Molecular Simulation of Adsorption and Diffusion in Nanoporous Rigid Amorphous Materials

Book Research Talk Session I: 12:00 – 12:55 p.m. Lipidome Dynamics in an Ovarian Cancer Mouse Model Olatomiwa O. Bifarin*, Samykuta Sah, David A. Gaul, Ruihong Chen, Murugesan Palaniappan, Facundo M. Fernández Ovarian cancer (OC) is the fifth leading cause of cancer-related death in women, and particularly deadly is high-grade serous carcinoma (HGSC), the most frequent type of OC. An HGSC Dicer-Pten Double-Knockout (DKO) mouse model was used to study the dynamics of lipidome changes in this OC subtype as the DKO mice mimic many features of the human disease. After two months of breeding, serum samples of DKO (n = 15) and DKO control (n = 15) mice were collected every two weeks for six months. Ultra-high performance liquid chromatography−mass spectrometry (UHPLC−MS) was used for serum lipidomic profiling. The dynamics of lipidomic changes were studied using univariate statistical methods, machine learning, and survival analysis. Given the different death rate profile of control and DKO mice, each time point collection was converted to a percentage of the total lifetime of the individual mice, making the group comparison more robust. Percentage lifetimes were then binned into five different stages (030%, 30-45%, 45-60%, 60-75%, and 75-100% lifetime). Hierarchical clustering analysis revealed the clustering of longitudinal lipid abundance trajectories into four selected clusters, associated with distinct lipid phenotypes. Machine learning was used for DKO classification for the five different stages of OC progression, with the highest classification performance at 4660% lifetime (Test set ROC AUC = 0.85). Altered lipid levels were observed for fatty acids (FA) and their derivatives, phospholipids, and sphingolipids. Early progression of OC is marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages were marked by more diverse lipids alterations. The alterations provided evidence of perturbations in cell membrane stability, cellular proliferation, and survival, as our study provides the first in-depth, longitudinal lipidome dynamics study of ovarian cancer in the DKO mouse model. Molecular Simulation of Adsorption and Diffusion in Nanoporous Rigid Amorphous Materials Raghuram Thyagarajan* and David S. Sholl The development of publicly available materials databases for materials including zeolites (IZASC), metal organic frameworks (CoRE-MOF), and inorganic materials (The Materials Project) has been a key enabler of high-throughput computational screening and data-driven discovery of materials for potential use in new technologies. We recently introduced a similar database for structures of porous rigid amorphous materials, an important class of materials for which no such resource was available. The database includes atomically detailed structures of disordered materials like amorphous carbons, kerogens, hyper-cross-linked polymers etc. generated using a wide range of simulation techniques by multiple research groups. We present extensive computational analyses for material characterization by calculating a series of scalar (e.g., accessible surface area) and vector (e.g., pore size distribution) descriptors. A variety of gas adsorption isotherms for both single component and binary mixtures are predicted for each structure. We also discuss the agreement between binary adsorption data and predictions from the Ideal Adsorbed Solution Theory. In addition to adsorption isotherms, we have computed the diffusion coefficients of CH4 and CO2 in several of these structures. We present a diverse collection of molecular diffusivities in amorphous materials and examine their concentration dependence by comparing data from adsorption and diffusion simulations. Characterization of Sulfur Aerosols in Fairbanks, AK During Extreme Winter Conditions Kayane Dingilian*, Michael Battaglia, James Campbell, Jingqiu Mao, Rodney Weber Sulfur aerosols play an important role in public health and meteorology. As one of the primary components of PM 2.5 (particulate matter at size 2.5 μm and below), they are highly toxic, and within clouds, they impact rainfall and the properties of the Earth’s atmosphere. Under the unique conditions of Fairbanks, AK in the winter, sulfur chemistry is not well-understood. In extreme cold and low light, traditional aerosol formation pathways are very slow – yet Fairbanks remains one of the most severely polluted cities in the world due in part to its strong temperature inversion patterns that trap unclean air at the surface. We recently identified hydroxymethane sulfonate (HMS) as an important constituent of sulfur-based aerosols in highly polluted events and isolated the species in ion chromatography analysis. A product of the reaction of formaldehyde and the ions sulfite and bisulfite, HMS is especially remarkable because its detection implies the presence of supercooled aqueous chemistry in normally freezing conditions. As part of an ongoing international effort led by the University of Alaska at Fairbanks, we employed both online instruments mist chamber, particle-into-liquid-sampler (PILS) as well as offline filter analyses of PM 2.5 to investigate the formation of HMS, sulfate, and other sulfur-based aerosols. We also gathered data on common pollutants such as chloride, nitrite, and nitrate, and we are studying their correlation to HMS and sulfate. Thus far, during January and February of 2022, we detected levels of HMS and sulfate as high as approximately 5 and 30 μg/m in PILS measurements. Characterizing the relative abundance of HMS and other sulfur-containing aerosols in PM 2.5 as well as their chemistry in relation to one another will not only improve the accuracy of atmospheric models but also advance the understanding of atmospheric chemistry and air quality under extreme conditions. Lightning Talk Session I: 1:00 – 1:30 p.m. Antarctic Krill (Euphausia superba) Kinematics in Relation to Chemical, Physical and Photic Stimuli: From Video Analysis to an Individual-based-model Nicole Hellessey*, Marc Weissburg, David Fields, and Nicholas Record Antarctic krill (Euphausia superba) are at the centre of the Antarctic ecosystem. Little is known about what stimuli cause individual krill swimming behaviours to change, what causes aggregation into schools and swarms, or what cues the movement and structure of aggregations. In this study we are constructing an individual-based-model (IBM) that scales up from individual to aggregate behaviour. Using a horizontal flume, we examined krill swimming kinematics and behaviour in relation to chemical, physical and photic stimuli. Our video analysis has the ability to clarify: search/foraging or avoidance behaviour in relation to chemical stimuli, the grouping and alignment of krill in a current and their ability to exit/enter a current (physical stimuli), energy efficient swimming (positive stimuli in high flow), maintenance of a heading under low photic conditions, as well as how search behaviours differ in low photic conditions. Krill swimming in high flow showed few strong directional changes (low frequency of large turn angles) that would indicate active searching, which may be a response to the energetic demands of not reducing drag when flow is high. In contrast, krill turned more frequently in low flow conditions, regardless of chlorophyll level, suggesting that the energetic demands of not aligning to current direction may constrain foraging. Roughly 1/3rd of krill tracks in low flow conditions had bimodal swim velocities and more skewed swim velocities, suggesting more behavioural flexibility when krill do not have to confront strong flows. Heading was more consistently aligned with the flow in low photic conditions which reduced their ability to “search” even when chemical stimuli were added to the low photic conditions. Krill swimming velocities decreased significantly in low photic as well as in negative chemical stimuli conditions. Information and analysis gathered from this study will be used to construct an individual-basedmodel (IBM) for Antarctic krill. PSP: A toolkit for efficient generation of 3D atomic-level polymer models Harikrishna Sahu*, Huan Tran, Kuan-Hsuan Shen, Joseph H. Montoya and Rampi Ramprasad Typically, 3D atomic models are required for physics-based simulations of materials. Within the specific area of polymer science, an automatic engine for generating such polymer models is needed. We have developed a python toolkit named Polymer Structure Predictor (PSP) for suggesting a hierarchy of polymer models, ranging from oligomer/infinite polymer chains to sophisticated amorphous models [1], which can be used downstream in physics-based simulations. The only input of PSP is the simplified molecular-input line-entry system (SMILES) strings of the repeat unit of the polymer. The performance of PSP was tested by comparing the generated models with the known experimental data of several polymers. The output files can be directly used with several computational software, such as VASP, ORCA, LAMMPS, and GAMESS, allowing automation for computing properties. PSP has already been used in the polymer version of computational autonomy for materials discovery (CAMD) [2], establishing extensive databases for polymer bandgap and charge injection barriers that power the Polymer Genome platform (www.polymergenome.org). PSP is expected to benefit a wide number of academic and industrial research activities, realizing automation in polymer discovery. In the context of the emerging polymer informatics ecosystem, this toolkit will substantially reduce efforts to develop extensive databases of computed polymer properties. Besides, simpler visual models of polymeric materials would likely aid in polymer synthesis research, benefiting experimental polymer scientists. References: [1] H. Sahu, K.-H. Shen, J. H. Montoya, H. Tran, R. Ramprasad, Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atom