Nanoholes on the basal plane of graphene can provide abundant mass transport channels and chemically active sites for enhancing the electrochemical performance, making this material highly promising in applications such as supercapacitors, batteries, desalination, molecule or ion detection, and biosensing. However, the current solution-based chemical etching processes to manufacture these nanoholes commonly suffer from low process efficiency, scalability, and controllability, because conventional bulk heating cannot promote the etching reactions. Herein, a novel manufacturing method is developed to address this issue using microwave irradiation to facilitate and control the chemical etching of graphene. In this process, microwave irradiation induces selective heating of graphene in the aqueous solution due to an energy dissipation mechanism coupled with the dielectric and conduction losses. This strategy brings a remarkable reduction of processing time from hour-scale to minute-scale compared to the conventional approaches. By further incorporating microwave pretreatment, it is possible to control the population and area percentage of the in-plane nanoholes on graphene. Theoretical calculations reveal that the nanoholes emerge and grow by a repeating reduction–oxidation process occurring at the edge-sites atoms around vacancy defects on the graphene basal plane. The reduced holey graphene oxide sheets obtained via the microwave-assisted chemical etching method exhibit great potentials in supercapacitors and electrocatalysis. Excellent capacitive performance and electrocatalytic activity are observed in electrochemical measurements. The improvements against the non-holey counterpart are attributed to the enhanced kinetics involving ion diffusion and heterogeneous charge transfer.

A skin lesion is a part of the skin which has an uncommon growth or appearance in comparison with the skin around it. While most are harmless, some can be warnings of skin cancer. Melanoma is the deadliest form of skin cancer and its early detection in dermoscopic images is crucial and results in increase in the survival rate. The clinical ABCD (asymmetry, border irregularity, color variation and diameter greater than 6mm) rule is one of the most widely used method for early melanoma recognition. However, accurate classification of melanoma is still extremely difficult due to following reasons(not limited to): great visual resemblance between melanoma and non-melanoma skin lesions, less contrast difference between skin and the lesions etc. There is an ever-growing need of correct and reliable detection of skin cancers. Advances in the field of deep learning deems it perfect for the task of automatic detection and is very useful to pathologists as they aid them in terms of efficiency and accuracy. In this thesis various state of the art deep learning frameworks are used. An analysis of their parameters is done, innovative techniques are implemented to address the challenges faced in the tasks, segmentation, and classification in skin lesions.• Segmentation is task of dividing out regions of interest. This is used to only keep the ROI and separate it from its background. • Classification is the task of assigning the image a class, i.e., Melanoma(Cancer) and Nevus(Not Cancer). A pre-trained model is used and fine-tuned as per the needs of the given problem statement/dataset. Experimental results show promise as the implemented techniques reduce the false negatives rate, i.e., neural network is less likely to misclassify a melanoma.

How to effectively and accurately describe, character and quantify the microstructure of the heterogeneous material and its 4D evolution process with time suffered from external stimuli or provocations is very difficult and challenging, but it’s significant and crucial for its performance prediction, processing, optimization and design. The goal of this research is to overcome these challenges by developing a series of novel hierarchical statistical microstructure descriptors called “n-point polytope functions” which is as known as Pn functions to quantify heterogeneous material’s microstructure and creating Pn functions related quantification methods which are Omega Metric and Differential Omega Metric to analyze its 4D processing.In this dissertation, a series of powerful programming tools are used to demonstrate that Pn functions can be used up to n=8 for chaotically scattered images which can hardly be distinguished by our naked eyes in chapter 3 to find or compare the potential configuration feature of structure such as symmetry or polygon geometry relation between the different targets when target’s multi-modal imaging is provided. These n-point statistic results calculated from Pn functions for features of interest in the microstructure can efficiently decompose the structural hidden features into a set of “polytope basis” to provide a concise, explainable, expressive, universal and efficient quantifying manner. In Chapter 4, the Pn functions can also be incorporated into material reconstruction algorithms readily for fast virtualizing 3D microstructure regeneration and also allowing instant material property prediction via analytical structure-property mappings for material design. In Chapter 5, Omega Metric and Differential Omega Metric are further created and used to provide a time-dependent reduced-dimension metric to analyze the 4D evaluation processing instead of using Pn functions directly because these 2 simplified methods can provide undistorted results to be easily compared. The real case of vapor-deposition alloy films analysis are implemented in this dissertation to demonstrate that One can use these methods to predict or optimize the design for 4D evolution of heterogeneous material. The advantages of the all quantification methods in this dissertation can let us economically and efficiently quantify, design, predict the microstructure and 4D evolution of the heterogeneous material in various fields.

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.

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

Single-layer pentagonal materials have received limited attention compared with their counterparts with hexagonal structures. They are two-dimensional (2D) materials with pentagonal structures, that exhibit novel electronic, optical, or magnetic properties. There are 15 types of pentagonal tessellations which allow plenty of options for constructing 2D pentagonal lattices. Few of them have been explored theoretically or experimentally. Studying this new type of 2D materials with density functional theory (DFT) will inspire the discovery of new 2D materials and open up applications of these materials in electronic and magnetic devices.In this dissertation, DFT is applied to discover novel 2D materials with pentagonal structures. Firstly, I examine the possibility of forming a 2D nanosheet with the vertices of type 15 pentagons occupied by boron, silicon, phosphorous, sulfur, gallium, germanium or tin atoms. I obtain different rearranged structures such as a single-layer gallium sheet with triangular patterns. Then the exploration expands to other 14 types of pentagons, leading to the discoveries of carbon nanosheets with Cairo tessellation (type 2/4 pentagons) and other patterns. The resulting 2D structures exhibit diverse electrical properties. Then I reveal the hidden Cairo tessellations in the pyrite structures and discover a family of planar 2D materials (such as PtP2), with a chemical formula of AB2 and space group pa ̄3. The combination of DFT and geometries opens up a novel route for the discovery of new 2D materials. Following this path, a series of 2D pentagonal materials such as 2D CoS2 are revealed with promising electronic and magnetic applications. Specifically, the DFT calculations show that CoS2 is an antiferromagnetic semiconductor with a band gap of 2.24 eV, and a N ́eel temperature of about 20 K. In order to enhance the superexchange interactions between the ions in this binary compound, I explore the ternary 2D pentagonal material CoAsS, that lacks the inversion symmetry. I find out CoAsS exhibits a higher Curie temperature of 95 K and a sizable piezoelectricity (d11=-3.52 pm/V). In addition to CoAsS, 34 ternary 2D pentagonal materials are discovered, among which I focus on FeAsS, that is a semiconductor showing strong magnetocrystalline anisotropy and sizable Berry curvature. Its magnetocrystalline anisotropy energy is 440 μeV/Fe ion, higher than many other 2D magnets that have been found.
Overall, this work not only provides insights into the structure-property relationship of 2D pentagonal materials and opens up a new route of studying 2D materials by combining geometry and computational materials science, but also shows the potential applications of 2D pentagonal materials in electronic and magnetic devices.

The structural and electronic properties of compositionally complex semiconductors have long been of both theoretical interest and engineering importance. As a new class of materials with an intrinsic compositional complexity, medium entropy alloys (MEAs) are immensely studied mainly for their excellent mechanical properties. The electronic properties of MEAs, however, are less well investigated. In this thesis, various properties such as electronic, spin, and thermal properties of two three-dimensional (3D) and two two-dimensional (2D) compositionally complex semiconductors are demonstrated to have promising various applications in photovoltaic, thermoelectric, and spin quantum bits (qubits).3D semiconducting Si-Ge-Sn and C3BN alloys is firstly introduced. Density functional theory (DFT) calculations and Monte Carlo simulations show that the Si1/3Ge1/3Sn1/3 MEA exhibits a large local distortion effect yet no chemical short-range order. Single vacancies in this MEA can be stabilized by bond reformations while the alloy retains semiconducting. DFT and molecular dynamics calculations predict that increasing the compositional disorder in SiyGeySnx MEAs enhances their electrical conductivity while weakens the thermal conductivity at room temperature, making the SiyGeySnx MEAs promising functional materials for thermoelectric devices. Furthermore, the nitrogen-vacancy (NV) center analog in C3BN (NV-C3BN) is studied to explore its applications in quantum computers. This analog possesses similar properties to the NV center in diamond such as a highly localized spin density and strong hyperfine interactions, making C3BN suitable for hosting spin qubits. The analog also displays two zero-phonon-line energies corresponding to wavelengths close to the ideal telecommunication band width, useful for quantum communications.
2D semiconducting transition metal chalcogenides (TMCs) and PtPN are also investigated. The quaternary compositionally complex TMCs show tunable properties such as in-plane lattice constants, band gaps, and band alignment, using a high through-put workflow from DFT calculations in conjunction with the virtual crystal approximation. A novel 2D semiconductor PtPN of direct bandgap is also predicted, based on pentagonal tessellation.
The work in the thesis offers guidance to the experimental realization of these novel semiconductors, which serve as valuable prototypes of other compositionally complex systems from other elements.

Metal-organic frameworks have made a feature in the cutting-edge technology with a wide variety of applications because they are the new material candidate as adsorbent or membrane with high surface area, various pore sizes, and highly tunable framework functionality properties. The emergence of two-dimensional (2D) metal-organic frameworks has surged an outburst of intense research to understand the feasible synthesis and exciting material properties of these class of materials. Despite their potential, studies to date show that it is extremely challenging to synthesize and manufacture 2D MOF at large scales with ultimate control over crystallinity and thickness.

The field of research to date has produced various synthesis routes which can further be used to design 2D materials with a range of organic ligands and metal linkers. This thesis seeks to extend these design rules to demonstrate the competitive growth of two- dimensional (2D) metal-organic frameworks(MOF) and their alloys to predict which ligands and metals can be combined, study the intercalation of Bromine in these frameworks and their alloys which leads to the discovery of reduced band gap in the layered MOF alloy.

In this study it has been shown that the key factor in achieving layered 2D MOFs and it relies on the use of carefully engineered ligands to terminate the out-of-plane sites on metal clusters thereby eliminating strong interlayer hydrogen bond formation.

The major contribution of pyridine is to replace interlayer hydrogen bonding or other weak chemical bonds. Overall results establish an entirely new synthesis method for producing highly crystalline and scalable 2D MOFs and their alloys. Bromine intercalation merits future studies on band gap engineering in these layered materials.

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.

Corrosion fatigue has been of prime concern in railways, aerospace, construction industries and so on. Even in the case of many medical equipment, corrosion fatigue is considered to be a major challenge. The fact that even high strength materials have lower resistance to corrosion fatigue makes it an interesting area for research. The analysis of propagation of fatigue crack growth under environmental interaction and the life prediction is significant to reduce the maintenance costs and assure structural integrity. Without proper investigation of the crack extension under corrosion fatigue, the scenario can lead to catastrophic disasters due to premature failure of a structure. An attempt has been made in this study to predict the corrosion fatigue crack growth with reasonable accuracy. Models that have been developed so far predict the crack propagation for constant amplitude loading (CAL). However, most of the industrial applications encounter random loading. Hence there is a need to develop models based on time scale. An existing time scale model that can predict the fatigue crack growth for constant and variable amplitude loading (VAL) in the Paris region is initially modified to extend the prediction to near threshold and unstable crack growth region. Extensive data collection was carried out to calibrate the model for corrosion fatigue crack growth (CFCG) based on the experimental data. The time scale model is improved to incorporate the effect of corrosive environments such as NaCl and dry hydrogen in the fatigue crack growth (FCG) by investigation of the trend in change of the crack growth. The time scale model gives the advantage of coupling the time phenomenon stress corrosion cracking which is suggested as a future work in this paper.