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KAIST Identifies Cause of Artifacts in Battery Nanoscale Analysis, Paving the Way for More Reliable Measurements
A signal that appears to show ions moving inside a battery may, in fact, be an illusion caused by an uneven surface. A KAIST research team has identified the origin of this type of artifacts, which can lead researchers to misinterpret what is happening inside a battery, and has developed a method to reduce it. The findings are expected to enable more accurate analysis of ion movement and improve the reliability of next-generation battery-material development, including that of solid-state and sodium-ion batteries.
KAIST (President Choongsik Bae) announced on September 7 that a research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the research groups of Professor Jong Min Yuk from the same department and Professor Nam-Soon Choi from the Department of Chemical and Biomolecular Engineering, has identified the cause of a measurement artifact in nanoscale battery analysis that can be mistaken for actual ion transport. The team also proposed a method for effectively reducing this artifact.
During charging and discharging, lithium or sodium ions move back and forth within a battery. The speed and ease with which these ions move affect the battery’s performance and lifespan. Developing better batteries therefore requires researchers to precisely determine where ions can move freely and where their movement is hindered.
One technique used for this type of analysis is Electrochemical Strain Microscopy (ESM), which is based on Atomic Force Microscopy (AFM). ESM scans the surface of a battery material with an extremely fine tip and measures nanoscale changes in the material associated with ion movement, allowing researchers to indirectly track ion transport.
The problem is that when the surface of a battery material is rough, similar signals can appear even in the absence of actual ion movement. If these signals are interpreted as evidence of ion transport, researchers may incorrectly identify where ions are moving within the material.
To investigate the origin of theseartifactss, the team created fine trenches on the surface of an ionically inactive single-crystal silicon sample. This provided an experimental environment in which no ions were moving, while the sample surface remained uneven.
The results quantitatively demonstrated that variations in surface height alone can alter the degree of contact between the microscope tip and the sample, producing signals similar to those generated by actual ion movement.
The same phenomenon was also observed in actual battery materials. When the team analyzed a graphite anode and the sodium solid electrolyte Na₂Zn₂TeO₆, the ESM signals likewise varied according to surface topography. This confirmed that the issue is not limited to a particular material but is a phenomenon that researchers must account for when conducting nanoscale analyses of a wide range of battery materials.
As a solution, the team proposed making the surfaces of battery materials as smooth and flat as possible. To achieve this, the researchers used a cooling cross-section polisher (CCP), which employs an argon (Ar) ion beam to precisely polish sample cross sections. Because argon is chemically inert under most conditions, this technique allows the surface to be processed precisely without significantly altering the properties of the sample.
This treatment substantially reduced surface roughness and, in turn, decreased measurement artifacts caused by uneven surfaces. The mechanism is comparable to a car moving up and down while traveling over a bumpy road: as the scanning tip passes over height variations on the surface of a battery material, the degree of contact between the tip and the sample changes. These changes can generate signals resembling those produced by actual ion movement.
In particular, the team examined signals detected at grain boundaries—the interfaces at which the small crystals that make up a battery material meet, much like the seams between adjacent tiles.
Before the surface was smoothed, strong ESM signals appeared at these grain boundaries. After the surface was polished, however, the enhanced signals disappeared. This finding indicates that some signals previously interpreted as evidence of “pathways that facilitate ion transport” may actually have resulted from variations in surface height rather than genuine ion movement.
This study is significant because it experimentally demonstrates how this type of measurement artifact arises in nanoscale battery analysis and shows that it can be reduced using the practical approach of smoothing battery-material surfaces.
The findings are expected to provide a more accurate understanding of where ions move freely and where their movement is hindered within a battery. Such insights could provide an important foundation for designing battery materials that facilitate ion transport, thereby enabling faster charging and longer battery life.
The team expects this analytical approach to be applicable not only to widely used lithium-ion batteries but also to next-generation battery systems. These include solid-state batteries, which use solid rather than liquid electrolytes, and sodium-ion batteries, which use sodium ions in place of lithium ions. The approach could help researchers more accurately understand how these batteries operate and support the design of new materials.
Furthermore, the accumulation of reliable nanoscale analysis data could provide high-quality training datasets for artificial intelligence (AI) and machine-learning research aimed at designing new battery materials and predicting their performance.
“This research clearly demonstrates how variations in surface height affect the results of nanoscale battery-material analysis,” said Professor Hong. “We expect our findings to enable more accurate tracking of ion movement within batteries and contribute to understanding the operating mechanisms of next-generation battery materials and designing improved materials.”
Dongyan Chen, a PhD student in the Department of Materials Science and Engineering, served as the first author of the study, which was published in Small Methods, an international journal specializing in materials science and nanotechnology.
Paper title: Quantitative Analysis of Topographic Crosstalk in DART-ESM Arising from Feedback-Loop-Delay-Induced Contact Stiffness Variations in Battery Materials
DOI: https://doi.org/10.1002/smtd.70763
This work was supported by National Research Foundation of Korea (NRF) grants funded by the Korean government’s Ministry of Science and ICT (MSIT) (Nos. RS-2026-25468150 and RS-2023-00247245).
2026.09.07 View 283 -
KAIST Turns Once-Troublesome Protons into an Energy Storage Resource for Batteries
Batteries that use water-based electrolytes have a relatively low risk of fire and are inexpensive, but they have faced limitations in storing large amounts of energy. KAIST researchers have succeeded in using a reaction previously regarded as a “troublemaker” that degrades battery performance to store more energy instead. This achievement opens up the possibility of next-generation water-based batteries capable of storing more energy while charging and discharging rapidly.
KAIST (President Choongsik Bae) announced on the 19th that a research team led by Sarah S. Park from the Department of Chemistry has developed a new electrode material that sequentially stores zinc ions (Zn²⁺) and protons. The material is based on a two-dimensional conductive metal–organic framework (MOF), a structure in which metals and organic molecules are connected to form microscopic pores.
Aqueous zinc-ion batteries use water-based electrolytes. An electrolyte is a substance that allows ions to move inside a battery. Because aqueous zinc-ion batteries have a relatively low risk of fire, are inexpensive, and impose a low environmental burden, they are attracting attention as a next-generation option for large-scale energy storage systems (ESS).
Because zinc ions carry electrical charge, are divalent ions and slow movement inside electrodes, making it difficult to achieve both high capacity and fast charge–discharge performance at the same time. Protons, by contrast, are extremely small and can move very quickly, making them an ideal charge carrier. However, when too many protons enter an electrode, byproducts form on the electrode surface, interfering with the movement of zinc ions and lowering battery performance. For this reason, protons have traditionally been regarded as “troublemakers” that degrade battery performance, and research has focused on suppressing their reactions.
The research team took a different approach. Instead of suppressing proton reactions, the research team precisely controlled the order in which zinc ions and protons are stored so that both could be used for energy storage.
To accomplish this, they introduced amine functional groups into the micropores of a two-dimensional conductive MOF, designing the material so that it would react with protons only when a certain voltage was reached. By designing the amine functional groups to store protons only at a specific voltage, the researchers enabled zinc ions to be stored first, followed by the additional storage of protons.
The principle is similar to first placing large pebbles in an empty bottle and then filling the gaps between them with fine sand. The electrode is used more efficiently by storing the relatively large zinc ions first and then adding the smaller protons.
In practice, zinc ions were stored first in the higher-voltage range, while protons were additionally stored as the voltage decreased. In the Cu₃(HHTATP)₂ electrode material developed by the research team, zinc ions and protons participated in energy storage sequentially in different voltage ranges.
The sequence is particularly important. If protons react too early, by-products may form and obstruct the movement of zinc ions. By enabling protons to react only after the zinc ions had been stored, the researchers reduced interference between the two storage processes.
The newly developed electrode material achieved a high storage capacity of 368.7 mAh g⁻¹ at 0.5 A g⁻¹. In simple terms, this means that a small amount of electrode material can store a large amount of electricity.
Even when the charging and discharging rate was increased sixteenfold, the electrode retained 46.9% of its initial storage capacity—nearly half. Batteries generally store less energy as their charging and discharging rates increase, but the new electrode maintained substantial storage capacity even under rapid charging and discharging conditions. It also demonstrated stable performance after more than 500 rapid charge–discharge cycles.
Using various X-ray analysis techniques, the research team confirmed that zinc ions were stored first, followed by additional proton storage. The researchers also found that the process of storing and subsequently releasing protons could be repeated.
This study is significant because it overcomes the difficulty of simultaneously achieving high capacity and rapid ion transport in porous electrode materials. In particular, it presents a new direction for developing next-generation aqueous zinc-ion batteries by demonstrating that the storage sequence of different ions can be controlled through molecular-level design.
The findings present a new design approach that could enable safe and economical aqueous zinc-ion batteries to store more energy while charging and discharging rapidly. The approach is expected to help improve the performance of water-based batteries used in applications such as large-scale energy storage systems.
Professor Sarah S. Park from KAIST said, “This study demonstrates that protons, previously regarded as ‘troublemakers’ that could degrade battery performance, can instead be used to store more energy. We expect that applying this principle to various electrode materials will lead to the development of batteries capable of rapidly storing larger amounts of energy.”
POSTECH doctoral student Geunchan Park and master student Gyuwon Lee, participated as co-first authors. The study was published in the international chemistry journal Chem on July 7.
Paper title: Sequential Zn²⁺–H⁺ Storage in a 2D Conductive Metal–Organic Framework for Advanced Aqueous Zinc-Ion Battery
DOI: 10.1016/j.chempr.2026.103129
Author information: Geunchan Park (co-first author), Gyuwon Lee (co-first author), Kangmin Kim (second author), Sarah S. Park (corresponding author)
This research was supported by the Basic Research Program of the National Research Foundation of Korea and the National Supercomputing Center.
2026.08.19 View 873 -
KAIST Develops Ultra-Precise Inspection Technology to Prevent Electric Vehicle Battery Fires
An ultra-precise inspection technology that could help prevent electric vehicle battery fires and improve battery safety has been developed. A KAIST research team has developed a method capable of detecting minute variations in battery electrode thickness that can contribute to thermal runaway with a precision equivalent to approximately one ten-thousandth the diameter of a human hair, all without disassembling or damaging the battery. The technology is expected to improve battery safety and quality by identifying invisible defects during the manufacturing process.
KAIST (President Choongsik Bae) announced on 23rd of July that a research team led by Professor Young-Jin Kim from the Department of Mechanical Engineering has developed a technology that measures the thickness of lithium-ion battery electrodes in a non-contact and non-destructive manner.
The technology combines terahertz waves (electromagnetic waves in the spectral region between light and radio waves) to obtain information from inside battery electrodes with an optical frequency comb, which divides the frequency of light into evenly spaced intervals like the markings on a ruler and serves as a reference for ultra-precise measurements.
The electrodes in lithium-ion batteries, which are widely used in electric vehicles, are essential components through which electric current flows. Even a slight variation in electrode thickness can cause current to become concentrated in certain areas when charging and discharging, generating heat. If the heat continues to accumulate, it may lead to thermal runaway, a phenomenon in which the internal temperature of a battery rises rapidly and can result in a fire or explosion. Maintaining uniform electrode thickness is therefore critically important during battery manufacturing.
Existing inspection technologies, however, have limitations when applied to production environments. X-ray computed tomography can provide detailed images of internal structures, but its relatively long inspection time makes it difficult to use on high-speed production lines. Ultrasonic acoustic microscopy requires direct contact with a liquid medium, while laser displacement sensors can perform rapid measurements but have difficulty precisely analyzing structures inside an electrode.
The research team overcame these limitations by combining optical frequency comb and terahertz technologies. The researchers first directed terahertz waves at a battery electrode and collected signals generated as the waves were repeatedly reflected within the electrode. They then used an optical frequency comb as a reference to analyze the signals with exceptionally high precision and calculate the electrode thickness. This enabled nanometer-scale measurements of the electrode’s internal structure without damaging the battery.
At the core of the technology is Fabry–Pérot interference, a regularly spaced interference pattern produced as terahertz waves repeatedly travel back and forth between the front and rear surfaces of an electrode. Much like measuring length by reading the markings on a ruler, the researchers precisely analyzed the interference pattern using the optical frequency comb as a reference to determine the electrode thickness.
As a result, the team successfully measured both the electrode thickness and its complex refractive index (a material’s optical property indicating how strongly it transmits and absorbs electromagnetic waves) in a single measurement without requiring a separate calibration process.
The researchers validated the technology using battery electrodes measuring between 50 and 150 micrometers in thickness, comparable to the diameter of a human hair. With a measurement time of just 0.2 seconds, the system detected thickness differences as small as 70.1 nanometers in the anode (approximately one fourteen-hundredth the diameter of a human hair) and 465.5 nanometers in the cathode. This measurement speed is considered sufficient for use on rapidly moving battery production lines.
When the measurement time was increased to 25.6 seconds, the precision improved further. The system distinguished differences as small as 7.8 nanometers in the anode (approximately one ten-thousandth the diameter of a human hair) and 25.2 nanometers in the cathode. This represents up to a 100-fold improvement in precision compared with conventional time-domain analysis methods, enabling the detection of thickness variations that are completely invisible to the naked eye.
The technology is not limited to measuring thickness at a single point. It can generate a three-dimensional map of thickness across an entire electrode and track gradual thickness variations in real time during production. The researchers also confirmed that the system could accurately measure an electrode tilted at an angle of approximately 45 degrees, demonstrating its potential for application to fast-moving, real-world battery manufacturing lines.
The study is significant because it presents a new inspection technology capable of identifying invisible microscopic defects during production without disassembling or damaging batteries. In addition to lithium-ion batteries, the technology is expected to serve as a key quality-control tool for manufacturing next-generation all-solid-state batteries, which use solid electrolytes instead of liquid electrolytes. By detecting defects at an early stage, the technology could improve battery safety and quality while enabling more stable manufacturing processes.
“This technology is an integrated metrology platform that can simultaneously measure electrode thickness and material properties without requiring a separate calibration process,” said Professor Kim. “We expect it to become a key technology for the real-time quality control of production lines for next-generation lithium-ion batteries and all-solid-state batteries.”
The study was led by Dr. Guseon Kang from the KAIST Department of Mechanical Engineering, currently with the Korea Institute of Industrial Technology, as the first author, with Professor Young-Jin Kim serving as the corresponding author. The research findings were published in the international journal Nature Communications on June 10.
Paper title: Nanometre-precision terahertz interferometry for battery electrode metrology
DOI: https://doi.org/10.1038/s41467-026-74193-8
This work was financially supported by the National Research Foundation of Korea (NRF) (RS-2024-00401786, RS-2025-00523273, RS-2025-25455397, RS-2026-25540567, and NRF-2022M1A3C2069728) and from the Korean government’s Defense Acquisition Program Administration (DAPA) (KRIT-CT-22-040).
2026.07.23 View 1750 -
KAIST’s Reliability-Aware AI Opens Path to Faster Cathode Design and Next-Generation Batteries
< (From front left) Professor Seungbum Hong, Professor EunAe Cho (From back left) Chaeyul Kang, Benediktus Madika, Jung Hyeon Moon, Taemin Park (Top) JooSung Shim >
The power that makes electric vehicles travel further and smartphones last longer comes from battery materials. Among them, the core material that directly determines the performance and lifespan of a battery is the cathode material. What if artificial intelligence could replace the numerous experiments required for battery material development? KAIST's research team has developed an artificial intelligence (AI) framework that presents both the particle size of cathode materials and prediction reliability even in situations where experimental data is insufficient, opening the possibility of expansion to next-generation energy technologies such as all-solid-state batteries.
KAIST announced on January 26th that a research team led by Professor Seungbum Hong of the Department of Materials Science and Engineering, in joint research with Professor EunAe Cho's team, has developed a machine learning framework that accurately predicts the particle size of battery cathode materials even when experimental data is incomplete and provides the degree of reliability of the results.
The cathode material inside the battery is the core material that allows lithium-ion batteries to store and use energy. Currently, the most widely used cathode material for electric vehicle batteries is an NCM-based metal oxide mixed with nickel (Ni), cobalt (Co), and manganese (Mn), which greatly affects the battery's lifespan, charging speed, driving range, and safety.
KAIST research team focused on the fact that the size of the very small primary particles that make up these cathode materials is a key factor in determining battery performance. This is because if the particles are too large, performance deteriorates, and conversely, if they are too small, stability problems may occur. Accordingly, the research team developed an AI-based technology that can accurately predict and control particle size.
< Battery performance prediction related (AI-generated image) >
In the past, to determine the particle size, numerous experiments had to be repeated while changing the sintering temperature, time, and material composition. However, in actual research fields, it was difficult to measure all conditions without omission, and experimental data were often missing, which limited the precise analysis of the relationship between process conditions and particle size.
To solve this problem, the research team designed an AI framework that supplements missing data and presents prediction results along with reliability. This framework is characterized by combining a technology (MatImpute) that supplements missing experimental data by considering chemical characteristics and a probabilistic machine learning model (NGBoost) that calculates prediction uncertainty.
This AI model does not stop at simply predicting particle size but also provides information on the extent to which the prediction can be trusted. This serves as an important criterion for deciding under what conditions to actually synthesize materials.
As a result of learning by expanding experimental data, the AI model showed a high prediction accuracy of about 86.6%. According to the analysis, it was found that the cathode material particle size is more significantly affected by process conditions such as baking temperature and time than by material components, which aligns well with existing experimental understanding.
To verify the reliability of the AI prediction, the research team conducted an experiment by newly producing four types of cathode material samples synthesized under manufacturing conditions not included in the existing data while maintaining the same metal component ratio of NCM811 (Ni 80% / Co 10% / Mn 10%) composition. As a result, the particle size predicted by the AI almost matched the actual microscopic measurement results, and most of the errors were 0.13 micrometers (μm) or less, which is much smaller than the thickness of a human hair. In particular, the actual experimental results were included within the prediction uncertainty range presented by the AI, confirming that not only the predicted value but also its reliability was valid.
< Distribution shift condition experiment verification using 4 types of samples >
This study is significant in that it has opened a way to find conditions with a high probability of success first without performing all experiments in battery research. Through this, it is expected to speed up the development of battery materials and significantly reduce unnecessary experiments and costs.
Professor Seungbum Hong said, "The key is that the AI presents not only the predicted value but also how much the result can be trusted," and added, "It will be of practical help in designing next-generation battery materials more quickly and efficiently."
In this study, Benediktus Madika, a doctoral student in the Department of Materials Science and Engineering, participated as the first author, and it was published on October 8, 2025, in 'Advanced Science', an internationally prestigious academic journal in the field of materials science and chemical engineering.
※ Paper Title: Uncertainty-Quantified Primary Particle Size Prediction in Li-Rich NCM Materials via Machine Learning and Chemistry-Aware Imputation, DOI: https://doi.org/10.1002/advs.202515694
Meanwhile, this research was conducted by researchers Benediktus Madika, Chaeyul Kang, JooSung Shim, Taemin Park, Jung Hyeon Moon, and the research team of Professor EunAe Cho and Professor Seungbum Hong, and was conducted with support from the Ministry of Science and ICT (MSIT) National Research Foundation of Korea (NRF) Future Convergence Technology Pioneer (Strategic) (Project No. RS-2023-00247245).
< Battery performance prediction (AI-generated image) >
2026.01.26 View 3193 -
Breaking Performance Barriers of All Solid State Batteries
< (Bottom, from left) Professor Dong-Hwa Seo, Researcher Jae-Seung Kim, (Top, from left) Professor Kyung-Wan Nam, Professor Sung-Kyun Jung, Professor Youn-Seok Jung >
Batteries are an essential technology in modern society, powering smartphones and electric vehicles, yet they face limitations such as fire explosion risks and high costs. While all-solid-state batteries have garnered attention as a viable alternative, it has been difficult to simultaneously satisfy safety, performance, and cost. Recently, a Korean research team successfully improved the performance of all-solid-state batteries simply through structural design—without adding expensive metals.
KAIST announced on January 7th that a research team led by Professor Dong-Hwa Seo from the Department of Materials Science and Engineering, in collaboration with teams led by Professor Sung-Kyun Jung (Seoul National University), Professor Youn-Suk Jung (Yonsei University), and Professor Kyung-Wan Nam (Dongguk University), has developed a design method for core materials for all-solid-state batteries that uses low-cost raw materials while ensuring high performance and low risk of fire or explosion.
Conventional batteries rely on lithium ions moving through a liquid electrolyte. In contrast, all-solid-state batteries use a solid electrolyte. While this makes them safer, achieving rapid lithium-ion movement within a solid has typically required expensive metals or complex manufacturing processes.
To create efficient pathways for lithium-ion transport within the solid electrolyte, the research team focused on "divalent anions" such as oxygen and sulfur . Divalent anions play a crucial role in altering the crystal structure by integrating into the basic framework of the electrolyte.
The team developed a technology to precisely control the internal structure of low-cost zirconium (Zr)-based halide solid electrolytes by introducing these divalent anions. This design principle, termed the "Framework Regulation Mechanism," widens the pathways for lithium ions and lowers the energy barriers they encounter during transport. By adjusting the bonding environment and crystal structure around the lithium ions, the team enabled faster and easier movement.
To verify these structural changes, the researchers utilized various high-precision analysis techniques, including:
High-energy Synchrontron X-ray diffraction(Synchrotron XRD)
Pair Distribution Function (PDF) analysis
X-ray Absorption Spectroscopy (XAS)
Density Functional Theory (DFT) modeling for electronic structure and diffusion.
The results showed that electrolytes incorporating oxygen or sulfur improved lithium-ion mobility by 2 to 4 times compared to conventional zirconium-based electrolytes. This signifies that performance levels suitable for practical all-solid-state battery applications can be achieved using inexpensive materials.
Specifically, the ionic conductivity at room temperature was measured at approximately 1.78 mS/cm for the oxygen-doped electrolyte and 1.01 mS/cm for the sulfur-doped electrolyte. Ionic conductivity indicates how quickly and smoothly lithium ions move; a value above 1 mS/cm is generally considered sufficient for practical battery applications at room temperature.
< Structural Regulation Mechanism of Zr-based Halide Electrolytes via Divalent Anion Introduction >
< Atomic Rearrangement of Solid Electrolyte for All-Solid-State Batteries (AI-generated image) >
Professor Dong-Hwa Seo stated, "Through this research, we have presented a design principle that can simultaneously improve the cost and performance of all-solid-state batteries using cheap raw materials. Its potential for industrial application is very high." Lead author Jae-Seung Kim added that the study shifts the focus from "what materials to use" to "how to design them" in the development of battery materials.
This study, with Jae-Seung Kim (KAIST) and Da-Seul Han (Dongguk University) as co-first authors, was published in the international journal Nature Communications on November 27, 2025.
Paper Title: Divalent anion-driven framework regulation in Zr-based halide solid electrolytes for all-solid-state batteries
DOI: https://www.nature.com/articles/s41467-025-65702-2
This research was supported by the Samsung Electronics Future Technology Promotion Center, the National Research Foundation of Korea, and the National Supercomputing Center.
2026.01.07 View 7756 -
KAIST Unveils Cause of Performance Degradation in Electric Vehicle High-Nickel Batteries: "Added with Good Intentions
<(From left in the front row) Professor Nam-Soon Choi, Professor Dong-Hwa Seo, (back row, from left) Ph.D candidate Gihoon Lee, Ph.D candidate Seung Hee Han, Ph.D candidate Jae-Seung Kim, (top) M.S candidate Junyoung Kim>
High-nickel batteries, which are high-energy lithium-ion batteries primarily used in electric vehicles, offer high energy density but suffer from rapid performance degradation. A research team from KAIST has, for the first time globally, identified the fundamental cause of the rapid deterioration (degradation) of high-nickel batteries and proposed a new approach to solve it.
KAIST announced on December 3rd that a research team led by Professor Nam-Soon Choi of the Department of Chemical and Biomolecular Engineering, in collaboration with a research team led by Professor Dong-Hwa Seo of the Department of Materials Science and Engineering, has revealed that the electrolyte additive 'succinonitrile (CN4), which has been used to improve battery stability and lifespan, is actually the key culprit causing performance degradation in high-nickel batteries.
In a battery, electricity is generated as lithium ions travel between the cathode and the anode. A small amount of CN4 is included in the electrolyte to facilitate the movement of lithium. The research team confirmed through computer calculations that CN4, which has two nitrile (-CN) structures, attaches excessively strongly to the nickel ions on the surface of the high-nickel cathode.
The nitrile structure is a 'hook-like' structure, where carbon and nitrogen are bound by a triple bond, making it adhere well to metal ions. This strong bonding destroys the protective electrical double layer (EDL) that should form on the cathode surface. During the charging and discharging process, the cathode structure is distorted (Jahn-Teller distortion), and even electrons from the cathode are drawn out to the CN4, leading to rapid damage of the cathode.
Nickel ions that leak out during this process migrate through the electrolyte to the anode surface, where they accumulate. This nickel acts as a 'bad catalyst' that accelerates electrolyte decomposition and wastes lithium, further speeding up battery degradation.
Various analyses confirmed that CN4 transforms the high-nickel cathode surface into an abnormal layer deficient in nickel, and changes the normally stable structure into an abnormal 'rock-salt structure'.
This proves the dual nature of CN4: while useful in LCO batteries (lithium cobalt oxide), it actually causes the structural collapse in high-nickel batteries with a high nickel ratio.
This research holds significant meaning as a precise analysis that goes beyond simple control of charging/discharging conditions, to even elucidating the actual electron transfer occurring between metal ions and electrolyte molecules. Based on this achievement, the research team plans to develop a new electrolyte additive optimized for high-nickel cathodes.
<Schematic diagram of the ligand coordination between CN₄ molecules and Ni³⁺ on the high-nickel cathode surface and the cathode structural degradation process>
Professor Nam-Soon Choi stated, "A precise, molecular-level understanding is essential to enhance battery lifespan and stability. This research will pave the way for the development of new additives that do not excessively bond with nickel, significantly contributing to the commercialization of next-generation high-capacity batteries."
This research, jointly led by Professor Nam-Soon Choi, Seung Hee Han, Junyoung Kim, and Gihoon Lee of the Department of Chemical and Biomolecular Engineering, and Professor Dong-Hwa Seo and Jae-Seung Kim of the Department of Materials Science and Engineering as co-first authors, was published online on November 14th in the prestigious international journal 'ACS Energy Letters' and was selected as the cover article.
※ Paper Title: Unveiling Bidentate Nitrile-Driven Structural Degradation in Ultra-High-Nickel Cathodes,
https://doi.org/10.1021/acsenergylett.5c02845
<Cover Page of International Journal(ACS Energy Letters)>
The research was supported by Samsung SDI.
2025.12.03 View 4328 -
Material Innovation Realized with Robotic Arms and AI, Without Human Researchers
<(From Left) M.S candidate Dongwoo Kim from KAIST, Ph.D candidate Hyun-Gi Lee from KAIST, Intern Yeham Kang from KAIST, M.S candidate Seongjae Bae from KAIST, Professor Dong-Hwa Seo from KAIST, (From top right, from left) Senior Researcher Inchul Park from POSCO Holdings, Senior Researcher Jung Woo Park, senior researcher from POSCO Holdings>
A joint research team from industry and academia in Korea has successfully developed an autonomous lab that uses AI and automation to create new cathode materials for secondary batteries. This system operates without human intervention, drastically reducing researcher labor and cutting the material discovery period by 93%.
* Autonomous Lab: A platform that autonomously designs, conducts, and analyzes experiments to find the optimal material.
KAIST (President Kwang Hyung Lee) announced on the 3rd of August that the research team led by Professor Dong-Hwa Seo of the Department of Materials Science and Engineering, in collaboration with the team of LIB Materials Research Center in Energy Materials R&D Laboratories at POSCO Holdings' POSCO N.EX.T Hub (Director Ki Soo Kim), built the lab to explore cathode materials using AI and automation technology.
Developing secondary battery cathode materials is a labor-intensive and time-consuming process for skilled researchers. It involves extensive exploration of various compositions and experimental variables through weighing, transporting, mixing, sintering*, and analyzing samples.
* Sintering: A process in which powder particles are heated to form a single solid mass through thermal activation.
The research team's autonomous lab combines an automated system with an AI model. The system handles all experimental steps—weighing, mixing, pelletizing, sintering, and analysis—without human interference. The AI model then interprets the data, learns from it, and selects the best candidates for the next experiment.
<Figure 1. Outline of the Anode Material Autonomous Exploration Laboratory>
To increase efficiency, the team designed the automation system with separate modules for each process, which are managed by a central robotic arm. This modular approach reduces the system's reliance on the robotic arm.
The team also significantly improved the synthesis speed by using a new high-speed sintering method, which is 50 times faster than the conventional low-speed method. This allows the autonomous lab to acquire 12 times more material data compared to traditional, researcher-led experiments.
<Figure 2. Synthesis of Cathode Material Using a High-Speed Sintering Device>
The vast amount of data collected is automatically interpreted by the AI model to extract information such as synthesized phases and impurity ratios. This data is systematically stored to create a high-quality database, which then serves as training data for an optimization AI model. This creates a closed-loop experimental system that recommends the next cathode composition and synthesis conditions for the automated system.
* Closed-loop experimental system: A system that independently performs all experimental processes without researcher intervention.
Operating this intelligent automation system 24 hours a day can secure more than 12 times the experimental data and shorten material discovery time by 93%. For a project requiring 500 experiments, the system can complete the work in about 6 days, whereas a traditional researcher-led approach would take 84 days.
During development, POSCO Holdings team managed the overall project planning, reviewed the platform design, and co-developed the partial module design and AI-based experimental model. The KAIST team, led by Professor Dong-hwa Seo, was responsible for the actual system implementation and operation, including platform design, module fabrication, algorithm creation, and system verification and improvement.
Professor Dong-Hwa Seo of KAIST stated that this system is a solution to the decrease in research personnel due to the low birth rate in Korea. He expects it will enhance global competitiveness by accelerating secondary battery material development through the acquisition of high-quality data.
<Figure 3. Exterior View (Side) of the Cathode Material Autonomous Exploration Laboratory>
POSCO N.EX.T Hub plans to apply an upgraded version of this autonomous lab to its own research facilities after 2026 to dramatically speed up next-generation secondary battery material development. They are planning further developments to enhance the system's stability and scalability, and hope this industry-academia collaboration will serve as a model for using innovative technology in real-world R&D.
<Figure 4. Exterior View (Front) of the Cathode Material Autonomous Exploration Laboratory>
The research was spearheaded by Ph.D. student Hyun-Gi Lee, along with master's students Seongjae Bae and Dongwoo Kim from Professor Dong-Hwa Seo’s lab at KAIST. Senior researchers Jung Woo Park and Inchul Park from LIB Materials Research Center of POSCO N.EX.T Hub's Energy Materials R&D Laboratories (Director Jeongjin Hong) also participated.
2025.08.06 View 8152 -
KAIST Extends Lithium Metal Battery Lifespan by 750% Using Water
Lithium metal, a next-generation anode material, has been highlighted for overcoming the performance limitations of commercial batteries. However, issues inherent to lithium metal have caused shortened battery lifespans and increased fire risks. KAIST researchers have achieved a world-class breakthrough by extending the lifespan of lithium metal anodes by approximately 750% only using water.
KAIST (represented by President Kwang Hyung Lee) announced on the 2nd of December that Professor Il-Doo Kim from the Department of Materials Science and Engineering, in collaboration with Professor Jiyoung Lee from Ajou University, successfully stabilized lithium growth and significantly enhanced the lifespan of next-generation lithium metal batteries using eco-friendly hollow nanofibers as protective layers.
Conventional protective layer technologies, which involve applying a surface coating onto lithium metal in order to create an artificial interface with the electrolyte, have relied on toxic processes and expensive materials, with limited improvements in the lifespan of lithium metal anodes.
< Figure 1. Schematic illustration of the fabrication process of the newly developed protective membrane by eco-friendly electrospinning process using water >
To address these limitations, Professor Kim’s team proposed a hollow nanofiber protective layer capable of controlling lithium-ion growth through both physical and chemical means. This protective layer was manufactured through an environmentally friendly electrospinning process* using guar gum** extracted from plants as the primary material and utilizing water as the sole solvent.
*Electrospinning process: A method where polymer solutions are subjected to an electric field, producing continuous fibers with diameters ranging from tens of nanometers to several micrometers.
**Guar gum: A natural polymer extracted from guar beans, composed mainly of monosaccharides. Its oxidized functional groups regulate interactions with lithium ions.
< Figure 2. Physical and chemical control of Lithium dendrite by the newly developed protective membrane >
The nanofiber protective layer effectively controlled reversible chemical reactions between the electrolyte and lithium ions. The hollow spaces within the fibers suppressed the random accumulation of lithium ions on the metal surface, stabilizing the interface between the lithium metal surface and the electrolyte.
< Figure 3. Performance of Lithium metal battery full cells with the newly developed protective membrane >
As a result, the lithium metal anodes with this protective layer demonstrated approximately a 750% increase in lifespan compared to conventional lithium metal anodes. The battery retained 93.3% of its capacity even after 300 charge-discharge cycles, achieving world-class performance.
The researchers also verified that this natural protective layer decomposes entirely within about a month in soil, proving its eco-friendly nature throughout the manufacturing and disposal process.
< Figure 4. Excellent decomposition rate of the newly developed protective membrane >
Professor Il-Doo Kim explained, “By leveraging both physical and chemical protective functions, we were able to guide reversible reactions between lithium metal and the electrolyte more effectively and suppress dendrite growth, resulting in lithium metal anodes with unprecedented lifespan characteristics.”
He added, “As the environmental burden caused by battery production and disposal becomes a pressing issue due to surging battery demand, this water-based manufacturing method with biodegradable properties will significantly contribute to the commercialization of next-generation eco-friendly batteries.”
This study was led by Dr. Jiyoung Lee (now a professor in the Department of Chemical Engineering at Ajou University) and Dr. Hyunsub Song (currently at Samsung Electronics), both graduates of KAIST’s Department of Materials Science and Engineering. The findings were published as a front cover article in Advanced Materials, Volume 36, Issue 47, on November 21.
(Paper title: “Overcoming Chemical and Mechanical Instabilities in Lithium Metal Anodes with Sustainable and Eco-Friendly Artificial SEI Layer”)
The research was supported by the KAIST-LG Energy Solution Frontier Research Lab (FRL), the Alchemist Project funded by the Ministry of Trade, Industry and Energy, and the Top-Tier Research Support Program from the Ministry of Science and ICT.
2024.12.12 View 14789 -
KAIST Develops a Multifunctional Structural Battery Capable of Energy Storage and Load Support
Structural batteries are used in industries such as eco-friendly, energy-based automobiles, mobility, and aerospace, and they must simultaneously meet the requirements of high energy density for energy storage and high load-bearing capacity. Conventional structural battery technology has struggled to enhance both functions concurrently. However, KAIST researchers have succeeded in developing foundational technology to address this issue.
< Photo 1. (From left) Professor Seong Su Kim, PhD candidates Sangyoon Bae and Su Hyun Lim of the Department of Mechanical Engineering >
< Photo 2. (From left) Professor Seong Su Kim and Master's Graduate Mohamad A. Raja of KAIST Department of Mechanical Engineering >
KAIST (represented by President Kwang Hyung Lee) announced on the 19th of November that Professor Seong Su Kim's team from the Department of Mechanical Engineering has developed a thin, uniform, high-density, multifunctional structural carbon fiber composite battery* capable of supporting loads, and that is free from fire risks while offering high energy density.
*Multifunctional structural batteries: Refers to the ability of each material in the composite to simultaneously serve as a load-bearing structure and an energy storage element.
Early structural batteries involved embedding commercial lithium-ion batteries into layered composite materials. These batteries suffered from low integration of their mechanical and electrochemical properties, leading to challenges in material processing, assembly, and design optimization, making commercialization difficult.
To overcome these challenges, Professor Kim's team explored the concept of "energy-storing composite materials," focusing on interface and curing properties, which are critical in traditional composite design. This led to the development of high-density multifunctional structural carbon fiber composite batteries that maximize multifunctionality.
The team analyzed the curing mechanisms of epoxy resin, known for its strong mechanical properties, combined with ionic liquid and carbonate electrolyte-based solid polymer electrolytes. By controlling temperature and pressure, they were able to optimize the curing process.
The newly developed structural battery was manufactured through vacuum compression molding, increasing the volume fraction of carbon fibers—serving as both electrodes and current collectors—by over 160% compared to previous carbon-fiber-based batteries.
This greatly increased the contact area between electrodes and electrolytes, resulting in a high-density structural battery with improved electrochemical performance. Furthermore, the team effectively controlled air bubbles within the structural battery during the curing process, simultaneously enhancing the battery's mechanical properties.
Professor Seong Su Kim, the lead researcher, explained, “We proposed a framework for designing solid polymer electrolytes, a core material for high-stiffness, ultra-thin structural batteries, from both material and structural perspectives. These material-based structural batteries can serve as internal components in cars, drones, airplanes, and robots, significantly extending their operating time with a single charge. This represents a foundational technology for next-generation multifunctional energy storage applications.”
< Figure 2. Supplementary cover of ACS Applied Materials & Interfaces >
Mohamad A. Raja, a master’s graduate of KAIST’s Department of Mechanical Engineering, participated as the first author of this research, which was published in the prestigious journal ACS Applied Materials & Interfaces on September 10. The paper was recognized for its excellence and selected as a supplementary cover article. (Paper title: “Thin, Uniform, and Highly Packed Multifunctional Structural Carbon Fiber Composite Battery Lamina Informed by Solid Polymer Electrolyte Cure Kinetics.” https://doi.org/10.1021/acsami.4c08698)
This research was supported by the National Research Foundation of Korea’s Mid-Career Researcher Program and the National Semiconductor Research Laboratory Development Program.
2024.11.27 View 15133 -
KAIST Develops a Fire-risk Free Self-Powered Hydrogen Production System
KAIST researchers have developed a new hydrogen production system that overcomes the current limitations of green hydrogen production. By using a water-splitting system with an aqueous electrolyte, this system is expected to block fire risks and enable stable hydrogen production.
KAIST (represented by President Kwang Hyung Lee) announced on the 22nd of October that a research team led by Professor Jeung Ku Kang from the Department of Materials Science and Engineering developed a self-powered hydrogen production system based on a high-performance zinc-air battery*.
*Zinc-air battery: A primary battery that absorbs oxygen from the air and uses it as an oxidant. Its advantage is long life, but its low electromotive force is a disadvantage.
Hydrogen (H₂) is a key raw material for synthesizing high-value-added substances, and it is gaining attention as a clean fuel with an energy density (142 MJ/kg) more than three times higher than traditional fossil fuels (gasoline, diesel, etc.). However, most current hydrogen production methods impose environmental burden as they emit carbon dioxide (CO₂).
While green hydrogen can be produced by splitting water using renewable energy sources such as solar cells and wind power, these sources are subject to irregular power generation due to weather and temperature fluctuations, leading to low water-splitting efficiency.
To overcome this, air batteries that can emit sufficient voltage (greater than 1.23V) for water splitting have been gaining attention. However, achieving sufficient capacity requires expensive precious metal catalysts and the performance of the catalyst materials becomes significantly degraded during prolonged charge and discharge cycles. Thus, it is essential to develop catalysts that are effective for the water-splitting reactions (oxygen and hydrogen evolution) and materials that can stabilize the repeated charge and discharge reactions (oxygen reduction and evolution) in zinc-air battery electrodes.
In response, Professor Kang's research team proposed a method to synthesize a non-precious metal catalyst material (G-SHELL) that is effective for three different catalytic reactions (oxygen evolution, hydrogen evolution, and oxygen reduction) by growing nano-sized, metal-organic frameworks on graphene oxide.
The team incorporated the developed catalyst material into the air cathode of a zinc-air battery, confirming that it achieved approximately five times higher energy density (797Wh/kg), high power characteristics (275.8mW/cm²), and long-term stability even under repeated charge and discharge conditions compared to conventional batteries.
Additionally, the zinc-air battery, which operates using an aqueous electrolyte, is safe from fire risks. It is expected that this system can be applied as a next-generation energy storage device when linked with water electrolysis systems, offering an environmentally friendly method for hydrogen production.
< Figure 1. Illustrations of a trifunctional graphene-sandwiched heterojunction-embedded layered lattice (G-SHELL) structure. Schematic representation of a) synthesis procedures of G-SHELL from a zeolitic imidazole framework, b) hollow core-layered shell structure with trifunctional sites for oxygen reduction evolution (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER), and c) heterojunctions, eterojunction-induced internal electric fields, and the corresponding band structure. >
Professor Kang explained, "By developing a catalyst material with high activity and durability for three different electrochemical catalytic reactions at low temperatures using simple methods, the self-powered hydrogen production system we implemented based on zinc-air batteries presents a new breakthrough to overcome the current limitations of green hydrogen production."
<Figure 2. Electrochemical performance of a ZAB-driven water-splitting cell with G-SHELL. Diagram of a self-driven water-splitting cell integrated by combining a ZAB with an alkaline water electrolyzer.>
PhD candidate Dong Won Kim and Jihoon Kim, a master's student in the Department of Materials Science and Engineering at KAIST, were co-first authors of this research, which was published in the international journal Advanced Science on September 17th in the multidisciplinary field of materials science. (Paper Title: “Trifunctional Graphene-Sandwiched Heterojunction-Embedded Layered Lattice Electrocatalyst for High Performance in Zn-Air Battery-Driven Water Splitting”)
This research was supported by the Nano and Material Technology Development Program of the Ministry of Science and ICT and the National Research Foundation of Korea’s Future Technology Research Laboratory.
2024.10.22 View 13634 -
KAIST Develops Technology for the Precise Diagnosis of Electric Vehicle Batteries Using Small Currents
Accurately diagnosing the state of electric vehicle (EV) batteries is essential for their efficient management and safe use. KAIST researchers have developed a new technology that can diagnose and monitor the state of batteries with high precision using only small amounts of current, which is expected to maximize the batteries’ long-term stability and efficiency.
KAIST (represented by President Kwang Hyung Lee) announced on the 17th of October that a research team led by Professors Kyeongha Kwon and Sang-Gug Lee from the School of Electrical Engineering had developed electrochemical impedance spectroscopy (EIS) technology that can be used to improve the stability and performance of high-capacity batteries in electric vehicles.
EIS is a powerful tool that measures the impedance* magnitude and changes in a battery, allowing the evaluation of battery efficiency and loss. It is considered an important tool for assessing the state of charge (SOC) and state of health (SOH) of batteries. Additionally, it can be used to identify thermal characteristics, chemical/physical changes, predict battery life, and determine the causes of failures. *Battery Impedance: A measure of the resistance to current flow within the battery that is used to assess battery performance and condition.
However, traditional EIS equipment is expensive and complex, making it difficult to install, operate, and maintain. Moreover, due to sensitivity and precision limitations, applying current disturbances of several amperes (A) to a battery can cause significant electrical stress, increasing the risk of battery failure or fire and making it difficult to use in practice.
< Figure 1. Flow chart for diagnosis and prevention of unexpected combustion via the use of the electrochemical impedance spectroscopy (EIS) for the batteries for electric vehicles. >
To address this, the KAIST research team developed and validated a low-current EIS system for diagnosing the condition and health of high-capacity EV batteries. This EIS system can precisely measure battery impedance with low current disturbances (10mA), minimizing thermal effects and safety issues during the measurement process.
In addition, the system minimizes bulky and costly components, making it easy to integrate into vehicles. The system was proven effective in identifying the electrochemical properties of batteries under various operating conditions, including different temperatures and SOC levels.
Professor Kyeongha Kwon (the corresponding author) explained, “This system can be easily integrated into the battery management system (BMS) of electric vehicles and has demonstrated high measurement accuracy while significantly reducing the cost and complexity compared to traditional high-current EIS methods. It can contribute to battery diagnosis and performance improvements not only for electric vehicles but also for energy storage systems (ESS).”
This research, in which Young-Nam Lee, a doctoral student in the School of Electrical Engineering at KAIST participated as the first author, was published in the prestigious international journal IEEE Transactions on Industrial Electronics (top 2% in the field; IF 7.5) on September 5th. (Paper Title: Small-Perturbation Electrochemical Impedance Spectroscopy System With High Accuracy for High-Capacity Batteries in Electric Vehicles, Link: https://ieeexplore.ieee.org/document/10666864)
< Figure 2. Impedance measurement results of large-capacity batteries for electric vehicles. ZEW (commercial EW; MP10, Wonatech) versus ZMEAS (proposed system) >
This research was supported by the Basic Research Program of the National Research Foundation of Korea, the Next-Generation Intelligent Semiconductor Technology Development Program of the Korea Evaluation Institute of Industrial Technology, and the AI Semiconductor Graduate Program of the Institute of Information & Communications Technology Planning & Evaluation.
2024.10.17 View 15431 -
KAIST Employs Image-recognition AI to Determine Battery Composition and Conditions
An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.
KAIST (President Kwang-Hyung Lee) announced on July 2nd that Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the Electronics and Telecommunications Research Institute (ETRI) and Drexel University in the United States, has developed a method to predict the major elemental composition and charge-discharge state of NCM cathode materials with 99.6% accuracy using convolutional neural networks (CNN)*.
*Convolutional Neural Network (CNN): A type of multi-layer, feed-forward, artificial neural network used for analyzing visual images.
The research team noted that while scanning electron microscopy (SEM) is used in semiconductor manufacturing to inspect wafer defects, it is rarely used in battery inspections. SEM is used for batteries to analyze the size of particles only at research sites, and reliability is predicted from the broken particles and the shape of the breakage in the case of deteriorated battery materials.
The research team decided that it would be groundbreaking if an automated SEM can be used in the process of battery production, just like in the semiconductor manufacturing, to inspect the surface of the cathode material to determine whether it was synthesized according to the desired composition and that the lifespan would be reliable, thereby reducing the defect rate.
< Figure 1. Example images of true cases and their grad-CAM overlays from the best trained network. >
The researchers trained a CNN-based AI applicable to autonomous vehicles to learn the surface images of battery materials, enabling it to predict the major elemental composition and charge-discharge cycle states of the cathode materials. They found that while the method could accurately predict the composition of materials with additives, it had lower accuracy for predicting charge-discharge states. The team plans to further train the AI with various battery material morphologies produced through different processes and ultimately use it for inspecting the compositional uniformity and predicting the lifespan of next-generation batteries.
Professor Joshua C. Agar, one of the collaborating researchers of the project from the Department of Mechanical Engineering and Mechanics of Drexel University, said, "In the future, artificial intelligence is expected to be applied not only to battery materials but also to various dynamic processes in functional materials synthesis, clean energy generation in fusion, and understanding foundations of particles and the universe."
Professor Seungbum Hong from KAIST, who led the research, stated, "This research is significant as it is the first in the world to develop an AI-based methodology that can quickly and accurately predict the major elemental composition and the state of the battery from the structural data of micron-scale SEM images. The methodology developed in this study for identifying the composition and state of battery materials based on microscopic images is expected to play a crucial role in improving the performance and quality of battery materials in the future."
< Figure 2. Accuracies of CNN Model predictions on SEM images of NCM cathode materials with additives under various conditions. >
This research was conducted by KAIST’s Materials Science and Engineering Department graduates Dr. Jimin Oh and Dr. Jiwon Yeom, the co-first authors, in collaboration with Professor Josh Agar and Dr. Kwang Man Kim from ETRI. It was supported by the National Research Foundation of Korea, the KAIST Global Singularity project, and international collaboration with the US research team. The results were published in the international journal npj Computational Materials on May 4. (Paper Title: “Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images”)
2024.07.02 View 15011