive manufacturing (AM) has received widespread attention due to its ability to produce parts with complicated design and better surface finish compared to other additive techniques. LPBF uses a laser heat source to melt layers of powder particles and manufactures a part based on the CAD design. This process can benefit significantly through computational modeling. The objective of this thesis was to understand the thermal transport, and fluid flow phenomena of the process, and to optimize the main process parameters such as laser power and scan speed through a combination of computational, experimental, and statistical analysis. A multi-physics model was built using to model temperature profile, bead geometry and elemental evaporation in powder bed process using a non-gaussian interaction between laser heat source and metallic powder. Owing to the scarcity of thermo-physical properties of metallic powders in literature, thermal conductivity, diffusivity, and heat capacity was experimentally tested up to a temperature of 1400 degrees C. The values were used in the computational model, which improved the results significantly. The computational work was also used to assess the impact of fluid flow around melt pool. Dimensional analysis was conducted to determine heat transport mode at various laser power/scan speed combinations. Convective heat flow proved to be the dominant form of heat transfer at higher energy input due to violent flow of the fluid around the molten region, which can also create keyhole effect. The last part of the thesis focused on gaining useful information about several features of the bead area such as contact angle, porosity, voids and melt pool that were obtained using several combinations of laser power and scan speed. These features were quantified using process learning, which was then used to conduct a full factorial design that allows to estimate the effect of the process parameters on the output features. Both single and multi-response analysis are applied to analyze the output response. It was observed that laser power has more influential effect on all the features. Multi response analysis showed 150 W laser power and 200 mm/s produced bead with best possible features.
ContributorsAhsan, Faiyaz (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Kwon, Beomjin (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021
168458-Thumbnail Image.png
Description
Cellular metamaterials arouse broad scientific interests due to the combination of host material and structure together to achieve a wide range of physical properties rarely found in nature. Stochastic foam as one subset has been considered as a competitive candidate for versatile applications including heat exchangers, battery electrodes, automotive, catalyst

Cellular metamaterials arouse broad scientific interests due to the combination of host material and structure together to achieve a wide range of physical properties rarely found in nature. Stochastic foam as one subset has been considered as a competitive candidate for versatile applications including heat exchangers, battery electrodes, automotive, catalyst devices, magnetic shielding, etc. For the engineering of the cellular foam architectures, closed-form models that can be used to predict the mechanical and thermal properties of foams are highly desired especially for the recently developed ultralight weight shellular architectures. Herein, for the first time, a novel packing three-dimensional (3D) hollow pentagonal dodecahedron (HPD) model is proposed to simulate the cellular architecture with hollow struts. An electrochemical deposition process is utilized to manufacture the metallic hollow foam architecture. Mechanical and thermal testing of the as-manufactured foams are carried out to compare with the HPD model. Timoshenko beam theory is utilized to verify and explain the derived power coefficient relation. Our HPD model is proved to accurately capture both the topology and the physical properties of hollow stochastic foam. Understanding how the novel HPD model packing helps break the conventional impression that 3D pentagonal topology cannot fulfill the space as a representative volume element. Moreover, the developed HPD model can predict the mechanical and thermal properties of the manufactured hollow metallic foams and elucidating of how the inevitable manufacturing defects affect the physical properties of the hollow metallic foams. Despite of the macro-scale stochastic foam architecture, nano gradient gyroid lattices are studied using Molecular Dynamics (MD) simulation. The simulation result reveals that, unlike homogeneous architecture, gradient gyroid not only shows novel layer-by-layer deformation behavior, but also processes significantly better energy absorption ability. The deformation behavior and energy absorption are predictable and designable, which demonstrate its highly programmable potential.
ContributorsDai, Rui (Author) / Nian, Qiong (Thesis advisor) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Liu, Yongming (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2021
171825-Thumbnail Image.png
Description
High-temperature mechanical behaviors of metal alloys and underlying microstructural variations responsible for such behaviors are essential areas of interest for many industries, particularly for applications such as jet engines. Anisotropic grain structures, change of preferred grain orientation, and other transformations of grains occur both during metal powder bed fusion additive

High-temperature mechanical behaviors of metal alloys and underlying microstructural variations responsible for such behaviors are essential areas of interest for many industries, particularly for applications such as jet engines. Anisotropic grain structures, change of preferred grain orientation, and other transformations of grains occur both during metal powder bed fusion additive manufacturing processes, due to variation of thermal gradient and cooling rates, and afterward during different thermomechanical loads, which parts experience in their specific applications, could also impact its mechanical properties both at room and high temperatures. In this study, an in-depth analysis of how different microstructural features, such as crystallographic texture, grain size, grain boundary misorientation angles, and inherent defects, as byproducts of electron beam powder bed fusion (EB-PBF) AM process, impact its anisotropic mechanical behaviors and softening behaviors due to interacting mechanisms. Mechanical testing is conducted for EB-PBF Ti6Al4V parts made at different build orientations up to 600°C temperature. Microstructural analysis using electron backscattered diffraction (EBSD) is conducted on samples before and after mechanical testing to understand the interacting impact that temperature and mechanical load have on the activation of certain mechanisms. The vertical samples showed larger grain sizes, with an average of 6.6 µm, a lower average misorientation angle, and subsequently lower strength values than the other two horizontal samples. Among the three strong preferred grain orientations of the α phases, <1 1 2 ̅ 1> and <1 1 2 ̅ 0> were dominant in horizontally built samples, whereas the <0 0 0 1> was dominant in vertically built samples. Thus, strong microstructural variation, as observed among different EB-PBF Ti6Al4V samples, mainly resulted in anisotropic behaviors. Furthermore, alpha grain showed a significant increase in average grain size for all samples with the increasing test temperature, especially from 400°C to 600°C, indicating grain growth and coarsening as potential softening mechanisms along with temperature-induced possible dislocation motion. The severity of internal and external defects on fatigue strength has been evaluated non-destructively using quantitative methods, i.e., Murakami’s square root of area parameter model and Basquin’s model, and the external surface defects were rendered to be more critical as potential crack initiation sites.
ContributorsMian, Md Jamal (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Shuaib, Abdelrahman (Committee member) / Mobasher, Barzin (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2022