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NoElastocaloric cooling is a promising solid-state alternative to vapor-compression refrigeration. In conventional systems, such as natural rubber, deformation induces entropy change accompanied by temperature release. Unloading the material restores the entropic state and is accompanied by cooling. Inverse elastocaloric effects have been detailed in shape memory alloys, where deformation induces loss of order and cooling. Here, we report on a distinctive inverse elastocaloric effect in liquid crystalline elastomers (LCEs) containing supramolecular hydrogen bonds. Upon deformation, the supramolecular LCE exhibits initial organization but then disorganizes as the intramesogenic hydrogen bonds are broken. Due to the liquid crystalline nature of the dimeric supramolecular bonds, the mechanochemical bond breakage manifests in a disruption in order. By disrupting the extent of liquid crystallinity in the system, we hypothesize that the network disorganizes to the deformation (e.g., entropy increases) and produces an inverse elastocaloric output.t Available 

Economic dispatch in a multi-microgrid (MMG) system involves an increasing number of states from distributed energy resources (DERs) compared to a single microgrid. In these cases, traditional reinforcement learning (RL) approaches may become computationally expensive or less effective in finding the least-cost solution. This paper presents a novel RL approach that employs local learning agents to interact with individual microgrid environments in a distributed manner and a global agent to search for actions to minimize system cost at the MMG system level. The proposed distributed RL framework is more efficient in learning the dispatch policy compared to conventional approaches. Case studies are performed on a 3-microgrid system with different types of DERs. Results substantiate the effectiveness of the proposed approach in comparison with conventional methods in terms of operation costs, computation time, and peak-to-average ratio.