Kaist

KAIST Develops Microbially Produced Adhesive Mater..

  < The research team. From left: Ph.D. student Minju Kang, Distinguished Professor Sang Yup Lee, Dr. Youngjoon Lee, and Dr. Gi Bae Kim (Department of Chemical and Biomolecular Engineering, KAIST) >   KAIST researchers have demonstrated the potential of using microorganisms to produce a bio-based alternative to petroleum-derived materials for hot-melt adhesives. These heat-activated glues are widely used in packaging, furniture, electronics, and automobiles. Using Escherichia coli, the team produced a new polymer from glucose and demonstrated adhesive performance and thermal properties that support its potential use as a hot-melt adhesive. KAIST (President Choongsik Bae) announced on September 17 that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering developed a microbial cell factory by engineering E. coli to produce the new aromatic polyhydroxyalkanoate (PHA) polymers from glucose for adhesive applications. The research team applied systems metabolic engineering, an approach that redesigns a microorganism's overall metabolism to produce a target compound, to re-engineer the metabolic pathways of E. coli. Through this approach, the researchers successfully produced two new adhesive materials from glucose, poly(4HB-co-PhLA) and poly(3HB-co-4HB-co-PhLA), both belonging to the PHA family of biodegradable polymers. Adhesives are essential materials used in a wide range of industries, including packaging materials, electronics, automobiles, and construction materials. In particular, hot-melt adhesives (HMAs) are solid adhesives that are melted with heat for bonding and then solidify as they cool. Because they do not require separate organic solvents and enable rapid bonding, they are widely used in industry. However, most hot-melt adhesives currently in use are based on petroleum-derived polymers such as ethylene-vinyl acetate (EVA). Although they offer excellent adhesive performance, most do not readily degrade in nature and can cause waste and microplastic problems. In particular, even when biodegradable packaging materials are used, if the adhesive does not degrade, the biodegradability and recyclability of the entire product may be reduced. To address this problem, the research team focused on PHA, a type of biodegradable plastic produced by microorganisms. PHA can be produced by microorganisms using renewable feedstocks such as glucose. It also has the advantage that properties such as polymer flexibility, strength, and heat resistance can be adjusted in various ways depending on which components are included and in what proportions. Using this principle, the research team incorporated 4-hydroxybutyrate (4HB) and phenyllactate (PhLA) into a single polymer. 4HB contributes to the material’s softness and adhesion, while PhLA increases the material’s rigidity and heat resistance. When the research team varied the relative proportions of the two components, the thermal properties and adhesive performance of the polymers also changed. In particular, polymers containing approximately 24–34 mol％ 4HB showed excellent adhesive performance. The research team designed intracellular metabolic pathways so that E. coli consumes glucose, produces 4HB and PhLA on its own, and then links them together into polymers. To address metabolic imbalances that arise when multiple monomers are produced simultaneously, the team adjusted the strength and timing of gene expression, added a CoA transferase to facilitate the reactions, and used a genome-scale metabolic model to identify and optimize metabolic pathways associated with insufficient precursor supply and production bottlenecks. As a result, through fed-batch fermentation, in which nutrients are continuously supplied during microbial cultivation, the team successfully produced 10.2 g of poly(4HB-co-PhLA) per liter of culture medium. By additionally introducing a biosynthetic pathway for producing 3HB, the team successfully produced up to 52.8 g/L of poly(3HB-co-4HB-co-PhLA). The research team also tested whether the produced polymers could be used as hot-melt adhesives. In a lap shear adhesion test using stainless steel as the substrate, poly(4HB-co-PhLA) recorded a lap-shear strength of 4.58 MPa. This is higher than the 4.20 MPa achieved by a commercial EVA adhesive. Poly(3HB-co-4HB-co-PhLA) also showed performance comparable to EVA in a wood adhesion test. In addition, poly(4HB-co-PhLA) retained a substantial level of adhesive strength even after repeated melting and rebonding. The incorporation of the PhLA component improved heat resistance. Furthermore, after treatment with lipase, an enzyme that breaks down fats, surface damage to the polymers was observed, while the molecular weight and overall mass of the polymers also decreased. These results indicate that the newly developed aromatic PHA can be degraded by enzymes. This study is significant not only for the development of an eco-friendly adhesive but also for demonstrating the potential of metabolic engineering to produce functional materials with tunable properties. Going forward, this approach could be extended into a biomanufacturing technology that tunes polymer properties, such as adhesion strength, flexibility, and heat resistance, to meet specific application needs, and produces such materials using microorganisms. The approach also makes it possible to produce functional polymers that have traditionally been produced through petrochemical processes, from renewable feedstocks such as glucose instead. This is expected to serve as a sustainable production technology not only for adhesives but for a wide range of functional polymer materials going forward. < Schematic summary of the study >   Distinguished Professor Sang Yup Lee said, “This study demonstrates that by engineering microbial metabolism, it is possible to go beyond simply producing polymers and directly produce functional materials,” adding, “In the future, by utilizing various non-natural monomers and microbial cell factories, this approach could be expanded into biomanufacturing technologies for the sustainable production not only of petroleum-based adhesive alternatives but also of a wide range of functional polymers.” The study, co-authored by Ph.D. student Minju Kang, Dr. Youngjoon Lee, and Dr. Gi Bae Kim of the KAIST Department of Chemical and Biomolecular Engineering, was published online on July 20 in the international journal Nature Communications. Paper title: Synthesis of poly(4HB-co-PhLA) and poly(3HB-co-4HB-co-PhLA) as sustainable hot-melt adhesives using engineered Escherichia coli DOI: 10.1038/s41467-026-75809-9 Author information: Minju Kang (KAIST), Youngjoon Lee (KAIST), Gi Bae Kim (KAIST), Sang Yup Lee (KAIST, corresponding author) This research was supported by the National Research Foundation of Korea through a grant funded by the Ministry of Science and ICT. (Grant No. 2022M3J5A1056117).  

KAIST Robot That Follows Verbal Directions and Che..

  < From left: Professor Myung Hyun, Ph.D. candidate Park Juhye, Master >   A robot that can follow a single verbal instruction, navigate an unfamiliar building, and verify that it has reached the correct destination has earned top honors in two international competitions. KAIST (President Chungsik Bae) announced on September 11 that a team led by Professor Hyun Myung of the Urban Robotics Lab in the School of Electrical Engineering took first place in an international challenge held in conjunction with ECCV 2026, a leading computer vision conference, and second place in a challenge at RSS 2026, a leading robotics conference. The team’s technology goes beyond conventional navigation. It enables robots to interpret their surroundings and independently verify whether they have made the right decision. Both competitions focused on embodied AI—the ability of a robot to understand human language, perceive its surroundings through cameras, make decisions, and act on them. Unlike AI systems that operate solely in the digital realm, embodied AI interacts directly with the physical world. Professor Myung’s research team joined forces with the Field Robotics Research Section of the Electronics and Telecommunications Research Institute (ETRI) to form the “URL-FRRS” team. The team placed first among 112 teams worldwide in the VLNVerse Challenge, a vision-language navigation competition held at the ECCV 2026 EMR Workshop in Malmö, Sweden on September 9. The competition tested whether a robot could reach the correct destination based solely on a stated goal, such as “Find the blue sofa in the living room,” or a multistep instruction, such as “Turn right at the end of the hallway and enter the second door.” When the research team analyzed why existing robots were failing, they found that, surprisingly, the more common cause was not an inability to find the route, but the robot mistakenly concluding it had “arrived” at the wrong location. A robot might, for example, pass the actual destination or mistake a similar-looking room for the target. To address this problem, the team developed CoRe-VLN, short for Coverage-based Recovery for Vision-Language Navigation. The technology prompts the robot to verify its location whenever it is about to stop, essentially asking itself, “Is this really the destination?” Once the robot determines that it may have reached its destination, it scans its surroundings again. An AI model capable of interpreting both text and images then checks whether the robot is in the correct room, whether the target object matches the specified color and shape, and whether the object is actually nearby. The process is similar to a person checking a sign or looking around after arriving at a meeting point to make sure they are in the right place. If the robot determines that it has stopped at the wrong location, it plans a new route and resumes its search. Because CoRe-VLN can be added to an existing navigation system without further training, it gives the robot a self-verification and recovery capability that can be readily applied in unfamiliar environments. The team achieved an average success rate of 90.7％ across the two competition tasks. Although it tied with the runner-up in success rate, it secured first place by submitting its results earlier. The third-place team recorded a success rate of 89.7％. < Final rankings of the VLNVerse Challenge 2026 >   The KAIST team also took second place in the NaviTrace Challenge at the RSS 2026 OWN Workshop, held in Sydney, Australia, in July. In this competition, a robot was given only a single photograph and a brief command, such as “Drive into the parking lot,” and had to determine the correct direction of travel. When entering a parking lot, people instinctively distinguish the entrance from the exit by interpreting road markings and observing the direction of other vehicles. These cues, however, are not explicitly stated in the command. To navigate spaces shared with people, robots must be able to infer such implicit rules directly from the scene before them. The team addressed this challenge by developing PRISM-Nav, a system in which multiple AI agents divide the work instead of relying on a single AI model to make every decision. One agent locates the destination, another identifies hazards such as stairs and curbs, and a third recognizes socially significant features such as sidewalks and crosswalks. A final agent integrates their findings and determines the direction of travel. The approach resembles a group where one person checks the route, another watches for hazards, and a third interprets traffic rules before they jointly choose the safest path. The researchers also streamlined communication among the AI agents. Instead of describing locations through lengthy text or numerical coordinates, the agents mark destinations and hazardous areas directly on an image and share the annotated image with one another, much like handing someone a marked-up map rather than explaining the route verbally. PRISM-Nav can also be deployed in new environments without retraining the AI models. The team scored 53 points in the competition, finishing second behind a joint team from Nanjing University and FiveAges. Notably, the team used multiple instances of the relatively inexpensive and lightweight Gemini 3 Flash model to outperform Gemini 3.1 Pro Preview, the more powerful model used by the organizers as a benchmark. The result demonstrated that coordinating several lightweight AI models can deliver better performance and cost efficiency than relying on a single, more expensive model to handle every task. < Example results from PRISM-Nav >   “We are delighted that the potential of our technology has been recognized in a competition with research teams from around the world. This achievement demonstrates that AI can make decisions and act independently in unfamiliar environments without additional training.” said Professor Myung. He added, “We plan to extend this approach beyond navigation to tasks such as identifying, picking up, and moving objects, ultimately developing robots that can verify and correct their own decisions.” As the technology advances, robots may move beyond simply executing human commands. Before acting, they could independently ask, “Is this the right place?”, “Is this the right object?”, or “Is this action safe?” Such capabilities could benefit not only delivery and guide robots but also robots operating alongside people in logistics centers, factories, hospitals, and other shared environments. < Certificate of the 2nd place award in the RSS 2026 NaviTrace Challenge >   Professor Myung’s laboratory also took first place in international challenges held at ICRA 2026, a leading robotics conference, and CVPR 2026, a leading computer vision conference, in June. The laboratory is now expanding its expertise in spatial cognition into the field of physical AI. This research was supported by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP). (END)  

Single Material Enables Efficient Charge Injection..

  < The research team. From left: Jeongin Yeo, PhD student (UNIST); Sungyeon Kim, PhD student (UNIST); Hanbin Cho, PhD student (KAIST); Professor Joonki Suh (KAIST); Seonguk Yang, PhD student (KAIST); Wenxuan Zhu, postdoctoral researcher (KAIST); and Gilhwan Do, master >   A new approach could help make future AI chips smaller and more energy-efficient. A KAIST-led research team has used a single material to address one of the major obstacles facing atomically thin semiconductors, the difficulty of efficiently injecting charge. The technology could contribute to next-generation AI and low-power semiconductor devices in which multiple ultrathin layers are vertically integrated to increase device density and performance. KAIST (President Choongsik Bae) announced on September 16 that a research team led by Professor Joonki Suh from the Department of Chemical and Biomolecular Engineering has developed a ‘universal van der Waals tunneling injector’ based on tin diselenide (SnSe2). The single-material injector efficiently supplies charge to two different types of atomically thin semiconductor channels. The study was conducted in collaboration with researchers from Yonsei University, the Beijing Computational Science Research Center in China, the Korea Institute of Science and Technology (KIST), Hanyang University, the Ulsan National Institute of Science and Technology (UNIST), and Samsung Electronics. A transistor is a microscopic switch that controls the flow of electric current. Nearly every semiconductor chip, from those used in smartphones and computers to advanced AI processors, contains vast numbers of transistors. Transistors can be broadly classified as n-type or p-type. In n-type transistors, ‘electrons’ carry the current, while in p-type transistors, current is carried by ‘holes’, which are empty electron states that act as positive charge carriers. Modern semiconductor chips combine these two types in complementary metal-oxide-semiconductor, or CMOS, circuits, which enable fast operation while minimizing power consumption. < Figure 1. Concept and interfacial analysis of the universal SnSe₂ van der Waals injector. >   Two-dimensional semiconductors, which can be less than a nanometer thick, have recently emerged as promising materials for making chips smaller and more energy-efficient. Their atomically thin structure also makes it possible to stack multiple device layers vertically, potentially allowing more transistors to be integrated within the same footprint. A persistent challenge, however, has been injecting charge efficiently into such ultrathin semiconductors. Depositing a conventional metal electrode directly onto a two-dimensional semiconductor can damage its delicate atomic structure during fabrication. The resulting interface can also create an energy barrier that obstructs the injection of electrons or holes. In atomically thin semiconductors, the ‘entry point’ for electrical charge has been a major bottleneck limiting transistor performance. Complicating matters further, n-type and p-type channels generally require different contact conditions and have therefore relied on separately optimized electrodes. The research team addressed this problem using a single material: tin diselenide (SnSe2), a layered semiconductor composed of tin (Sn) and selenium (Se). Instead of forming strong chemical bonds with the semiconductor channel, SnSe2 can be placed in contact with it through weak interatomic attractions known as van der Waals forces. This approach preserves the atomically thin channel and creates a clean, uniform interface. Crucially, SnSe2 provides a different but favorable charge-injection pathway depending on the semiconductor with which it is paired. When combined with p-type tungsten diselenide (WSe2), SnSe2 forms a type-III, or ‘broken-gap’, band alignment that enables efficient band-to-band tunneling. In this process, charge carriers pass through an energy barrier quantum mechanically rather than having to acquire enough energy to travel over it. When combined with n-type molybdenum disulfide (MoS2), the same SnSe2 injector forms a different junction. An applied gate field reshapes and narrows the electron-injection barrier, allowing electrons to tunnel through it. In other words, whereas n-type and p-type transistors previously required separately optimized charge-injection contacts, SnSe2 serves as a single, shared injector that adapts to both channel polarities through different tunneling mechanisms. The resulting performance improvements were substantial. In p-type WSe2 transistors, the SnSe2 injector increased the maximum drive current by more than 1,000 times compared with conventional nickel electrodes. Drive current refers to the maximum current that a transistor can deliver when switched on. < Figure 2. Concept diagram of the universal van der Waals tunneling injector for monolayer CMOS. >   In n-type MoS₂ transistors, the SnSe2 injector enabled steep switching and an on/off current ratio exceeding one billion (109). This means that the device conducts current effectively in its on state while strongly suppressing current in its off state. The team also fabricated a CMOS inverter, a basic building block of digital circuits that combines n-type and p-type transistors, and confirmed that it operated reliably in response to repeated input signals. The key contribution of this research is that it demonstrated a single material can efficiently supply charge to both types of ultrathin semiconductors, moving beyond the conventional approach of using separate electrodes for n-type and p-type devices. With further advances in direct growth, large-area fabrication, and device integration, two-dimensional semiconductors could eventually be stacked in multiple layers to form three-dimensional chips. Such architectures could accommodate more transistors within the same footprint and perform more functions while consuming less power, contributing to the development of next-generation AI processors and ultra-low-power electronic devices. “This study demonstrates that efficient charge injection, one of the most challenging bottlenecks in monolayer two-dimensional semiconductors, can be addressed using a single material platform,” said Professor Joonki Suh. “With further advances in direct growth and large-area processing technologies, this approach could accelerate the practical implementation of low-power two-dimensional CMOS integrated circuits.” The study was led by KAIST PhD candidate Hanbin Cho as first author, with Professor Kyungmin Ko from Yonsei University and Professor Joonki Suh from KAIST serving as co-corresponding authors. The paper was published online on August 12 in the international journal Advanced Materials. Paper title: “A Universal van der Waals Tunneling Injector for Monolayer CMOS” DOI: 10.1002/adma.74597 Authors: Hanbin Cho of KAIST, first author; Professor Kyungmin Ko of Yonsei University and Professor Joonki Suh of KAIST, co-corresponding authors This work was primarily supported by Samsung Electronics Co., Ltd. It also received support from Korean funding agencies, including the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT and the Ministry of Education.  

KAIST and KIOM Develop a Wearable Microneedle Elec..

  < Researchers. From left: Dr. Sanghun Lee (KIOM), Dr. Se Kyun Bang (KIOM), Heesoo Kim (PhD student, KAIST), and Professor Jae-Woong Jeong (KAIST). >   A "wearable electroceutical" has been developed that could reduce the need to visit a hospital or rely on painkillers every time pain occurs. When attached to the skin, it modulates pain through electrical stimulation and can be remotely controlled via smartphone even across long distances, such as between Korea and the United States. KAIST (President Choongsik Bae) announced on September 13 that a joint research team led by Professor Jae-Woong Jeong from the School of Electrical Engineering at KAIST and Dr. Sanghun Lee from the KIOM (Korea Institute of Oriental Medicine, President Sung-Kyu Kho) has developed a wearable electroceutical platform that combines a wireless microneedle (fine needles that adhere to the skin) electroceutical with Internet of Things (IoT)-based remote control technology. < Figure 1. Schematic illustration of the wireless IoT-based microneedle electroceutical system for personalized and remote pain management. >   The key feature of this research is that a single small skin-attached device integrates stable electrical stimulation, smartphone-based remote control, and automatic stimulation based on the body's physiological state. Once its efficacy and safety are validated with actual patients, the technology may be used for personalized pain management at home or in daily life. The need for such an approach is particularly relevant for chronic pain management. Painkillers are widely used to treat chronic pain, but long-term medication use raises concerns about side effects and dependency. In particular, opioid analgesics used for conditions such as cancer pain carry an increasing risk of tolerance and misuse with prolonged use, which has spurred research into "electroceuticals", devices that modulate nerves through electrical stimulation as an alternative to drugs. However, existing electroceuticals have limitations. Implantable devices require surgery, and skin-attached electrodes may fail to deliver electrical current properly depending on skin conditions such as sweat or dead skin cells. A concentrated current at specific sites can also raise skin temperature or pose a risk of burns. To address these issues, the research team developed a temperature-responsive, conductive, and adhesive microneedle electrode (a fine needle electrode that conducts electricity well while adhering to the skin). Because the microneedles penetrate the highly resistive stratum corneum, the device can deliver stable electrical stimulation while reducing the influence of sweat and dead skin cells. The team also coated the electrode with a conductive hydrogel (a gel-like material that retains a large amount of water) so that current spreads evenly rather than concentrating at the needle tips. In addition to improving electrical performance, the electrode was designed with a built-in thermal safety mechanism. When skin temperature rises abnormally, the electrode's adhesion weakens and it detaches from the skin on its own, helping reduce the risk of skin burns that could occur during electrical stimulation. Beyond the skin interface itself, the research team integrated the device with IoT-based remote management. Using a smartphone and cloud server, a healthcare provider can control the device's operating time and electrical stimulation in real time or on a scheduled basis, even when located far from the patient. The team confirmed that the device could be remotely controlled even across long international distances, such as between Korea and the United States. Following future clinical validation, this could develop into a home-based or remote pain management approach in which patients use the electroceutical under medical supervision without needing to visit a hospital. The platform further extends beyond remote control by enabling automatic operation based on the body's physiological state. Using a photoplethysmography (PPG) sensor (a technology that measures pulse and blood flow changes using light), the team detected pain-related stress states and, based on this, implemented a closed-loop (a method that automatically adjusts treatment while continuously checking the body's condition) therapy function that automatically triggers electrical stimulation. The researchers evaluated the performance of the platform in both animal experiments and a small-scale human study. In animal experiments, current was delivered more effectively than with conventional gel electrodes, and pain-relieving effects were also confirmed. In a small-scale study involving healthy adults, changes in skin sensory pain thresholds (the level of stimulation at which pain begins to be felt) following electrical stimulation were observed to assess the potential for application in humans. However, direct analgesic effects in this study were confirmed only through animal experiments. The research team noted that further clinical studies are needed to confirm therapeutic efficacy and the safety of long-term use in actual chronic pain patients. < Figure 2. Configuration and characteristics of the thermoresponsive, conductive, and adhesive microneedle electroceutical. Schematic (a) and photograph (b) of the wireless microneedle electroceutical patch. Photograph of the thermoresponsive, conductive, and adhesive microneedle electrode (c) and schematic illustrating enhanced current delivery efficiency (d). Low skin-contact impedance (e) and temperature-dependent variable adhesion (f) of the conductive, adhesive microneedle electrode. >   Professor Jae-Woong Jeong from the KAIST School of Electrical Engineering said, "By combining a stable skin interface with IoT-based remote management, we have expanded the potential for wearable electroceuticals in daily life. We hope that, following future clinical validation, this approach can evolve into a personalized digital healthcare platform that enables pain management tailored to each patient's condition." Dr. Sanghun Lee from the KIOM said, "We hope this research will be integrated with future wearable acupuncture technologies to contribute to the development of a new non-pharmacological pain management approach that stimulates acupoints through electrical stimulation." The study, co-first-authored by Heesoo Kim, a PhD student at KAIST, and Dr. Se Kyun Bang from the KIOM, was published in the international journal Nature Communications on August 28th. Paper title: Wireless IoT-Enabled Microneedle Electroceutical for Personalized and Connected Pain Management, DOI: 10.1038/s41467-026-76527-y Author information: Heesoo Kim (KAIST, co-first author), Se Kyun Bang (KIOM/UST, co-first author), Sanghun Lee (KIOM, co-corresponding author), Jae-Woong Jeong (KAIST, corresponding author), and 10 others Demonstration video: https://www.dropbox.com/scl/fo/zrglys1t1c49l5u1xo08e/AJsbboH3sQMFag-6aO6sSSs?rlkey=dnux0jj80bdczws22u0dq7ugx&e=1&dl=0 This work was supported by the National Research Foundation of Korea (RS-2022-NR067853, RS-2025-02218624, RS-2024-00335066), and by the Korea Institute of Oriental Medicine (KSN2511012 and KSN2511013).  

KAIST Identifies Cause of Artifacts in Battery Nan..

  < The research team. From left: Professor Nam-Soon Choi, Department of Chemical and Biomolecular Engineering; Dongyan Chen, PhD student, Department of Materials Science and Engineering; Professor Seungbum Hong, Department of Materials Science and Engineering; and Professor Jong Min Yuk, Department of Materials Science and Engineering. >   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. < Figure 1. Schematic illustrating the trace–retrace discrepancy observed in DART-ESM measurements. >   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. < Figure 2. AFM images of an NZTO solid electrolyte for sodium-ion batteries, comparing samples mechanically polished with sandpaper and samples after CCP treatment. After CCP treatment, the standard deviation of the ESM amplitude decreases, and the consistency between amplitude trace and retrace improves. >   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).  

KAIST Develops AI Technology That Fixes SQL Errors..

  < The research team. From left: PhD student Geonho Lee, first author, and Professor Min-Soo Kim, corresponding author. >   “Find the best-selling product from last year.” When an AI system attempts to answer a question like this by querying a company database, even a single reference to a nonexistent item can cause the query to fail. Until now, correcting such an error often required regenerating the entire SQL query from scratch. A KAIST research team has developed a technology that instead identifies and fixes only the erroneous part. The technology is expected to make AI-powered data retrieval faster and more accurate, accelerating the adoption of AI work assistants in enterprise environments. KAIST (President Choongsik Bae) announced on September 4 that a research team led by Professor Min-Soo Kim from the School of Computing has developed SafeQL, a technology that detects and corrects errors that arise when natural-language questions are translated into Structured Query Language (SQL). < Figure 1. Comparison of the SQL error correction processes used by conventional methods and SafeQL. >   Text-to-SQL technology enables AI systems to convert everyday questions, such as “Which product saw the largest increase in sales last year?” or “Which items are running low in stock?”, into SQL queries. This allows users to retrieve sales, customer, and inventory data using natural language without having to understand complex database commands. However, AI systems can make mistakes when generating SQL. For example, they may refer to a table or column that does not exist or join tables incorrectly. These errors can prevent the query from running and leave the AI unable to retrieve the requested data. Conventional correction methods send the database error back to a large language model (LLM) and ask it to regenerate the entire query. This is similar to rewriting an entire report to correct a single word. In the process, parts that were already correct may be altered, new errors may be introduced, and repeated calls to the LLM increase both cost and processing time. SafeQL takes a different approach. Instead of discarding and regenerating the entire query after an execution failure, it interprets feedback from the database management system to precisely locate the faulty component, such as a relation, attribute, function, or value. It then incrementally repairs that component while preserving the valid structure and logic of the original query. To achieve this, the research team developed a “safe query space” approach. Among the candidate corrections that can be executed on the database, SafeQL searches for the one closest to the query originally generated by the AI. The system prioritizes the most promising candidates and filters out unsuitable ones in advance, reducing the time required for correction. The team implemented SafeQL as a PostgreSQL extension and integrated it with the database system’s parser, binder, and type analyzer. This enables SafeQL to precisely locate errors even in complex SQL queries. The system calls the LLM again when search-based refinement cannot resolve the error within a predefined threshold, thereby reducing unnecessary AI use. The research team evaluated SafeQL using BIRD and Spider, two widely used benchmarks for assessing the database querying capabilities of AI systems. On the BIRD benchmark, SafeQL resolved execution errors in up to 87.4％ of initially erroneous SQL queries and improved execution accuracy by up to 5.8 percentage points over the unrefined baseline. Compared with regenerating the entire query, SafeQL reduced token use by a factor of up to 15.1 and refinement latency by a factor of up to 29.6. SafeQL is expected to be particularly useful for enterprises that handle large volumes of data requests. If an error occurs while an AI system is searching internal sales, customer, or inventory data, SafeQL can repair only the affected part instead of regenerating the entire query. This can reduce the cost and time required to operate enterprise AI systems and support reliable work automation powered by AI agents and corporate data. < Figure 2. SafeQL’s efficient SQL search process using pruning. >   Professor Min-Soo Kim said, “For AI to perform real-world tasks in enterprise environments, it must be able to accurately retrieve the data it needs.” He added, “When AI makes an error during a database search, SafeQL fixes only the affected part instead of starting over from scratch. By reducing errors, costs, and processing time, we expect the technology to accelerate reliable AI-powered work automation.” Geonho Lee, a PhD student in the KAIST School of Computing, participated in the study as first author, with Professor Min-Soo Kim serving as corresponding author. The findings will be presented at the International Conference on Very Large Data Bases (VLDB), a leading international database conference, to be held in Boston, USA, from September 1 to 5. Paper title: SafeQL: Search-based Refinement for Safe and Efficient LLM-based Text-to-SQL DOI: 10.14778/3819518.3819545 Authors: Geonho Lee (KAIST, first author) and Min-Soo Kim (KAIST, corresponding author) This research was supported by the National Research Foundation of Korea (NRF) and the SW Star Lab program of the Institute of Information & Communications Technology Planning & Evaluation (IITP), both funded by the Ministry of Science and ICT (MSIT).  

KAIST Tames a Semiconductor Greenhouse Gas 6,000 T..

  < Researchers. KAIST research team (from left) Dr. Seunghyuck Chi, Postdoctoral Researcher, Department of Chemical and Biomolecular Engineering; Professor Minkee Choi, Department of Chemical and Biomolecular Engineering >   Among the gases used in semiconductor manufacturing, tetrafluoromethane (CF₄) is a greenhouse gas over 6,000 times more potent than carbon dioxide. A KAIST research team has developed a technology that removes this gas with high efficiency while extending the usable lifetime of the catalyst that helps break it down by harnessing the ‘power of disorder,’ in which mixing multiple metal atoms together actually stabilizes the catalyst’s structure. KAIST (President Choongsik Bae) announced on September 3 that a research team led by Professor Minkee Choi from the Department of Chemical and Biomolecular Engineering, working in collaboration with researchers from Samsung Electronics, has developed a new catalyst capable of removing CF₄, a greenhouse gas used in processes such as the fabrication of fine semiconductor circuits with high efficiency over long periods of use. CF₄ is used in processes such as dry etching, in which unwanted portions of a semiconductor wafer are selectively removed to create fine circuit patterns. The problem lies in the CF₄ left over after use. Because its carbon and fluorine atoms are bound together extremely tightly, the gas does not easily decompose, and once released into the atmosphere, it can persist for roughly 50,000 years. Its impact on global warming is also more than 6,000 times greater than that of carbon dioxide. To prevent CF₄ from being released as is, semiconductor manufacturing sites currently decompose it at high temperatures using steam and a catalyst. A catalyst speeds up chemical reactions, much like those used to reduce pollutants in car exhaust. However, conventional catalysts have suffered from declining performance the longer they are used. This is because hydrogen fluoride (HF), generated as CF₄ decomposes, combines with moisture to create a highly corrosive environment, causing the catalyst’s fine particles to aggregate or its structure to change. When small catalyst particles clump together into larger masses, the surface area in contact with the CF₄ to be treated shrinks, and performance declines accordingly. The research team solved this problem, paradoxically, by harnessing the ‘power of disorder.’ Mixing multiple atom types creates a complex, disordered structure that resists phase changes and remains stable. This process is called entropy stabilization. In simple terms, it is a principle in which evenly mixing multiple kinds of atoms makes it difficult for a catalyst to clump together or change into another structure. Using this principle, the research team evenly incorporated multiple metals — aluminum (Al), zinc (Zn), gallium (Ga), nickel (Ni), and cobalt (Co) — into a single aluminate crystal structure. Aluminate is a material in which several metals are bonded around a basic framework of aluminum and oxygen. Through this approach, the team developed an ‘entropy-stabilized aluminate (ESA) catalyst’ that resists aggregation and structural deformation even under the harsh conditions of high temperature, moisture, and fluorine occurring together. The performance gap was clear. The new catalyst’s intrinsic activity for decomposing CF₄ was approximately 2.3 times higher than that of a conventional alumina catalyst. Notably, in an accelerated test conducted at about 800°C for 150 hours, the CF₄ conversion of the conventional alumina catalyst dropped from 93％ to 48％. The new catalyst, by contrast, maintained a high level, declining only from 98％ to 92％. This demonstrated that the catalyst can remove CF₄ with high efficiency while sustaining its performance over extended periods. The researchers also revealed the decomposition mechanism of CF₄. To do this, they used oxygen isotopes, which allow the movement of oxygen atoms to be tracked. In simple terms, this involves attaching a ‘tag’ to oxygen atoms so that where the oxygen comes from and where it moves to during the reaction can be traced. The results confirmed that the catalyst first uses the oxygen within its own structure to decompose CF₄, and that the reaction continues as surrounding steam replenishes the oxygen that has been depleted. In effect, the catalyst functions as a kind of ‘oxygen refill system,’ in which steam restores the oxygen the catalyst draws upon. Through this, the research team provided the world’s first experimental confirmation of a CF₄ decomposition process that had previously only been proposed in theory. < Samsung Electronics >   The significance of this research goes beyond developing a single catalyst that decomposes CF₄ effectively; it presents a new catalyst design strategy capable of achieving both high decomposition performance and a long service life at the same time. The approach is expected to be applicable to the future development of catalysts for treating a range of semiconductor process gases by varying the types and combinations of metals used. Professor Choi said, “By applying the principle that disorder in nature can actually make a structure more stable to catalyst design, we achieved both high CF₄ decomposition performance and long-term stability at the same time.” He added, “This work is meaningful in that it presents a new materials design strategy that can be extended to catalysts for treating a range of semiconductor process gases by varying the types and combinations of metals used.” The study was led by Dr. Seunghyuck Chi, a postdoctoral researcher in KAIST’s Department of Chemical and Biomolecular Engineering, who served as first author, with researchers from Samsung Electronics participating as co-authors. The findings were published in June in the international chemistry journal Angewandte Chemie International Edition. Paper title: Entropy-Stabilized Aluminate Catalysts that Break the Activity–Stability Tradeoff in CF₄ Hydrolysis, DOI: 10.1002/anie.6752036 This research was supported by the National Research Foundation of Korea (RS‐2024‐00333937 and RS‐2024‐00405261).  

KAIST Skin-Conformable Micro-LED Mask Boosts Skin ..

  < Members of the research team. From left, Min Seo Kim (co-first author), Professor Keon Jae Lee >   Home beauty devices that let users care for their skin conveniently at home have grown popular recently, but conventional LED masks are limited not only by their rigid structures but also by their point-emitting LEDs, which must be positioned away from the skin to spread light over a broader area. This inherently prevents close skin contact and increases optical loss. KAIST (President Chung-Sik Bae) announced on September 2 that a joint research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering confirmed skin-brightening and elasticity-improving effects using a face-conforming LED mask. The mask combines a flexible surface-emitting micro-LED layer, consisting of a dense micro-LED array and a light-diffusing layer for uniform illumination, with a three-dimensional elastic scaffold that conforms to facial contours. In a 2024 study published in Advanced Materials, Professor Lee clinically demonstrated that a flexible surface-emitting micro-LED mask produced up to 340％ greater improvement in deep skin elasticity than conventional LED masks. In the present study, the 3D elastic scaffold adapted to different facial contours, increasing skin-contact area from 46.9％ to 78.1％ and reducing the light-source-to-skin distance to 1.8 mm, while achieving 93.83％ light uniformity across eight facial measurement sites. < Figure 1. Structure and operation of the Skin-Conformable Surface-Emitting Micro-LED Mask with a three-dimensional elastic scaffold. >   The researchers evaluated the mask in a split-face clinical study involving 33 participants. All of the participants received PN injections across the entire face, while the micro-LED mask was applied to only one side for eight weeks. The micro-LED-treated side showed greater improvement across all five skin-brightening indices, including skin brightness, tone uniformity, skin exfoliation, melasma count, and melasma area. Consistent with the clinical findings, human-derived skin tissue treated with micro-LEDs also showed reduced expression of the melanogenesis-related markers MITF and TYR, supporting a direct contribution of the LED treatment to the brightening effect. PN injections are primarily known for skin rejuvenation, with limited evidence of a direct skin-brightening effect when used alone. < Figure 2. Mechanism underlying the combined cosmetic effects of PN skin-rejuvenation injections and the Skin-Conformable Surface-Emitting Micro-LED Mask >   The synergy between PN injection and the micro-LED mask was also evident in skin regeneration and post-procedure recovery. Deep skin elasticity improved by 12.8％ on the LED＋PN side, compared with 3.1％ on the PN-only side, representing approximately 4.1 times the improvement observed with PN alone. Skin-barrier recovery was faster, while post-procedure redness was reduced to a greater extent, consistent with the known anti-inflammatory and tissue-repair effects of red-light photobiomodulation. These findings suggest that the LED mask may boost mitochondrial ATP production in the skin and activate regenerative responses that PN alone cannot fully induce. Professor Lee said, “This study clinically demonstrates that home-use LED masks can extend beyond skin rejuvenation to skin brightening and highlights the importance of delivering light in close contact with the skin. When combined with in-clinic skin-rejuvenation injections, the mask can substantially improve skin elasticity and accelerate post-procedure recovery, creating a new clinic-to-home care platform.” This study was conducted jointly by researchers from KAIST and AMOREPACIFIC. A related product based on the technology is scheduled to launch in Japan in Q4 2026 and enter the U.S. market in Q1 2027. The resulting paper, titled “Clinical validation of skin brightening and rejuvenation enabled by a skin-conformable surface-emitting micro-LED mask with injection,” was published in Nano Energy  (Vol. 157, Article 112288; DOI: 10.1016/j.nanoen.2026.112288). < Figure 3. Clinical and ex vivo validation of combined PN injections and surface-emitting micro-LED treatment. >  

KAIST Develops a Soft 3D-Printed Robotic Hand that..

  < The research team. (From left) Dr. Younghan Song (KIST), Professor Bumsoo Park (Seoul National University of Science and Technology), Professor Seungchul Lee (KAIST), and Dr. Jongbeom Na (KIST) >   3D printers that once could only produce rigid objects can now create products as soft and stretchable as rubber. A team of Korean researchers used AI to identify the optimal "recipe" for a material that can be printed into complex shapes while stretching to more than six times its original length. The material is expected to expand the range of applications for 3D printing, from robotic hands to form-fitting wearable devices and custom medical devices. KAIST (President Choong-Sik Bae) announced on September 1 that a research team led by Professor Seungchul Lee from the Department of Mechanical Engineering, working with Dr. Jongbeom Na's team at the Korea Institute of Science and Technology’s (KIST, President Sang-Rok Oh) Extreme Materials Research Center and Professor Bumsoo Park from the Department of Manufacturing Systems and Design Engineering (MSDE) at Seoul National University of Science and Technology (SEOULTECH, President Dong-Hwan Kim), had used AI to develop a material that is both 3D-printable and highly stretchable, like rubber. The need for such materials — soft, stretchable, and capable of forming complex shapes — has been growing as soft robots that come into direct contact with people, wearable devices worn on the body, and medical devices custom-fitted to patients have drawn increasing attention. The 3D printing technology the team used, Digital Light Processing (DLP), cures a liquid material into a desired shape by exposing it to light. While DLP can quickly produce complex structures, making a material more stretchable and durable tends to raise its viscosity to the point that it no longer flows well enough to be printed. Conversely, thinning the material to make it easier to print reduces its stretchability and strength. Thus, developing a material that is both easy to print and highly stretchable was the central challenge. The team used AI to identify the optimal "material recipe" that satisfies both conditions. Notably, the training data included not only materials that print well, but also highly viscous materials that are difficult to print. The researchers cured various liquid material formulations in small molds and measured how stretchable and hard they were, how quickly they cured under light, and how well they flowed. This produced a dataset linking a wide range of material formulations to their respective properties. < Figure 1. AI-based material design framework proposed in this study and its application to soft robotics >   The team then used machine learning to examine the relationship between material formulation and performance. Based on this, the AI identified the optimal material combination that is both 3D-printable and highly stretchable. The material identified by the AI printed reliably on a DLP 3D printer and showed high stretchability, extending to more than six times its original length when pulled, without easily tearing. To verify its real-world potential, the team 3D-printed a "soft actuator" using the material. A soft actuator is a device that uses air pressure and other means to create gentle, muscle-like movement. When inflated with air, it expanded like a balloon and bent as naturally as a human finger. < Figure 2. AI-based design and mechanical properties of the 3D-printing material >   A soft robotic hand made by combining several actuators lifted a 1 kg water bottle and successfully and stably grasped objects of varying shapes and rigidity, from fragile eggs to glass bottles, an egg carton, and a computer mouse. Beyond developing a single highly stretchable material, this research is significant for presenting an AI-based method for more quickly identifying materials with desired properties. Previously, researchers had to directly formulate and test countless materials to find the optimal combination. Going forward, AI can first identify promising material combinations based on experimental data, which researchers then verify through testing, thereby reducing trial and error and shortening material development time. "This research is significant as it shows that combining researchers' experimental data with artificial intelligence can efficiently identify optimal material combinations that were previously difficult to find," explained Professor Seungchul Lee. "We expect it to be used to more rapidly develop 3D-printing materials with the performance needed across a range of fields, including soft robots, wearable devices, and custom medical devices." < Figure 3. Soft robotic gripper fabricated using the newly developed material >   The study, with Dr. Younghan Song and Professor Bumsoo Park as co-first authors, was published in the international journal Nature Communications on June 4. Paper title: Machine learning guided formulation design of digital light processing printable elastomers beyond viscosity stretchability tradeoff DOI: https://doi.org/10.1038/s41467-026-73735-4 This research was supported by the Ministry of Trade, Industry and Resource's Machinery and Equipment Industry Technology Development Program (20023762), and by the Ministry of Science and ICT's Nano & Material Technology Development Program (RS-2026-25534767) and Excellent New Researcher Program (RS-2024-00350423).  

KAIST Opens the Era of Industrial-Scale Microbial ..

  < Members of the research team, from left: Distinguished Professor Sang Yup Lee, doctoral student Seok Yeong Jung, and Sol Choi, CEO of SilicoBio. >   The question is no longer whether microbial foods can be made. The question now is who can turn them into an industry first. KAIST researchers have comprehensively analyzed the conditions required for the microbial food industry to succeed across manufacturing, markets, and regulation, and have proposed growth strategies for the next-generation protein industry. KAIST (President Choongsik Bae) announced on the 31st of July that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, together with researchers from SilicoBio, a KAIST faculty startup, has comprehensively analyzed the conditions needed for the microbial food industry to succeed in terms of manufacturing, market entry, and regulatory readiness, and has presented an industrialization strategy and roadmap. < [Figure 1] Regional distribution of global microbial food companies and production infrastructure, highlighting gaps in manufacturing capacity >   This study is significant in that it did not develop a new microorganism or production technology, but instead systematically analyzed the key challenges involved in connecting laboratory-based core technologies to real-world industry. In particular, by presenting an integrated perspective that encompasses manufacturing readiness, market entry strategies, and regulatory responses, the study proposes a direction for developing microbial foods beyond the next-generation protein industry into a future biomanufacturing platform. It is expected to serve as an important milestone for strengthening national biomanufacturing competitiveness and fostering the global sustainable food industry. The researchers analyzed that competition in the microbial food industry is shifting from productivity at the laboratory level to manufacturing readiness. They identified stable raw material supply and quality control, control and safety assurance of non-model microorganisms, reduction of downstream processing costs, and regulatory compliance for byproduct recycling as key factors that will determine the pace of commercialization. Manufacturing Readiness refers to the level at which a laboratory technology can be reliably produced at industrial scale. Non-model microorganisms are microorganisms with high industrial potential but insufficient accumulated research infrastructure. Downstream processing refers to the processes of separating, purifying, concentrating, and drying target components after fermentation. < [Figure 2] Predictable regulation—not technology alone—is critical to market entry for microbial foods. This figure analyzes regulatory and policy barriers by comparing the European Union’s Novel Food authorization process with the U.S. GRAS notice procedure. >   The researchers particularly emphasized that future competitiveness will depend less on the excellence of any single technology and more on the ability to build integrated manufacturing platforms. An Integrated Manufacturing Platform refers to a production system that operates the entire process as one connected framework, from strain development and large-scale fermentation to purification, quality control, and product formulation. Even for the same microbial food product, the choice of raw material can affect pretreatment costs and quality variability, while the choice of strain and fermentation process can greatly influence production cost, energy use, and product quality. The researchers therefore concluded that future industrial competitiveness will depend on how quickly companies can build manufacturing platforms that optimize these factors in an integrated way. On the market side, the researchers also identified the conditions needed for the microbial food industry to succeed. Based on consumer surveys and industry cases, they found that microbial foods cannot spread simply by emphasizing environmental sustainability. Consumers place importance on taste, texture, familiarity, and safety, while food manufacturers value functionality that can be applied to actual products. Companies and investors, meanwhile, consider the predictability of regulatory approval procedures and speed of market entry to be especially important. In other words, the microbial food market has entered an industrial stage where not only technology, but also product development capability and regulatory readiness are evaluated together. The researchers also argued that microbial foods should not be viewed merely as an alternative protein industry. They suggested that microbial foods have the potential to develop into a core platform for precision fermentation-based functional food ingredients, high-value biomaterials, and circular biomanufacturing. Precision Fermentation is a technology that uses microorganisms to selectively produce specific proteins or functional substances. Circular Biomanufacturing refers to a sustainable manufacturing system that uses byproducts and renewable resources to produce new bio-based products. This means that microbial foods could become not only a future food source, but also a new production system connecting the global food, materials, and biomanufacturing industries. The industrialization strategy proposed in this study is also closely aligned with the business direction of SilicoBio, which participated in the joint research. Based on the manufacturing readiness strategy presented in the study, SilicoBio is working to build a platform that connects microbial proteins and functional food ingredients to industrial-scale fermentation, scale-up, and product development. Scale-up refers to the process of expanding production from laboratory scale to industrial scale. Distinguished Professor Sang Yup Lee of KAIST said, “As global competition surrounding synthetic biology and biomanufacturing intensifies, microbial foods are growing into a key industry that will shape national biomanufacturing competitiveness beyond future food.” He added, “Going forward, competitiveness will be determined by how quickly we can build an industrialization ecosystem that connects core technologies to real production and markets.” A SilicoBio representative said, “Our goal is to connect the industrialization strategy proposed in this study to actual production and commercialization,” adding, “We will build a platform capable of stably producing microbial-based next-generation foods and functional biomaterials.” < [Figure 3] Microbial foods can expand beyond alternative proteins into a circular biomanufacturing platform. This conceptual diagram illustrates the key conditions for industrializing microbial foods and their potential pathways for expansion. >   This study, with Seok Yeong Jung, a doctoral student in the Department of Chemical and Biomolecular Engineering, as first author and researchers from SilicoBio participating as co-authors, was published on July 17 in the international journal One Earth (Impact Factor 15.3, JCR top 2.07％). Paper title: Microbial foods as scalable platforms toward a circular protein economy for sustainable nutrition DOI: https://doi.org/10.1016/j.oneear.2026.101772 Authors: Sang Yup Lee (KAIST, corresponding author), Seok Yeong Jung (KAIST, first author), Sol Choi (SilicoBio, second author), Jun-Woo Kim (SilicoBio and Inha University, third author), and two others SilicoBio is a KAIST faculty startup founded in June 2025 by Distinguished Professor Sang Yup Lee, a world-renowned scholar in synthetic biology. The company focuses on connecting laboratory-level achievements in systems metabolic engineering to real industrialization. By combining KAIST’s core technologies with the industrialization experience of personnel from CJ BIO, SilicoBio has built a team capable of reviewing not only strain design, but also industrial-scale fermentation and scale-up, material purification and product development, pilot production, and process validation. Based on this foundation, SilicoBio is pursuing a phased commercialization strategy, starting with next-generation protein products and expanding into functional ingredients and eventually new drug and novel material candidates. This research was supported by the “Development of Next-Generation Biorefinery Core Technologies to Lead the Biochemical Industry” project under the Petroleum-Alternative Eco-Friendly Chemical Technology Development Program funded by the Ministry of Science and ICT, and by the “Advancement of a Synthetic Biology-Based Industrial Cell Factory Platform and Commercialization of High-Value Functional Biomaterials” project under the Deep Science Startup Activation Support Program funded by the Commercialization Promotion Agency for R&D Outcome.

KAIST Develops Smartphone-Based Technology to Dete..

  < The research team. From left: Jonghyuk Yun , KAIST Ph.D. student (first author); Sean Rui Xiang Tan , researcher at the National University of Singapore (NUS); and Jun Han, professor in the KAIST School of Computing (corresponding author). >   A smartphone can now be transformed into a “hidden-camera detector.” KAIST researchers have developed an AI technology that can detect hidden cameras using only a smartphone and a low-cost LED device. This new security technology enables users to protect their privacy more easily and is expected to help prevent illegal filming in everyday spaces such as hotels and short-term rentals. KAIST (President Choongsik Bae) announced on August 30 that a research team led by Professor Jun Han of the School of Computing, in collaboration with the National University of Singapore and Singapore Management University, has developed “SweepLED,” a technology that detects hidden cameras by attaching an LED case to a smartphone. < Figure1. Overview of SweepLED. The system scans objects using a smartphone case equipped with an LED array and detects hidden cameras by analyzing the distinctive reflection patterns of camera lenses under changing illumination. >     As hidden cameras are increasingly being installed in everyday spaces such as hotels, short-term rentals, and restrooms, the need is growing for detection technology that everyday users can easily use. However, existing portable detectors require users to visually identify bright reflective spots, which can lead to false positives by mistaking reflections from metal, glass, or glossy plastic surfaces for camera lenses. SweepLED works by keeping the smartphone camera fixed while changing only the direction of the LED illumination, then analyzing the patterns of reflected light that appear on object surfaces. Reflections from ordinary glossy objects tend to move or disappear depending on the direction of the light. In contrast, camera lenses show distinctive deformation patterns in their reflections due to their internal lens, aperture, and sensor structures. The research team uses deep learning-based analysis to distinguish these differences in temporal reflection patterns. While conventional detection methods rely on the user’s eyes to simply look for “bright spots,” SweepLED is different in that it analyzes both the movement and shape changes of reflections across multiple lighting angles. This enables more reliable detection of hidden camera lenses inside various everyday objects commonly found in lodging spaces, such as chargers, clocks, remote controls, and everyday objects. The research team evaluated SweepLED on 30 objects that may be found in real-world environments and found that it achieved approximately 94％ detection accuracy. It also took less than five seconds to inspect a single object. < Figure 2. SweepLED’s hidden-camera detection pipeline. The system identifies potential reflections in the captured video sequence and detects hidden cameras by analyzing reflection patterns across frames. It combines detection results from multiple viewpoints to improve detection reliability. >   In addition, the core components of the LED case attached to the smartphone cost less than USD 7, or about KRW 10,000, demonstrating the potential for this technology to be developed into an affordable detection tool that general users can easily access. Professor Jun Han said, “Hidden cameras pose a serious threat to personal safety and privacy in everyday spaces,” adding, “This research is meaningful in that it combines low-cost smartphone-based hardware with AI analysis to present the possibility of a practical detection technology that even non-experts can use.” < Figure 3. SweepLED hardware prototype and operation. A low-cost PCB-based LED array and a modular smartphone-mounted case enable video capture in real-world environments. >   This paper, with KAIST doctoral student Jonghyuk Yun as first author, was presented on June 20 at ACM MobiSys 2026, one of the leading international conferences in the field of mobile computing. Paper title: Hide-and-Sweep: Detecting Concealed Cameras via LED Illumination Sweeps https://doi.org/10.1145/3812835.3814866 Author information: Jonghyuk Yun (first author), Jaeyoung Moon, Yunseo Park, Sean Rui Xiang Tan, Byunghyun Kim, Rajesh Krishna Balan, and Professor Jun Han (corresponding author) This research was supported by the STEAM Global Convergence Research Support Program and the Mid-Career Researcher Program of the Ministry of Science and ICT and the National Research Foundation of Korea.  

Next-Generation Biological Foundation Model, K-Fol..

  < (From left) KAIST Professors Byung-Ha Oh, Gyuri Lee, Sung Ju Hwang, Woo Youn Kim, Sungsoo Ahn, and Ho Min Kim >   “Design a drug candidate that binds effectively to this protein.” In response to such a request, AI predicts the protein’s three-dimensional structure, analyzes which compounds are most likely to bind to it, and designs promising drug candidates. KAIST researchers have developed K-Fold, the world's fastest Bio-AI model for protein structure prediction, which also supports drug candidate design. KAIST (President Choongsik Bae) announced on August 28 that it had formed “Team KAIST” after being selected as the lead institution for the Ministry of Science and ICT’s “AI Specialized Foundation Model Project” and unveiled K-Fold, a next-generation Bio-AI model developed by Team KAIST. Team KAIST is led by Professor Woo Youn Kim from the Department of Chemistry. His research group, together with Professors Sung Ju Hwang and Sungsoo Ahn's groups at the Kim Jaechul Graduate School of AI, developed the AI model. Professors Byung-Ha Oh, Ho Min Kim, and Gyuri Lee from the Department of Biological Sciences oversaw protein data construction and validation. HITS, a KAIST faculty startup, integrated K-Fold into HyperLab, its web-based AI research platform, enabling researchers to use the model in real-world research workflows. In addition, the Korea Pharmaceutical and Bio-Pharma Manufacturers Association (KPBMA) and the Korea Biotechnology Industry Organization (KoreaBIO) will lead efforts to raise awareness of K-Fold’s achievements and promote its use across the industry. K-Fold’s defining capability is its ability to predict the binding between proteins and drug candidates—a critical step in drug discovery. Drug development begins with determining the structure of a disease-related protein and identifying, among numerous compounds, those most likely to bind to the protein and produce the desired effect. K-Fold not only predicts a protein’s three-dimensional structure, but also calculates where and how a drug candidate is likely to bind, helping researchers identify promising candidates more quickly. K-Fold goes beyond predicting the structure of a single protein. It can also predict the structures formed when different biomolecules interact, including protein–protein and protein–drug candidate complexes, as well as complexes involving DNA and RNA. In the project’s stage evaluation conducted in March, its accuracy in predicting molecular complex structures was assessed as approaching that of AlphaFold3, developed by Google DeepMind. In an in-house performance evaluation conducted by the research team in August, K-Fold also outperformed existing global models in selected evaluation categories. K-Fold demonstrated particularly strong performance in predicting how drug candidates bind to and act on key therapeutic targets, including G protein-coupled receptors (GPCRs) and kinases, which are major drug targets for cancer and other diseases. It also performed strongly in targeted protein degradation (TPD), an emerging drug discovery approach designed to directly eliminate disease-causing proteins. K-Fold also significantly increased the speed of structure prediction. Conventional protein structure prediction models often require a complex preprocessing step that searches for and compares large amounts of data on similar proteins before calculating a structure. K-Fold applies a new approach that does not depend on this process, eliminating the need for preprocessing calculations and increasing structure prediction speeds by up to 25 times compared with existing models. This means that researchers can evaluate more drug candidates within the same amount of time. By reducing the time and computing resources required for structure prediction, K-Fold can help rapidly identify the most promising compounds from a vast pool of candidates and narrow the selection for experimental validation. “National competitiveness in the AI era depends on sovereign AI capabilities, which is the crucial ability to develop and deploy core technologies independently,” said KAIST President Choongsik Bae. “K-Fold is significant because it combines homegrown AI technology with biotechnology to challenge the world’s leading technologies and translates that capability into a service applicable to real-world drug discovery. KAIST will continue to strengthen Korea’s technological sovereignty in AI and its future competitiveness by advancing the convergence of foundational AI technologies with science and technology.” The research team went beyond developing K-Fold as a standalone model, turning it into an AI research service that researchers can use through a conversational web-based interface. K-Fold has been integrated into HyperLab, a multi-agent platform developed by HITS, a KAIST faculty startup. This allows researchers to use the model without having to build their own high-performance computing infrastructure or operate complex AI software. For example, if a researcher asks the AI to “design an antibody that binds strongly to this protein,” it provides step-by-step support for predicting the protein’s structure, designing candidates with a high likelihood of binding, and computationally evaluating the results. Researchers can also ask it to “find a suitable peptide candidate for this cancer target protein.” In practical terms, instead of moving between multiple software tools to calculate structures and analyze results, researchers can simply state their research objective and have the AI carry out the necessary analyses and design tasks in sequence. To support these capabilities, HyperLab incorporates approximately 120 computational tools and 160 specialized functions for structure prediction, drug design, and the analysis of life science data, including genomic and proteomic data. It also connects more than 100 specialized databases with a large-scale knowledge graph, enabling the platform to retrieve relevant scientific information and apply it to its analyses. HyperLab aims to serve as an AI Co-Scientist that assists researchers throughout the research process by supporting the full workflow, from understanding a research question and selecting the appropriate tools to predicting structures, analyzing results, and iteratively improving designs. Bio AI is emerging as a critical technology capable of reducing the time and cost required for drug discovery, driving intense competition among global technology companies and major research institutions in the United States, the United Kingdom, and China. The development of K-Fold is significant because it lays the foundation for sovereign bio AI by securing core bio AI technology domestically, rather than relying solely on overseas models, and making it available for real-world research. The achievement was first presented at the 2026 Annual Meeting of the Korean Federation of Biomolecular Science, held on June 23, where Professor Woo Youn Kim from the KAIST Department of Chemistry delivered a keynote lecture titled “Generative Drug Design Powered by Agentic AI.” “K-Fold was developed not to follow existing models, but to overcome the limitations of conventional approaches through a new AI architecture,” explained Professor Kim. “We will develop it into an AI-for-Science platform that makes world-class bio AI technology accessible to researchers everywhere.” The Team KAIST consortium plans to release K-Fold free of charge. HyperLab will provide beta access to researchers in Korea and abroad, and gradually expand its commercial services by the end of this year. Meanwhile, industry training on K-Fold is gaining momentum. An online session hosted by KoreaBIO on August 27 attracted 85 participants, while 118 have registered for KPBMA’s hybrid session on September 1. Designed primarily for researchers and practitioners at pharmaceutical and biotech companies, the training covers how to use K-Fold and presents case studies of its application to drug design. The initiative is intended to accelerate the adoption of sovereign bio-AI across the industry. Related website: [HyperLab Co-Scientist] https://hyperlab.ai/features-co-scientist K-Fold–HyperLab 3.0 Introduction Video: https://drive.google.com/file/d/1MxhZr-C3pQdIVn1EP44hQICj8G70is3_/view This research was supported by the Ministry of Science and ICT (MSIT, PJT-25-100009)