CO2 Absorption Kinetics of Micro-encapsulated Ionic Liquids

Engineering encapsulated ionic liquids for next-generation applications

Ionic liquids (ILs) have attracted considerable attention in several sectors (from energy storage to catalysis, from drug delivery to separation media) owing to their attractive properties, such as high thermal stability, wide electrochemical window, and high ionic conductivity. However, their high viscosity and surface tension compared to conventional organic solvents can lead to unfavorable transport properties. To circumvent undesired kinetics effects limiting mass transfer, the discretization of ILs into small droplets has been proposed as a method to increase the effective surface area and the rates of mass transfer. In the present review paper, we summarize the different methods developed so far for encapsulating ILs in organic or inorganic shells and highlight characteristic features of each approach, while outlining potential applications. The remarkable tunability of ILs, which derives from the high number of anions and cations currently available as well as their permutations, combines with the possibility of tailoring the composition, size, dispersity, and properties (e.g., mechanical, transport) of the shell to provide a toolbox for rationally designing encapsulated ILs for next-generation applications, including carbon capture, energy storage devices, waste handling, and microreactors. We conclude this review with an outlook on potential applications that could benefit from the possibility of encapsulating ILs in organic and inorganic shells.

Encapsulated ionic liquids (ILs) are candidate materials for several applications owing to the attractive properties of ILs combined with the enhanced mass transfer rate obtained through the discretization of ILs in small capsules.

Capsules of Reactive Ionic Liquids for Selective Capture of Carbon Dioxide at Low Concentrations

The task-specific ionic liquid (IL), 1-ethyl-3-methylimidazolium 2-cyanopyrolide ([EMIM][2-CNpyr]), was encapsulated with polyurea (PU) and graphene oxide (GO) sheets via a one-pot Pickering emulsion, and these capsules were used to scrub CO₂ (0-5000 ppm) from moist air. Up to 60 wt % of IL was achieved in the synthesized capsules, and we demonstrated comparable gravimetric CO₂ capacities to zeolites and enhanced absorption rates compared to those of bulk IL due to the increased gas/liquid surface-to-volume area. The reactive IL capsules show recyclability upon mild temperature increase compared to zeolites that are the conventional absorber materials for CO₂ scrubbing. The measured breakthrough curves in a fixed bed under 100% relative humidity establish the utility of reactive IL capsules as moisture-stable scrubber materials to separate CO₂ from air, outperforming zeolites owing to their higher selectivity. It is shown that thermal stability, CO₂ absorption capacity, and rate of uptake by IL capsules can be further modulated by incorporating low-viscosity and nonreactive ILs to the capsule core. This study demonstrates an alternative and facile approach for CO₂ scrubbing, where separation from gas mixtures with extremely low partial pressures of CO₂ is required.

Review of liquid nano-absorbents for enhanced CO2 capture

Liquid nano-absorbents have become a topic of interest as a result of their enhanced mass-transfer performance for CO₂ capture. They are believed to have revolutionized the conventional CO₂ chemisorption process by largely improving CO₂ capture kinetics and reducing the energy requirement for solvent regeneration. Two classes of nanomaterial-based CO₂ capture absorbents, amine-based nanoparticle suspensions (nanofluids) and nanoparticle organic hybrid materials (NOHMs), have been developed, with significant progress achieved in recent decades. This review addresses two key questions for these two state-of-the-art nanomaterials: how are the physical and chemical properties of the prepared liquid nano-absorbents transformed relative to those of the base fluids? And how does the transformation of the properties affect the CO₂ capture behavior? While the current synthesis procedure for liquid nano-absorbents is quite straightforward, more advanced synthesis methods for long-term nanoparticle stability have been suggested for the future. Nanofluids have been shown to increase the CO₂ uptake by over 20% and the CO₂ capture rate by 2-93% compared with the values observed with neat amine solvents. Nanoparticles with catalytic effects on CO₂ capture can significantly increase the CO₂ desorption rate by as high as 4000%. NOHMs exhibit the interesting feature of enhanced mass transfer in CO₂ capture because of the unique pathway network that is created in them for CO₂ to reach specific functional groups. NOHMs promise an effect of combined CO₂ capture and conversion, and can be used especially as electrolytes for CO₂ electro-reduction. However, there are still some challenges for the application of these materials in real life, such as poor stability and high viscosity. Therefore, efficient CO₂ capture processes using these solvents need to be urgently developed and studied in the future.

Automated detection and sorting of microencapsulation via machine learning

Microfluidic-based microencapsulation requires significant oversight to prevent material and quality loss due to sporadic disruptions in fluid flow that routinely arise. State-of-the-art microcapsule production is laborious and relies on experts to monitor the process, e.g. through a microscope. Unnoticed defects diminish the quality of collected material and/or may cause irreversible clogging. To address these issues, we developed an automated monitoring and sorting system that operates on consumer-grade hardware in real-time. Using human-labeled microscope images acquired during typical operation, we train a convolutional neural network that assesses microencapsulation. Based on output from the machine learning algorithm, an integrated valving system collects desirable microcapsules or diverts waste material accordingly. Although the system notifies operators to make necessary adjustments to restore microencapsulation, we can extend the system to automate corrections. Since microfluidic-based production platforms customarily collect image and sensor data, machine learning can help to scale up and improve microfluidic techniques beyond microencapsulation.