(313g) Experimental Evaluation of Hygroscopic Hydrogels for Large-Scale, Low-Temperature Thermal Energy Storage

Thermal energy storage (TES) systems are vital in addressing the intermittent nature of renewable energy sources and achieving more reliable, sustainable power solutions. Among the various TES methods (sensible, latent, and thermochemical), thermochemical energy storage (TCES) stands out for its notably high energy density and ability to retain thermal energy over long periods with minimal losses. Conventional TCES materials, such as metal hydrides, zeolites, and salt hydrates, perform well in theory due to favorable reaction enthalpies but may require high regeneration temperatures, be costly to mass-produce, or suffer limited cyclability, thus restricting widespread adoption in residential and commercial settings. To address these limitations, we recently developed a large-scale thermal storage solution based on hygroscopic hydrogels infused with hygroscopic salts in a pure water vapor environment. By engineering the polymer matrix to prevent salt deliquescence while maximizing water absorption capacity, this hydrogel-salt composite offers high uptake, improved kinetics, and enduring cyclic stability. The hydrogel’s simplicity and tunable properties allow for the optimization of key parameters, including water uptake, thermal conductivity, and operational temperature range. Unlike traditional thermochemical reactants and systems, the composite requires significantly lower desorption temperatures, on the order of 40-80 °C, facilitating integration into existing systems such as solar-driven or low-grade industrial/HVAC heat, allowing for improved cooling efficiency, reduced peak loads, and enhanced utilization of renewable energy. Furthermore, the material surpasses many established sorbent-based TES media in cost-effectiveness, operating range, and manufacturability. Loaded with four grams of LiCl per gram of polymer, the composite exhibited an exceptionally high volumetric energy density exceeding 200 Wh/L for a full-scale TES system, rivaling the performance of top-tier thermochemical storage materials. However, despite it’s gained popularity for the use in TCES systems, the cyclicity and mass transport performance stability for a long operational device has not been shown.

To verify this cyclic performance of our hydrogel in a pure vapor environment, we constructed a specialized large-scale vacuum dynamic vapor sorption (vacuum DVS) apparatus, able to facilitate 3-5 order of magnitude larger samples than current single species sorption characterization instruments. This system precisely regulates temperature, pressure, and vapor composition, enabling experimentation with samples greatly larger than those in typical benchtop vacuum DVS or sorption devices. A high-precision mass balance with temperature and drift compensation is installed inside the chamber, and a dedicated viewport permits video recording of the hydrogel during each sorption phase. We conducted extensive cyclic testing, equivalent to over two years of service life, to assess the hydrogel’s durability. Even after prolonged operation, the salt-impregnated hydrogel preserved its high-water uptake, demonstrating greater reliability compared to conventional sorbents like silica gels and zeolites, which often degrade over time.

Beyond traditional gravimetric methods, we employed advanced computer vision techniques to monitor volumetric changes, transforming straightforward two-dimensional images into water concentration measurements. By applying cutting-edge video segmentation and tracking, we gained deeper insights into swelling and shrinking processes under pure vapor conditions, thereby confirming the hydrogel’s robust structural integrity and validating its reliable cyclicity. These imaging techniques, combined with accurate mass-balance data, ensure a comprehensive perspective on the system’s operation. Finally, mass transfer modeling was used to predict the hydrogel sorption kinetics and shown to accurately predicted both charge and discharge processes. This predictive capability allows for more precise estimates of potential energy and power densities across a wide range of system configurations and cycle durations.