Dual-Functional Phosphomolybdic Acid-Polypyrrole-Ionic Liquid Nanocomposites for Energy Storage and Hydrogen Evolution: Experimental and Theoretical Studies

Abstract

Recent advancements in pseudocapacitive materials for energy storage and catalytic activities highlight the benefits of incorporating nanostructured active materials. This study investigates the modification of polypyrrole (PPy) surfaces using polyoxometalates H4[PVMo11O40] (PVMo11) combined with ionic liquids Hexadecyltrimethylammonium chloride (CTAC) and 1-Benzyl-3-methylimidazolium chloride (BMI). Among various synthesized nanocomposites, PVMo11-BMI-PPy demonstrated superior electrochemical properties in a 0.25 M H2SO4 aqueous electrolyte, achieving a remarkable specific capacitance of 400 F g-1, an energy density of 49.5 Wh kg-1 and power density of 906 W kg-1 at a current density of 1 A g-1. It depicts a capacitive contribution of 94.5% at 10 mV s-1 with an impressive cyclic retention of 91.1% and 98.9 % coulombic efficiency after 10000 GCD cycles. Additionally, PVMo11-BMI-PPy exhibited outstanding electrocatalytic activity for the hydrogen evolution reaction (HER), achieving the highest catalytic activity of 19 mV at a current density of 10 mA cm−2, outperforming the benchmark Pt catalyst. Its superior performance is underscored by a high TOF value of 6.91× 10−7 s-1 and excellent long-term stability in 0.5 M H2SO4 over 24 hours. It is a promising candidate for bifunctional activities such as energy storage and catalytic applications. Additionally, density functional theory (DFT) studies were conducted to gain insights into the enhanced performance of PVMo11-BMI-PPy. The thermodynamic and electronic characteristics indicate that the V site of PVMo11-BMI-PPy offers the most efficient and balanced catalytic environment for the hydrogen evolution reaction (HER) compared to all other sites examined. These theoretical findings align well with experimental observations, demonstrating superior HER activity for the PVMo11-BMI-PPy catalyst, thereby confirming the computational predictions.