Kaist

Neural Implant in Korea Remotely Controlled from t..

  < From left, Eun Young Jeong, doctoral student in KAIST >     A researcher in Chicago remotely controls a miniaturized brain implant in Daejeon, Korea — over the internet. Korean researchers have developed a wireless device that can deliver drugs and light to precisely modulate targeted neurons from anywhere in the world. The technology is expected to overcome the constraints of distance and location, supporting long-term studies of brain disorders and the future development of therapeutic devices. KAIST (President Choongsik Bae) announced on August 27 that a research team led by Professor Jae-Woong Jeong from the School of Electrical Engineering, in collaboration with Professor Wha Young Kim's team at Yonsei University College of Medicine, has developed an IoT-enabled wireless neural implant that integrates drug delivery, optical stimulation, wireless communication, and internet-based remote control into a single miniaturized device. < Figure 1. Conceptual diagram of the IoT-based wireless neural implant. >   Conventional studies involving optical stimulation or drug delivery to the brain often required bulky equipment connected by wires, restricting the natural movement of experimental animals. Even wireless devices had their own limitations, often requiring researchers to operate them at close range, thereby restricting experimental flexibility and introducing the so-called “observer effect”. To overcome these limitations, the research team developed the brain implant with IoT connectivity. Even without being physically present in the laboratory, researchers can remotely administer drugs or stimulate specific brain neurons with light in real time via the internet. The device can also be programmed to operate automatically at a preset time. The device is about the size of a sugar cube and is designed not to interfere with the animal's natural behavior. Researchers no longer need to repeatedly approach or handle equipment near the animal, reducing the stress caused by a researcher's presence, which can otherwise affect the animal's behavior and bias experimental results. The implant contains a microfluidic system that precisely delivers drugs to a targeted region of the brain, as well as a micro-LED that enables optical control of specific neurons. Drug delivery and optical stimulation can be controlled independently, or the two functions can be combined. The drug reservoir is designed to be magnetically detachable. Even after the drug is depleted, researchers can replace or refill the reservoir without the need for additional implantation surgery, enabling long-term, repeated experiments. The research team implanted the device in rats and verified its performance over a four-week period. In particular, a researcher in Chicago successfully operated the brain implant in Daejeon, Korea, in real time via the internet, demonstrating that the device can operate reliably over intercontinental distances. The team also conducted an experiment in which cocaine was wirelessly administered to a rat's brain while specific neurons were simultaneously stimulated with light. The results showed that addiction-related behavioral responses could be suppressed, demonstrating the potential of combining drug delivery and optical stimulation for neural circuit research. By eliminating the need for researchers to operate equipment directly beside experimental animals, this technology enables long-term studies of the relationship between brain circuits and behavior under naturalistic conditions. It is expected to be useful for studying conditions that involve long-term changes in neural circuit function and behavior, such as addiction, depression, and neurodegenerative diseases. The technology could ultimately pave the way for intelligent implantable medical devices that combine brain-state sensing with AI to deliver drugs or neural stimulation precisely when needed. < Figure 2. Configuration and operation of the wireless brain implant. >   Professor Jae-Woong Jeong from KAIST said, “This technology transforms wireless brain implants that use light and drugs from short-range control tools into IoT-based brain engineering platforms capable of long-term, automated, and remote experimentation.” He added, “In the long term, it could contribute to the development of intelligent implantable medical devices for the diagnosis and treatment of brain disorders.” Professor Wha Young Kim from Yonsei University said, “This platform allows researchers to remotely and precisely control specific brain circuits over extended periods while animals move freely under naturalistic conditions.” She added, “It is expected to become an important tool for identifying causal relationships between neural circuits and behavior in disease models such as addiction, depression, and neurodegenerative disorders.” Eun Young Jeong, a doctoral student in KAIST's School of Electrical Engineering, and Jong Woo Park, a doctoral student at Yonsei University College of Medicine, served as co-first authors. The study was published on July 29 in the international journal Science Advances. Paper title: IoT-enabled wireless neural implant for chronic, programmable neuropharmacology and optogenetics, DOI: 10.1126/sciadv.aee8648 This research was supported by the Mid-Career Researcher Program and Basic Research Laboratory Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT, as well as the Industrial Technology Alchemist Project of the Ministry of Trade, Industry and Energy.

KAIST Uses Light to Distinguish Real from Fake wit..

  < The research team. From left: Dr. Geon Gug Yang and Professor Sang Ouk Kim from KAIST. In the circles, from left: Professor Seok Joon Kwon and Ph.D. student Seong-Gyun Im from Sungkyunkwan University. >   Shine a light, and the real can be distinguished from the fake. KAIST researchers have developed a security technology that uses unique “artificial fingerprints” created by the random assembly of nanoparticles. Although extremely difficult to replicate, these fingerprints can be conveniently authenticated using only a smartphone flashlight and a laser pointer, opening up potential applications in anti-counterfeiting and electronic device authentication. KAIST announced on August 26 that a research team led by Professor Sang Ouk Kim from the Department of Materials Science and Engineering, in collaboration with a team led by Professor Seok Joon Kwon of Sungkyunkwan University, has developed a new foundational security technology based on randomly assembled colloidal nanopatterns—unique microscopic patterns formed by particles too small to be seen with the naked eye. The technology enables authentication using everyday light sources such as smartphone flashlights and laser pointers. < Figure 1. Schematic illustration of polycrystalline colloidal self-assembly and the PUF authentication method. >   Recent advances in artificial intelligence have made cyberattacks increasingly sophisticated, while future quantum computers may pose a threat to conventional cryptographic systems. As a result, growing attention is being paid to technologies that use the unique physical characteristics of products or devices themselves for security, in addition to software-based encryption. A physical unclonable function, or PUF, is a security technology that uses minute physical differences naturally generated during the manufacturing process as security information. Just as every person has a unique fingerprint, microscopic particles form a different arrangement each time they assemble. Even when the same materials and process are used, reproducing the exact positions and orientations of the particles is extremely difficult. The researchers used these differences as “artificial fingerprints” for authenticating products and devices. However, conventional high-security PUFs typically require expensive microscopes, spectroscopic equipment, or imaging systems to read information from their tiny and complex structures, making them difficult to use conveniently in everyday settings. The research team focused on solving this dilemma between high security and easy authentication. By using the self-assembly of spherical particles hundreds of nanometers in size on a water surface, the team created unique structures composed of many small crystalline domains with different sizes and orientations. These structures are different every time they are made, making them difficult to replicate, while also producing clear optical signals when illuminated. The research team implemented an authentication method in which the two patterns generated by each product are registered in advance and subsequently compared with those observed from the actual product. In other words, a single “nanofingerprint” is authenticated using two different light sources: a flashlight and a laser. Much like identifying a person using both their face and fingerprint, verifying one nanostructure in two independent ways strengthens security. When illuminated with ordinary light, such as a smartphone flashlight, the nanostructure produces a unique color and reflection pattern depending on the particle arrangement. When illuminated with a laser pointer, the microscopic particle structure scatters the light in multiple directions, producing a second distinctive optical pattern. To create a counterfeit, a forger would have to reproduce not only the nanoparticle structure itself, but also the exact color and reflection pattern produced under a flashlight and the optical pattern generated under laser illumination—making replication extremely difficult. The researchers also successfully transferred the nanostructures onto a variety of surfaces, including flexible plastics, metals, transparent films, and hydrogels—soft, gel-like materials capable of retaining large amounts of water. The technology could be used to assign a unique “hardware ID” to electronic products and Internet of Things devices for product authentication. It could also serve as an anti-counterfeiting label for luxury goods, artworks, and pharmaceuticals. Because it can be applied to transparent films, it may also be developed into security stickers that do not obscure a product’s design or appearance. < Figure 2. Demonstration of polycrystalline PUF devices for passport, biometric, and underwater authentication. >   Professor Sang Ouk Kim of KAIST’s Department of Materials Science and Engineering said, “The key achievement of this study is that it combines randomly formed structures that are extremely difficult to replicate with a simple authentication method using readily available tools such as a flashlight or laser pointer.” He added, “We expect the technology to develop into a next-generation security solution that can be readily used in everyday applications, including electronic device authentication and anti-counterfeiting labels.” Dr. Geon Gug Yang of KAIST’s Department of Materials Science and Engineering and Ph.D. student Seong-Gyun Im of Sungkyunkwan University’s Department of Chemical Engineering contributed equally as co-first authors. Professors Sang Ouk Kim of KAIST and Seok Joon Kwon of Sungkyunkwan University served as co-corresponding authors. The results were published online on July 23 in the international journal Nature Communications. Paper title: “Dual-space visible light authentication toward high security physical unclonable function” DOI: https://doi.org/10.1038/s41467-026-75781-4 This research was supported by the Mid-Career Researcher Program and the InnoCORE Program funded by the Ministry of Science and ICT, as well as by the Samsung Research Funding & Incubation Center for Future Technology.  

KAIST Identifies a Route to Faster-Charging, Longe..

  < The research team. Clockwise from bottom right: Professor Kang Taek Lee (Department of Mechanical Engineering, KAIST); Professor EunAe Cho (Department of Materials Science and Engineering, KAIST); Yejin Kang, PhD candidate (Department of Mechanical Engineering, KAIST). >     Can electric vehicles charge quickly without sacrificing battery longevity? A KAIST research team has identified a potential solution using a three-dimensional digital twin—a virtual model that recreates the internal microstructure of a real battery electrode. The team found that fast-charging performance and degradation behavior are influenced not only by the amounts of materials and pore space within the electrode, but also by how they are distributed. KAIST (President Choongsik Bae) announced on August 24 that a research team led by Professor Kang Taek Lee from the Department of Mechanical Engineering, in collaboration with Professor EunAe Cho of the Department of Materials Science and Engineering, constructed a 3D digital twin informed by the microstructure and specifications of a commercial graphite anode. Using the model, the researchers quantitatively analyzed localized degradation mechanisms that arise during fast charging. A lithium-ion battery anode consists of graphite, which stores lithium; a binder that holds the graphite particles together; and electrolyte-filled pore space where lithium ions travel. When a battery is charged, lithium ions move into the graphite particles in the anode where they are intercalated and stored. But if charging happens too quickly, some lithium ions cannot enter the graphite in time and instead build up as metallic lithium on the surface—a phenomenon called Li plating. It is similar to cars piling up at the entrance of a parking lot when too many arrive at once and cannot get inside fast enough. If this continues, it can degrade both battery performance and lifespan. During charging, a thin protective film also forms on the graphite surface called the solid electrolyte interphase (SEI) layer. A properly formed SEI layer is necessary, but if it becomes too thick or uneven, it can degrade battery performance. In addition, as lithium enters the graphite particles during charging, the particles expand and push against the surrounding material, creating mechanical stress inside the electrode. These processes occur simultaneously at the microscale, making their individual effects difficult to distinguish experimentally. Existing computational models have also relied mainly on the electrode's average properties, making it hard to capture the complex internal structure and location-dependent behavior within the electrode. To address this, the research team built a "3D digital twin" of the battery electrode based on the structure of an actual commercial graphite anode. The team reconstructed the graphite particles, the binder that holds them together, and the electrolyte-filled pores through which lithium ions travel—all in three dimensions. < Figure 1. Schematic representation of the digital-twin-based anode degradation analysis. >   Using this virtual electrode, the researchers varied the electrode thickness, porosity, and the distribution of the binder, then simulated fast charging to analyze how lithium ions moved. They also examined where Li plating occured, how the protective film formed, and which parts of the anode experienced concentrated stress. The results showed that even when the overall charge capacities were similar, the internal degradation behavior of the anodes could differ significantly depending on how the binder and pore space were arranged inside the electrode. In 50-micrometer (μm) anodes, the difference in charge capacities due to binder distribution was within 4％—meaning there was little apparent difference in charging performance. Inside the electrode, however, the locations where lithium was intercalated and where performance-degrading reactions occurred differed clearly. In particular, when the binder was concentrated near the separator, the available pore space for lithium-ion transport decreased, making it more difficult for lithium ions to move through the anode. It is much like how a narrower road causes traffic congestion. In this case, Li plating near the current collector increased by more than 10％ compared to the anode with an evenly distributed binder. Conversely, when the binder was spread relatively evenly throughout the electrode, lithium-ion transport became more uniform, and the protective film also formed more uniformly. This difference grew larger as the electrode became thicker. In 83 μm-thick anodes, the charge capacity difference between the two binder distributions widened to about 18％. This suggests that making thicker electrodes to store more energy requires carefully designing not just how much material is used, but exactly how it is arranged inside. The location of pore space also affected the stress the electrode experienced. Where there was enough pore space, the surrounding area could accommodate the graphite particles as they expanded during charging. Where pore space was insufficient, the graphite particles had no room to expand, concentrating stress in specific areas. < Figure 2. Electrochemical, side-reaction, and mechanical behavior analyzed in this study. >   Through this study, the research team proposed a new design direction for fast-charging lithium-ion batteries: rather than simply looking at how much binder and pore space an electrode contains, researchers should also consider where and how they are distributed. Using a 3D digital twin makes it possible to examine potential problems inside a battery in virtual space before building and testing multiple electrode designs by hand. The approach is expected to help identify optimal electrode structure, contributing to the development of batteries that can charge faster while maintaining longer service life. "This research is significant in that it used a 3D digital twin to uncover internal battery problems that were difficult to detect from overall charging performance alone," said Professor Lee. He added that properly arranging the binder and pore space inside the electrode could help design batteries that store more energy while charge faster, and last longer. The study, with KAIST PhD candidate Yejin Kang from the Department of Mechanical Engineering as first author, was published in the international journal InfoMat (Impact Factor 19.6) and was for the journal’s back cover on July 7. Paper title: Digital twin quantifies spatial-heterogeneity-driven failure in fast-charging lithium-ion battery anodes, DOI: https://doi.org/10.1002/inf2.70141 This research was supported by the Ministry of Science and ICT's Mid-Career Researcher Support Program, its Convergence Technology Development Program, and the InnoCORE Research Center.    < InfoMat’s back cover image >

KAIST Uses Surface ‘Defects’ to Enhance Droplet Fo..

  < From left: Professor Youngsuk Nam, Ph.D. candidate Jun Soo Kim, Professor Sung Gap Im, and Ph.D. candidate Minjeong Kang. >   A technology that boosts condensation heat transfer performance by up to 5.5 times that of conventional copper surfaces has been developed by helping water droplets form more readily and detach more quickly. It is expected to help improve the energy efficiency of power plants and desalination facilities and enhance the cooling performance of electronic devices. KAIST (President Choongsik Bae) announced on August 23 that a joint research team led by Professor Youngsuk Nam from the Department of Mechanical Engineering and Professor Sung Gap Im from the Department of Chemical and Biomolecular Engineering has developed a technology that controls the thickness and structure of an ultrathin polymer coating applied to a surface, allowing more water droplets to form and the resulting droplets to detach more quickly as water vapor turns into liquid water. Condensation is the process by which water vapor turns into liquid water. It is easy to observe in everyday life, as when droplets form on the surface of a cold beverage cup. In industrial settings, it is widely used to convert steam back into water at power plants, obtain fresh water from seawater, and remove heat generated by electronic devices. During condensation, rapidly removing water from the surface is essential. On ordinary metal surfaces, small droplets merge to form a thin water film. This water film adds thermal resistance, impeding heat flow and reducing heat transfer efficiency, much like layers of winter clothing that slow the loss of body heat. By contrast, when water forms as small droplets and continuously detaches, it repeatedly exposes fresh surface area. This phenomenon, in which water condenses as droplets, is known as dropwise condensation. Put simply, instead of water continuously covering the surface, droplets repeatedly form and fall away. This allows heat to be transferred more effectively. Existing technologies, however, faced a dilemma. Roughening the surface to create more sites where droplets could first form caused the droplets to become caught on the structures and prevented them from detaching easily. Conversely, smoothing the surface helped droplets detach but reduced the number of sites available for new droplets to form. In other words, surface features that promote droplet formation can also make droplets harder to remove, creating a fundamental trade-off between nucleation and droplet mobility. The research team solved this problem by using nanoscale polymer aggregates that had previously been regarded as ‘defects’ in polymer films. The team used initiated chemical vapor deposition (iCVD), a process that deposits gas-phase precursors onto a surface to create an ultrathin polymer film. When the polymer film was made thinner, small polymer aggregates formed densely across the surface and served as nucleation sites where water droplets could readily begin to form. As a result, approximately three times more droplets formed on the thin polymer films than on the thicker films. The team then added a heat treatment step to reduce the force holding droplets to the surface. This allowed droplets to detach easily before growing large. In other words, thinning the polymer film increased the number of sites where droplets could form, while heat treatment helped the resulting droplets detach quickly. The key advance was to control these two competing effects separately: film thickness increased droplet nucleation, while thermal treatment promoted droplet removal. New droplets form again where previous droplets have detached. Much like the next person taking a seat as soon as it becomes vacant, the faster droplets form and detach, the more frequently the surface is renewed, allowing heat to transfer more efficiently during condensation. The research team coated copper tubes commonly used in actual condensers with the polymer film and evaluated their performance. The maximum condensation heat transfer coefficient, a measure of heat transfer ability, reached approximately 88 kW·m⁻²·K⁻¹. This represented heat transfer performance up to approximately 5.5 times higher than that of a conventional copper surface with a water film. The coating also performed more than 50％ better than a conventional hydrophobic coating surface. < Conceptual illustration of the study. A nanoscale polymer film deposited on a copper tube by initiated chemical vapor deposition (iCVD) >   Unlike conventional approaches focused on making surfaces smooth or hydrophobic, this study actively used small surface ‘defects.’ The researchers found that nanoscale particles previously regarded as features to be eliminated could instead help droplets form, and they incorporated that finding into a new surface-design strategy. If applied to power plants or industrial heat exchangers, the technology could help improve energy efficiency by transferring heat more effectively. It is also expected to enable more effective water collection in desalination and water-harvesting devices and faster heat removal for improved cooling of electronic devices. Professor Nam said, “This research is meaningful because it uses nanostructures previously regarded as defects as features that help droplets form. We have presented a new method for improving heat transfer efficiency by separately controlling droplet formation and removal.” He added, “Because this technology can form extremely thin, uniform coatings even on surfaces with complex shapes, we expect it to be used in various energy and environmental applications, including industrial heat exchangers.” Jun Soo Kim, a researcher in the Department of Mechanical Engineering, and Minjeong Kang, a researcher in the Department of Chemical and Biomolecular Engineering, co-authored the study as first authors. The results were published online in the international journal Nature Communications on July 16. Paper title: Rational design of polymer film morphology via structure–performance linkage for enhanced condensation performance DOI: https://doi.org/10.1038/s41467-026-75621-5 This research was supported by the Mid-Career Researcher Program (Ministry of Science and ICT and the National Research Foundation of Korea), the SME Technology Innovation Development Program (Ministry of SMEs and Startups and the Korea Technology and Information Promotion Agency for SMEs), and the Deep-Tech Startup Activation Support Program (Ministry of Science and ICT and Commercialization Promotion Agency for R&D Outcomes, COMPA).

KAIST Develops Core Technology to Reverse Biologic..

  < Top right: Dr. Jongwan Kim (co-first author), Department of Bio and Brain Engineering, KAIST. Group photo (from left in the back row): Dr. Jonghoon Lee (co-author), Dr. Seong-Hoon Jang (co-first author), Corbin Hopper, Ph.D. student (co-author); (front row): Professor Kwang-Hyun Cho. >   Once a cell has locked into an abnormal state — the way cancer cells do — can it ever be restored back to normal? A KAIST research team has identified the ‘molecular lock’ that keeps cells trapped in an altered state, opening a new path toward releasing that lock and reversing a cell’s fate. KAIST (President Choongsik Bae) announced on the 21st of August that a research team led by Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering has, for the first time, identified the causal circuits responsible for irreversibility in intracellular molecular networks and developed a fundamental control technology called ROOT that can regulate these circuits and restore biological states to their original condition. Cells in the human body change their state in response to external stimuli. In many cases, however, these state changes are irreversible, in the sense that cells do not return to their original state even after the stimulus disappears. < [Figure 1] The ROOT framework for identifying and controlling the cause of irreversible cell-state transitions >   Irreversibility is essential for maintaining normal biological processes, such as a cell differentiating into one with a specific function. At the same time, it can also drive disease progression — for example, in epithelial–mesenchymal transition, which gives cancer cells the ability to migrate into and invade surrounding tissue. Complicating matters, the circuits that maintain these state changes inside a cell are highly intricate: more than a thousand positive feedback loops are woven throughout the network, in which one molecule activates a series of other molecules that in turn reactivate the original molecule. This is similar to the feedback screech produced when a microphone is placed next to a speaker, where a sound repeatedly amplifies itself. Even a change that starts with an external stimulus can persist after the stimulus is gone, simply because the cell’s own molecules keep reinforcing one another. Until now, it has been extremely difficult to determine which of these countless circuits is actually responsible for locking a cell into an irreversible state. To solve this problem, the team developed ROOT technology, short for Revelation Of the Original circuit of irreversible Transition, which works by representing intracellular regulatory processes as computational logic models and analyzing them through systems biology techniques. Using ROOT, the research team successfully simulated the process in which cells maintain a signal even after an external stimuli is removed, allowing them to identify a set of core circuits that cause irreversibility, which they defined as the “irreversibility kernel.” Going beyond identifying the cause, the team also proposed two groundbreaking control strategies. < [Figure 2] Process of identifying the irreversibility kernel and deriving control strategies using ROOT >   The first, “resetting control,” restores a cell to its state before the change while leaving the cell’s underlying irreversible property intact — comparable to leaving the lock itself in place, but opening the locked door and returning to the starting point. The second, “reversing control,” removes the source of irreversibility itself, allowing a cell to move freely between different states — comparable to disabling the mechanism that automatically locks a door each time it closes, so that afterward the door can be opened and closed again. The team applied the new technique to various biological models, including B-cell differentiation, epithelial–mesenchymal transition in lung cancer, and enterocyte and beta-cell differentiation models based on single-cell transcriptome data, in which the ROOT method accurately identified causal circuits that matched known cell-fate determinants. The team also proposed more effective resetting control strategies, demonstrating that the method can be broadly applied even to models built from real experimental data. Rather than simply removing cells that have become fixed in an abnormal state, as in cancer or aging, the technology is expected to help identify and control the core circuits that keep cells trapped in that state, enabling new treatment strategies that restore cells to a normal condition. Professor Kwang-Hyun Cho said, “The core achievement of this study is identifying the causal circuits behind cells that, once changed, do not return to their original state, and developing a technology to control these circuits and restore cells to their previous condition.” He added, “We expect this technology to be used in developing new treatment strategies that restore abnormally fixed cell states — such as those seen in cancer and aging — back to normal.” < [Figure 3] Analysis of irreversibility in hematopoietic stem cell differentiation using ROOT >   This study was co-led by Dr. Jongwan Kim and Dr. Seong-Hoon Jang of KAIST’s Department of Bio and Brain Engineering as co-first authors, with participation from Dr. Jonghoon Lee and Ph.D. student Corbin Hopper. The research was published on August 13 in Proceedings of the National Academy of Sciences of the United States of America (PNAS), one of the world’s leading scientific journals. Paper title: The structural origin of irreversible transitions in biological networks, DOI: https://doi.org/10.1073/pnas.2600800123   This research was supported by the Mid-Career Researcher Program and the Basic Research Laboratory Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT.  

KAIST Solves 3D Memory Reliability Problem with ˝O..

  < The research team. Back row, from left: Hyeonjin Lee, Researcher (UNIST); Jimin Kwon, Professor (KAIST); Hyeonho Gu, Researcher (KAIST); Haksoon Jung, Dr. (KAIST); Yongwoo Lee, Dr. (KAIST). Front row, from left: Minho Park, Researcher (UNIST); Youngmin Jo, Dr. (KAIST); Heesoo Yang, Researcher (UNIST); Seunghun Baek, Researcher (UNIST). >   As AI systems become more advanced, memory is required to transfer larger amounts of data at higher speeds. But conventional planar semiconductor scaling is running out of room. A KAIST research team has now addressed a key weakness in three-dimensional, vertically stacked memory devices, opening a new path to faster, more power-efficient AI semiconductors. KAIST (President Choongsik Bae) announced on August 25 that a research team led by Professor Jimin Kwon from the School of Electrical Engineering has developed a new multilayer interlayer dielectric structure that reduces defects and significantly enhances the performance of oxide vertical channel transistors (VCTs), a next-generation memory device. The study was conducted in collaboration with researchers from UNIST, Yonsei University, and other Korean institutions. DRAM, which serves as the main memory in computers, has advanced over the past several decades by scaling down device size while reducing power leakage. More recently, vertical channel structures, in which current flows vertically, have become a key technology for increasing memory density. The challenge is oxygen vacancies — defects caused by the absence of oxygen atoms in the oxide semiconductor — which destabilizes the material's electrical properties. But oxygen cannot simply be supplied without limit: when oxygen is supplied to suppress oxygen vacancies, some of the oxygen tends to migrate further, reaching the metal electrode and oxidizing it, which degrades device performance instead. The channel needed oxygen; the electrode did not. Therefore, selectively controlling oxygen flow became a key challenge. The KAIST team developed a new multilayer interlayer dielectric consisting of silicon nitride/silicon dioxide/silicon nitride (SiN/SiO₂/SiN), engineered to function as an "oxygen tunnel" that steers oxygen selectively toward the channel while blocking its path to the electrode. The structure enabled stable compensation of oxygen vacancies in the oxide semiconductor while simultaneously suppressing unwanted oxidation at the electrode, thereby resolving the trade-off. As a result, the researchers achieved world-class current density and data retention time in oxide vertical channel transistors. < Figure 1. Oxide Vertical Channel Transistor with an Oxygen-Tunnel Structure >   The device also demonstrated outstanding operational stability. Even after more than ten million cycles of harsh electrical stress testing, the threshold voltage shift remained below 50 millivolts (mV), confirming its high reliability as a memory device. The team further evaluated system-level performance by integrating conventional silicon CMOS technology with the new oxide semiconductor platform. The results suggest that this approach could significantly improve the performance of next-generation compute-in-memory (CIM) systems, intelligent semiconductors that perform AI computation directly inside memory. Hyeonho Gu, the first author of the study, said, “This research is significant because it goes beyond improving memory density and addresses the long-standing instability problem in 3D devices through a new approach based on oxygen migration control.” He added, “We expect this technology to play a key role in accelerating the commercialization of ultra-low-power, high-performance compute-in-memory systems required for the AI era.” This study was led by KAIST researcher Hyeonho Gu as the first author and was published on May 20 in Advanced Functional Materials, a leading international journal in materials science. The paper was also selected as a Front Cover article in recognition of its academic significance and originality. < Figure 2. Research Image Selected as the Front Cover of Advanced Functional Materials >   Paper title: Oxygen-Tunnel Indium Tin Oxide Vertical Channel Transistors with Enhanced Current Density and Reliability for Monolithic 3D Compute-In-Memory Systems DOI: https://doi.org/10.1002/adfm.202531989 Author information: Hyeonho Gu (KAIST, first author); Yongwoo Lee (KAIST, corresponding author); Haksoon Jung (KAIST, corresponding author); Jimin Kwon (KAIST, corresponding author); Hoichang Jeong (UNIST, co-author); Yanfeng Zhao (UNIST, co-author); Heesoo Yang (UNIST, co-author); Minho Park (UNIST, co-author); Hyeonjin Lee (UNIST, co-author); Seunghun Baek (UNIST, co-author); Minju Song (UNIST, co-author); Junghwan Kim (UNIST, co-author); Youngmin Jo (KAIST, co-author); Hyunjin Park (Korea Research Institute of Chemical Technology, co-author); Munhyeon Kim (Seoul National University of Science and Technology, co-author); Jae-Joon Kim (Seoul National University, co-author); Kyuho Jason Lee (Yonsei University, co-author); and Byungjo Kim (UNIST, co-author). This research was supported by the National Semiconductor Laboratory Program and the Excellent Young Researcher Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT; the Broadcast and Telecommunications Industry Technology Development Program of the Institute of Information & Communications Technology Planning & Evaluation; and the Super Gap Technology Development Program of the Korea Evaluation Institute of Industrial Technology, funded by the Ministry of Trade, Industry and Energy.

KAIST Develops Semiconductor Neuron That Tunes Noi..

  < Members of the research team. From left: Dae Hee Kim (Ph.D. candidate), Tae Wook Ko (Ph.D. candidate), Dr. Do Hoon Kim (first author), Seo Eun Jang (Master >   In electronic devices, irregular fluctuations in signals are generally referred to as “noise.” Because noise interferes with accurate information processing, conventional semiconductor technology has mainly treated it as something to be reduced or eliminated. However, neurons in the human brain do not respond in exactly the same way every time, even to the same stimulus. Tiny internal variations in neurons change when and how often neurons are fired, and this probabilistic operation is one of the brain’s key information-processing features. Inspired by this, KAIST researchers have developed a next-generation semiconductor technology that does not remove current noise generated in memristors, but instead tunes it to a desired level and uses it to process different types of signals. KAIST (President Choongsik Bae) announced on the August 16 that a research team led by Professor Kyung Min Kim from the Department of Materials Science and Engineering has developed a new neuromorphic neuron technology that uses noise generated in semiconductor devices for information processing, enabling selective encoding of time-series signals across different frequency bands. ※ Neuromorphic technology: A technology that processes information by mimicking the way the human brain and neurons operate. In general, noise generated in semiconductors is regarded as an obstacle to accurate signal processing. For this reason, most electronic devices are designed to reduce or eliminate noise as much as possible. The human brain, however, works differently. Neurons, the nerve cells of the brain, do not always respond in the same way to the same stimulus because of internal probabilistic fluctuations. This irregularity actually helps the brain flexibly respond to a wide range of situations and sensory signals. < Figure 1. Comparison of biological neurons and memristor-based probabilistic neurons >   The research team used a memristor in this study. A memristor is a semiconductor device that changes its resistance state in response to electrical stimulation and remembers that state. Until now, current noise generated in memristors has mainly been used for random number generation, which creates unpredictable numbers, or for probabilistic computing. However, previous studies have largely focused on using the inherent randomness of memristors as it is. Technologies that can tune probabilistic response characteristics according to need had not been sufficiently realized. < Figure 2. Memristor noise and spike-generation characteristics dependent on the resistance state >   The key insight of this study is that when the resistance state of a memristor is changed, the magnitude and behavior of its current noise also change. By presetting the resistance state of the memristor, the probability of spike generation and the response range can vary even under the same input. Using this principle, the research team implemented a “programmable probabilistic neuron (PPN)” that treats noise not simply as instability, but as an information-processing resource that can be tuned in a desired way. This neuron can be configured to respond differently depending on how rapidly an input signal changes, in other words, its frequency. By changing only the resistance state of the memristor, the same circuit can be switched to respond sensitively to slow human activity signals in the hertz (Hz) range or fast speech signals in the kilohertz (kHz) range. Hz and kHz are units that indicate how many times a signal repeats per second, with 1 kHz equal to 1,000 Hz. < Figure 3. Reconfigurable time-series encoding for human activity and speech signals >   In simple terms, a single artificial neuron can be reconfigured according to the speed of the signal it needs to process. When processing slowly changing signals such as human movement, it can operate in a way suited to slow variations; when processing rapidly changing signals such as speech, it can be adjusted to capture short and fast changes effectively. The research team verified the technology using signals with different frequency ranges. The system encoded and classified human activity signals in the Hz range and speech signals in the kHz range, achieving accuracies of 94.8％ in human activity recognition and 95.0％ in speech recognition. Professor Kyung Min Kim said, “The significance of this study lies in demonstrating that memristor noise can be harnessed as a tunable information-processing resource, rather than simply treated as an error or instability,” adding, “Because the same hardware can be reconfigured for signals of different speeds and frequencies, it could be used as a signal-processing technology for future low-power edge neuromorphic systems.” This study was led by Dr. Do Hoon Kim from the Department of Materials Science and Engineering as first author, and was published in the internationally renowned materials science journal Advanced Materials on August 05.   Paper title: Noise-Tunable Memristor Enabling Programmable Probabilistic Neurons for Frequency-Selective Time-Series Signal Encoding, DOI: https://doi.org/10.1002/adma.74529   This research was supported by the Basic Research Program in Science and Engineering and the PIM Artificial Intelligence Semiconductor Core Technology Development Program of the Ministry of Science and ICT and the National Research Foundation of Korea.

Patient-Specific ˝Blood–Brain Tumor Barrier Chip˝ ..

  < Members of the research team. Top row, from left: Gaeun Lee, Ph.D. candidate at Sungkyunkwan University; Professor Jungho Ahn of Sungkyunkwan University; Professor Jaejoon Lim of Bundang CHA Medical Center; and Professor Youn-Jung Kang of CHA University. Bottom front row: Minsu Ryoo, Ph.D. candidate at KAIST, and Professor Song Ih Ahn of KAIST. Bottom back row: Nayeong Kang, master’s student at KAIST, and Jinwoo Jung, Ph.D. candidate at KAIST. >   Even for the same brain tumor treated with the same anticancer drug, the effect can differ from patient to patient. A Korean research team has developed a chip that recreates a patient's own tumor cells together with the surrounding peritumoral vascular environment, making it possible to predict patient-specific treatment responses in advance. KAIST (President Choongsik Bae) announced on August 18 that a research team led by Professor Song Ih Ahn from the Department of Mechanical Engineering, in collaboration with Professor Jungho Ahn's team at Sungkyunkwan University, Professor Jaejoon Lim of CHA Bundang Medical Center, and Professor Youn-Jung Kang's team at CHA University, has developed a patient-specific blood–brain tumor barrier (BBTB) chip capable of predicting treatment responses in glioblastoma patients. Glioblastoma is one of the most lethal malignant brain tumors, hard to treat because the cancer cells spread rapidly into normal brain tissue and tumor characteristics differ from patient to patient. The brain is also protected by a "blood–brain barrier," which blocks harmful substances in the blood from entering brain tissue. The problem is that it blocks anticancer drugs too, so not enough of the drug reaches the tumor. When glioblastoma develops, this vascular barrier changes as well. How much it changes differs from patient to patient, which is one reason the same drug can work differently in different people. Predictions of treatment response have so far relied mainly on tumor genetic information and biomarkers, which cannot capture the patient-specific vascular environment around the tumor or the resulting drug response. To address this, the team built a chip that includes not only the patient's tumor cells but also the vascular barrier. This barrier is the "route" an anticancer drug must travel to reach the tumor. Patient-derived glioblastoma cells were co-cultured with brain vascular endothelial cells and astrocytes inside a small microfluidic chip. Together they recreate the boundary where tumor tissue meets normal brain tissue. The design also accommodates perivascular and immune cells, allowing the tumor's vascular environment to be reproduced more precisely. < Figure 1. The upper-left schematic shows the structure of a microfluidic chip designed to recreate the glioblastoma margin. >   Using tumor cells from three glioblastoma patients, the team built patient-mimicking BBTB chips and applied temozolomide (TMZ) and bevacizumab (BEV), the standard agents in glioblastoma treatment, to compare responses. The three patients showed the same result on conventional genetic testing, the MGMT promoter methylation biomarker, and were therefore expected to respond similarly. On the chip, however, both the vascular barrier characteristics and the drug responses differed from one patient to another. The chip results showed a high level of agreement with the patients' actual clinical courses, indicating that outcomes can vary with the state of the vascular barrier even when genetic information is similar. The core advance is that the platform evaluates not only whether cancer cells respond to a drug, but how a treatment acts within a given patient's tumor environment. If predictive performance and reproducibility are validated in a larger cohort, several agents could be tested on a chip made from a patient's own tumor cells to select the most promising strategy in advance. The same environment could also serve to screen new drug candidates. Professor Song Ih Ahn said, "This study is meaningful in that it presents a platform that recreates patient-derived tumor cells together with the blood–brain tumor barrier, allowing patient-to-patient differences in treatment response to be evaluated in a way that closely reflects reality," adding, "We hope to validate it in a larger patient population and develop it into a preclinical evaluation platform for establishing personalized treatment strategies and for new drug development." < Figure 2. Conceptual illustration of the research (AI-generated image) >   Minsu Ryoo, a doctoral student in KAIST's Department of Mechanical Engineering, and Gaeun Lee, a doctoral student at Sungkyunkwan University, participated as co-first authors. The results were published on June 27, 2026, in Small, an international journal in materials and nanoscience, and were selected as the journal's Front Cover.   Paper title: Human Blood-Brain Tumor Barrier on a Chip to Investigate Personalized Treatment for Glioblastoma Patients DOI: 10.1002/smll.202506712   This research was supported by the Korea–US Collaborative Research Fund of the Ministry of Health and Welfare and the Korea Health Industry Development Institute (RS-2024-00468873), and by the Early-Career Researcher Program of the Ministry of Science and ICT and the National Research Foundation of Korea (RS-2026-25487930).

KAIST Develops High-Efficiency, Eco-Friendly Hydro..

  < The research team. From left: Dr. Hongju Lee, currently a postdoctoral researcher at KIST (first author); Professor Tae-Hyun Bae of the KAIST Department of Chemical and Biomolecular Engineering (corresponding author); and Suhyeon Choi, a Ph.D. candidate in the KAIST Department of Chemical and Biomolecular Engineering (second author). >   For hydrogen to be widely used as a clean energy source in everyday life, technologies that can extract only hydrogen with high purity from mixed gases are essential. KAIST researchers have presented a new strategy for developing high-performance separation membranes that can selectively filter hydrogen for clean hydrogen energy production. KAIST (President Choongsik Bae) announced on the 13th of August that a research team led by Professor Tae-Hyun Bae of the Department of Chemical and Biomolecular Engineering has successfully introduced hydrogen-selective transport pathways at the angstrom scale inside polymer membranes and clarified their separation performance through the concept of “network completeness.” ＊Angstrom (Å): An extremely small unit of length used to measure wavelengths of light or the size of atoms and molecules. One angstrom is one hundred-millionth of a centimeter, or one ten-billionth of a meter, roughly one-millionth the thickness of a human hair. Hydrogen is drawing attention as an eco-friendly energy source because it does not emit pollutants when used. However, separating hydrogen with high purity from mixed gases generated during production remains a key challenge for commercialization. Crystalline porous materials such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) are advantageous because their pores can be designed uniformly. However, they are difficult to fabricate over large areas without defects and have limitations in separating small molecules such as hydrogen. Polymer membranes, by contrast, are easier to process and scale up to large areas, but because their pore formation is difficult to control precisely, it has been challenging to raise their separation performance beyond a certain level. To combine the advantages of both types of materials, the research team designed a modular network structure in which polymer chains are linked by crosslinkers. In this process, the team focused on the limitation that conventional indicators such as the degree of crosslinking (CD) and effective crosslinking degree (ECD), which have been used to describe the extent of crosslinking, cannot determine whether pores useful for separation have actually been formed. The researchers therefore proposed a new metric called the Bridge Connectivity Degree (BCD), which refers to the proportion of crosslinkers that are connected at both ends to form complete pathways. This made it possible to quantitatively apply the concept of “complete framework connectivity,” which has been emphasized in inorganic porous materials, to polymer networks as well. The newly developed membrane, ms-oDMB-DB50, achieved a high bridge connectivity degree of 73％, and both its hydrogen permeability and hydrogen/nitrogen selectivity improved significantly compared with the original material, DB50. Analysis showed that the membrane contains numerous ultramicropores smaller than 3 Å, which carbon dioxide cannot access. The research team also proposed a “density-probe method,” using helium molecules, which are smaller than hydrogen, as probes to experimentally verify the existence of these ultramicropores. The newly developed membrane also operated stably for 100 hours without any loss of performance. Its tensile strength, the force the membrane can withstand without breaking, was about twice that of previously reported high-performance polymer membranes, confirming that it also has the robust durability needed for industrial processes. Dr. Hongju Lee said, “There have been previous attempts to combine the advantages of these two types of materials, but this study is different in that it defines ‘how completely the network is connected’ as a quantitative value and directly links that value to separation performance,” adding, “We hope this study will serve as a starting point for extending reticular synthesis, a design principle used for inorganic molecular sieves, to polymer membranes.” Professor Tae-Hyun Bae said, “By stitching polymer chains together with crosslinkers that fit together like Lego blocks, we formed a network inside the membrane that selectively allows only small hydrogen gas molecules to pass through.” < <Figure 1> Research overview: [Left] Schematic illustration of a strategy for improving the hydrogen separation performance of polymer molecular-sieve membranes by increasing the bridge connectivity degree (BCD). [Right] Changes in hydrogen permeability and hydrogen/impurity selectivity—key performance indicators for hydrogen separation membranes—compared with performance benchmarks. The structural evolution directly associated with performance enhancement is also illustrated schematically. >   This paper was led by Dr. Hongju Lee, currently a postdoctoral researcher at the Korea Institute of Science and Technology, as first author, with Professor Tae-Hyun Bae as corresponding author. The research was published on July 23 in the international journal Nature Communications. Paper title: Network completeness enables angstrom-scale transport pathways in polymer membranes, DOI: https://doi.org/10.1038/s41467-026-73860-   Author information: Hongju Lee, formerly of KAIST and currently at the Korea Institute of Science and Technology, first author; Suhyeon Choi, KAIST, second author; and Tae-Hyun Bae, KAIST, corresponding author This research was supported by the 2025 Global C.L.E.A.N. Project and the Mid-Career Researcher Program under the Basic Research Program, funded by the Ministry of Science and ICT.      

KAIST Develops AI to Detect ‘Foreign-Linked Opinio..

  < People. From left: Jaehong Kim, Ph.D. candidate at the KAIST Graduate School of Culture Technology; Hyeonseung Kim, master’s student at the KAIST Graduate School of Culture Technology; Jiseon Kim, Ph.D. candidate at the KAIST School of Computing; Professor Alice Oh of the KAIST School of Computing; Professor Thorsten Holz of the Max Planck Institute for Security and Privacy in Germany; Professor Wonjae Lee of the KAIST Graduate School of Culture Technology; and Professor Meeyoung Cha of KAIST and the Max Planck Institute for Security and Privacy >   During election seasons or major national issues, online news comment sections often become heated spaces of conflict across gender, generation, and political lines. For years, there have been persistent concerns that behind some of these conflicts may lie “foreign winds,” or interventions by foreign actors seeking to manipulate public opinion and deepen social divisions. A KAIST research team has now developed a technology that uses big data on two decades worth of news comments and artificial intelligence (AI) to precisely detect traces of such hidden influence operations. KAIST (President Choongsik Bae) announced on the 12th of August that a joint research team led by Professor Wonjae Lee of the Graduate School of Culture Technology, Professor Meeyoung Cha of the School of Computing (Scientific director at Max Planck Institute for Security and Privacy) and Professor Alice Oh of the School of Computing, in collaboration with Professor Thorsten Holz of the Max Planck Institute, has developed an explainable AI technology that automatically detects patterns suspected of foreign-linked influence operations in online news comments and provides specific evidence for its judgments. The organized and repeated posting of comments or content by certain actors to shape public opinion in a desired direction is known as an “online influence operation.” Existing AI-based detection technologies have had a key limitation: even when they classify certain accounts as belonging to a “blacklist,” they often fail to provide clear evidence explaining why those accounts should be considered influence-operation accounts. < Figure 1. A framework for explainable comment-level troll detection. >   To overcome this limitation, the research team used 70 foreign-linked accounts previously identified by the Institute for National Security Strategy as starting points. They then tracked groups of accounts connected to them or repeatedly commenting on the same news articles, ultimately collecting and analyzing a large-scale dataset of 110 million comments posted on Naver News over a 20-year period from 2006 to 2025. In particular, the AI developed by the research team examines accounts through a careful three-step process. First, it checks whether there are clues suggesting that the author may be linked to a foreign source. Second, it examines whether the comment contains emotionally polarizing expressions, such as moral condemnation or blind praise. Third, it identifies which country or target the emotional framing is directed toward. The model does not stop at simply labeling an account as suspicious. It also highlights the specific phrases in the comments that served as the basis for its judgment. The team further combined this with multidimensional behavioral-pattern analysis, including account activity frequency, account lifespan, and activity links with other suspected accounts. As a result, among approximately 4 million Naver News users, the model ultimately identified 23,998 accounts exhibiting patterns consistent with suspected public-opinion manipulation. The analysis also revealed the more subtle strategy of these suspected accounts. Their main target was not the victory of a particular political camp, but rather the maximization of division and confrontation within Korean society. Among the top 10 targets that drew the highest public engagement, measured through likes and other reactions, seven were prominent domestic political figures. Notably, the attacks were not concentrated on a single party or ideology. Former and current presidents, presidential candidates, and political parties from both progressive and conservative camps were targeted across the spectrum. According to the research team, this suggests a sophisticated strategy aimed not so much at supporting a particular group, but at inflaming domestic political conflict and increasing social distrust and polarization. This study is significant because it provides data-based evidence for influence-operation activity that had previously been discussed largely in terms of suspicion, while also offering a potential defense mechanism for protecting healthy online public discourse. In the future, portal platforms and related organizations could use this technology during elections or national crises to monitor the influx of suspicious accounts in real time and prioritize the review of coordinated attacks against domestic political figures. However, the research team emphasized that the AI should not be used to block accounts indiscriminately, but rather as an explainable content-moderation tool that supports the judgment of expert reviewers. < Figure 2. Temporal patterns of troll activity from 2006 to 2025. >   Professor Wonjae Lee said, “By analyzing 20 years of data, we found that suspected accounts tended to use messages criticizing Korea and domestic political figures rather than directly praising foreign countries, and that these messages gained higher visibility,” adding, “This research can provide empirical criteria for when and which messages platforms and monitoring organizations should prioritize for review, especially during socially sensitive periods such as elections.” Professor Alice Oh said, “This is a meaningful achievement in which AI precisely identified not only the surface meaning of words in massive comment datasets, but also subtle emotional patterns and organized behavioral signals intended to provoke conflict,” adding, “It can become a powerful defense system against online influence operations, which are becoming increasingly sophisticated.” Professor Meeyoung Cha said, “This study goes beyond simple blacklist-account analysis and represents the outcome of actionable data science that addresses real-world problems and drives practical change,” adding, “In an online environment where social conflict is deepening, we hope this technology will serve as a practical tool for protecting the transparency and trustworthiness of the digital public sphere.” This research was led by KAIST Ph.D. candidate Jaehong Kim and master’s student Hyeonseung Kim as co-first authors. The paper is scheduled to be presented at the USENIX Security Symposium 2026, one of the most prestigious conferences in the field of computer security. Paper title: Cross-National Information Attacks: A Two-Decade Analysis of Troll Behavior in Korea, DOI: 10.48550/arXiv.2606.22785   This research was supported by the Hyundai Motor Chung Mong-Koo Foundation, the Institute of Information & Communications Technology Planning & Evaluation, and the National Research Foundation of Korea, funded by the Ministry of Science and ICT.

KAIST Shapes a ˝ Templates a ‘Gas Lattice’ in Poro..

  < (From left) Professor Jihan Kim, Ph.D. students Younghun and Dohoon Kim, Department of Chemical and Biomolecular Engineering, KAIST >   Capturing carbon or storing hydrogen to combat global warming requires compressing gases into sponge-like porous materials. Until now, gas molecules were thought to adsorb in a disordered manner throughout the pores. But what if invisible gas molecules could be lined up in regular order — like ice crystals or LEGO bricks? KAIST (President Choongsik Bae) announced on August 11 that a research team led by Professor Jihan Kim of the Department of Chemical and Biomolecular Engineering has developed a computational framework that combines large-scale screening of metal–organic frameworks (MOFs)＊ with machine-learning-guided inverse design. Focusing on the “gas lattice”—a crystal-like ordered state formed by gas molecules under confinement—the framework enables researchers to explore a vast range of MOF structures and design candidate porous materials capable of stabilizing desired gas arrangements. ＊Metal–organic framework (MOF): a material built from metal ions or clusters connected by organic linkers to create countless microscopic pores; MOFs are promising eco-friendly materials used to store or separate gases. Using xenon (Xe), a monatomic noble gas, as a model system, the research team identified a specific cobalt-based porous material — Co-CAU-36 — that stabilizes xenon in a regular lattice. Computer simulations (GCMC) confirmed that xenon inside this material does not spread out randomly, but instead lines up in a body-centered cubic (BCC) lattice, a well-defined, crystal-like arrangement. This is a breakthrough because gas crystallization was achieved within the pores without the extreme bulk pressures normally required by using the pore structure as a ‘template’. Striking results also emerged when the team examined the separation of xenon (Xe) and krypton (Kr), a gas mixture of industrial importance. Inside the framework, xenon preferentially occupies an ordered shell region, displacing krypton toward the pore core — a separation behavior that had not been reported before. < Figure 1. Conceptual shift from host-only adsorption design to framework-templated gas lattice design. Conventional MOF design optimizes host–guest interactions by tuning metal nodes and organic linkers, while treating the adsorbed phase as disordered and evaluating it using averaged descriptors such as uptake and selectivity. In contrast, gas lattice design includes guest–guest correlations and commensurate packing within the pore as design variables, enabling a crystalline-like, structure-defined adsorbed phase and more tunable adsorption responses. >   To show that the phenomenon could be deliberately designed rather than occurring incidentally, the researchers combined machine learning with a genetic algorithm and used inverse design to identify candidate porous structures targeting BCC- and FCC-like lattices. The findings may have applications in advanced energy and environmental technologies that depend on precise control of molecular arrangement, including carbon capture and separation, selective catalytic reactions, and gas storage. "This research is the first demonstration of a gas forming a crystal-like ordered state inside a porous material," said Professor Jihan Kim. He added that the work's significance lies in moving beyond conventional approaches focused primarily on increasing adsorption capacity, toward treating the arrangement of gas molecules itself as a design target. "If this approach can be extended to more complex molecules, such as carbon dioxide or water, it could become an important starting point for designing tailored materials for gas separation and storage," Professor Kim added. Younghun Kim and Dohoon Kim, PhD candidates in KAIST's Department of Chemical and Biomolecular Engineering, are co-first authors, with Seungwoo Kim, a master's candidate, and Yunsung Lim, a PhD, serving as co-authors. The findings were published online on June 23 in the international academic journal Nature Communications. < Figure. 2 Structural and thermodynamic analysis of xenon gas lattice formation in Co-CAU-36. Comparison of xenon adsorption configurations with a reference BCC lattice template, together with time-averaged density maps, adsorption isotherms, and energy-grid analysis, shows that xenon in Co-CAU-36 undergoes sequential shell occupancy, pore filling, and gas lattice formation, ultimately stabilizing a BCC-like xenon lattice. >   Paper title: Framework-templated gas lattices in metal-organic frameworks DOI: 10.1038/s41467-026-74776-5 This work was supported by grants from the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (Project Numbers RS-2024-00451160 and RS-2024-00435493).  

KAIST Held Inauguration Ceremony for 18th Presiden..

  < President Choongsik Bae delivering a presentation >   KAIST announced that it held an inauguration ceremony for its 18th president, Choongsik Bae, at the KAIST Auditorium on Monday, August 10. At the ceremony, the university unveiled "Fundamentals First, Innovation Forward" as its new vision. The ceremony officially presented President Bae's philosophy on university governance and his vision for KAIST's future to the KAIST community and the public. Departing from the conventional format of a formal inaugural address, President Bae personally explained his vision and the strategies for implementing it. Professor Yiyun Kang of the Department of Industrial Design directed the stage production, bringing KAIST's future vision to life through an intuitive and immersive presentation. < President Choongsik Bae delivering his inaugural address >   The event built on the innovation advanced under KAIST's 17th president, Kwang Hyung Lee, while introducing new leadership and development strategies that will guide the university toward its 60th anniversary. Distinguished guests from Korea and abroad attended, including Deputy Prime Minister and Minister of Science and ICT Kyung Hoon Bae, former KAIST President Kwang Hyung Lee, and ambassadors to Korea from key countries. < Congratulatory address by Kyunghoon Bae, Deputy Prime Minister and Minister of Science and ICT >   In his inaugural address, President Bae presented "Continuity & Innovation" as the central philosophy of his administration. He aimed to preserve the values KAIST has cultivated over the past 55 years -- Creativity, Challenge, and Caring -- while pursuing innovation across education, research, entrepreneurship, and administration in response to AI-driven transformation and intensifying global competition for technological leadership. The new vision, "Fundamentals First, Innovation Forward," rests on two foundational principles: people strongly grounded in fundamental disciplines, humanistic insight, and AI capabilities; and an organization characterized by autonomy, accountability, and efficiency. On these foundations, KAIST aims to achieve world-class excellence in education, research, entrepreneurship, and internationalization. < President Choongsik Bae announcing KAIST’s new vision >   To realize this vision, KAIST will pursue the following five development strategies, collectively called the Beyond Series: Beyond AI – AI for Everyone: Create a leading environment for education and research that moves beyond today's AI toward Humanistic AI, Democratic AI, and Agentic AI. Beyond Laboratory – Innovative Research and Entrepreneurship: Move beyond the laboratory to advance deep-tech innovation in partnership with industry and society and build a global startup ecosystem. Beyond Barriers – An Efficient and Open University: Remove barriers so that members can devote themselves to research and education, underpinned by transparent governance and a culture and systems built on trust. Beyond Carbon – Sustainability and a Greener Future: Strengthen research to address the climate crisis and create an environmentally responsible, carbon-neutral campus grounded in ESG and the UN Sustainable Development Goals. Beyond KAIST – Toward the World and the Future through Global Connect: Connect global talent, universities, research institutions, companies, and local communities; foster a more international campus; expand international joint research; and strengthen global and regional partnerships. KAIST plans to make AI not merely a technology for specific disciplines or specialists, but a common language and general-purpose tool across all fields. By strengthening foundational education and interdisciplinary AI education, KAIST aims to push beyond merely using AI effectively toward leading AI innovation. KAIST will also expand research in physical AI, AI that operates in the real world, including robotics, autonomous driving, and advanced manufacturing, as well as in strategic technologies such as quantum science, climate technology, and energy technology. Building on world-class basic research, KAIST will expand industry collaboration, technology commercialization, and global entrepreneurship, creating a cycle in which research outcomes drive innovation in industry and society. KAIST will expand the establishment of corporate satellite laboratories and collaborative research centers. It will also support joint research and development with companies by building AI Autonomous Labs that integrate AI into the R&D process, creating a new research environment in which AI designs and conducts experiments and analyzes the results. The university will introduce specialized entrepreneurship education for newly admitted students and establish a model that combines classroom instruction with hands-on training, involving alumni entrepreneurs and industry professionals. The inauguration also featured case studies of KAIST alumni using AI to drive innovation in industry and research. Dr. Hyeon-Sook Yoon from Korea Shipbuilding & Offshore Engineering (KSOE) presented the use of digital twins in the shipbuilding and maritime industries, while Dr. Ji-Yong Shin from Samsung Electronics' Semiconductor R&D Center discussed the use of AI in semiconductor manufacturing. Professor Joonsik Hwang of KAIST then discussed the development and applications of physical AI in automobiles, mobility, robotics, and other fields. KAIST plans to build an AI Native Campus that organically connects AI Interactive Education in education, AI Autonomous Labs in research, AI Agent Administration in administration, and AI Energy Convergence in infrastructure. KAIST will also build an AI-based digital administration system to streamline or eliminate unnecessary regulations and procedures so that faculty and students can focus more fully on education and research. The campus will also become a living lab where climate and energy technologies are developed and validated, while global cooperation will be strengthened by recruiting outstanding international students and faculty, expanding international joint research, and broadening dual-degree programs. In his address, President Bae said, “We will carry forward the proud tradition we have inherited: our vision of becoming a Global Value-Creative Leading University and our C-Cube core values of Creativity, Challenge, and Caring. Building on this foundation, we will pursue the innovation needed to move toward our new goal, Fundamentals First, Innovation Forward.” He added, “Grounded in strong fundamentals across both our people and our institution, we will advance five strategic priorities—AI, global entrepreneurship, a stronger focus on education and research, sustainable growth, and internationalization—and further establish KAIST as a world-leading university.” He also emphasized, “I will listen with an open mind and act with determination. As both a facilitator and a servant leader, I will empower every member of the KAIST community to pursue their aspirations with confidence and fulfillment. Together, we will take KAIST beyond innovation—establishing it as a university that sets new standards and a national innovation platform shaping the future of science and technology in Korea.” President Bae is an internationally recognized mechanical engineer and energy scientist specializing in carbon-neutral transportation power systems and sustainable mobility technologies. He earned his bachelor’s and master’s degrees in aerospace engineering from Seoul National University and a Ph.D. in mechanical engineering from Imperial College London. Since joining KAIST in 1998, he has served in leadership roles including Chair of the Department of Mechanical Engineering, Dean of the College of Engineering, and Director of the Mobile Clinic Module Project during the COVID-19 pandemic, gaining broad experience in education, research, and university administration. He has also contributed to energy and carbon-neutrality research and to national science and technology policy as chair of the International Energy Agency's Technology Collaboration Programme on Sustainable Combustion, chair of the Climate Division of the Ministry of Foreign Affairs' Science and Technology Diplomacy Advisory Committee, and chair of the Society of Carbon-Neutral Fuel Technology. He was the first Korean researcher in the powertrain field to be elected an SAE Fellow and has received honors including a Presidential Commendation and a Merit Award from the National Assembly of the Republic of Korea. KAIST presented the inauguration as a ceremony marking the start of a new presidency and as a forum for sharing the university's future vision and implementation strategies. The occasion marked KAIST's move beyond "a KAIST that embraces challenges" toward "a KAIST that sets the next standard for innovation," as it pursues its goal of becoming a world-leading university for innovation. < President Choongsik Bae waving the university flag >