A new class of electronic materials from food and foodstuff was developed to form a “toolkit” for edible electronics along with inorganic materials. Electrical components like resistors, capacitors and inductors were fabricated with such materials and tested. Applicable devices such as filters, microphones and pH sensors were built with edible materials. Among the applications, a wireless edible pH sensor was optimized in terms of form factor, fabrication process and cost. This dissertation discusses the material sciences of food industry, design and fabrication of electronics and biomedical engineering by demonstrating edible electronic materials, components and devices such as filters, microphones and pH sensors. pH sensors are optimized for two different generations of design and fabrication.

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.

Special thermal interface materials are required for connecting devices that operate at high temperatures up to 300°C. Because devices used in power electronics, such as GaN, SiC, and other wide bandgap semiconductors, can reach very high temperatures (beyond 250°C), a high melting point, and high thermal & electrical conductivity are required for the thermal interface material. Traditional solder materials for packaging cannot be used for these applications as they do not meet these requirements. Sintered nano-silver is a good candidate on account of its high thermal and electrical conductivity and very high melting point. The high temperature operating conditions of these devices lead to very high thermomechanical stresses that can adversely affect performance and also lead to failure. A number of these devices are mission critical and, therefore, there is a need for very high reliability. Thus, computational and nondestructive techniques and design methodology are needed to determine, characterize, and design the packages. Actual thermal cycling tests can be very expensive and time consuming. It is difficult to build test vehicles in the lab that are very close to the production level quality and therefore making comparisons or making predictions becomes a very difficult exercise. Virtual testing using a Finite Element Analysis (FEA) technique can serve as a good alternative. In this project, finite element analysis is carried out to help achieve this objective. A baseline linear FEA is performed to determine the nature and magnitude of stresses and strains that occur during the sintering step. A nonlinear coupled thermal and mechanical analysis is conducted for the sintering step to study the behavior more accurately and in greater detail. Damage and fatigue analysis are carried out for multiple thermal cycling conditions. The results are compared with the actual results from a prior study. A process flow chart outlining the FEA modeling process is developed as a template for the future work. A Coffin-Manson type relationship is developed to help determine the accelerated aging conditions and predict life for different service conditions.

Polyurea is a highly versatile material used in coatings and armor systems to protect against extreme conditions such as ballistic impact, cavitation erosion, and blast loading. However, the relationships between microstructurally-dependent deformation mechanisms and the mechanical properties of polyurea are not yet fully understood, especially under extreme conditions. In this work, multi-scale coarse-grained models are developed to probe molecular dynamics across the wide range of time and length scales that these fundamental deformation mechanisms operate. In the first of these models, a high-resolution coarse-grained model of polyurea is developed, where similar to united-atom models, hydrogen atoms are modeled implicitly. This model was trained using a modified iterative Boltzmann inversion method that dramatically reduces the number of iterations required. Coarse-grained simulations using this model demonstrate that multiblock systems evolve to form a more interconnected hard phase, compared to the more interrupted hard phase composed of distinct ribbon-shaped domains found in diblock systems. Next, a reactive coarse-grained model is developed to simulate the influence of the difference in time scales for step-growth polymerization and phase segregation in polyurea. Analysis of the simulated cured polyurea systems reveals that more rapid reaction rates produce a smaller diameter ligaments in the gyroidal hard phase as well as increased covalent bonding connecting the hard domain ligaments as evidenced by a larger fraction of bridging segments and larger mean radius of gyration of the copolymer chains. The effect that these processing-induced structural variations have on the mechanical properties of the polymer was tested by simulating uniaxial compression, which revealed that the higher degree of hard domain connectivity leads to a 20% increase in the flow stress. A hierarchical multiresolution framework is proposed to fully link coarse-grained molecular simulations across a broader range of time scales, in which a family of coarse-grained models are developed. The models are connected using an incremental reverse–mapping scheme allowing for long time scale dynamics simulated at a highly coarsened resolution to be passed all the way to an atomistic representation.

Though a single mode of energy transfer, optical radiation meaningfully interacts with its surrounding environment at over a wide range of physical length scales. For this reason, its reconstruction and measurement are of great importance in remote sensing, as these multi-scale interactions encode a great deal of information about distant objects, surfaces, and physical phenomena. For some remote sensing applications, obtaining a desired quantity of interest does not necessitate the explicit mapping of each point in object space to an image space with lenses or mirrors. Instead, only edge rays or physical boundaries of the sensing instrument are considered, while the spatial intensity distribution of optical energy received from a distant object informs its position, optical characteristics, or physical/chemical state.

Admittedly specialized, the principals and consequences of non-imaging optics are nevertheless applicable to heterogeneous semiconductor integration and automotive light detection and ranging (LiDAR), two important emerging technologies. Indeed, a review of relevant engineering literature finds two under-addressed remote sensing challenges. The semiconductor industry lacks an optical strain metrology with displacement resolution smaller than 100 nanometers capable of measuring strain fields between high-density interconnect lines. Meanwhile, little attention is paid to the per-meter sensing characteristics of scene-illuminating flash LiDAR in the context of automotive applications, despite the technology’s much lower cost. It is here that non-imaging optics offers intriguing instrument design and explanations of observed sensor performance at vastly different length scales.

In this thesis, an effective non-contact technique for mapping nanoscale mechanical strain fields and out-of-plane surface warping via laser diffraction is demonstrated, with application as a novel metrology for next-generation semiconductor packages. Additionally, object detection distance of low-cost automotive flash LiDAR, on the order of tens of meters, is understood though principals of optical energy transfer from the surface of a remote object to an extended multi-segment detector. Such information is of consequence when designing an automotive perception system to recognize various roadway objects in low-light scenarios.

Flexible conducting materials have been in the forefront of a rapidly transforming electronics industry, focusing on wearable devices for a variety of applications in recent times. Over the past few decades, bulky, rigid devices have been replaced with a surging demand for thin, flexible, light weight, ultra-portable yet high performance electronics. The interconnects available in the market today only satisfy a few of the desirable characteristics, making it necessary to compromise one feature over another. In this thesis, a method to prepare a thin, flexible, and stretchable inter-connect is presented with improved conductivity compared to previous achievements. It satisfies most mechanical and electrical conditions desired in the wearable electronics industry. The conducting composite, prepared with the widely available, low cost silicon-based organic polymer - polydimethylsiloxane (PDMS) and silver (Ag), is sandwiched between two cured PDMS layers. These protective layers improve the mechanical stability of the inter-connect. The structure can be stretched up to 120% of its original length which can further be enhanced to over 250% by cutting it into a serpentine shape without compromising its electrical stability. The inter-connect, around 500 µm thick, can be integrated into thin electronic packaging. The synthesis process of the composite material, along with its electrical and mechanical and properties are presented in detail. Testing methods and results for mechanical and electrical stability are also illustrated over extensive flexing and stretching cycles. The materials put into test, along with conductive silver (Ag) - polydimethylsiloxane (PDMS) composite in a sandwich structure, are copper foils, copper coated polyimide (PI) and aluminum (Al) coated polyethylene terephthalate (PET).

The focus of this dissertation is first on understanding the difficulties involved in constructing reduced order models of structures that exhibit a strong nonlinearity/strongly nonlinear events such as snap-through, buckling (local or global), mode switching, symmetry breaking. Next, based on this understanding, it is desired to modify/extend the current Nonlinear Reduced Order Modeling (NLROM) methodology, basis selection and/or identification methodology, to obtain reliable reduced order models of these structures. Focusing on these goals, the work carried out addressed more specifically the following issues:

i) optimization of the basis to capture at best the response in the smallest number of modes,

ii) improved identification of the reduced order model stiffness coefficients,

iii) detection of strongly nonlinear events using NLROM.

For the first issue, an approach was proposed to rotate a limited number of linear modes to become more dominant in the response of the structure. This step was achieved through a proper orthogonal decomposition of the projection on these linear modes of a series of representative nonlinear displacements. This rotation does not expand the modal space but renders that part of the basis more efficient, the identification of stiffness coefficients more reliable, and the selection of dual modes more compact. In fact, a separate approach was also proposed for an independent optimization of the duals. Regarding the second issue, two tuning approaches of the stiffness coefficients were proposed to improve the identification of a limited set of critical coefficients based on independent response data of the structure. Both approaches led to a significant improvement of the static prediction for the clamped-clamped curved beam model. Extensive validations of the NLROMs based on the above novel approaches was carried out by comparisons with full finite element response data. The third issue, the detection of nonlinear events, was finally addressed by building connections between the eigenvalues of the finite element software (Nastran here) and NLROM tangent stiffness matrices and the occurrence of the ‘events’ which is further extended to the assessment of the accuracy with which the NLROM captures the full finite element behavior after the event has occurred.

Soft materials are matters that can easily deform from their original shapes and structures under thermal or mechanical stresses, and they range across various groups of materials including liquids, foams, gels, colloids, polymers, and biological substances. Although soft materials already have numerous applications with each of their unique characteristics, integrating materials to achieve complementary functionalities is still a growing need for designing advanced applications of complex requirements. This dissertation explores a unique approach of utilizing intermolecular interactions to accomplish not only the multifunctionality from combined materials but also their tailored properties designed for specific tasks. In this work, multifunctional soft materials are explored in two particular directions, ionic liquids (ILs)-based mixtures and interpenetrating polymer network (IPN).

First, ILs-based mixtures were studied to develop liquid electrolytes for molecular electronic transducers (MET) in planetary exploration. For space missions, it is challenging to operate any liquid electrolytes in an extremely low-temperature environment. By tuning intermolecular interactions, the results demonstrated a facile method that has successfully overcome the thermal and transport barriers of ILs-based mixtures at extremely low temperatures. Incorporation of both aqueous and organic solvents in ILs-based electrolyte systems with varying types of intermolecular interactions are investigated, respectively, to yield optimized material properties supporting not only MET sensors but also other electrochemical devices with iodide/triiodide redox couple targeting low temperatures.

Second, an environmentally responsive hydrogel was synthesized via interpenetrating two crosslinked polymer networks. The intermolecular interactions facilitated by such an IPN structure enables not only an upper critical solution temperature (UCST) transition but also a mechanical enhancement of the hydrogel. The incorporation of functional units validates a positive swelling response to visible light and also further improves the mechanical properties. This studied IPN system can serve as a promising route in developing “smart” hydrogels utilizing visible light as a simple, inexpensive, and remotely controllable stimulus.

Over two directions across from ILs to polymeric networks, this work demonstrates an effective strategy of utilizing intermolecular interactions to not only develop multifunctional soft materials for advanced applications but also discover new properties beyond their original boundaries.

Structural assemblies for military applications must be guaranteed to withstand normal operating environments. Traditionally, experimental testing is performed on a prototype of the object to understand how it will behave under potential failure conditions. However, this process can be time-consuming and expensive, and it is often desired to have preliminary information to guide the design of the components. Consequently, a finite element analysis (FEA) can be performed using computational tools to approximate the failure behavior of the object before experiments are performed. This can provide information for a faster preliminary evaluation of the design, which very useful when implementing new technologies in the defense sector.
Currently, a new design for collapsible, lightweight ammunition package (LAP) has been proposed for military applications. The design employs rubber gaskets which enable the LAP to fold when it is empty, in addition to carbon fiber walls which decrease weight while increasing strength. To evaluate the new design, it is desired to perform a finite element analysis to simulate the behavior of the can under various drop impact conditions. Because the design includes complex joinery, which is often difficult to model, the purpose of this thesis project is to determine the most effective methodology to define the physical system using finite elements for impact simulations, and consequently perform the desired analysis for the LAP.

This investigation focuses on the development of uncertainty modeling methods applicable to both the structural and thermal models of heated structures as part of an effort to enable the design under uncertainty of hypersonic vehicles. The maximum entropy-based nonparametric stochastic modeling approach is used within the context of coupled structural-thermal Reduced Order Models (ROMs). Not only does this strategy allow for a computationally efficient generation of samples of the structural and thermal responses but the maximum entropy approach allows to introduce both aleatoric and some epistemic uncertainty into the system.

While the nonparametric approach has a long history of applications to structural models, the present investigation was the first one to consider it for the heat conduction problem. In this process, it was recognized that the nonparametric approach had to be modified to maintain the localization of the temperature near the heat source, which was successfully achieved.

The introduction of uncertainty in coupled structural-thermal ROMs of heated structures was addressed next. It was first recognized that the structural stiffness coefficients (linear, quadratic, and cubic) and the parameters quantifying the effects of the temperature distribution on the structural response can be regrouped into a matrix that is symmetric and positive definite. The nonparametric approach was then applied to this matrix allowing the assessment of the effects of uncertainty on the resulting temperature distributions and structural response.

The third part of this document focuses on introducing uncertainty using the Maximum Entropy Method at the level of finite element by randomizing elemental matrices, for instance, elemental stiffness, mass and conductance matrices. This approach brings some epistemic uncertainty not present in the parametric approach (e.g., by randomizing the elasticity tensor) while retaining more local character than the operation in ROM level.

The last part of this document focuses on the development of “reduced ROMs” (RROMs) which are reduced order models with small bases constructed in a data-driven process from a “full” ROM with a much larger basis. The development of the RROM methodology is motivated by the desire to optimally reduce the computational cost especially in multi-physics situations where a lack of prior understanding/knowledge of the solution typically leads to the selection of ROM bases that are excessively broad to ensure the necessary accuracy in representing the response. It is additionally emphasized that the ROM reduction process can be carried out adaptively, i.e., differently over different ranges of loading conditions.