Advances in modeling gas adsorption in porous materials for the characterization applications

The dissertation studies methods for mesoporous materials characterization using adsorption at various levels of scale and complexity. It starts with the topic introduction, necessary notations and definitions, recognized standards, and a literature review. Synthesis of novel materials requires tailoring of the characterization methods and their thorough testing. The second chapter presents a nitrogen adsorption characterization study for silica colloidal crystals (synthetic opals). These materials have cage-like pores in the range of tens of nanometers. The adsorption model can be described within a macroscopic approach, based on the Derjaguin-Broekhoff-de Boer (DBdB) theory of capillary condensation. A kernel of theoretical isotherms is built and applied to the solution of the adsorption integral equation to derive the pore-size distribution from experimental data. The technique is validated with a surface modification of the samples so that it changes the interaction but not the pore size. The second chapter deals with the characterization of three-dimensional ordered mesoporous (3DOm) carbons. Similar to opals, these materials have cage-like mesopores, however, these pores are connected with large windows. These windows affect the adsorption process and calculated pore-size distributions. The grand canonical Monte Carlo simulations with derived solid-fluid potentials, which take into account the 3DOm carbons geometry, confirm the critical role of interconnections, their size, and number, for correct interpretation of adsorption data for the PSD calculations. The fourth chapter discusses a method for the pore size estimation that can serve as an alternative to the adsorption isotherms analysis. It is based on measurements of elastic properties of liquid that can be useful for the pore size estimation. A Vycor glass sample, a disordered mesoporous material with channel-like pores having a characteristic size of ca. 6-8 nm, is considered. The changes in longitudinal and shear moduli from the experimental data and molecular simulations are predicted with a near-quantitative agreement. Then, it follows by their relation of the moduli to the pore size, which is promising for characterization. The last fifth chapter considers a promising Monte Carlo method, the Kinetic Monte Carlo (kMC) algorithm. This method is efficient for the vapor-liquid equilibrium prediction in dense regions. This chapter shows a benchmark with conventional Metropolis et al algorithms as well as a parallelization scheme of the kMC algorithm

Bio-psycho-social factors’ associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants

Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6–82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions’ influence on brain age in future studies.publishedVersio