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Continuous greenhouse gas monitoring at sub-zero temperatures is needed for monitoring greenhouse gas emission in cold environments such as the Arctic tundra. This work reports a single-frequency electrochemical impedance sensing (SF-EIS) method for real-time continuous monitoring of carbon dioxide (CO2) at a wide range of temperatures (−15 to 40 °C) by using robust ionic liquid (IL) sensing materials and noninvasive, low-power, and low-cost impedance readout mechanisms since they cause minimal changes in the sensing interface, avoiding the baseline change for long-term continuous sensing. In addition, a miniaturized planar electrochemical sensor was fabricated that incorporates a hydrophobic 1-butyl-1-methylpyrrolidinium bis(trifluromethylsulfonyl)imide ([Bmpy][NTf2]) IL electrolyte and Pt black electrode materials. The high viscosity of the ILs facilitates the formation of thin, ordered, and concentrated layers of ionic charges, and the inverse relationship of IL viscosity with temperature makes them especially suited for impedance sensing at low temperatures. The unique low-temperature properties of ILs together with EIS transduction mechanisms are shown to be sensitive and selective for continuously monitoring CO2 at a −15 to 40 °C temperature range via impedance changes at a specifically selected frequency at the open circuit potential (OCP). Molecular dynamics simulations revealed insights into the structure and dynamics of the IL at varying temperatures in the presence of methane and CO2 and provided potential explanations for the observed sensing results. The miniaturized and flexible planar electrochemical sensor with the [Bmpy][NTf2] electrolyte was tested repeatedly at subzero temperatures over a 58-day period, during which good stability and repeatability were obtained. The CO2 impedance sensor was further tested for sensing CO2 from soil samples and shows promising results for their use in real-time monitoring of greenhouse gas emissions in cold temperatures such as permafrost soils. 

Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users’ social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques. To tackle the above challenges, this work proposes a Knowledge-aware Coupled Graph Neural Network (KCGN) that jointly injects the inter-dependent knowledge across items and users into the recommendation framework. KCGN enables the high-order user- and item-wise relation encoding by exploiting the mutual information for global graph structure awareness. Additionally, we further augment KCGN with the capability of capturing dynamic multi-typed user-item interactive patterns. Experimental studies on real-world datasets show the effectiveness of our method against many strong baselines in a variety of settings. Source codes are available at: https://github.com/xhcdream/KCGN.