Matching Items (7)

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A Computational Investigation of Theoretical GeSn Alloys

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In materials science, developing GeSn alloys is major current research interest concerning the production of efficient Group-IV photonics. These alloys are particularly interesting because the development of next-generation semiconductors for

In materials science, developing GeSn alloys is major current research interest concerning the production of efficient Group-IV photonics. These alloys are particularly interesting because the development of next-generation semiconductors for ultrafast (terahertz) optoelectronic communication devices could be accomplished through integrating these novel alloys with industry-standard silicon technology. Unfortunately, incorporating a maximal amount of Sn into a Ge lattice has been difficult to achieve experimentally. At ambient conditions, pure Ge and Sn adopt cubic (α) and tetragonal (β) structures, respectively, however, to date the relative stability and structure of α and β phase GeSn alloys versus percent composition Sn has not been thoroughly studied. In this research project, computational tools were used to perform state-of-the-art predictive quantum simulations to study the structural, bonding and energetic trends in GeSn alloys in detail over a range of experimentally accessible compositions. Since recent X-Ray and vibrational studies have raised some controversy about the nanostructure of GeSn alloys, the investigation was conducted with ordered, random and clustered alloy models.
By means of optimized geometry analysis, pure Ge and Sn were found to adopt the alpha and beta structures, respectively, as observed experimentally. For all theoretical alloys, the corresponding αphase structure was found to have the lowest energy, for Sn percent compositions up to 90%. However at 50% Sn, the correspondingβ alloy energies are predicted to be only ~70 meV higher. The formation energy of α-phase alloys was found to be positive for all compositions, whereas only two beta formation energies were negative. Bond length distributions were analyzed and dependence on Sn incorporation was found, perhaps surprisingly, not to be directly correlated with cell volume. It is anticipated that the data collected in this project may help to elucidate observed complex vibrational properties in these systems.

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Date Created
  • 2019-05

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Prediction of Linear Epitopes by a Machine Learning Algorithm Developed Using the Immunosignature Technology

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Elucidation of Antigen-Antibody (Ag-Ab) interactions is critical to the understanding of humoral immune responses to pathogenic infection. B cells are crucial components of the immune system that generate highly specific

Elucidation of Antigen-Antibody (Ag-Ab) interactions is critical to the understanding of humoral immune responses to pathogenic infection. B cells are crucial components of the immune system that generate highly specific antibodies, such as IgG, towards epitopes on antigens. Serum IgG molecules carry specific molecular recognition information concerning the antigens that initiated their production. If one could read it, this information can be used to predict B cell epitopes on target antigens in order to design effective epitope driven vaccines, therapies and serological assays. Immunosignature technology captures the specific information content of serum IgG from infected and uninfected individuals on high density microarrays containing ~105 nearly random peptide sequences. Although the sequences of the peptides are chosen to evenly cover amino acid sequence space, the pattern of serum IgG binding to the array contains a consistent signature associated with each specific disease (e.g., Valley fever, influenza) among many individuals. Here, the disease specific but agnostic behavior of the technology has been explored by profiling molecular recognition information for five pathogens causing life threatening infectious diseases (e.g. DENV, WNV, HCV, HBV, and T.cruzi). This was done by models developed using a machine learning algorithm to model the sequence dependence of the humoral immune responses as measured by the peptide arrays. It was shown that the disease specific binding information could be accurately related to the peptide sequences used on the array by the machine learning (ML) models. Importantly, it was demonstrated that the ML models could identify or predict known linear epitopes on antigens of the four viruses. Moreover, the models identified potential novel linear epitopes on antigens of the four viruses (each has 4-10 proteins in the proteome) and of T.cruzi (a eukaryotic parasite which has over 12,000 proteins in its proteome). Finally, the predicted epitopes were tested in serum IgG binding assays such as ELISAs. Unfortunately, the assay results were inconsistent due to problems with peptide/surface interactions. In a separate study for the development of antibody recruiting molecules (ARMs) to combat microbial infections, 10 peptides from the high density peptide arrays were tested in IgG binding assays using sera of healthy individuals to find a set of antibody binding termini (ABT, a ligand that binds to a variable region of the IgG). It was concluded that one peptide (peptide 7) may be used as a potential ABT. Overall, these findings demonstrate the applications of the immunosignature technology ranging from developing tools to predict linear epitopes on pathogens of small to large proteomes to the identification of an ABT for ARMs.

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Date Created
  • 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.

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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Agent

Created

Date Created
  • 2020

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Multiscale Modeling of Structure-Property Relationships in Polymers with Heterogenous Structure

Description

The exceptional mechanical properties of polymers with heterogeneous structure, such as the high toughness of polyethylene and the excellent blast-protection capability of polyurea, are strongly related to their morphology and

The exceptional mechanical properties of polymers with heterogeneous structure, such as the high toughness of polyethylene and the excellent blast-protection capability of polyurea, are strongly related to their morphology and nanoscale structure. Different polymer microstructures, such as semicrystalline morphology and segregated nanophases, lead to coordinated molecular motions during deformation in order to preserve compatibility between the different material phases. To study molecular relaxation in polyethylene, a coarse-grained model of polyethylene was calibrated to match the local structural variable distributions sampled from supercooled atomistic melts. The coarse-grained model accurately reproduces structural properties, e.g., the local structure of both the amorphous and crystalline phases, and thermal properties, e.g., glass transition and melt temperatures, and dynamic properties: including the vastly different relaxation time scales of the amorphous and crystalline phases. A hybrid Monte Carlo routine was developed to generate realistic semicrystalline configurations of polyethylene. The generated systems accurately predict the activation energy of the alpha relaxation process within the crystalline phase. Furthermore, the models show that connectivity to long chain segments in the amorphous phase increases the energy barrier for chain slip within crystalline phase. This prediction can guide the development of tougher semicrystalline polymers by providing a fundamental understanding of how nanoscale morphology contributes to chain mobility. In a different study, the macroscopic shock response of polyurea, a phase segregated copolymer, was analyzed using density functional theory (DFT) molecular dynamics (MD) simulations and classical MD simulations. The two models predict the shock response consistently up to shock pressures of 15 GPa, beyond which the DFT-based simulations predict a softer response. From the DFT simulations, an analysis of bond scission was performed as a first step in developing a more fundamental understanding of how shock induced material transformations effect the shock response and pressure dependent strength of polyurea subjected to extreme shocks.

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Agent

Created

Date Created
  • 2017

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Theoretical characterization of zinc phthalocyanine and porphyrin analogs for organic solar cell absorption

Description

The absorption spectra of metal-centered phthalocyanines (MPc's) have been investigated since the early 1960's. With improved experimental techniques to characterize this class of molecules the band assignments have advanced. The

The absorption spectra of metal-centered phthalocyanines (MPc's) have been investigated since the early 1960's. With improved experimental techniques to characterize this class of molecules the band assignments have advanced. The characterization remains difficult with historic disagreements. A new push for characterization came with a wave of interest in using these molecules for absorption/donor molecules in organic photovoltaics. The use of zinc phthalocyanine (ZnPc) became of particular interest, in addition to novel research being done for azaporphyrin analogs of ZnPc.

A theoretical approach is taken to research the excited states of these molecules using time-dependent density functional theory (TDDFT). Most theoretical results for the first excited state in ZnPc are in only limited agreement with experiment (errors near 0.1 eV or higher). This research investigates ZnPc and 10 additional porphyrin analogs. Excited-state properties are predicted for 8 of these molecules using ab initio computational methods and symmetry breaking for accurate time- dependent self-consistent optimization. Franck-Condon analysis is used to predict the Q-band absorption spectra for all 8 of these molecules. This is the first time that Franck-Condon analysis has been reported in absolute units for any of these molecules. The first excited-state energy for ZnPc is found to be the closest to experiment thus far using a range-separated meta-GGA hybrid functional. The theoretical results are used to find a trend in the novel design of new porphyrin analog molecules.

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Agent

Created

Date Created
  • 2014

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Design of protein-based hybrid catalysts for fuel production

Description

One of the greatest problems facing society today is the development of a

sustainable, carbon neutral energy source to curb the reliance on fossil fuel combustion as the primary source of

One of the greatest problems facing society today is the development of a

sustainable, carbon neutral energy source to curb the reliance on fossil fuel combustion as the primary source of energy. To overcome this challenge, research efforts have turned to biology for inspiration, as nature is adept at inter-converting low molecular weight precursors into complex molecules. A number of inorganic catalysts have been reported that mimic the active sites of energy-relevant enzymes such as hydrogenases and carbon monoxide dehydrogenase. However, these inorganic models fail to achieve the high activity of the enzymes, which function in aqueous systems, as they lack the critical secondary-shell interactions that enable the active site of enzymes to outperform their organometallic counterparts.

To address these challenges, my work utilizes bio-hybrid systems in which artificial proteins are used to modulate the properties of organometallic catalysts. This approach couples the diversity of organometallic function with the robust nature of protein biochemistry, aiming to utilize the protein scaffold to not only enhance rates of reaction, but also to control catalytic cycles and reaction outcomes. To this end, I have used chemical biology techniques to modify natural protein structures and augment the H2 producing ability of a cobalt-catalyst by a factor of five through simple mutagenesis. Concurrently I have designed and characterized a de novo peptide that incorporates various iron sulfur clusters at discrete distances from one another, facilitating electron transfer between the two. Finally, using computational methodologies I have engineered proteins to alter the specificity of a CO2 reduction reaction. The proteins systems developed herein allow for study of protein secondary-shell interactions during catalysis, and enable structure-function relationships to be built. The complete system will be interfaced with a solar fuel cell, accepting electrons from a photosensitized dye and storing energy in chemical bonds, such as H2 or methanol.

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Created

Date Created
  • 2016

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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,

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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Agent

Created

Date Created
  • 2020