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Browsing by Author "Deshmukh, Sanket A."

Now showing 1 - 20 of 20
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    Accelerating Catalyst Discovery via Ab Initio Machine Learning
    Li, Zheng (Virginia Tech, 2019-12-03)
    In recent decades, machine learning techniques have received an explosion of interest in the domain of high-throughput materials discovery, which is largely attributed to the fastgrowing development of quantum-chemical methods and learning algorithms. Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory to facilitate the discovery of high-performance catalysts. The framework, which integrates the descriptor-based kinetic analysis, material fingerprinting and machine learning algorithms, can rapidly explore a broad range of materials space with enormous compositional and configurational degrees of freedom prior to the expensive quantum-chemical calculations and/or experimental testing. Importantly, advanced machine learning approaches (e.g., global sensitivity analysis, principal component analysis, and exploratory analysis) can be utilized to shed light on the underlying physical factors governing the catalytic activity on a diverse type of catalytic materials with different applications. Chapter 1 introduces some basic concepts and knowledge relating to the computational catalyst design. Chapter 2 and Chapter 3 demonstrate the methodology to construct the machine-learning models for bimetallic catalysts. In Chapter 4, the multi-functionality of the machine-learning models is illustrated to understand the metalloporphyrin's underlying structure-property relationships. In Chapter 5, an uncertainty-guided machine learning strategy is introduced to tackle the challenge of data deficiency for perovskite electrode materials design in the electrochemical water splitting cell.
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    Advancing computational materials design and model development using data-driven approaches
    Sose, Abhishek Tejrao (Virginia Tech, 2024-02-02)
    Molecular dynamics (MD) simulations find their applications in fundamental understanding of molecular level mechanisms of physical processes. This assists in tuning the key features affecting the development of the novel hybrid materials. A certain application demanding the need for a desired function can be cherished through the hybrids with a blend of new properties by a combination of pure materials. However, to run MD simulations, an accurate representation of the interatomic potentials i.e. force-fields (FF) models remain a crucial aspect. This thesis intricately explores the fusion of MD simulations, uncertainty quantification, and data-driven methodologies to accelerate the computational design of innovative materials and models across the following interconnected chapters. Beginning with the development of force fields for atomic-level systems and coarse-grained models for FCC metals, the study progresses into exploring the intricate interfacial interactions between 2D materials like graphene, MoS2, and water. Current state-of-the-art model development faces the challenge of high dimensional input parameters' model and unknown robustness of developed model. The utilization of advanced optimization techniques such as particle swarm optimization (PSO) integrated with MD enhances the accuracy and precision of FF models. Moreover, the bayesian uncertainty quantification (BUQ) assists FF model development researchers in estimating the robustness of the model. Furthermore, the complex structure and dynamics of water confined between and around sheets was unraveled using 3D Convolutional Neural Networks (3D-CNN). Specifically, through classification and regression models, water molecule ordering/disordering and atomic density profiles were accurately predicted, thereby elucidating nuanced interplays between sheet compositions and confined water molecules. To further the computational design of hybrid materials, this thesis delves into designing and investigating polymer composites with functionalized MOFs shedding light on crucial factors governing their compatibility and performance. Therefore, this report includes the study of structure and dynamics of functionalized MOF in the polymer matrix. Additionally, it investigates the biomedical potential of porous MOFs as drug delivery vehicles (DDVs). Often overlooked is the pivotal role of solvents (used in MOF synthesis or found in relevant body fluids) in the drug adsorption and release process. This report underscores the solvent's impact on drug adsorption within MOFs by comparing results in its presence and absence. Building on these findings, the study delves into the effects of MOF functionalization on tuning the drug adsorption and release process. It further explores how different physical and chemical properties influence drug adsorption within MOFs. Furthermore, the research explores the potential of functionalized MOFs for improved carbon capture, considering their application in energy-related contexts. By harnessing machine learning and deep learning, the thesis introduces innovative pathways for material property prediction and design, emphasizing the pivotal fusion of computational methodologies with data-driven approaches to advance molecular-level understanding and propel future material design endeavors.
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    Coarse-grained molecular dynamics integrated with convolutional neural network for comparing shapes of temperature sensitive bottlebrushes
    Joshi, Soumil Y.; Singh, Samrendra; Deshmukh, Sanket A. (Nature Portfolio, 2022-03-18)
    Quantification of shape changes in nature-inspired soft material architectures of stimuli-sensitive polymers is critical for controlling their properties but is challenging due to their softness and flexibility. Here, we have computationally designed uniquely shaped bottlebrushes of a thermosensitive polymer, poly(N-isopropylacrylamide) (PNIPAM), by controlling the length of side chains along the backbone. Coarse-grained molecular dynamics simulations of solvated bottlebrushes were performed below and above the lower critical solution temperature of PNIPAM. Conventional analyses (free volume, asphericity, etc.) show that lengths of side chains and their immediate environments dictate the compactness and bending in these architectures. We further developed 100 unique convolutional neural network models that captured molecular-level features and generated a statistically significant quantification of the similarity between different shapes. Thus, our study provides insights into the shapes of complex architectures as well as a general method to analyze them. The shapes presented here may inspire the synthesis of new bottlebrushes.
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    A Comparison between the Lower Critical Solution Temperature Behavior of Polymers and Biomacromolecules
    Xie, Yuxin; Li, Nan K.; Singh, Abhishek; Deshmukh, Sanket A.; Yingling, Yaroslava G. (MDPI, 2022-03-18)
    All-atom molecular dynamics (MD) simulations are employed to compare the lower critical solution temperature (LCST) behaviors of poly(N-isopropylacrylamide) (PNIPAM) and elastin-like polypeptides (ELPs) with the canonical Val-Pro-Gly-Val-Gly ((VPGVG)n) sequence over a range of temperatures from 280 K to 380 K. Our simulations suggest that the structure of proximal water dictates the conformation of both the (VPGVG)n ELPs and PNIPAM chains. Specifically, the LCST transition in ELPs can be attributed to a combination of thermal disruption of the network of the proximal water near both hydrophilic and hydrophobic groups in the backbone and side-chain of (VPGVG)n, resulting in a reduction in solvent accessible surface area (SASA). This is accompanied with an increase in the secondary structure above its LCST. In the case of PNIPAM, the LCST transition is a result of a combination of a reduction in the hydrophobic SASA primarily due to the contributions of isopropyl side-chain and less to the backbone and the formation of intra-chain hydrogen bonds between the amide groups on the side-chain above its LCST.
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    Developing Fast and Accurate Water Models for Atomistic Molecular Dynamics Simulations
    Xiong, Yeyue (Virginia Tech, 2021-09-15)
    Water models are of great importance for different fields of studies such as fluid mechanics, nano materials, and biomolecule simulations. In this dissertation, we focus on the water models applied in atomistic simulations, including those of biomolecules such as proteins and DNA. Despite water's simple structure and countless studies carried out over the decades, the best water models are still far from perfect. Water models are normally divided into two types--explicit model and implicit model. Here my research is mainly focused on explicit models. In explicit water models, fixed charge n-point models are most widely used in atomistic simulations, but have known accuracy drawbacks. Increasing the number of point charges, as well as adding electronic polarizability, are two common strategies for accuracy improvements. Both strategies come at considerable computational cost, which weighs heavily against modest possible accuracy improvements in practical simulations. With a careful comparison between the two strategies, results show that adding polarizability is a more favorable path to take. Optimal point charge approximation (OPCA) method is then applied along with a novel global optimization process, leading to a new polarizable water model OPC3-pol that can reproduce bulk liquid properties of water accurately and run at a speed comparable to 3- and 4-point non-polarizable water models. For practical use, OPC3-pol works with existing non-polarizable AMBER force fields for simulations of globular protein or DNA. In addition, for intrinsically disordered protein simulations, OPC3-pol fixes the over-compactness problem of the previous generation non-polarizable water models.
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    Development of Transferable Coarse-Grained Models of Amino Acids
    Conway, Olivia Kristine (Virginia Tech, 2019-10-01)
    There are twenty standard amino acids that are the structural units of biomolecules and biomaterials such as proteins and peptide amphiphiles (PAs). The focus of this study was to develop accurate transferable coarse-grained (CG) models of those amino acids. In CG models, several atoms are represented together as a single pseudo-atom or "bead," which can allow the modeling of processes like self-assembly of biomolecules and biomaterials through reduction of degrees of freedom and corresponding increased computational speed. A 2:1 to 4:1 mapping scheme, in which a CG bead is comprised of two to four heavy atoms, respectively, and associated hydrogens, has been employed to represent functional groups in the amino acids. The amino acid backbone atoms are modeled as two beads while the side chains are modeled with one to three beads, and each terminus is modeled as one bead. The bonded parameters for the CG models were obtained from bond, angle, and dihedral distributions from all-atom molecular dynamics (MD) simulations of dipeptides. Non-bonded parameters were optimized using the particle swarm optimization (PSO) method to reproduce experimental properties (heat of vaporization, surface tension, and density) of analogues of the side chains, termini, and backbone groups of the amino acids. These CG models were used to study the self-assembly pathways and mechanisms of the PA c16-AHL3K3-CO2H in the presence of explicit CG water.
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    Femtosecond Laser Pulse Driven Melting in Gold Nanorod Aqueous Colloidal Suspension: Identification of a Transition from Stretched to Exponential Kinetics
    Li, Yuelin; Lin, Xiao-Min; Wen, Haidan; Walko, Donald A.; Deshmukh, Sanket A.; Subbaraman, Ram; Sankaranarayanan, Subramanian K. R. S.; Gray, Stephen K.; Ho, Phay (Nature, 2015-01-30)
    Many potential industrial, medical, and environmental applications of metal nanorods rely on the physics and resultant kinetics and dynamics of the interaction of these particles with light. We report a surprising kinetics transition in the global melting of femtosecond laser-driven gold nanorod aqueous colloidal suspension. At low laser intensity, the melting exhibits a stretched exponential kinetics, which abruptly transforms into a compressed exponential kinetics when the laser intensity is raised. It is found the relative formation and reduction rate of intermediate shapes play a key role in the transition. Supported by both molecular dynamics simulations and a kinetic model, the behavior is traced back to the persistent heterogeneous nature of the shape dependence of the energy uptake, dissipation and melting of individual nanoparticles. These results could have significant implications for various applications such as water purification and electrolytes for energy storage that involve heat transport between metal nanorod ensembles and surrounding solvents.
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    Linking Thermophysical Transitions and Rheological Properties to Polymer Foaming Outcomes with Carbon Dioxide
    Sarver, Joseph Arron (Virginia Tech, 2022-06-01)
    Interest in high-pressure and supercritical fluids as physical blowing agents for polymer foaming is driving a renewed need for the fundamental understanding of polymer thermophysical and rheological properties in the presence of dense fluids. In particular, carbon dioxide is often studied as a physical blowing agent because of its readily accessible critical point (31.1 ℃ and 73.8 MPa) and relatively high solubility levels in polymer materials. The basic principle involved is to dissolve the supercritical fluid in the polymer at high pressures and then impose a pressure reduction to initiate bubble nucleation and growth. The outcomes depend on the thermophysical and rheological properties of the polymer under the prevailing process conditions in the fluid. The present dissertation explores the high-pressure characterization and foaming of thermoplastic elastomers and seeks to link polymer thermophysical and rheological properties to polymer foaming outcomes with carbon dioxide as a physical blowing agent. A major focus of this dissertation has been the development of novel high-pressure characterization techniques to understand polymer behavior at high pressure. These techniques include (1) high-pressure torsional braid analysis (HP-TBA), (2) magnetic suspension balance (MSB), and (3) unique high-pressure batch foaming cells. HP-TBA allows for the assessment of the depression in thermal transitions (Tg and/or Tm/Tc) and the changes in rheological properties like modulus or rigidity of polymer systems exposed to carbon dioxide. MSB provides for the assessment of the amount of carbon dioxide that sorbs into a polymer material at a given temperature and pressure. Unique confined foaming strategies have been developed to translate information learned from batch-scale experimentation to practical industrial applications. The polymer systems of interest are thermoplastic elastomers including poly(ethylene-co-vinyl acetate) (EVA) and poly(ethylene-co-vinyl acetate-co-carbon monoxide) (EVACO). These materials find use in numerous commercial applications including adhesives, compatibilizers, and foams. Their foams are noted to undergo significant degrees of expansion followed by unfavorable post-foaming collapse. In the first part of this study, the foaming of neat EVACO and EVA with carbon dioxide was explored. The blending of these polymers was then explored to regulate foam expansions and control the pore morphology development. The foamability of the polymers and their blends was explored under both isothermal and gradient conditions to assess the temperature effects on foaming outcomes at a given pressure. In the second part of this study foaming of EVACO was explored in relationship to the depressed thermal transitions of the polymer in the presence of carbon dioxide. Accompanying the depressed melting transition is a sharp reduction in the modulus or rigidity of the polymer material. By studying foaming outcomes near the melting transition rational windows for foaming exploration can be evaluated to generate foams that display more favorable bulk foam densities and minimal foam collapse. This part demonstrates that linking foaming conditions to the relative rigidity or melt strength of EVACO in carbon dioxide allows for the determination of the lower pressure where foaming will occur and the upper pressure beyond which further foam density reductions are not significant. The third part of this study explores the foaming of EVACO with carbon dioxide under batch, confined foaming conditions where the foam expansion is restricted in order to again control the foaming outcomes and prevent foam collapse. A practical question is the scale-up of batch foaming processes which likely will be conducted with injection molding or extrusion type processes. Studying batch foaming in confinement allows for a better understanding of the factors that may affect foam development that may be more readily translated to industrial practice. The fourth part of this study examines the role of crystallinity and block copolymer composition in altering the polymer behavior in carbon dioxide. Several EVACO polymers with varying ethylene, vinyl acetate, and carbon monoxide content have been explored to study how block copolymer composition affects the thermophysical and rheological properties along with the sorption of carbon dioxide at high pressure.
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    Machine Learning and Multivariate Statistics for Optimizing Bioprocessing and Polyolefin Manufacturing
    Agarwal, Aman (Virginia Tech, 2022-01-07)
    Chemical engineers have routinely used computational tools for modeling, optimizing, and debottlenecking chemical processes. Because of the advances in computational science over the past decade, multivariate statistics and machine learning have become an integral part of the computerization of chemical processes. In this research, we look into using multivariate statistics, machine learning tools, and their combinations through a series of case studies including a case with a successful industrial deployment of machine learning models for fermentation. We use both commercially-available software tools, Aspen ProMV and Python, to demonstrate the feasibility of the computational tools. This work demonstrates a novel application of ensemble-based machine learning methods in bioprocessing, particularly for the prediction of different fermenter types in a fermentation process (to allow for successful data integration) and the prediction of the onset of foaming. We apply two ensemble frameworks, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to build classification and regression models. Excessive foaming can interfere with the mixing of reactants and lead to problems, such as decreasing effective reactor volume, microbial contamination, product loss, and increased reaction time. Physical modeling of foaming is an arduous process as it requires estimation of foam height, which is dynamic in nature and varies for different processes. In addition to foaming prediction, we extend our work to control and prevent foaming by allowing data-driven ad hoc addition of antifoam using exhaust differential pressure as an indicator of foaming. We use large-scale real fermentation data for six different types of sporulating microorganisms to predict foaming over multiple strains of microorganisms and build exploratory time-series driven antifoam profiles for four different fermenter types. In order to successfully predict the antifoam addition from the large-scale multivariate dataset (about half a million instances for 163 batches), we use TPOT (Tree-based Pipeline Optimization Tool), an automated genetic programming algorithm, to find the best pipeline from 600 other pipelines. Our antifoam profiles are able to decrease hourly volume retention by over 53% for a specific fermenter. A decrease in hourly volume retention leads to an increase in fermentation product yield. We also study two different cases associated with the manufacturing of polyolefins, particularly LDPE (low-density polyethylene) and HDPE (high-density polyethylene). Through these cases, we showcase the usage of machine learning and multivariate statistical tools to improve process understanding and enhance the predictive capability for process optimization. By using indirect measurements such as temperature profiles, we demonstrate the viability of such measures in the prediction of polyolefin quality parameters, anomaly detection, and statistical monitoring and control of the chemical processes associated with a LDPE plant. We use dimensionality reduction, visualization tools, and regression analysis to achieve our goals. Using advanced analytical tools and a combination of algorithms such as PCA (Principal Component Analysis), PLS (Partial Least Squares), Random Forest, etc., we identify predictive models that can be used to create inferential schemes. Soft-sensors are widely used for on-line monitoring and real-time prediction of process variables. In one of our cases, we use advanced machine learning algorithms to predict the polymer melt index, which is crucial in determining the product quality of polymers. We use real industrial data from one of the leading chemical engineering companies in the Asia-Pacific region to build a predictive model for a HDPE plant. Lastly, we show an end-to-end workflow for deep learning on both industrial and simulated polyolefin datasets. Thus, using these five cases, we explore the usage of advanced machine learning and multivariate statistical techniques in the optimization of chemical and biochemical processes. The recent advances in computational hardware allow engineers to design such data-driven models, which enhances their capacity to effectively and efficiently monitor and control a process. We showcase that even non-expert chemical engineers can implement such machine learning algorithms with ease using open-source or commercially available software tools.
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    Modeling the Nucleation and Growth of Colloidal Nanoparticles
    Mozaffari, Saeed (Virginia Tech, 2020-02-05)
    Controlling the size and size distribution of colloidal nanoparticles have gained extraordinary attention as their physical and chemical properties are strongly affected by size. Ligands are widely used to control the size and size distribution of nanoparticles; however, their exact roles in controlling the nanoparticle size distribution and the way they affect the nucleation and growth kinetics are poorly understood. Therefore, understanding the nucleation and growth mechanisms and developing theoretical/modeling framework will pave the way towards controlled synthesis of colloidal nanoparticles with desired sizes and polydispersity. This dissertation focuses on identifying the possible roles of ligands and size on the kinetics of nanoparticle formation and growth using in-situ characterization tools such as small-angle X-ray scattering (SAXS) and kinetic modeling. The presented work further focuses on developing kinetic models to capture the main nucleation and growth reactions and examines how ligand-metal interactions could potentially alter the rate of nucleation and growth rates, and consequently the nanoparticle size distribution. Additionally, this work highlights the importance of using multi-observables including the concentration of nanoparticles, size and/or precursor consumption, and polydispersity to differentiate between different nucleation and growth models and extract accurate information on the rates of nanoparticle nucleation and growth. Specifically, during the formation and growth of colloidal nanoparticles, complex reactions are occurring and as such nucleation and growth can take place through various reaction pathways. Therefore, sensitivity analysis was applied to effectively compare different nucleation and growth models and identify the most important reactions and obtain a reduced model (e.g. a minimalistic model) required for efficient data analysis. In the following chapters, a more sophisticated modeling approach is presented (population balance model) capable of capturing the average-properties of nanoparticle size distribution. PBM allows us to predict the growth rate of nanoparticles of different sizes, the ligand surface coverage for each individual size, and the parameters involved in altering the size distribution. Additionally, thermodynamic calculations of nanoparticle growth and ligand-metal binding as a function of size and ligand surface coverage were conducted to further shed light on the kinetics of nanoparticle formation and growth. The combination of kinetic modeling, in-situ SAXS and thermodynamic calculations can significantly advance the understanding of nucleation and growth mechanisms and guide toward controlling size and polydispersity.
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    Modelling drug adsorption in metal-organic frameworks: the role of solvent
    Sose, Abhishek T.; Cornell, Hannah D.; Gibbons, Bradley J.; Burris, Ashley A.; Morris, Amanda J.; Deshmukh, Sanket A. (2021-05-20)
    Solvent plays a key role in biological functions, catalysis, and drug delivery. Metal-organic frameworks (MOFs) due to their tunable functionalities, porosities and surface areas have been recently used as drug delivery vehicles. To investigate the effect of solvent on drug adsorption in MOFs, we have performed integrated computational and experimental studies in selected biocompatible MOFs, specifically, UiO-AZB, HKUST-1 (or CuBTC) and NH2-MIL-53(Al). The adsorption of three drugs, namely, 5-fluorouracil (5-FU), ibuprofen (IBU), and hydroxyurea (HU) were performed in the presence and absence of the ethanol. Our computational predictions, at 1 atmospheric pressure, showed a reasonable agreement with experimental studies performed in the presence of ethanol. We find that in the presence of ethanol the drug molecules were adsorbed at the interface of solvent and MOFs. Moreover, the computationally calculated adsorption isotherms suggested that the drug adsorption was driven by electrostatic interactions at lower pressures (<10(-4) Pa). Our computational predictions in the absence of ethanol were higher compared to those in the presence of ethanol. The MOF-adsorbate interaction (U-HA) energy decreased with decrease in the size of a drug molecule in all three MOFs at all simulated pressures. At high pressure the interaction energy increases with increase in the MOFs pore size as the number of molecules adsorbed increases. Thus, our research shows the important role played by solvent in drug adsorption and suggests that it is critical to consider solvent while performing computational studies.
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    Molecular Dynamic Simulation of Polysiloxane
    Chaney, Harrison Matthew (Virginia Tech, 2023-04-10)
    Polymer Derived Ceramics are a promising class of Materials that allow for higher levels of tunability and shaping that traditional sintering methods do not allow for. Polysiloxanes are commonly used as a precursor for these types of material because of their highly tunable microstructures by adjusting the side groups on the initial polymer. These Polymers are generally cross linked and pyrolyzed in inert atmospheres to form the final polymer. The microstructures of Polymer Derived Ceramics is complex and hard to observe due to the size of each microstructure region and the proximity in the periodic table that the elements present have. The process of forming phases such as Graphitic Carbon, Amorphous Carbon, Silicon Carbide. Silicon Oxide, and SiliconOxycarbide are not well understood. Simulation provides a route to understanding the phenomenon behind these phase formations. Specifically, Molecular dynamics simulation paired with the Reaxff forcefield provides a framework to simulate the complex processes involved in pyrolysis such as chemical reactions and a combination of thermodynamic and kinetic interactions. This Thesis examines firstly the size effect that a system can have on phase separation and the change in composition. Showing that size plays a major role in how the system develops and limits the occurrence of specific reactions. Secondly, this thesis shows that using polymer precursors with different initial polymer components leads to vastly different microstructures and yield. This provides insights into how the transition from polymer to ceramic takes place on a molecular level.
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    Molecular Structure and Dynamics of Novel Polymer Electrolytes Featuring Coulombic Liquids
    Yu, Zhou (Virginia Tech, 2019-01-25)
    Polymer electrolytes are indispensable in numerous electrochemical systems. Existing polymer electrolytes rarely meet all technical demands by their applications (e.g., high ionic conductivity and good mechanical strength), and new types of polymer electrolytes continue to be developed. In this dissertation, the molecular structure and dynamics of three emerging types of polymer electrolytes featuring Coulombic liquids, i.e., polymerized ionic liquids (polyILs), nanoscale ionic materials (NIMs), and polymeric ion gels, were investigated using molecular dynamics (MD) simulations to help guide their rational design. First, the molecular structure and dynamics of a prototypical polyILs, i.e., poly(1-butyl-3-vinylimidazolium hexafluorophosphate), supported on neutral and charged quartz substrates were investigated. It was found that the structure of the interfacial polyILs is affected by the surface charge on the substrate and deviates greatly from that in bulk. The mobile anions at the polyIL-substrate interfaces diffuse mainly by intra-chain hopping, similar to that in bulk polyILs. However, the diffusion rate of the interfacial mobile anions is much slower than that in bulk due to the slower decay of their association with neighboring polymerized cations. Second, the structure and dynamics of polymeric canopies in the modeling NIMs where the canopy thickness is much smaller than their host nanoparticle were studied. Without added electrolyte ions, the polymeric canopies are strongly adsorbed on the solid substrate but maintain modest in-plane mobility. When electrolyte ion pairs are added, the added counter-ions exchange with the polymeric canopies adsorbed on the charged substrate. However, the number of the adsorbed electrolyte counter-ions exceeds the number of desorbed polymeric canopies, which leads to an overscreening of the substrate's charge. The desorbed polymers can rapidly exchange with the polymers grafted electrostatically on the substrate. Finally, the molecular structure and dynamics of an ion gel consisting of PBDT polyanions and room-temperature ionic liquids (RTIL) were studied. First, a semi-coarse-grained model was developed to investigate the packing and dynamics of the ions in this ion gel. Ions in the interstitial space between polyanions exhibit distinct ordering, which suggests the formation of a long-range electrostatic network in the ion gel. The dynamics of ions slow down compared to that in bulk due to the association of the counter-ions with the polyanions' sulfonate groups. Next, the RTIL-mediated interactions between charged nanorods were studied. It was discovered that effective rod-rod interaction energy oscillates with rod-rod spacing due to the interference between the space charge near each rod as the two rods approach each other. To separate two rods initially positioned at the principal free energy minimum, a significant energy barrier (~several kBT per nanometer of the nanorod) must be overcome, which helps explain the large mechanical modulus of the PBDT ion gel reported experimentally.
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    Multiscale Modeling of Effects of Solute Segregation and Oxidation on Grain Boundary Strength in Ni and Fe Based Alloys
    Xiao, Ziqi (Virginia Tech, 2023-01-13)
    Nickel and iron-based alloys are important structure and cladding materials for modern nuclear reactors due to their high mechanical properties and high corrosion resistance. To understand the radiative and corrosive environment influence on the mechanical strength, computer simulation works are conducted. In particular, this dissertation is focused on multiscale modeling of the effects of radiation-induced solute segregation and oxidation on grain boundary (GB) strength in nickel-based and iron-based alloys. Besides the atomistic scale density functional theory (DFT) based calculations of GB strength, continuum-scale cohesive zone model (CZM) is also used to simulate intergranular fracture at the microstructure scale. First, the effects of solute or impurity segregation at GBs on the GB strength are studied. Thermal annealing or radiation induced segregation of solute and impurity elements to GBs in metallic alloys changes GB chemistry and thus can alter the GB cohesive strength. To understand the underlying mechanisms, first principles based DFT calculations are conducted to study how the segregation of substitutional solute and impurity elements (Al, C, Cr, Cu, P, Si, Ti, Fe, which are present in Ni-based X-750 alloys) influences the cohesive strength of Σ3(111),Σ3(112),Σ5(210) and Σ5(310) GBs in Ni. It is found that C and P show strong embrittlement potencies while Cr and Ti can strengthen GBs in most cases. Other solute elements, including Si, have mixed but insignificant effects on GB strength. In terms of GB character effect, these solute and impurity elements modify the GB strength of the Σ5(210) GB most and that of the Σ3(111) least. Detailed analyses of solute-GB chemical interactions are conducted using electron localization function, charge density map, partial density of states, and Bader charge analysis. The results suggest that the bond type and charge transfer between solutes and Ni atoms at GBs may play important roles on affecting the GB strength. For non-metallic solute elements (C, P, Si), their interstitial forms are also studied but the effects are weaker than their substitutional counterparts. Nickel-base alloys are also susceptible to stress corrosion cracking (SCC), in which the fracture mainly propagates along oxidized grain boundaries (GBs). To understand how oxidation degrades GB strength, the next step is to use density functional theory (DFT) calculations to study three types of oxidized interfaces: partially oxidized GBs, fully oxidized GBs, and oxide/metal interface, using Ni as a model system. For partially oxidized GBs, both substitutional and interstitial oxygen atoms of different concentrations are inserted at three Ni GBs: Σ3(111) coherent twin, Σ3(112) incoherent twin, and Σ5(210). Simulation results show that the GB strength decreases almost linearly with the increasing oxygen coverage at all GBs. Typically, substitutional oxygen causes a stronger embrittlement effect than interstitial oxygen, except at the Σ3(111). In addition, the oxygen-induced mechanical distortion has a much smaller contribution to the embrittlement than its chemical effect, except for oxygen interstitials at the Σ3(111). For the fully oxidized GBs, three NiO GBs of the same types are studied. Although the strengths of Σ3(112) and Σ5(210) NiO GBs are much weaker than the Ni counterparts, the Σ3(111) NiO GB has a higher strength than that in Ni, indicating that Σ3(111) GB may be difficult to fracture during SCC. Finally, the strength of a Ni/NiO interface is found to be the weakest among all interfaces studied, suggesting the metal/oxide interface could be a favorable crack initiation site when the tensile stress is low. Furthermore, the effects of co-segregation of oxygen and solute/impurity elements on GB strength are studied by DFT, using the 5(210) GB in an face-centered-cubic (FCC) Fe as a model system. Four elements (Cr, Ni, P, Si) that are commonly present in stainless steels are selected. Regarding the effects of single elements on GB strength, Ni and Cr are found to the increase the GB strength, while both P and Si have embrittlement effects. When each of them is combined with oxygen at the GB, the synergetic effect can be different from the linear sum of individual contributions. The synergetic effect also depends on the spatial arrangement of solute elements and oxygen. If they are aligned on the same plane at the GB, the synergetic effect is similar to the linear sum, although P and Si show stronger embrittlement potencies when they combine with both interstitial and substitutional oxygen. When they are arranged on a trans-plane structure, only nickel combined with oxygen show larger embrittlement potencies than the linear sum in all cases. Crystal Orbital Hamilton Populations analysis of bonding and anti-bonding states is conducted to interpret how the interaction between solutes and oxygen impacts GB strength. Finally, the continuum-scale CZM method, which is based on the bilinear mixed mode traction separation law, is used to model SCC-induced intergranular fracture in polycrystalline Ni and Fe based alloys in the MOOSE framework. The previous DFT results are used to justify the input parameters for the oxidation-induced GB strength degradation. In this study, it is found that the crack path does not always propagate along the weak GBs. As expected, the fracture prefers to occur at the GB orientations perpendicular to the loading direction. In addition, triple junctions can arrest or deflect fracture propagation, which is consistent with experimental observations. Simulation results also indicate that percolated weak GBs will lead to a much lower fracture stress compared to the discontinuous ones.
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    Reconstruction of Rhodium Clusters During CO Oxidation and Consequences on The Reaction Mechanism
    Albrahim, Malik Ali M. (Virginia Tech, 2023-05-16)
    Heterogeneous catalysis plays a significant role in the chemical industry and the global economy. Most heterogeneous catalysts in the chemical industry and laboratory consist of supported metal nanoparticles, clusters and isolated (single) atoms. Understanding structure sensitivity and identifying the active site or sites are crucially essential for designing efficient catalysts. To determine the active sites of a catalyst for a particular chemical reaction, in-situ/operando spectroscopy, such as diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and X-ray absorption fine structure (XAFS) spectroscopy, is usually implemented as characterization tools. However, understanding the limitation of the characterization tools is crucial to eliminate misleading conclusions. Therefore, the main object of this work is not only to characterize the catalyst before and after the reaction but to investigate the reliability of the characterization tools as well as the stability of the metal clusters and single atoms during CO oxidation. There are four main findings that will be present in this work. First, a high-flux X-ray beam can induce structural change that leads to a reduction of the metal and agglomeration of metal clusters. This finding is very important since X-ray beam damage is uncommon for heterogeneous catalysis as for homogeneous catalysts and biological samples. In the study, the effect of high-flux X-ray on the Rh clusters and nanoparticles was highlighted along with providing mitigation strategies in order to reduce the damage caused by the high-flux X-ray beam. The second important finding is about the characterization of Rh clusters and nanoparticles during CO reduction treatment using DRIFTS. In this study, the integration of low-temperature CO oxidation kinetics as a characterization tool with DRIFTS, XAFS and scanning/transmission electron microscopy (STEM) was found to be necessary to improve the characterization of Rh single atoms. Implementing CO oxidation measurements at low temperatures can provide a rough estimation of the percentage of Rh single atoms. The third finding is related to the stability of Rh clusters upon exposure to CO, oxygen and CO oxidation at different temperatures. The study shows an unexpected dynamic structural change that the Rh cluster undergoes during exposure to oxygen even at room temperature in which the Rh clusters disperse to form Rh single atoms. This dispersion phenomenon was found to be size, gas environment and temperature dependent. For example, small clusters tend to disperse while large nanoparticles resist dispersion. additionally, increasing the temperature to ∼ 160 with CO and oxygen lead to an increase in the percentage of Rh single atoms. More importantly, the dispersed catalyst (Rh single atoms) exhibits higher CO oxidation activity than Rh nanoparticles by 350x. This finding can also be used for Rh single atoms synthesis for different oxide supports such as MgAl2O4, TiO2, and CeO2. Finally, the fourth finding is about investigating the CO oxidation kinetics and mechanism. The kinetics of Rh single atoms differ from Rh nanoparticles. Implementing in-situ spectroscopy helps to identify the resting state of the Rh complex during CO oxidation which is Rh(CO)2. By combining CO oxidation kinetics and in-situ spectroscopy, the plausible mechanism was suggested to be Eley-Rideal/Mars Van Krevelen mechanism.
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    Supramolecular Peptide Nanostructures Regulate Catalytic Efficiency and Selectivity
    Li, Zhao; Joshi, Soumil Y.; Wang, Yin; Deshmukh, Sanket A.; Matson, John B. (Wiley-V C H, 2023-05)
    We report three constitutionally isomeric tetrapeptides, each comprising one glutamic acid (E) residue, one histidine (H) residue, and two lysine (K-S) residues functionalized with side-chain hydrophobic S-aroylthiooxime (SATO) groups. Depending on the order of amino acids, these amphiphilic peptides self-assembled in aqueous solution into different nanostructures:nanoribbons, a mixture of nanotoroids and nanoribbons, or nanocoils. Each nanostructure catalyzed hydrolysis of a model substrate, with the nanocoils exhibiting the greatest rate enhancement and the highest enzymatic efficiency. Coarse-grained molecular dynamics simulations, analyzed with unsupervised machine learning, revealed clusters of H residues in hydrophobic pockets along the outer edge of the nanocoils, providing insight for the observed catalytic rate enhancement. Finally, all three supramolecular nanostructures catalyzed hydrolysis of the l-substrate only when a pair of enantiomeric Boc-l/d-Phe-ONp substrates were tested. This study highlights how subtle molecular-level changes can influence supramolecular nanostructures, and ultimately affect catalytic efficiency.
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    Sustainable Water through Innovation in Membranes & Materials (SWIMM)
    Martin, Stephen M.; Baird, Donald G.; Achenie, Luke E. K.; Deshmukh, Sanket A.; Foster, Earl Johan; He, Jason; Vikesland, Peter J.; Edwards, Marc A.; Deitrich, Andrea; Dillard, David A.; Lesko, John J.; Moore, Robert Bowen; Long, Timothy E.; Riffle, Judy S.; Morris, Amanda J.; Cheng, Shengfeng; Edgar, Kevin J.; Moeltner, Klaus; Xia, Kang; Stewart, Ryan D.; Badgley, Brian D.; Hedrick, Valisa E.; Gohlke, Julia M.; Duncan, Susan E. (Virginia Tech, 2017-05-15)
    Water scarcity is mainly caused by overwhelming human consumption and contamination, from production of water-thirsty meats and vegetables, biofuel crop production, industrial uses, and rapid urbanization. The scale of water scarcity makes it an interconnected global issue and efforts to minimize the gap between water supply and demand are critical...
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    Theoretical Modeling of Polymeric and Biological Nanostructured Materials
    Rahmaninejad, Hadi (Virginia Tech, 2023-02-23)
    Polymer coatings on periodic nanostructures have facilitated advanced applications in various fields. The performance of these structures is intimately linked to their nanoscale characteristics. Smart polymer coatings responsive to environmental stimuli such as temperature, pH level, and ionic strength have found important uses in these applications. Therefore, to optimize their performance and improve their design, precise characterization techniques are essential for understanding the nanoscale properties of polymer coating, especially in response to stimuli and interactions with the surrounding medium. Due to their layered compositions, applying non-destructive measurement methods by X-ray/neutron scattering is optimal. These approaches offer unique insights into the structure, dynamics, and kinetics of polymeric coatings and interfaces. The caveat is that scattering methods require non-trivial data modeling, particularly in the case of periodic structures, which result in strong correlations between scattered beams. The dynamical theory (DT) model offers an exact model for interpreting off-specular signals from periodically structured surfaces and has been validated on substrates measured by neutron scattering. In this dissertation, we improved the model using a computational optimization approach that simultaneously fits specular and off-specular scattering signals and efficiently retrieves the three-dimensional sample profile with high precision. In addition, we extended this to the case of X-ray scattering. We applied this approach to characterize polymer brushes for nanofluidic applications and protein binding to modulated lipid membranes. This approach opens new possibilities in developing soft matter nanostructured substrates with desired properties for various applications.
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    Transferable Coarse-Grained Models: From Hydrocarbons to Polymers, and Backmapped by Machine Learning
    An, Yaxin (Virginia Tech, 2021-01-11)
    Coarse-grained (CG) molecular dynamics (MD) simulations have seen a wide range of applications from biomolecules, polymers to graphene and metals. In CG MD simulations, atomistic groups are represented by beads, which reduces the degrees of freedom in the systems and allows larger timesteps. Thus, large time and length scales could be achieved in CG MD simulations with inexpensive computational cost. The representative example of large time- and length-scale phenomena is the conformation transitions of single polymer chains as well as polymer chains in their architectures, self-assembly of biomaterials, etc. Polymers exist in many aspects of our life, for example, plastic packages, automobile parts, and even medical devices. However, the large chemical and structural diversity of polymers poses a challenge to the existing CG MD models due to their limited accuracy and transferabilities. In this regard, this dissertation has developed CG models of polymers on the basis of accurate and transferable hydrocarbon models, which are important components of the polymer backbone. CG hydrocarbon models were created with 2:1 and 3:1 mapping schemes and their force-field (FF) parameters were optimized by using particle swarm optimization (PSO). The newly developed CG hydrocarbon models could reproduce their experimental properties including density, enthalpy of vaporization, surface tension and self-diffusion coefficients very well. The cross interaction parameters between CG hydrocarbon and water models were also optimized by the PSO to repeat the experimental properties of Gibbs free energies and interfacial tensions. With the hydrocarbon models as the backbone, poly(acrylic acid) (PAA) and polystyrene (PS) models were constructed. Their side chains were represented by one COOH (carboxylic acid) and three BZ beads, respectively. Before testing the PAA and PS models, their monomer models, propionic acid and ethylbenzene, were created and validated, to confirm that the cross interactions between hydrocarbon and COOH beads, and between hydrocarbon and BZ beads could be accurately predicted by the Lorentz-Berthelot (LB) combining rules. Then the experimental properties, density of polymers at 300 K and glass transition temperatures, and the conformations of their all-atom models in solvent mixtures of water and dimethylformamide (DMF) were reproduced by the CG models. The CG PAA and PS models were further used to build the bottlebrush copolymers of PAA-PS and to predict the structures of PAA-PA in different compositions of binary solvents water/DMF. Although CG models are useful in understanding the phenomena at large time- or length- scales, atomistic information is lost. Backmapping is usually involved in reconstructing atomistic models from their CG models. Here, four machine learning (ML) algorithms, artificial neural networks (ANN), k-nearest neighbor (kNN), gaussian process regression (GPR), and random forest (RF) were developed to improve the accuracy of the backmapped all-atom structures. These optimized four ML models showed R2 scores of more than 0.99 when testing the backmapping against four representative molecules: furan, benzene, naphthalene, graphene.
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    Understanding the Impact of High Aspect Ratio Nanoparticles on Desalination Membrane Performance
    Smith, Ethan D. (Virginia Tech, 2020-04-16)
    Access to clean water is one of the world's foremost challenges that has been addressed on a large scale by membrane-based separation processes for the last six decades. Commercial membrane technology within one operation, reverse osmosis, has remained consistent since the late 1970s, however within the last two decades, access to nanotechnology has created a realm of study involving thin film nanocomposite (TFN) membranes, in which nanoparticles are incorporated into existing membrane designs. Desirable properties of the nanoparticles may positively impact qualities of the membrane like performance, anti-fouling behavior, and physical strength. In the present work, two types of nanoparticles have been evaluated for their potential as TFN additives: cellulose nanocrystals (CNCs) and metal-organic framework (MOF) nanorods. CNCs were chosen due to their high aspect ratios, mechanical strength, and potential for surface functionalization. MOF nanorods are also of interest given their aspect ratios and potential for functionalization, but they also possess defined pores, the sizes of which may be tuned with post-synthetic modification. Both CNCs and MOF nanorods were incorporated into TFN membranes via interfacial polymerization, and the resulting membranes were characterized using a variety of techniques to establish their performances, but also to gain insight into how the presence of each nanoparticle might be affecting the membrane active layer formation. A resulting CNC membrane (0.5 wt% loading) exhibited a 160% increase in water flux and an improvement in salt rejection to 98.98 ± 0.41 % compared to 97.53 ± 0.31 % for a plain polyamide control membrane. Likewise, a MOF nanorod membrane (0.01 wt% loading) with a high ratio of acid chain modification exhibited a 95% flux increase with maintained high salt rejection. For the CNCs, the flux increase is attributed to the formation of nanoscale voids along the length of each particle that form during the interfacial polymerization. These nanochannels introduce new rapid water transport pathways within the active layer of each membrane while maintaining ion rejection. The proposed mechanism for the MOF nanorods also introduces nanochannels into each membrane, but the presence of each nanorod's pore structure may offer another transport pathway for water molecules, one that varies with pore size. In combination, these results have allowed the study of molecular transport of water molecules and various ion species within the active layer of a thin film composite RO membrane. Understanding these phenomena will allow the development of smarter membrane materials to address present-day and future separations challenges. Carbon nanotubes are also demonstrated as surface modifiers for forward osmosis (FO) membranes to address issues unique to the FO process, namely reverse solute flux (RSF). This method shows promise, as a coating density of 0.97 g/m2 reduced RSF for many draw solution species, including a 55% reduction for sodium chloride.