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 polyoxometalate H₄[PVMo₁₁O₄₀].xH₂O (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 W h kg⁻¹ and a power density of 906 W kg⁻¹ at a current density of 1 A g⁻¹. It achieves a capacitive contribution of 94.5% at 10 mV s⁻¹ with an impressive cyclic retention rate of 91.1% and a coulombic efficiency of 98.9% after 10 000 GCD cycles. Additionally, PVMo₁₁-BMI-PPy exhibited outstanding electrocatalytic activity for 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 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 hydrogen evolution reaction (HER) compared with all other sites examined. These theoretical findings align well with experimental observations, demonstrating superior HER activity of the PVMo₁₁-BMI-PPy catalyst, thereby confirming the computational predictions.