Matching Items (22)
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 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.
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.
ContributorsTheisen, Rebekah (Author) / Adams, James B (Thesis advisor) / Li, Jian (Committee member) / Ponce, Fernando (Committee member) / Arizona State University (Publisher)
Created2014
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 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.
ContributorsLi, Yiyang (Author) / Oswald, Jay (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Solanki, Kiran (Committee member) / Chamberlin, Ralph (Committee member) / Rajagopalan, Jagannathan (Committee member) / Arizona State University (Publisher)
Created2017
Description
The majority of trust research has focused on the benefits trust can have for individual actors, institutions, and organizations. This “optimistic bias” is particularly evident in work focused on institutional trust, where concepts such as procedural justice, shared values, and moral responsibility have gained prominence. But trust in institutions may not be exclusively good. We reveal implications for the “dark side” of institutional trust by reviewing relevant theories and empirical research that can contribute to a more holistic understanding. We frame our discussion by suggesting there may be a “Goldilocks principle” of institutional trust, where trust that is too low (typically the focus) or too high (not usually considered by trust researchers) may be problematic. The chapter focuses on the issue of too-high trust and processes through which such too-high trust might emerge. Specifically, excessive trust might result from external, internal, and intersecting external-internal processes. External processes refer to the actions institutions take that affect public trust, while internal processes refer to intrapersonal factors affecting a trustor’s level of trust. We describe how the beneficial psychological and behavioral outcomes of trust can be mitigated or circumvented through these processes and highlight the implications of a “darkest” side of trust when they intersect. We draw upon research on organizations and legal, governmental, and political systems to demonstrate the dark side of trust in different contexts. The conclusion outlines directions for future research and encourages researchers to consider the ethical nuances of studying how to increase institutional trust.
ContributorsNeal, Tess M.S. (Author) / Shockley, Ellie (Author) / Schilke, Oliver (Author)
Created2016
Description
High-entropy alloys (HEAs) is a new class of materials which have been studied heavily due to their special mechanical properties. HEAs refers to alloys with multiple equimolar or nearly equimolar elements. HEAs show exceptional and attractive properties currently absent from conventional alloys, which make them the center of intense investigation. HEAs obtain their properties from four core effects that they exhibit and most of the work on them have been dedicated to study their mechanical properties. In contrast, little or no research have gone into studying the functional or even thermal properties of HEAs. Some HEAs have also shown exceptional or very high melting points. According to the definition of HEAs, Si-Ge-Sn alloys with equal or comparable concentrations of the three group IV elements belong to the category of HEAs. Thus, the equimolar components of Si-Ge-Sn alloys probably allow their atomic structures to display the same fundamental effects of metallic HEAs. The experimental fabrication of such alloys has been proven to be very difficult, which is mainly due to differences between the properties of their constituent elements, as indicated from their binary phase diagrams. However, previous computational studies have shown that SiGeSn HEAs have some very interesting properties, such as high electrical conductivity, low thermal conductivity and semiconducting properties. In this work, going for a complete characterization of the SiGeSn HEA properties, the melting point of this alloy is studied using classical molecular dynamics (MD) simulations and density functional theory (DFT) calculations. The aim is to investigate the effects of high Sn content in this alloy on the melting point compared with the traditional SiGe alloys. Classical MD simulations results strongly indicates that none of the available empirical potentials is able to predict accurate or reasonable melting points for SiGeSn HEAs and most of its subsystems. DFT calculations results show that SiGeSn HEA have a melting point which represent the mean value of its constituent elements and that no special deviations are found. This work contributes to the study of SiGeSn HEA properties, which can serve as guidance before the successful experimental fabrication of this alloy.
ContributorsAlqaisi, Ahmad Madhat Odeh (Author) / Hong, Qi-Jun (Thesis advisor) / Zhuang, Houlong (Thesis advisor) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2023
Description
Anthropogenic processes have increased the concentration of toxic Se, As and N in water. Oxo-anions of these species are poisonous to aquatic and terrestrial life. Current remediation techniques have low selectivity towards their removal. Understanding the chemistry and physics which control oxo-anion adsorption on metal oxide and the catalytic nitrate reduction to inform improved remediation technologies can be done using Density functional theory (DFT) calculations. The adsorption of selenate, selenite, and arsenate was investigated on the alumina and hematite to inform sorbent design strategies. Adsorption energies were calculated as a function of surface structure, composition, binding motif, and pH within a hybrid implicit-explicit solvation strategy. Correlations between surface property descriptors including water network structure, cationic species identity, and facet and the adsorption energies of the ions show that the surface water network controls the adsorption energy more than any other, including the cationic species of the metal-oxide. Additionally, to achieve selectivity for selenate over sulphate, differences in their electronic structure must be exploited, for example by the reduction of selenate to selenite by Ti3+ cations.
Thermochemical or electrochemical reduction pathways to convert NO3- to N2 or NH3, which are benign or value-added products, respectively are examined over single-atom electrocatalysts (SAC) in Cu. The activity and selectivity for nitrate reduction are compared with the competitive hydrogen evolution reaction (HER). Cu suppresses HER but produces toxic NO2- because of a high activation barrier for cleaving the second N-O bond. SACs provide secondary sites for reaction and break traditional linear scaling relationships. Ru-SACs selectively produce NH3 because N-O bond scission is facile, and the resulting N remains isolated on SAC sites; reacting with H+ from solvating H2O to form ammonia. Conversely, Pd-SAC forms N2 because the reduced N* atoms migrate to the Cu surface, which has a low H availability, allowing N atoms to combine to N2. This relation between N* binding preference and reduction product is demonstrated across an array of SAC elements.
Hence, the solvation effects on the surface critically alter the activity of adsorption and catalysis and the removal of toxic pollutants can be improved by altering the surface water network.
ContributorsGupta, Srishti (Author) / Muhich, Christopher L (Thesis advisor) / Singh, Arunima (Committee member) / Emady, Heather (Committee member) / Westerhoff, Paul (Committee member) / Deng, Shuguang (Committee member) / Arizona State University (Publisher)
Created2023
Description
Secondary active transporters play significant roles in maintaining living cells' homeostasis by utilizing the electrochemical gradient in driving ions or protons as the source of free energy to transport substrate through biological membranes.A broadly recognized molecular framework, the alternating access model, describes the transport mechanism as the transporter undergoes conformational changes between different conformations and alternatingly exposes its binding site to intracellular and extracellular sides and, thus, exchanges ion and substrate in a cyclical manner.
Recent progress in structural biology brought the first-ever structural insights into the mammalian Cation-Proton Antiporters (CPA) family of proteins.
However, the dynamic atomic-level information about the interactions between the newly discovered structures and the bound ion or the corresponding substrate remains unknown.
With Molecular Dynamics (MD), multiple spontaneous ion binding events were observed in the equilibrium simulations, revealing the binding site topology of Horse Sodium-Proton Exchanger 9 (NHE9) and Bison Sodium-Proton Antiporter 2 (NHA2) in their preferred protonation state.
Further investigation into more CPA homologs compared various aspects, including sequence identity, binding site topology, and energetic properties, and obtained general insights into the similarities shared by the binding process of CPA members.
The putative binding site and other conserved residues in their actively ion-bound poses were identified for each model, and their similarities were compared.
The energetic properties accessed by the three-dimensional free energy profile, initially found to be binding unfavorable for the experimental structures, were recalculated based on the simulation data. The updated results show consistency with the correct binding affinity as indicated by the experimental methods.
This work provided a general picture of the structures and the ion-protein interaction of CPA proteins and serves as comprehensive guidance for any related future structural and computational work.
ContributorsZhang, Chenou (Author) / Beckstein, Oliver (Thesis advisor) / Ozkan, Banu (Committee member) / Ros, Robert (Committee member) / Singharoy, Abhishek (Committee member) / Arizona State University (Publisher)
Created2023
Description
The origin of life remains unknowable to current science. Scientists cannot see into the origin of life on Earth, and until humanity discovers life elsewhere in the universe and begin to compare this alien life to Earth, it is likely to be undiscoverable. However, alien life may be so different from life as it is currently known that it may not be recognizable when it is found. Therefore, astrobiology needs a universal theory for life to avoid detection methods being biased towards Earth-based life. This also extends to the instrumentation sent into space, which should be built to detect universal properties of life. Assembly theory, a novel measure of complexity and arguably the only testable agnostic biosignature in current science, is used here to provide precision requirements for mass spectrometry instrumentation on future spaceflight missions with the goal of finding life elsewhere. Universal properties are not only applicable to the origins of life, but also to technologically advanced societies. Predictable patterns are found in today’s industrially based society, such as energy usage as a function of population density. These patterns may serve as the basis for technosignatures that are evidence of advanced extraterrestrial civilizations. Patters found in patent chemistry are explored, as well as predictions of chemical complexity based on assembly theory, to determine how complex chemistry is built by human society and which statistical patterns may be found in extraterrestrial civilizations. Moving beyond astrobiology, science cannot be done in a vacuum but must be communicated and taught to others. Topics such as a universal definition of life, biosignatures, and increasing complexity mean nothing without interest and engagement from others, particularly students. To this end, transformative pedagogical tools are used, particularly sociotransformative constructivism (sTc), to build and teach an Earth Science and Astrobiology curriculum to a classroom of high school incarcerated students. The impact of this class on their science learning and how they personally identify as scientists is studied.
ContributorsMalloy, John (Author) / Walker, Sara (Thesis advisor) / Reano, Darryl (Committee member) / Hartnett, Hilairy (Committee member) / Trembath-Reichert, Elizabeth (Committee member) / Cronin, Leroy (Committee member) / Arizona State University (Publisher)
Created2023
Description
Chemical Reaction Networks (CRNs) provide a useful framework for modeling andcontrolling large numbers of agents that undergo stochastic transitions between a set of states
in a manner similar to chemical compounds. By utilizing CRN models to design agent control
policies, some of the computational challenges in the coordination of multi-agent systems can
be overcome. In this thesis, a CRN model is developed that defines agent control policies for a
multi-agent construction task. The use of surface CRNs to overcome the tradeoff between
speed and accuracy of task performance is explained. The computational difficulties involved
in coordinating multiple agents to complete collective construction tasks is then discussed. A
method for stochastic task and motion planning (TAMP) is proposed to explain how a TAMP
solver can be applied with CRNs to coordinate multiple agents.
This work defines a collective construction scenario in which a group of noncommunicating agents must rearrange blocks on a discrete domain with obstacles into a
predefined target distribution. Four different construction tasks are considered with 10, 20,
30, or 40 blocks, and a simulation of each scenario with 2, 4, 6, or 8 agents is performed. As the
number of blocks increases, the construction problem becomes more complex, and a given
population of agents requires more time to complete the task. Populations of fewer than 8
agents are unable to solve the 30-block and 40-block problems in the allotted simulation time,
suggesting an inflection point for computational feasibility, implying that beyond that point
the solution times for fewer than 8 agents would be expected to increase significantly. For a
group of 8 agents, the time to complete the task generally increases as the number of blocks
increases, except for the 30-block problem, which has specifications that make the task slightly
easier for the agents to complete compared to the 20-block problem. For the 10-block and 20-
block problems, the time to complete the task decreases as the number of agents increases;
however, the marginal effect of each additional two agents on this time decreases. This can be
explained through the pigeonhole principle: since there area finite number of states, when the
number of agents is greater than the number of available spaces, deadlocks start to occur and
the expectation is that the overall solution time to tend to infinity.
ContributorsKamojjhala, Pranav (Author) / Berman, Spring (Thesis advisor) / Fainekos, Gergios E (Thesis advisor) / Pavlic, Theodore P (Committee member) / Arizona State University (Publisher)
Created2022
Description
Molecular recognition forms the basis of all protein interactions, and therefore is crucial for maintaining biological functions and pathways. It can be governed by many factors, but in case of proteins and peptides, the amino acids sequences of the interacting entities play a huge role. It is molecular recognition that helps a protein identify the correct sequences residues necessary for an interaction, among the vast number of possibilities from the combinatorial sequence space. Therefore, it is fundamental to study how the interacting amino acid sequences define the molecular interactions of proteins. In this work, sparsely sampled peptide sequences from the combinatorial sequence space were used to study the molecular recognition observed in proteins, especially monoclonal antibodies. A machine learning based approach was used to study the molecular recognition characteristics of 11 monoclonal antibodies, where a neural network (NN) was trained on data from protein binding experiments performed on high-throughput random-sequence peptide microarrays. The use of random-sequence microarrays allowed for the peptides to be sparsely sampled from sequence space. Post-training, a sequence vs. binding relationship was deduced by the NN, for each antibody. This in silico relationship was then extended to larger libraries of random peptides, as well as to the biologically relevant sequences (target antigens, and proteomes). The NN models performed well in predicting the pertinent interactions for 6 out of the 11 monoclonal antibodies, in all aspects. The interactions of the other five monoclonal antibodies could not be predicted well by the models, due to their poor recognition of the residues that were omitted from the array. Furthermore, NN predicted sequence vs. binding relationships for 3 other proteins were experimentally probed using surface plasmon resonance (SPR). This was done to explore the relationship between the observed and predicted binding to the arrays and the observed binding on different assay platforms. It was noted that there was a general motif dependent correlation between predicted and SPR-measured binding. This study also indicated that a combined reiterative approach using in silico and in vitro techniques is a powerful tool for optimizing the selectivity of the protein-binding peptides.
ContributorsBisarad, Pritha (Author) / Woodbury, Neal W (Thesis advisor) / Green, Alexander A (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2022
Description
Understanding solvent-mediated interactions in biomolecular systems at the molecular level is important for the development of predictive models for processes such as protein folding and ligand binding to a host biomolecule. Solvent-mediated interactions can be quantified as changes in the solvation free energy of solvated molecules. Theoretical models of solvent-mediated interactions thus need to include ensemble-averaged solute-solvent interactions. In this thesis, molecular dynamics simulations were coupled with the 3D-2PT method to decompose solvation free energies into spatially resolved local contributions. In the first project, this approach was applied to benzene derivatives to guide the development of efficient and predictive models of solvent-mediated interactions in the context of computational drug design. Specifically, the effects of carboxyl and nitro groups on solvation were studied due to their similar sterical requirements but distinct interactions with water. A system of solvation free energy arithmetics was developed and showed that non-additive contributions to the solvation free energy originate in electrostatic solute-solvent interactions, which are qualitatively reproduced by computationally efficient continuum models. In the second project, a simple model system was used to analyze hydrophilic water-mediated interactions (water-mediated hydrogen bonds), which have been previously suggested to play a key role in protein folding. Using the spatially resolved analysis of solvation free energies, the sites of bridging water molecules were identified as the primary origin of solvent-mediated forces and showed that changes in hydration shell structure can be neglected. In the third project, the analysis of solvation free energy contributions is applied to proteins in inhomogeneous electric fields to explore water-mediated contributions to protein dielectrophoresis. The results provide a potential explanation for negative dielectrophoretic forces on proteins, which have been observed experimentally but cannot be explained with previous theoretical models.
ContributorsLazaric, Aleksandar (Author) / Heyden, Matthias (Thesis advisor) / Ozkan, Banu S (Committee member) / Sulc, Petr (Committee member) / Arizona State University (Publisher)
Created2022