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Abstract Electrochemical CO2 reduction reaction (CO2RR) to produce value‐added multi‐carbon chemicals has been an appealing approach to achieving environmentally friendly carbon neutrality in recent years. Despite extensive research focusing on the use of CO2 to produce high‐value chemicals like high‐energy‐density hydrocarbons, there have been few reports on the production of propane (C3H8), which requires carbon chain elongation and protonation. A rationally designed 0D/2D hybrid Cu2O anchored‐Ti3C2Tx MXene catalyst (Cu2O/MXene) is demonstrated with efficient CO2RR activity in an aqueous electrolyte to produce C3H8. As a result, a significantly high Faradaic efficiency (FE) of 3.3% is achieved for the synthesis of C3H8 via the CO2RR with Cu2O/MXene, which is ≈26 times higher than that of Cu/MXene prepared by the same hydrothermal process without NH4OH solution. Based on in‐situ attenuated total reflection‐Fourier transform infrared spectroscopy (ATR‐FTIR) and density functional theory (DFT) calculations, it is proposed that the significant electrocatalytic conversion originated from the synergistic behavior of the Cu2O nanoparticles, which bound the *C2 intermediates, and the MXene that bound the *CO coupling to the C3 intermediate. The results disclose that the rationally designed MXene‐based hybrid catalyst facilitates multi‐carbon coupling as well as protonation, thereby manipulating the CO2RR pathway.

Abstract The battery‐like systems utilizing redox materials present a promising avenue for electrochemical desalination with reduced energy consumption. However, several attempts have focused on reducing the operational voltage to minimize energy consumption, and efforts to enhance the performance of such systems as energy storage devices remain limited. Herein, a system with a high energy density capable of sustaining freshwater production was proposed. Furthermore, in this proposed system, the electrolytes with different pH values were utilized to implement an operational voltage of 2 V or higher. Accordingly, an alkaline Zn electrolyte was paired with acidic MnO2 or VOSO4 electrolyte. During the process, potential side reactions such as chlorine evolution and proton crossover were not observed, even without water‐splitting reactions. Ultimately, at operational voltages exceeding 2 V, the system achieved a desalination rate of 0.104 mg/cm2/min and demonstrated maximum energy discharge of up to 2.4 Wh/L. These designed systems pave the way towards a more environmentally friendly and efficient approach to desalination.

Rechargeable aqueous Zn–CO2 battery is a promising technology that combines energy storage and CO2 utilization to meet the needs of sustainable and eco–friendly energy. However, the practical application of this technology is hindered by factors such as the low current density, low Faradaic efficiency of value–added products, high overpotential, and limited cycles. Cathode catalysts, which are crucial components, significantly influence the performance of the system. There have been several updated publications on cathode materials for aqueous Zn–CO2 batteries, but there are few reports of high–performance Zn–CO2 batteries. Herein, a new system for achieving high performance by injecting gas–phase CO2 was reported. Unlike conventional Zn–CO2 batteries, the problem of low CO2 solubility was overcome by injecting gaseous CO2. The designed Zn–gaseous CO2 battery, powered by Ag or Au catalysts, achieved that Faradaic efficiencies of CO were 92.72 % and 89.56 % at 4 mA/cm2, respectively. The maximum power density of 5.52 mW/cm2 and 7.89 mW/cm2 for Ag and Au catalysts, respectively, were obtained, which were much larger than the power density of conventional aqueous Zn–CO2 batteries. In addition, using X–ray diffraction, ex–situ monitoring of the reaction characterizations on Ag and Au cathodes was carried out and the durability of 600 min was achieved, when using Au cathode. These results offer a practical way to design a Zn–CO2 battery for high-performance energy storage and CO2 utilization.

Technology advancements in energy storage, photocatalysis, and sensors have generated enormous impedimetric data. Electrochemical impedance spectroscopy (EIS) results play an essential role in analyzing the interfacial properties of materials. Nonetheless, in many situations, the data is misinterpreted due to the complexity of the electrochemical system or the compromise between the experimental result and the theoretical model, resulting in partiality in the interpretation process, especially for the impedimetric results. Typically, the experimenter interprets impedimetric results using a searching approach based on a theoretical model until the best‐fitting model is obtained, which is a time‐consuming process, and errors can occur. To reduce misinterpretation by the experimenter, herein, the machine‐learning strategy is demonstrated for the classification of an EIS circuit model and parameter prediction using a deep neural network (DNN). The DNN model shows a highly accurate classifier for the commonly used EIS circuit with an average area under the receiver operating characteristic curve of more than 0.95. Additionally, the model demonstrates high accuracy in the prediction of EIS parameters on a complex EIS system, with a maximum R2 of 0.999. These reveal that the machine‐learning strategy may open a new room for studying electrochemical systems.