In traditional battery designs, anodes are typically constructed from bulk materials, often contributing to the overall weight of the battery. In the case of magnesium metal batteries, a bare current collector made of copper or zinc serves as the anode. During the charging phase of the battery, magnesium ions from the cathode deposit onto this collector, creating a metallic layer that functions as the anode. This innovative approach provides a significant advantage: it reduces the material weight, making the batteries more compact and lower in production costs. However, one of the major technological hurdles facing this system is the propensity for dendrite growth—a phenomenon that can lead to short circuits and ultimately reduce the efficacy and safety of the battery.

To address these issues, a team of researchers led by Associate Professor Hee-Dae Lim from Hanyang University in South Korea has pioneered a novel facet-guided metal plating strategy. This innovative approach aims to control the deposition of magnesium onto the anode surface, enhancing battery performance while simultaneously suppressing dendrite formation. Dr. Lim elucidates the fundamental concepts behind this groundbreaking research, explaining that the team focused on a “crystallographic strategy” that employs a facet-oriented zinc host with a particularly tailored surface chemistry.

In standard practice, the materials chosen for battery current collectors are generally polycrystalline. Though widely utilized, this type of structure presents significant challenges due to its random grain orientation and dense grain boundaries that can create numerous hotspots during metal deposition. These microscopic inconsistencies can lead to vertical growth of magnesium, which, in turn, contributes to the formation of dendrites that degrade the battery’s performance over time. The research team made decisive strides by opting for zinc as the host metal, recognized for its structural similarity to magnesium, which enhances compatibility during the plating process.

The research group employed meticulous engineering techniques to expose a thermodynamically stable (002) facet of zinc, characterized by a flat and smooth surface. By aligning the host material in this way, magnesium can spread uniformly across the surface layer, effectively reducing the likelihood of hotspots that contribute to dendrite growth. To create this facet, the team subjected bare zinc foil to thermal annealing. This process not only enhances the crystalline structure but also reinforces the surface properties necessary for effective magnesium deposition.

Following the thermal treatment, the bare zinc foil was further subjected to reactive ion etching to create a polished surface known as P-Zn(002). This critical step involved minimizing the effects of grain boundaries—a significant contributing factor to uneven growth during the magnesium plating process. Experimental tests have shown that this finely tuned P-Zn(002) surface significantly mitigates dendrite formation and enhances the stability of the battery. The uniform horizontal growth of magnesium observed in tests suggests that the crystal orientation and surface chemistry played a crucial role in achieving desired outcomes.

The research findings highlight a compelling achievement, as the anode-free magnesium cell employing the P-Zn(002) substrate successfully retained approximately 87.58% of its original capacity after over 900 cycles at a current density of 200 mA/g. This level of performance is noteworthy, particularly when compared to traditional magnesium and lithium-based batteries that often struggle with capacity retention and failure under similar conditions.

Dr. Lim emphasizes that the implications of this research extend beyond mere laboratory success; it proposes a new conceptual framework for next-generation magnesium-metal batteries with high energy densities. Such technological advancements are aligned with future energy requirements, particularly for the evolving landscape of renewable energy systems and smart grid infrastructure. The work exemplifies how strategic control at the atomic level can yield significant breakthroughs in energy storage technology.

Moreover, the study underscores the importance of interdisciplinary approaches bridging materials science, electrochemistry, and chemical engineering. By merging insights from these fields, the research team has contributed valuable knowledge that may stimulate further innovations in battery technology, while also addressing pressing concerns regarding sustainability and efficiency in energy storage systems.

In conclusion, this pivotal research led by Professor Hee-Dae Lim and his team demonstrates an impressive leap forward in anode-free battery technology. By employing facet-guided metal plating techniques, they have illustrated the potential for crystallographic control to create stable anode surfaces, paving the way for practical applications of magnesium metal batteries. As researchers continue to innovate and refine these technologies, the future of energy storage appears promising, a vital component for advancing the utilization of renewable energy and meeting the demands of a sustainable future.

Subject of Research: Magnesium metal batteries
Article Title: Facet-Guided in-Plane Metal Plating via Accelerated Surface Diffusion in Mg Metal Batteries
News Publication Date: 10-Sep-2025
Web References: https://doi.org/10.1002/aenm.202503832
References: 10.1002/aenm.202503832
Image Credits: Hee-Dae Lim from Hanyang University

Keywords

• Anode-free batteries
• Magnesium metal batteries
• Dendrite formation
• Energy storage technology
• Crystallographic control
• Electrochemistry
• Nanotechnology
• Metal plating techniques
• Renewable energy systems
• Materials science

SOM represents a paradigm shift in battery health and usability assessment. Instead of merely reporting raw battery percentages or offering generalized estimates, SOM integrates a deep understanding of both the battery’s physical state and the complexities of the mission environment. Its algorithm takes into account not only the internal electrochemical data of the battery but aligns that information with contextual factors such as traffic dynamics, elevation profiles, and ambient temperature fluctuations. This holistic perspective enables a real-time, task-specific prediction of whether the battery can successfully power a given operation.

This hybrid intelligence approach, as elucidated by Mihri Ozkan, a leading engineering professor at UCR, entails the fusion of data-driven machine learning with rigorous physical law-based modeling. The SOM system transcends the limitations of traditional battery evaluation approaches: classical physics-based models offer predictability but lack adaptability, while pure machine learning methods provide flexibility yet often operate as “black boxes,” lacking interpretability and physical justification. SOM’s innovation lies in marrying these two methods to produce a model that is both accurate and explainable.

Technically, SOM’s core utilizes neural networks that learn from empirical datasets documenting battery charge-discharge cycles, voltage and current fluctuations, heat generation, and degradation patterns over extended time frames. Simultaneously, it enforces constraints derived from electrochemical principles and thermodynamics, ensuring that predictions remain physically tenable. This dual-framework resilience empowers SOM to maintain high accuracy even when subjected to stressors like abrupt temperature drops or demanding elevation climbs, conditions that notoriously confound conventional battery management systems.

To validate this methodology, the UCR team employed extensive datasets publicly available from aerospace and academic institutions, including NASA and Oxford University. These datasets encompass comprehensive battery operational records, capturing real-world fluctuations in voltage, current, temperature, and state of charge under varying environmental conditions. In direct comparison to legacy diagnostic techniques, the SOM model demonstrated a marked improvement in its predictive precision, reducing voltage prediction errors by 0.018 volts, temperature prediction errors by 1.37 degrees Celsius, and charge state estimation errors by 2.42%.

What distinguishes SOM is its shift away from static, retrospective measures like “percent charged” to dynamic, prospective forecasts. For instance, an electric vehicle equipped with SOM could alert its driver that while the planned route is mostly feasible, a recharge stop might be necessary halfway. Similarly, in applications such as drone flight management, SOM’s nuanced predictions can specify whether a mission is viable under present wind and temperature conditions, thus preventing unexpected operational failures.

Importantly, this intelligent system transforms complex, often abstract data points into actionable insights, significantly improving safety margins and operational reliability. By interpreting nuanced battery behaviors in light of mission-specific demands, SOM facilitates smarter energy management decisions, enhancing endurance, reliability, and planning across a wide spectrum of mobility and storage technologies including consumer vehicles, unmanned aerial systems, and grid-scale storage solutions.

While promising, the SOM framework currently faces one main hurdle: computational complexity. The intricate algorithms necessitate processing power beyond what is conventionally feasible for embedded systems typical in today’s battery management architectures. This limitation highlights ongoing engineering challenges in optimizing the algorithms for real-time, resource-limited environments without diminishing prediction accuracy.

However, optimism prevails among the researchers. Continued refinement, algorithmic efficiency advancements, and hardware integration innovations could soon position SOM as a standard feature within electric vehicles and beyond. Its adaptability encompasses future potential with emerging battery chemistries too—such as sodium-ion, solid-state, and flow batteries—extending its impact far beyond lithium-ion technology’s current dominance.

Looking forward, the UCR team aspires to conduct comprehensive field-testing of SOM within operational environments to assess its practical utility and robustness under diverse conditions. These experiments will be critical for validating the framework’s generalizability and helping tailor it to heterogeneous energy applications. The vision is clear: a universal, mission-aware battery diagnostic tool that enhances confidence in energy autonomy, safety, and efficiency across the automotive industry and numerous other sectors reliant on energy storage.

SOM’s innovative fusion of electrochemical science and neural networks signifies a transformative step in battery technology. By making battery diagnostics mission-focused and predictive rather than retrospective, it promises to fundamentally reshape how we interact with energy storage devices, bringing real-world intelligence into electric mobility and beyond.

Subject of Research: Battery management and diagnostic technology integrating neural networks with electrochemical principles.

Article Title: State of mission: Battery management with neural networks and electrochemical AI

News Publication Date: 7-Oct-2025

Web References: http://dx.doi.org/10.1016/j.isci.2025.113593

Image Credits: Mihri Ozkan/UCR

Keywords

Electric vehicles, Vehicles, Transportation engineering, Automobiles, Batteries, Lithium ion batteries, Electrochemistry, Electricity, Electrochemical cells