(114b) Modeling and Testing of Functionalized Interfaces for Electrochemical Separations | AIChE

(114b) Modeling and Testing of Functionalized Interfaces for Electrochemical Separations

Authors 

Glezakou, V. A. - Presenter, Pacific Northwest National Laboratory
Nguyen, M. T., Pacific Northwest National Laboratory
Zhang, D., Pacific Northwest National Laboratory
Tan, S., University of Wyoming
Wang, X., PNNL
Johnson, G., PNNL
Rousseau, R., ORNL
The selective and efficient separation of target ions from solutions is a challenging problem that demands novel methods with ease of adapatiblity. [1] Separation processes are an essential component of a sustainable future that will enable clean water production and recovery of natural resources for energy manufacturing and storage. [2] Among the different separation processes, electrochemical methods provide a sustainable platform for addressing the issues of water purification and wastewater treatment, with several advantages over traditional methods such as fast kinetics, reusability, and modularity.[3] Ionic liquids with different chemical functionalities offer a path for modulating the hydrophobicity and desolvation process of ions occurring at electrode interfaces, but the molecular-level interactions between ionic liquids and cations needs to be better understood before we can develop functionalized electrodes with tailored properties for specific ionic and molecular separations. [4,5] In this work [6], we investigated the formation and properties of anionic 1-ethyl-3-methylimidazolium chloride [EMIM]x[Cl]x+1– (x = 1–10) IL clusters and their suitability for enhancing the electrochemical adsorption and separation of ions from solution employing a combination of experiments and theory. We applied a state-of-the-art global optimization tool, NWPEsSe[7], to determine the structures of ionic liquid clusters and evaluated their relative stability toward dissociation along with complementary electrospray ionization mass spectrometry experiments. We also benchmarked the calculated size-dependent electronic structure of these ionic liquid clusters using experimentally determined electron binding energies from negative-ion photoelectron spectroscopy. Our simulations provided electrostatic potential maps of different ionic liquid clusters that enabled us to locate the most favorable interaction sites for ion adsorption. Using these theoretical molecular level insights into the bonding, structural rigidity, and electrostatic properties of ionic liquid clusters, the experimental team tailored functionalized electrodes with a new set of ionic liquid that were evaluated for targeted ion adsorption. Our combined results show that the size and structure of ionic liquid clusters play a dominant role in determining the energy barriers for ion desolvation/adsorption and interfacial charge transfer, thereby providing a path to tune selective ion adsorption on functionalized electrodes during electrochemical separations.

References

[1] “A Research Agenda for Transforming Separation Science”, National Academies of Sciences, Engineering, and Medicine, 2019. National Academies Press.

[2] Sholl, David S., and Ryan P. Lively. "Seven chemical separations to change the world." Nature 532.7600 (2016): 435-437.

[3] Radjenovic, Jelena, and David L. Sedlak. "Challenges and opportunities for electrochemical processes as next-generation technologies for the treatment of contaminated water." Environmental science & technology 49.19 (2015): 11292-11302.

[4] Palakkal, Varada Menon, et al. "Advancing electrodeionization with conductive ionomer binders that immobilize ion-exchange resin particles into porous wafer substrates." npj Clean Water 3.1 (2020): 1-10.

[5] Chen, Ji, ed. Application of ionic liquids on rare earth green separation and utilization. Springer Berlin Heidelberg, 2016.

[6] Baxter, Eric T., et al. "Functionalization of Electrodes with Tunable [EMIM] x [Cl] x+ 1–Ionic Liquid Clusters for Electrochemical Separations." Chemistry of Materials 34.6 (2022): 2612-2623

[7] Zhang, Jun, et al. "NWPEsSe: an adaptive-learning global optimization algorithm for nanosized cluster systems." Journal of Chemical Theory and Computation 16.6 (2020): 3947-3958.