High-performance electrode material that withstands seawater!
KIMS develops MXene-based highly stable electrode material for seawater electrolysis
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Suppression of Chlorine Evolution Reaction and Improvement of Electrode Durability in Seawater Electrolysis Using the Developed Catalyst Electrode
view moreCredit: Korea Institute of Materials Science (KIMS)
Dr. Juchan Yang’s research team at the Hydrogen & Battery Materials Center, from the Energy & Environment Materials Research Division of the Korea Institute of Materials Science (KIMS), has developed a composite catalyst using the novel material MXene that suppresses the generation of chloride ions-one of the key challenges in seawater electrolysis. This research outcome is expected to accelerate the practical application of seawater electrolysis technology by enabling stable hydrogen production even in seawater.
Hydrogen is gaining attention as an eco-friendly energy source that emits no carbon. However, conventional water electrolysis technologies primarily use clean freshwater, which leads to high production costs and raises concerns over water resource availability. Seawater electrolysis an alternative that directly uses seawater has emerged to address these drawbacks. Nonetheless, a critical challenge remains: chloride (Cl⁻) ions present in seawater can easily corrode the electrolysis electrodes, significantly shortening the lifespan of hydrogen production systems.
MXene is a two-dimensional nanomaterial composed of metals and either carbon or nitrogen. It possesses excellent electrical conductivity and can be combined with various metal compounds, making it well-suited for use as an electrode material. However, it has a notable limitation: its high reactivity with oxygen and water makes it prone to oxidation, which hinders its long-term stability and application.
To address this issue, the research team intentionally oxidized the MXene to form a stable conductive structure and fabricated an electrode composite catalyst by combining it with nickel ferrite (NiFe₂O₄), an oxygen evolution catalyst, using a high-energy ball milling process. The resulting composite catalyst exhibited approximately five times higher current density and twice the durability compared to conventional catalysts. In addition, it demonstrated excellent repulsion toward chloride ions, effectively preventing electrode corrosion. Through this process, the team achieved high uniformity and reproducibility, laying the groundwork for large-scale production. Furthermore, beyond laboratory-scale catalyst performance evaluations, the team successfully validated the material’s performance in an actual electrolysis unit cell, confirming its practical applicability.
This technology is highly significant in that it overcomes the limitations of conventional MXene-based materials by simultaneously securing both conductivity and durability, making it suitable for application in seawater electrolysis electrodes. Moreover, by developing a high-performance electrode material that suppresses corrosion issues in seawater electrolysis, it is expected to accelerate practical implementation and contribute to the global expansion of hydrogen production infrastructure.
Dr. Juchan Yang, the principal investigator at KIMS, stated, “This study is significant in that it addresses the issue of chloride ions in seawater by utilizing the novel material MXene.” He added, “We are actively conducting follow-up demonstration research to further advance this technology into a sustainable hydrogen production solution.”
This research was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the National Research Council of Science & Technology (NST), and was conducted in collaboration with Professor Hyun-Kon Song’s team from the Department of Energy and Chemical Engineering at Ulsan National Institute of Science and Technology (UNIST, President Chong Rae Park). The research findings were published on June 30 in ACS Nano (Impact Factor: 16), a prestigious journal in the field of nanoscience.
Schematic Overview of MXene-Based Catalyst Development for Seawater Electrolysis and Durability Evaluation in a Single Cell. TiOx/C@NFO is the name given to the final composite catalyst produced by ball milling MXene and nickel ferrite
Credit
Korea Institute of Materials Science (KIMS)
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About Korea Institute of Materials Science(KIMS)
KIMS is a non-profit government-funded research institute under the Ministry of Science and ICT of the Republic of Korea. As the only institute specializing in comprehensive materials technologies in Korea, KIMS has contributed to Korean industry by carrying out a wide range of activities related to materials science including R&D, inspection, testing&evaluation, and technology support.
Journal
ACS Nano
Article Title
Durable Seawater Electrolysis through the Synergistic Effect of Oxidized MXene/Nickel Ferrite Composite Electrocatalyst
How deep learning is accelerating multiscale design of porous electrodes for flow cells
Science China Press
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Framework of the deep learning model for multiscale electrode optimization.
view moreCredit: ©Science China Press
To meet urgent net-zero goals, the global energy system is shifting from fossil fuels to renewable sources such as solar and wind. Because these sources are intermittent, efficient storage and conversion are essential. Electrochemical technologies including fuel cells, water electrolyzers, and redox-flow batteries are promising because they decouple energy from power and can operate flexibly. A central bottleneck, however, is the porous electrode. Its complex micro and nano scale features create anisotropic mass transport that is hard to predict and optimize, slowing progress toward higher energy and power density.
In this study, a research team developed a deep learning approach called Electrode Net to accelerate porous-electrode design without sacrificing accuracy. The method represents three-dimensional electrode geometry using signed distance fields and then applies a three-dimensional convolutional neural network to learn the link between structure and transport performance. This combination captures geometry cleanly and enables fast, reliable predictions.
To train and test the model, the researchers built a validated pore-network framework and assembled a comprehensive dataset of 15,433 porous samples paired with their anisotropic transport properties. Across benchmarks, Electrode Net achieved a coefficient of determination (R2) greater than 0.95, outperforming other advanced models on the same tasks. Speed is a key advantage. Guided by the signed distance field representation, Electrode Net cuts computation time by as much as 96% than the conventional numerical simulation models, while maintaining high fidelity. In practice, this turns many hours of simulation into minutes or seconds of model inference, enabling rapid screening of large design spaces.
The team further validated the approach on real electrodes from three technology classes: fuel cells, water electrolyzers, and redox-flow batteries. In each case, the model delivered excellent predictive accuracy, demonstrating strong cross-system generalization and suggesting that the framework can be adopted across diverse subjects.
Beyond fast predictions, the researchers introduced a practical multiscale design workflow. Electrode Net first estimates pore-scale, anisotropic transport parameters. These parameters are then embedded into cell-scale simulations to guide device-level optimization under realistic operating constraints. Using the gas diffusion layer of a proton-exchange-membrane fuel cell as an example, the workflow produced electrode designs with significantly higher limiting power density and limiting current density.
Together, these results show that learning directly from three-dimensional structure can remove a long-standing bottleneck in electrochemical device development. Electrode Net offers a general and scalable path to design porous electrodes faster and more accurately, helping advance next-generation clean-energy technologies.
Journal
Science Bulletin
Method of Research
Computational simulation/modeling
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