The study, led by Professor Kang Taek Lee of the Department of Mechanical Engineering at the Korea Advanced Institute of Science and Technology, or KAIST, was conducted with Professor EunAe Cho of the Department of Materials Science and Engineering. Instead of treating a battery electrode as a uniform block with average properties, the researchers recreated its internal architecture in three dimensions, including graphite particles, binder material, and electrolyte-filled pores. The resulting model was designed to behave like a virtual counterpart of a commercial graphite anode.

That internal structure matters because a lithium-ion battery is not simply a container in which ions move smoothly from one side to another. During charging, lithium ions travel through the electrolyte-filled pores of the anode and enter graphite particles, where they are stored between layers of carbon atoms. When charging is too rapid, however, the ions may reach the graphite surface faster than they can be absorbed. Instead of intercalating into the graphite, they can accumulate as metallic lithium on the surface, a damaging process known as lithium plating.

Lithium plating is one of the most important obstacles to extreme fast charging. It can consume active lithium, reduce the battery’s usable capacity, and in some cases create structures that increase the risk of internal short circuits. At the same time, a thin protective layer called the solid electrolyte interphase, or SEI, forms on the graphite surface. The SEI is essential because it helps stabilize the electrode, but excessive or uneven growth consumes electrolyte and lithium, raises resistance, and can prevent ions from reaching the graphite efficiently.

The anode also undergoes mechanical changes during charging. As lithium enters graphite, the particles expand and push against neighboring particles, binder regions, and pore walls. If the surrounding structure has sufficient empty space, that expansion can be accommodated with less damage. If the local pore volume is too limited, mechanical stress becomes concentrated in particular regions. Because lithium transport, SEI growth, lithium plating, and mechanical deformation occur simultaneously at microscopic scales, experiments that measure only total capacity can miss the earliest signs of failure.

To expose these hidden processes, the KAIST team reconstructed the three-dimensional arrangement of the graphite particles, polymer binder, and pores in a commercial anode. The researchers then altered key structural parameters in the virtual electrode, including its thickness, porosity, and the spatial distribution of the binder. They simulated fast-charging conditions and tracked where lithium ions moved, where lithium plating occurred, how the SEI developed, and which areas experienced the greatest mechanical stress.

The simulations revealed that two electrodes with nearly identical overall compositions and apparent charging capabilities can behave very differently internally. In 50-micrometer-thick anodes, changing the binder distribution produced a capacity difference of less than 4 percent, a variation that might appear relatively minor in conventional battery testing. Yet the simulations showed clear differences in the locations where lithium was inserted into graphite and where degradation reactions were concentrated.

One particularly important result emerged when binder was concentrated near the separator, the membrane that separates the anode from the cathode while allowing lithium ions to pass. The binder occupied space that could otherwise support ion transport, effectively narrowing the pathways through which ions moved into the electrode. This created a microscopic bottleneck similar to traffic congestion on a narrowed road. Under those conditions, lithium plating near the current collector increased by more than 10 percent compared with an anode in which the binder was distributed more evenly.

A more uniform binder arrangement produced more consistent ion transport and helped the SEI form more evenly across the electrode. The contrast became substantially stronger as the anode grew thicker. In an 83-micrometer electrode, the difference in charge capacity between the two binder distributions reached approximately 18 percent. The finding highlights a growing challenge in battery engineering: thicker electrodes can store more energy per unit of area, but their greater transport distance makes them more sensitive to local variations in pores, binder, and particle arrangement.

The researchers also found that pore-space distribution influenced mechanical damage. Regions with adequate pore volume could absorb some of the expansion of graphite particles during charging, while densely packed areas forced particles against one another and developed concentrated stress. These localized effects may not immediately appear in a battery’s total voltage or capacity, but they can gradually accelerate structural damage and amplify other degradation mechanisms. The study therefore suggests that electrode design must consider not only how much graphite, binder, and pore space are present, but also their precise locations.

The digital-twin strategy could allow battery developers to test virtual electrode designs before producing and cycling large numbers of physical prototypes. By revealing where transport bottlenecks, lithium plating, uneven SEI growth, and mechanical stress are likely to occur, the model may help engineers optimize electrodes for fast charging while preserving energy density and service life. Professor Lee said the work demonstrates how three-dimensional modeling can uncover internal battery problems that remain invisible when researchers rely only on overall charging performance. The study, led by KAIST PhD candidate Yejin Kang as first author, was published in InfoMat and featured on the journal’s back cover. Its results point toward a future in which the microscopic architecture of an electrode is designed as carefully as its chemical ingredients.

Subject of Research: Three-dimensional digital-twin modeling of graphite lithium-ion battery anodes, fast-charging degradation, lithium plating, SEI formation, ion transport, binder distribution, pore structure, and mechanical stress.

Article Title: Digital twin quantifies spatial-heterogeneity-driven failure in fast-charging lithium-ion battery anodes

News Publication Date: August 24, 2026

Web References: https://doi.org/10.1002/inf2.70141

References: Kang, Y. et al., “Digital twin quantifies spatial-heterogeneity-driven failure in fast-charging lithium-ion battery anodes,” InfoMat, DOI: 10.1002/inf2.70141. Article publication date: July 7, 2026.

Image Credits: KAIST

Keywords

Lithium-ion batteries, electric vehicles, fast charging, battery degradation, lithium plating, graphite anodes, digital twins, solid electrolyte interphase, electrode microstructure, binder distribution, pore structure, battery materials, energy storage, KAIST.

Sodium-ion batteries are attracting intense interest because sodium is far more abundant than lithium and is widely distributed across Earth’s crust. That abundance could help reduce raw-material costs and ease pressure on lithium supplies. Sodium-based cells also offer safety and low-temperature advantages, making them attractive for applications ranging from stationary energy storage to electric vehicles. However, their commercial progress depends on solving a major problem: many sodium-ion cathodes lose capacity rapidly after repeated charging and discharging.

The Tokyo University of Science team focused on O3-type sodium nickel manganese oxide, written chemically as O3-Na[Ni1/2Mn1/2]O2, or NNMO. This layered material begins with a favorable, stoichiometric sodium arrangement and can deliver relatively high reversible capacity. Yet sodium ions move in and out of its crystal structure during battery operation, causing large changes in the spacing and volume of the layered lattice. Over time, these repeated structural shifts can trigger cracking, phase transformations, loss of crystallinity and severe capacity fading.

To investigate how scandium works, the researchers introduced Sc3+ ions into NNMO in two ways. In the first approach, scandium was incorporated directly into the bulk crystal structure through a doping process. The resulting materials were labeled NNMSOx, with the number representing the scandium content. The researchers paid particular attention to NNMSO8, which demonstrated the strongest cycling performance among the doped compositions. In the second approach, they treated NNMO particles with a scandium isopropoxide solution and then annealed them at 800 degrees Celsius, producing a surface-modified material known as NNMO-SC800.

The difference between the two approaches became strikingly clear when the materials were tested in coin-type sodium cells. After 100 charge-discharge cycles, undoped NNMO retained only 18.6 percent of its original capacity. By comparison, NNMSO8 retained 67.8 percent, while NNMO-SC800 retained 75.4 percent. These results show that both bulk doping and surface coating can dramatically improve durability, although they do so through different chemical and structural mechanisms.

Inside the doped material, electrochemically inactive Sc3+ ions occupy positions normally associated with transition metals. Their ionic size is comparable to that of the nickel and manganese ions in the host lattice, allowing them to become part of the layered framework without simply forming a separate phase. Because scandium does not participate in the same redox reactions as the active transition metals, it helps immobilize nearby sodium ions. These relatively fixed sodium ions function like structural pillars, supporting the layers as sodium is extracted and reinserted during operation.

This internal stabilization also changes the battery’s electrochemical signature. NNMSO8 displayed a smoother charging and discharging profile than the undoped cathode. The researchers attributed this behavior to suppression of sodium-ion and vacancy ordering, a process in which sodium ions and empty sites arrange themselves into ordered patterns during cycling. Such ordering can promote abrupt structural changes and voltage plateaus. By disrupting it, scandium doping allows sodium ions to move through the cathode more smoothly while reducing the size of harmful volume fluctuations.

The coated material followed a different path. In NNMO-SC800, scandium was found mainly at the particle surface, where it formed a phase resembling O3-NaScO2. This protective layer did not substantially alter the crystal structure inside the cathode. Instead, it acted as a barrier between the active electrode and the electrolyte, suppressing parasitic reactions that gradually consume active sodium, damage the surface and accelerate interfacial degradation. The coating therefore improved cycling stability without producing the smoother voltage profile observed in the bulk-doped material.

Tests in full sodium-ion cells further demonstrated the practical significance of the findings. The researchers paired the modified cathodes with hard-carbon anodes and operated the cells for 300 cycles. The full cell using NNMSO8 retained 71.4 percent of its initial capacity, while the cell using NNMO-SC800 retained an impressive 91.2 percent. The results suggest that surface protection is especially powerful for preserving capacity over extended operation, while bulk doping provides important resistance to structural collapse. Neither strategy alone solved every degradation pathway: coating could not fully prevent long-term loss of crystallinity, and doping did not completely eliminate capacity fading.

The researchers say the most promising future direction may be to combine both approaches, creating cathodes that are reinforced internally and protected externally. Scandium provides an exceptionally clear model for understanding how these mechanisms operate, but its cost and limited availability make it unlikely to be the final commercial solution. The next challenge will be to identify more abundant elements that can reproduce scandium’s ability to stabilize sodium-ion battery structures and protect their surfaces. If successful, this design principle could help transform sodium-ion batteries into longer-lasting, lower-cost alternatives for the rapidly expanding energy-storage market.

Subject of Research: Not applicable

Article Title: Scandium doping and coating for improving O3-NaNi1/2Mn1/2O2 electrode in sodium battery

News Publication Date: 8 August 2026

Web References: https://www.tus.ac.jp/en/mediarelations/

References: Small, DOI: 10.1002/smll.75049

Image Credits: Professor Shinichi Komaba and Associate Professor Shinichi Kumakura, Tokyo University of Science, Japan

Keywords

Sodium-ion batteries, scandium doping, surface coating, cathode materials, energy storage, battery technology, electrochemistry, electric vehicles, sustainable energy, materials science

Traditional ether-based electrolytes face a critical hurdle during the charging process. As lithium ions are released from the cathode, the solvents and anions must desolvate to accommodate ion transport. This dynamic desolvation intensifies oxidative breakdown of the electrolyte and perpetuates continuous consumption of electrolyte components, which in turn degrades the solvation structure and undermines redox stability over extended cycling. The result is a progressive decline in battery performance and lifespan.

To counteract these issues, the research team developed an innovative approach by incorporating a targeted ligand anti-solvent (TLAS) into an anion-rich ether electrolyte matrix. In its static state, the TLAS exhibits minimal interaction with lithium ions, thus maintaining the original solvation environment. However, under the influence of the intense electric field present at the positive electrode during high-voltage operation, the TLAS dynamically reorients and actively coordinates at the interface. This unique adaptive coordination effectively replaces the conventional solvent and anion decoordination-recoordination process on the cathode surface.

This TLAS-driven dynamic solvation mechanism significantly curtails electrolyte reconstruction and stabilizes the interphase, effectively reducing oxidative decomposition. The result is a markedly improved cycling stability, as confirmed by performance metrics from lithium metal pouch cells assembled with this gradient solvation electrolyte. One such cell demonstrated an impressive energy density of 450 Wh kg⁻¹ and sustained over 750 cycles while retaining 80% of its capacity—a remarkable improvement over existing systems.

Taking this strategy further, the researchers validated a high-energy pouch cell configuration that achieved an even higher energy density of 605 Wh kg⁻¹. This cell maintained 96% capacity retention after 150 cycles, underscoring the robustness of the gradient electrolyte design under demanding conditions. These results not only highlight the practical viability of this electrolyte engineering approach but also suggest its potential scalability toward commercial battery applications.

The implications of these advancements extend beyond lithium metal batteries. The concept of gradient solvation, enabled by dynamic solvation and targeted ligand anti-solvents, opens up new avenues for electrolyte design in various metal-ion battery chemistries. By modulating solvation behavior at electrified interfaces, it becomes possible to tailor electrolyte properties for enhanced electrochemical stability and longevity.

As the demand for high-energy, durable battery systems continues to surge in electric vehicles and grid storage, this discovery offers a promising pathway to overcoming longstanding limitations. The integration of gradient solvation electrolytes not only elevates lithium metal battery performance but also accelerates the broader quest for next-generation energy storage solutions with superior safety, efficiency, and lifespan.

This pioneering work showcases the power of molecular-level manipulation within electrolytes to transform battery technologies. Future efforts will likely explore optimizing the composition and operational conditions of gradient solvation systems to further enhance their commercial appeal and functional adaptability.

Subject of Research: Lithium metal batteries, electrolyte engineering, solvation chemistry, high-voltage full cells

Article Title: Single-phase gradient-solvation-electrolyte-stabilized Li metal batteries

Article References:
Yang, W., Cai, J., Chen, A. et al. Single-phase gradient-solvation-electrolyte-stabilized Li metal batteries. Nature (2026). https://doi.org/10.1038/s41586-026-10732-z

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41586-026-10732-z

Lithium-ion batteries have become an essential component in modern technology, powering everything from smartphones to electric vehicles. As the demand for reliable and efficient energy storage solutions continues to escalate, efficient monitoring and predictive maintenance of battery health have become paramount. A precise understanding of a battery’s state-of-health can preemptively address issues such as reduced capacity, overcharging, and premature failure, and this is where the new model developed by An, Ma, and Du stands to make considerable impacts.

The core of the researchers’ work is an attention-based bidirectional LSTM network, a type of recurrent neural network (RNN) specifically designed to capture temporal dependencies in sequential data. The bidirectional approach allows the model to learn from both past and future contexts, optimizing its accuracy in prediction tasks. The attention mechanism further enhances this by enabling the model to focus on significant features of the data, allowing it to weigh different input sequences more effectively, which results in improved predictive performance.

Data for training this sophisticated model has been meticulously gathered from real-world applications, ensuring that the results are both relevant and applicable. The researchers conducted extensive experiments with various configurations and datasets, revealing that the attention-based LSTM not only outperformed traditional statistical methods but also other machine learning techniques in predicting lithium-ion battery SOH. This is crucial because accurate SOH prediction can significantly disrupt current paradigms in battery management systems, allowing for more adaptive and predictive approaches.

By employing this advanced model, battery manufacturers can implement more effective monitoring solutions that can forecast potential failures well before they occur. This not only extends the asset lifespan but also optimizes the overall operational efficiency of battery-powered devices and systems. Consequently, the implications extend beyond individual devices, potentially influencing entire industries reliant on battery technologies, significantly reducing service delays and maintenance costs.

Another major contribution of this research is its potential to address concerns related to sustainability and environmental impact. Lithium-ion batteries, while prevalent, also pose disposal challenges due to their toxic components. Improved prediction of degradation rates and health management can lead to more informed decisions regarding recycling and end-of-life management of batteries, facilitating a circular economy in this tech-driven sector. By extending battery life, this model could assist in reducing waste, promoting sustainability, and contributing to more environmentally friendly energy solutions.

The researchers also emphasize the adaptability of their model. As battery chemistry evolves with advancements in technology, the LSTM’s architecture can be adjusted and retrained with new datasets, ensuring that the model remains relevant and accurate amidst rapid changes in the field. This adaptability is particularly crucial in today’s fast-paced technological landscape, where new battery materials and designs are continuously emerging.

Moreover, the implications of this research extend into the realm of smart cities and renewable energy integration. As the world pivots towards sustainable energy solutions, the role of energy storage, particularly via lithium-ion batteries, will become even more central. The ability to accurately forecast battery health will support efficient energy deployment strategies, enhancing grid reliability and allowing for the better integration of second-life applications for batteries that can no longer effectively serve their original purpose.

In light of these findings, it is clear that attention-based bidirectional LSTM models represent a significant leap forward in battery management research. As industries strive for efficiency and sustainability, innovative solutions such as this will play a pivotal role in driving adoption rates of renewable energy technologies, facilitating the transition towards more sustainable energy systems globally.

The researchers also call for collaboration between academia and industry to ensure that their findings are translated into practical applications. As researchers create models that push the boundaries of what’s possible, industry players must work synergistically to implement these innovations in real-world scenarios effectively.

The fundamental question remains: how can the insights derived from this advanced modeling technique influence the next generation of battery technologies? The promise of improved SOH prediction through sophisticated modeling techniques could signal seismic shifts in how industries manage energy resources, ensuring that lithium-ion batteries remain a cornerstone of modern energy storage solutions.

Much work lies ahead, but the potential is undeniably vast. As industries continue to demand improved efficiency and performance from lithium-ion technologies, attention-based LSTM models may become essential tools in achieving these goals.

In conclusion, this innovative research by An, Ma, and Du illuminates the path forward for enhanced battery health management and predictive maintenance solutions. By bridging theoretical advancements with practical applications, their findings encourage a deeper exploration of machine learning techniques in energy storage systems, shaping the future of battery technologies and their myriad applications.

Subject of Research: Lithium-ion battery state-of-health prediction through advanced modeling techniques.

Article Title: Attention-based bidirectional LSTM model construction and application for lithium-ion battery state-of-health prediction.

Article References:

An, Z., Ma, J., Du, X. et al. Attention-based bidirectional LSTM model construction and application for lithium-ion battery state-of-health prediction.
Ionics (2025). https://doi.org/10.1007/s11581-025-06678-3

Image Credits: AI Generated

DOI: https://doi.org/10.1007/s11581-025-06678-3

Keywords: Lithium-ion batteries, state-of-health prediction, attention-based LSTM, deep learning, energy storage, battery management systems, predictive maintenance, sustainability.