In silico screening of nanoporous materials for urea removal in hemodialysis applications

The design of miniaturized hemodialysis devices, such as wearable artificial kidneys, requires regeneration of the dialysate stream to remove uremic toxins from water. Adsorption has the potential to capture such molecules, but conventional adsorbents have low urea/water selectivity. In this work, we performed a comprehensive computational study of 560 porous crystalline adsorbents comprising mainly covalent organic frameworks (COFs), as well as some siliceous zeolites, metal organic frameworks (MOFs) and graphitic materials. An initial screening using Widom insertion method assessed the excess chemical potential at infinite dilution for water and urea at 310 K, providing information on the strength and selectivity of urea adsorption. From such analysis it was observed that urea adsorption and urea/water selectivity increased strongly with fluorine content in COFs, while other compositional or structural parameters did not correlate with material performance. Two COFs, namely COF-F6 and Tf-DHzDPr were explored further through Molecular Dynamics simulations. The results agree with those of the Widom method and allow to identify the urea binding sites, the contribution of electrostatic and van der Waals interactions, and the position of preferential urea–urea and urea–framework interactions. This study paves the way for a well-informed experimental campaign and accelerates the development of novel sorbents for urea removal, ultimately advancing on the path to achieve wearable artificial kidneys

Prediction of the performance of pre-packed purification columns through machine learning

Pre‐packed columns have been increasingly used in process development and biomanufacturing thanks to their ease of use and consistency. Traditionally, packing quality is predicted through rate models, which require extensive calibration efforts through independent experiments to determine relevant mass transfer and kinetic rate constants. Here we propose machine learning as a complementary predictive tool for column performance. A machine learning algorithm, extreme gradient boosting, was applied to a large data set of packing quality (plate height and asymmetry) for pre‐packed columns as a function of quantitative parameters (column length, column diameter, and particle size) and qualitative attributes (backbone and functional mode). The machine learning model offered excellent predictive capabilities for the plate height and the asymmetry (90 and 93%, respectively), with packing quality strongly influenced by backbone (∼70% relative importance) and functional mode (∼15% relative importance), well above all other quantitative column parameters. The results highlight the ability of machine learning to provide reliable predictions of column performance from simple, generic parameters, including strategic qualitative parameters such as backbone and functionality, usually excluded from quantitative considerations. Our results will guide further efforts in column optimization, for example, by focusing on improvements of backbone and functional mode to obtain optimized packings

Effect of urea concentration on the viscosity and thermal stability of aqueous NaOH/urea cellulose solutions

Aqueous solutions of sodium hydroxide (NaOH) and urea are a known and versatile solvent for cellulose. The dissolution of cellulose occurs at subambient temperatures through the formation of a cellulose-NaOH-urea “inclusion complex” (IC). NaOH and urea form a hydrate layer around the cellulose chains preventing chain agglomeration. Urea is known to stabilize the solution but its direct role is unknown. Using viscometry and quartz crystal microbalance with dissipation monitoring (QCM-D) it could be shown that the addition of urea reduced the solutions viscosity of the tested solutions by almost 40% and also increased the gelation temperature from approximately 40°C to 90°C. Both effects could also be observed in the presence of additional cellulose powder serving as a physical cross-linker. Using Fourier transform infrared (FTIR) spectroscopy during heating, it could be shown that a direct interaction occurs between urea and the cellulose molecules, reducing their ability to form hydrogen bonds with neighbouring chains