Advanced alkaline electrolyzer design for cost reduction

The recent worldwide move towards decarbonization and integration of renewable energy has heightened the search to develop cost-effective production of hydrogen by means of water electrolysis. Among the technologies which are accessible, the most commercially developed is alkaline water electrolysis (AWE), but the main problem is still to reduce its costs. This article discusses future design approaches that can help to minimize the cost of production by enhancing electrode design, catalyst activity and thermal conductivity in future generation alkaline electrolyzers. Based on the results of experimental and computational research, the study focuses on the role of the electrode surface modification, optimization of mass transportation, and three-dimensional current collectors in improving the reaction kinetics and reducing energy losses (Zhang et al., 2025; Schneider et al., 2024; Riaz et al., 2025). The paper also explores how the optimization of artificial intelligence (AI)-controlled and 3D printing technologies can be implemented to monitor in real-time and design scalable cell designs (Bhuiyan et al., 2025; Wang et al., 2026). The paper through a hybrid approach to the models of material selection and cost-performance analysis illustrates that the cost of hydrogen production can be brought down to USD 2.00/kg in 2030 through a combination of high-efficiency nickel-based catalysts and thermally optimized cells. The study determines three main foundational pillars of cost-reduction: (1) advanced electrode architectures based on three-dimensional (3D) porous nickel-based scaffolds, which improve the active surface area and reduce ohmic losses; (2) hybrid Fe-Ni-Co catalytic layers, which are able to increase hydrogen evolution (HER) and oxygen evolution (OER) reaction kinetics; and (3) dynamic thermal control with the help of phase-change materials that stabilize temperatures fluctuations and extend the lifespan of components. An additional predictive control algorithm that enhances energy efficiency is the one that is based on a neural-network optimization and provides a means of real-time adjustment of current densities and electrolyte flow. Computer-based simulation indicates that under best-case conditions, the integrated system would be able to produce certain energy of approximately 36 kWh kg-1 of H2; optimized configurations, performed with one of our TEA, would obtain approximately 47 kWh kg-1 of H2, reducing the cost of production to about USD 2.25 kg -1, with potential to reach even USD 1.85 kg -1 in the most favorable electricity price and scale conditions. The development of green hydrogen based on coupling materials science with digital process optimization is a scalable route to cost-effective green hydrogen.