Matching Items (56)

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Learning Scalable Dynamical Models for Predicting Atomic Structures of High-Entropy Alloys

Description

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

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.

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2021-05

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Synthesis and Characterization of Zinc Oxide Nanowire-Reinforced Shear Thickening Fluids for Enhanced Soft-Body Armor Applications

Description

The development of stab-resistant Kevlar armor has been an ongoing field of research
since the late 1990s, with the ultimate goal of improving the multi-threat capabilities of
traditional soft-body armor while significantly improving its protective efficiency - the amount
of

The development of stab-resistant Kevlar armor has been an ongoing field of research
since the late 1990s, with the ultimate goal of improving the multi-threat capabilities of
traditional soft-body armor while significantly improving its protective efficiency - the amount
of layers of armor material required to defeat threats. To create a novel, superior materials
system to reinforce Kevlar armor for the Norica Capstone project, this thesis set out to
synthesize, recover, and characterize zinc oxide nanowire colloids.

The materials synthesized were successfully utilized in the wider Capstone effort to
dramatically enhance the protective abilities of Kevlar, while the data obtained on the 14
hydrothermal synthesis attempts and numerous challenges at recovery provided critical
information on the synthesis parameters involved in the reliable, scalable mass production of the
nanomaterial additive. Additionally, recovery was unconventionally facilitated in the absence of
a vacuum filtration apparatus with nanoscale filters by intentionally inducing electrostatic
agglomeration of the nanowires during standard gravity filtration. The subsequent application of
these nanowires constituted a pioneering use in the production of nanowire-reinforced
STF-based Kevlar coatings, and support the future development and, ultimately, the
commercialization of lighter and more-protective soft armor systems.

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2019-05

Experimental and Theoretical Investigation of Tetrel Clathrates for Li-ion Batteries: Electrochemistry, Structure and Applications

Description

Current Li-ion battery technologies are limited by the low capacities of theelectrode materials and require developments to meet stringent performance demands for
future energy storage devices. Electrode materials that alloy with Li, such as Si, are one
of the most

Current Li-ion battery technologies are limited by the low capacities of theelectrode materials and require developments to meet stringent performance demands for
future energy storage devices. Electrode materials that alloy with Li, such as Si, are one
of the most promising alternatives for Li-ion battery anodes due to their high capacities.
Tetrel (Si, Ge, Sn) clathrates are a class of host-guest crystalline structures in which
Tetrel elements form a cage framework and encapsulate metal guest atoms. These
structures can form with defects such as framework/guest atom substitutions and
vacancies which result in a wide design space for tuning materials properties. The goal of
this work is to establish structure property relationships within the context of Li-ion
battery anode applications. The type I Ba 8 Al y Ge 46-y clathrates are investigated for their
electrochemical reactions with Li and show high capacities indicative of alloying
reactions. DFT calculations show that Li insertion into the framework vacancies is
favorable, but the migration barriers are too high for room temperature diffusion. Then,
guest free type I clathrates are investigated for their Li and Na migration barriers. The
results show that Li migration in the clathrate frameworks have low energy barriers (0.1-
0.4 eV) which suggest the possibility for room temperature diffusion. Then, the guest
free, type II Si clathrate (Na 1 Si 136 ) is synthesized and reversible Li insertion into the type
II Si clathrate structure is demonstrated. Based on the reasonable capacity (230 mAh/g),
low reaction voltage (0.30 V) and low volume expansion (0.21 %), the Si clathrate could
be a promising insertion anode for Li-ion batteries. Next, synchrotron X-ray
measurements and pair distribution function (PDF) analysis are used to investigate the
lithiation pathways of Ba 8 Ge 43 , Ba 8 Al 16 Ge 30 , Ba 8 Ga 15 Sn 31 and Na 0.3 Si 136 . The results
show that the Ba-clathrates undergo amorphous phase transformations which is distinct
from their elemental analogues (Ge, Sn) which feature crystalline lithiation pathways.
Based on the high capacities and solid-solution reaction mechanism, guest-filled
clathrates could be promising precursors to form alloying anodes with novel
electrochemical properties. Finally, several high temperature (300-550 °C)
electrochemical synthesis methods for Na-Si and Na-Ge clathrates are demonstrated in a
cell using a Na β’’-alumina solid electrolyte.

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Date Created
2021

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Computational Design of Compositionally Complex 3D and 2D Semiconductors

Description

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

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.

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2020

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Exploring Pentagonal Geometries for Discovering Novel Two-Dimensional Materials

Description

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

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.

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Date Created
2020

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Utilization of Computational Techniques in the Development of Functional Materials

Description

Functional materials can be characterized as materials that have tunable properties and are attractive solutions to the improvement and optimization of processes that require specific physiochemical characteristics. Through tailoring and altering these materials, their characteristics can be fine-tuned for specific

Functional materials can be characterized as materials that have tunable properties and are attractive solutions to the improvement and optimization of processes that require specific physiochemical characteristics. Through tailoring and altering these materials, their characteristics can be fine-tuned for specific applications. Computational modeling proves to be a crucial methodology in the design and optimization of such materials. This dissertation encompasses the utilization of molecular dynamics simulations and quantum calculations in two fields of functional materials: electrolytes and semiconductors. Molecular dynamics (MD) simulations were performed on ionic liquid-based electrolyte systems to identify molecular interactions, structural changes, and transport properties that are often reflected in experimental results. The simulations aid in the development process of the electrolyte systems in terms of concentrations of the constituents and can be invoked as a complementary or predictive tool to laboratory experiments. The theme of this study stretches further to include computational studies of the reactivity of atomic layer deposition (ALD) precursors. Selected aminosilane-based precursors were chosen to undergo density functional theory (DFT) calculations to determine surface reactivity and viability in an industrial setting. The calculations were expanded to include the testing of a semi-empirical tight binding program to predict growth per cycle and precursor reactivity with a high surface coverage model. Overall, the implementation of computational methodologies and techniques within these applications improves materials design and process efficiency while streamlining the development of new functional materials.

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Date Created
2021

Atomic-Scale Simulations of Si-Ge-Sn Alloys Using Deep-Learning-Based Interatomic Potentials

Description

Accurate knowledge and understanding of thermal conductivity is very important in awide variety of applications both at microscopic and macroscopic scales. Estimation,however varies widely with respect to scale and application. At a lattice level, calcu-lation of thermal conductivity

Accurate knowledge and understanding of thermal conductivity is very important in awide variety of applications both at microscopic and macroscopic scales. Estimation,however varies widely with respect to scale and application. At a lattice level, calcu-lation of thermal conductivity of any particular alloy require very heavy computationeven for a relatively small number of atoms. This thesis aims to run conventionalmolecular dynamic simulations for a particular supercell and then employ a machinelearning based approach and compare the two in hopes of developing a method togreatly reduce computational costs as well as increase the scale and time frame ofthese systems. Conventional simulations were run using interatomic potentials basedon density function theory-basedab initiocalculations. Then deep learning neuralnetwork based interatomic potentials were used run similar simulations to comparethe two approaches.

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Date Created
2021

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Food‐Based Edible Electronics

Description

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

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.

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Date Created
2021

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Synthesis of Monolayer Janus Transition Metal Dichalcogenides

Description

2D materials with reduced symmetry have gained great interest in the past decade due to the arising quantum properties introduced by the structural asymmetry. A particular example is called 2D Janus materials. Named after Roman god Janus with two faces,

2D materials with reduced symmetry have gained great interest in the past decade due to the arising quantum properties introduced by the structural asymmetry. A particular example is called 2D Janus materials. Named after Roman god Janus with two faces, Janus materials have different chemical compositions on the two sides of materials, leading to a structure with broken mirror symmetry. Electronegativity difference of the facial elements induces a built-in polarization field pointing out of the plane, which has driven a lot of theory predictions on Rashba splitting, high- temperature ferromagnetism, Skyrmion formation, and so on. Previously reported experimental synthesis of Janus 2D materials relies on high-temperature processing, which limits the crystallinity of as produced 2D layers. In this dissertation, I present a room temperature selective epitaxial atomic re- placement (SEAR) method to convert CVD-grown transition metal dichalcogenides (TMDs) into a Janus structure. Chemically reactive H2 plasma is used to selectively etch off the top layer of chalcogen atoms and the introduction of replacement chalco- gen source in-situ allows for the achievement of Janus structures in one step at room temperature. It is confirmed that the produced Janus monolayers possess high crys- tallinity and good excitonic properties. Moving forward, I show the fabrication of lateral and vertical heterostructures of Janus materials, which are predicted to show exotic properties because of the intrinsic polarization field. To efficiently screen other kinds of interesting Janus structures, a new plasma chamber is designed to allow in-situ optical measurement on the target monolayer during the SEAR process. Successful conversion is seen on mechanically exfoliated MoSe2 and WSe2, and insights into reaction kinetics are gain from Raman spectra evolution. Using the monitoring ability, Janus SNbSe is synthesized for the first time. It’s also demonstrated that the overall crystallinity of as produced Janus monolayer
SWSe and SMoSe are correlated with the source of monolayer TMDs.
Overall, the synthesis of the Janus monolayers using the described method paves the way to the production of highly crystalline Janus materials, and with the in-situ monitoring ability, a deeper understanding of the mechanism is reached. This will accelerate future exploration of other Janus materials synthesis, and confirmation and discovery of their exciting quantum properties.

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Date Created
2021

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Segmentation and Classification of Melanoma

Description

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

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.

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Date Created
2021