Electrochemically Grown Ultrathin Platinum Nanosheet Electrodes with Ultralow Loadings for Energy-Saving and Industrial-Level Hydrogen Evolution

Amorphous Iridium Oxide-Integrated Anode Electrodes with Ultrahigh Material Utilization for Hydrogen Production at Industrial Current Densities

Herein, ionomer-free amorphous iridium oxide (IrOx) thin electrodes are first developed as highly active anodes for proton exchange membrane electrolyzer cells (PEMECs) via low-cost, environmentally friendly, and easily scalable electrodeposition at room temperature. Combined with a Nafion 117 membrane, the IrOx-integrated electrode with an ultralow loading of 0.075 mg cm-2 delivers a high cell efficiency of about 90%, achieving more than 96% catalyst savings and 42-fold higher catalyst utilization compared to commercial catalyst-coated membrane (2 mg cm-2). Additionally, the IrOx electrode demonstrates superior performance, higher catalyst utilization and significantly simplified fabrication with easy scalability compared with the most previously reported anodes. Notably, the remarkable performance could be mainly due to the amorphous phase property, sufficient Ir3+ content, and rich surface hydroxide groups in catalysts. Overall, due to the high activity, high cell efficiency, an economical, greatly simplified and easily scalable fabrication process, and ultrahigh material utilization, the IrOx electrode shows great potential to be applied in industry and accelerates the commercialization of PEMECs and renewable energy evolution.

The impact of support electronegativity on the electrochemical properties of platinum

Modulating the electronic structure of platinum (Pt) through a support is an important strategy for enhancing its electrocatalytic properties. In this work, to explore the impact of support electronegativity on Pt's catalytic activity for hydrogen evolution, we chose diverse metals with varying electronegativities that are stable in acidic solutions, such as titanium (Ti), molybdenum (Mo), and tungsten (W), as supports. Ti is the optimal support according to density functional theory (DFT) calculations. As expected, the Pt@Ti catalyst demonstrated remarkable efficiency in the hydrogen evolution reaction (HER), displaying a minimal overpotential of 13 mV at -10 mA cm-2, a Tafel slope of 34.5 mV dec-1, and sustained durability over 110 h in a 0.5 M H2SO4 solution. To unravel the metal-support interaction (MSI) between Pt and Ti, a comprehensive exploration encompassing both experimental investigations and DFT calculations was undertaken. The results elucidate that the outstanding HER performance of Pt@Ti stems from robust synergies forged between Pt and Ti atoms within the Ti support. This work not only furnishes a technique for producing electrocatalysts with superior efficiency and stability but also streamlines the process of choosing the most appropriate metal support. Moreover, it enhances comprehension of the interaction between Pt and the metal support.

Ultra-Efficient and Cost-Effective Platinum Nanomembrane Electrocatalyst for Sustainable Hydrogen Production

Hydrogen production through hydrogen evolution reaction (HER) offers a promising solution to combat climate change by replacing fossil fuels with clean energy sources. However, the widespread adoption of efficient electrocatalysts, such as platinum (Pt), has been hindered by their high cost. In this study, we developed an easy-to-implement method to create ultrathin Pt nanomembranes, which catalyze HER at a cost significantly lower than commercial Pt/C and comparable to non-noble metal electrocatalysts. These Pt nanomembranes consist of highly distorted Pt nanocrystals and exhibit a heterogeneous elastic strain field, a characteristic rarely seen in conventional crystals. This unique feature results in significantly higher electrocatalytic efficiency than various forms of Pt electrocatalysts, including Pt/C, Pt foils, and numerous Pt single-atom or single-cluster catalysts. Our research offers a promising approach to develop highly efficient and cost-effective low-dimensional electrocatalysts for sustainable hydrogen production, potentially addressing the challenges posed by the climate crisis.

Interfacial Electronic Modulation of Dual-Monodispersed Pt-Ni3S2 as Efficacious Bi-Functional Electrocatalysts for Concurrent H2 Evolution and Methanol Selective Oxidation

Constructing the efficacious and applicable bi-functional electrocatalysts and establishing out the mechanisms of organic electro-oxidation by replacing anodic oxygen evolution reaction (OER) are critical to the development of electrochemically-driven technologies for efficient hydrogen production and avoid CO2 emission. Herein, the hetero-nanocrystals between monodispersed Pt (~ 2 nm) and Ni3S2 (~ 9.6 nm) are constructed as active electrocatalysts through interfacial electronic modulation, which exhibit superior bi-functional activities for methanol selective oxidation and H2 generation. The experimental and theoretical studies reveal that the asymmetrical charge distribution at Pt-Ni3S2 could be modulated by the electronic interaction at the interface of dual-monodispersed heterojunctions, which thus promote the adsorption/desorption of the chemical intermediates at the interface. As a result, the selective conversion from CH3OH to formate is accomplished at very low potentials (1.45 V) to attain 100 mA cm-2 with high electronic utilization rate (~ 98%) and without CO2 emission. Meanwhile, the Pt-Ni3S2 can simultaneously exhibit a broad potential window with outstanding stability and large current densities for hydrogen evolution reaction (HER) at the cathode. Further, the excellent bi-functional performance is also indicated in the coupled methanol oxidation reaction (MOR)//HER reactor by only requiring a cell voltage of 1.60 V to achieve a current density of 50 mA cm-2 with good reusability.

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.