(492c) Enabling Materials for Wearable Hemodialysis Via Molecular Simulations and Machine Learning
AIChE Annual Meeting
2024
2024 AIChE Annual Meeting
Separations Division
Bioseparations: Process and Modeling I
Wednesday, October 30, 2024 - 8:48am to 9:06am
The design of miniaturized hemodialysis devices, such as wearable artificial kidneys, requires regeneration of the dialysate stream to remove uremic toxins from water.
Adsorbents have the potential to capture such molecules, but conventional adsorbents have low urea/water selectivity.
In this work, we performed a comprehensive computational screening 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 molecular 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 longer Molecular Dynamics (MD) 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.
Water molecules were then added to the picture and the effect of water-urea interactions estimated on the adsorption and removal of urea with selected COF materials using longer MD simulations.
The data generated with molecular simulations were used to train a Machine Learning algorithm that was used to predict the separation efficiency of a wider database of COF materials, considering different chemical and structural features based also on XRD spectra, to classify the porous materials.
The analysis was extended to include other uremic toxins relevant for the wearable artificial kidney, i.e. uric acid, creatinine, etc. to accelerate the development of novel sorbents for uremic toxins removal and pave the way for a well-informed experimental campaign.
In conclusion, the screening indicates that the large freedom in linkers’ selection gives space for optimizing and tailoring the frameworks on the desired separation.
Furthermore, the computational method here presented is suitable for a fast preliminary screening of large materials databases and consistent with longer MD simulations. Such procedure can be used to inform and shorten the experimental campaign and also to explore hypothetical structures, not yet synthesized, assisting the design of new materials with target performance.
Adsorbents have the potential to capture such molecules, but conventional adsorbents have low urea/water selectivity.
In this work, we performed a comprehensive computational screening 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 molecular 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 longer Molecular Dynamics (MD) 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.
Water molecules were then added to the picture and the effect of water-urea interactions estimated on the adsorption and removal of urea with selected COF materials using longer MD simulations.
The data generated with molecular simulations were used to train a Machine Learning algorithm that was used to predict the separation efficiency of a wider database of COF materials, considering different chemical and structural features based also on XRD spectra, to classify the porous materials.
The analysis was extended to include other uremic toxins relevant for the wearable artificial kidney, i.e. uric acid, creatinine, etc. to accelerate the development of novel sorbents for uremic toxins removal and pave the way for a well-informed experimental campaign.
In conclusion, the screening indicates that the large freedom in linkers’ selection gives space for optimizing and tailoring the frameworks on the desired separation.
Furthermore, the computational method here presented is suitable for a fast preliminary screening of large materials databases and consistent with longer MD simulations. Such procedure can be used to inform and shorten the experimental campaign and also to explore hypothetical structures, not yet synthesized, assisting the design of new materials with target performance.