Protein-surface interactions are exploited in various processes in life sciences and biotechnology. Many of such processes are performed in presence of a buffer system, which is generally believed to have an influence on the protein-surface interaction but is rarely investigated systematically. Combining experimental and theoretical methodologies, we herein demonstrate the strong influence of the buffer type on protein-surface interactions. Using state of the art chromatographic experiments, we measure the interaction between individual amino acids and silica, as a reference to understand protein-surface interactions. Among all the 20 proteinogenic amino acids studied, we found that arginine (R) and lysine (K) bind most strongly to silica, a finding validated by free energy calculations. We further measured the binding of R and K at different pH in presence of two different buffers, MOPS (3-(N-morpholino)propanesulfonic acid) and TRIS (tris(hydroxymethyl)aminomethane), and find dramatically different behavior. In presence of TRIS, the binding affinity of R/K increases with pH, whereas we observe an opposite trend for MOPS. These results can be understood using a multiscale modelling framework combining molecular dynamics simulation and Langmuir adsorption model. The modelling approach helps to optimize buffer conditions in various fields like biosensors, drug delivery or bio separation engineering prior to the experiment.

Multiple Linear Regression Analysis as a part of machine learning is employed to develop equations for the quick and accurate prediction of methane uptake and working capacity of metal-organic frameworks...

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Propylene is a vital component for the petrochemical industry. The overwhelming majority of the studies is focused on propylene/propane separation. Nevertheless, as the ultimate goal is to obtain propylene as the end product, propylene-selective MOFs necessitate a demanding and complex desorption process, which consumes a substantial amount of energy and involves intricate operations. Consequently, having an adsorbent that preferentially adsorbs propane instead would be more advantageous. This approach would enable the production of high-purity propylene in a single step and lead to significant reductions in energy consumption and the quantity of adsorbent required. The Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 database is utilized for the computational screening of flexible MOFs using molecular dynamics simulations to identify materials with strong potential for separating propane/propylene via self-diffusion. This screening process is the first to fully consider the impact of framework flexibility on the discovery of guest self-diffusion coefficients. The study underscores the significance of accounting for framework flexibility in investigations of gas molecules transport in MOFs, showcases the potential of using data-driven approaches to identify high-performance materials, and suggests methods for enhancing the predictive capabilities of screening workflows. The top 5 MOFs from the CoRE MOF database for propane and propylene separation at a temperature of 298 K are identified. It is observed that the presence of carbonyl groups greatly enhances the separation properties between propane and propylene. Subsequently, a machine learning approach is utilized to develop a model and uncover important features. The machine learning model's predicted values for self-diffusion are reasonably consistent with the data obtained from molecular dynamics simulations.