Kaist

Robot Valley Project Activation of the Korean styl..

  < From left: Top Excellence Award winner Robolight (Pre-startup Founder Han-seol Choi), Top Excellence Award winner Coils (CEO Seong-ryeol Heo), Professor Jung Kim of KAIST, Grand Prize winner Noman (CEO Jung-wook Moon), Professor Kyoungchul Kong of KAIST, CEO Dae-hee Park of Daejeon Creative Economy Innovation Center, Excellence Award winner Gigaflops (CEO Min-tae Kim), Excellence Award winner BLUE APEX (Pre-startup Founder Na-hyeon Kwon) >     KAIST announced on December 10th that KAIST Holdings (CEO Hyeonmin Bae), a specialized technology commercialization investment institution, successfully held the '2025 KAIST Hu-Robotics Startup Cup' on the 9th at the main building of Daejeon Startup Park. This was held as part of the Robot Valley Project, aiming to discover and foster promising startup teams in the robotics field and establish a robot scale-up ecosystem based on a technology platform. This competition was conducted as a core program of the Robot Valley Project (Deep-Tech Scale-up Valley Fostering Project), which is promoted by the Ministry of Science and ICT and supported by Daejeon Metropolitan City. The competition proceeded through a meet-up day with KAIST Mechanical Engineering researchers, robotics companies like Angel Robotics and Twinny, and startup experts such as Bluepoint, leading to the final round. Throughout this process, a support system for the scale-up of robot startups was established, linking technology verification, strengthening entrepreneurial capabilities, and investment linkage. KAIST Holdings and the Deep-Tech Valley Project Group (hereinafter referred to as the Project Group) stated that this competition marks the beginning of 'establishing a Korean-style Robot and AI startup ecosystem.' Their goal through the Robot Valley Project is to create a Korean-style robot scale-up ecosystem centered around Daejeon and KAIST, and furthermore, to build a technology circulation structure utilizing verified technology platforms.   KAIST has produced successful scale-up cases in the robotics field, such as Rainbow Robotics and Angel Robotics. However, the recent robotics industry has seen a rapid increase in technological difficulty due to the convergence of mechanical engineering, AI, and control software, creating structural limitations for early-stage founders to challenge alone. To solve this, the Project Group proposed the 'Scale-up Valley Construction Strategy,' which opens up the verified technologies of established senior companies to junior founders. This strategy focuses on supporting startups to concentrate on developing market-ready robot services and applications on top of verified technology platforms, rather than consuming excessive time on developing basic hardware like motors and controllers. The Angel Robotics technology platform, presented as the core underlying technology of this strategy, consists of actuators, control modules, and core software. KAIST plans to gradually open up these foundational technologies for use by early-stage startup teams. The Project Group emphasized that enabling startup teams to utilize such technology platforms from the initial stage is the core infrastructure for accelerating the Korean-style robot startup ecosystem. A total of 21 teams participated in this competition, including pre-startup founders (Track A) and early-stage startups established within 3 years (Track B), all possessing human-centered robotics technology and convergence business models. After fierce preliminaries, 8 teams advanced to the final round, and a total of 5 teams were finally selected: one Grand Prize winner, two Choi Woo-sung (Top Excellence Award) winners, and two Excellence Award winners. The Grand Prize was awarded to 'Noman' for proposing an integrated system for a strawberry farm work robot and a rotating vertical cultivation module. The Woo-sung Choi (Top Excellence Award) went to 'Robolight' and 'Coils.' The Excellence Award was awarded to BLUE APEX and Gigaflops. Professor Jung Kim, Head of the KAIST Mechanical Engineering Department and General Manager of the Robot Valley Project, said, "This competition has become the starting point for discovering future robot unicorns. For the next three years, we will continue to provide practical support for the growth of robot startups, and KAIST will play a leading role in building and expanding the deep-tech robot ecosystem centered in Daejeon." < Group Photo of Award Winners > Meanwhile, this competition was jointly hosted and organized by the Ministry of Science and ICT, Daejeon Metropolitan City, and the Research and Business Development Special Zone Foundation, as well as startup support organizations including KAIST, KAIST Holdings, Daejeon Technopark, and Daejeon Creative Economy Innovation Center.

KAIST Predicts Human Group Behavior with AI！ 1st P..

  <(From Left) Ph.D candidate Geon Lee, Ph.D candidate Minyoung Choe, M.S candidate Jaewan Chun, Professor Kijung Shin, M.S candidate Seokbum Yoon>   KAIST (President Kwang Hyung Lee) announced on the 9th of December that Professor Kijung Shin’s research team at the Kim Jaechul Graduate School of AI has developed a groundbreaking AI technology that predicts complex social group behavior by analyzing how individual attributes such as age and role influence group relationships. With this technology, the research team achieved the remarkable feat of winning the Best Paper Award at the world-renowned data mining conference “IEEE ICDM,” hosted by the Institute of Electrical and Electronics Engineers (IEEE). This is the highest honor awarded to only one paper out of 785 submissions worldwide, and marks the first time in 23 years that a Korean university research team has received this award, once again demonstrating KAIST’s technological leadership on the global research stage. Today, group interactions involving many participants at the same time—such as online communities, research collaborations, and group chats—are rapidly increasing across society. However, there has been a lack of technology that can precisely explain both how such group behavior is structured and how individual characteristics influence it at the same time. To overcome this limitation, Professor Kijung Shin’s research team developed an AI model called “NoAH (Node Attribute-based Hypergraph Generator),” which realistically reproduces the interplay between individual attributes and group structure. NoAH is an artificial intelligence that explains and imitates what kinds of group behaviors emerge when people’s characteristics come together. For example, it can analyze and faithfully reproduce how information such as a person’s interests and roles actually combine to form group behavior. As such, NoAH is an AI that generates “realistic group behavior” by simultaneously reflecting human traits and relationships. It was shown to reproduce various real-world group behaviors—such as product purchase combinations in e-commerce, the spread of online discussions, and co-authorship networks among researchers—far more realistically than existing models. < The process of generating group interactions using NoAH > Professor Kijung Shin stated, “This study opens a new AI paradigm that enables a richer understanding of complex interactions by considering not only the structure of groups but also individual attributes together,” and added, “Analyses of online communities, messengers, and social networks will become far more precise.” This research was conducted by a team consisting of Professor Kijung Shin and KAIST Kim Jaechul Graduate School of AI students: master’s students Jaewan Chun and Seokbum Yoon, and doctoral students Minyoung Choe and Geon Lee, and was presented at IEEE ICDM on November 18.  ※ Paper title: “Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes”  Original paper: https://arxiv.org/abs/2509.21838 < Photo from the award ceremony held on November 14 at the International Spy Museum in Washington, D.C.> Meanwhile, including this award-winning paper, Professor Shin’s research team presented a total of four papers at IEEE ICDM this year. In addition, in 2023, the team also received the Best Student Paper Runner-up (4th place) at the same conference. This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-202400457882, AI Research Hub Project) (RS-2019-II190075, Artificial Intelligence Graduate School Program (KAIST)) (No. RS-2022-II220871, Development of AI Autonomy and Knowledge Enhancement for AI Agent Collaboration).      

KAIST Removes 99.9％ of Ultrafine Dust Using Nano W..

  <(From Left) Ph.D candidate Sungyoon Woo, Professor Il-Doo Kim, Professor Seung S.Lee, Ph.D candiate Jihwan Chae, Researcher Jiyeon Yu, (Upper Right) Dr. Yujang Cho>   A KAIST research team has drawn attention by developing a new water-based air purification technology that combines “nano water droplets that capture dust” with a “nano sponge structure that autonomously draws up water,” enabling dust removal using nano water droplets without filters, self-supplied water operation, and long-term, quiet, and safe performance.   KAIST (President Kwang Hyung Lee) announced on the December 8 that a joint research team led by Professor Il-Doo Kim of the Department of Materials Science and Engineering and Professor Seung S. Lee of the Department of Mechanical Engineering developed a new water electrospray–based air purification device that rapidly removes ultrafine dust without filters, generates no ozone, and operates with ultra-low power consumption.   The research team confirmed that this device overcomes the limitations of conventional air purifiers by eliminating the need for filter replacement, producing no ozone, and removing even extremely fine ultrafine dust as small as PM0.3 (diameter 0.3 μm), which is about 1/200 the thickness of a human hair, within a short time. In addition, it demonstrated high stability and durability without performance degradation even during long-term use.   This device was created by combining Professor Seung S. Lee’s “ozone-free water electrospray” technology with Professor Il-Doo Kim’s “hygroscopic nanofiber Emitter” technology.   Inside the device are a high-voltage electrode, a nanofiber absorber that autonomously draws up water, and polymer microchannels that transport water via capillary action. Thanks to this structure, a self-pumped configuration is achieved in which water is automatically supplied without a pump, enabling stable long-term water electrospray operation.   Tests conducted by the research team in a 0.1 m3 experimental chamber showed that the device removed 99.9％ of various particles in the PM0.3–PM10 range within 20 minutes. In particular, it exhibited outstanding performance by removing 97％ of PM0.3 ultrafine dust, which is difficult to eliminate using conventional filter-based air purifiers, within just 5 minutes.   Even after 30 consecutive tests and 50 hours of continuous operation, the device operated stably without performance degradation, and its power consumption was approximately 1.3 W, which is lower than that of a smartphone charger and only about 1/20 that of conventional HEPA (High Efficiency Particulate Air) filter–based air purifiers.   In addition, because there is no filter, there is no pressure loss in airflow and almost no noise is generated.   This technology maintains high-efficiency purification performance while generating no ozone at all, presenting the potential for a next-generation eco-friendly air purification platform.   In particular, with advantages such as elimination of filter replacement costs, ultra-low power operation, and secured long-term stability, it is expected to expand into various fields including indoor environments as well as automotive, cleanroom, portable, and wearable air purification modules.   Commercialization of this technology is currently underway through A2US Co., Ltd., a university spin-off company from Professor Seung S. Lee’s laboratory.   A2US Co., Ltd. won a CES 2025 Innovation Award and plans to launch a portable air purifier product in 2026. The product is equipped not only with fine dust removal using nano water droplets but also with odor removal and pathogen sterilization functions.     <Figure1.Design and Operating Mechanism of a Miniature Air-Purification Device Based on Cone-Jet Water Electrospray Using a Self-Pumping Hygroscopic (PVA–PAA–MMT) Nanofiber Membrane (PPM-NFM) Emitter.>     <Figure 2. (a) Schematic of the Self-Pumping Hygroscopic Nanofiber Membrane (PPM-NFM) Emitter, and (b) Corresponding Photograph and Surface Scanning Microscopy Images.>          

KAIST, Production Temperature ↓ by 500°C, Power Ou..

  <(Top row, from left) Professor Kang Taek Lee, Ph.D candidate Yejin Kang, Dr. Dongyeon Kim, (Bottom row, from left) M.S candidate Mincheol Lee, Ph.D candidate Seeun Oh, Ph.D candidate Seungsoo Jang, Ph.D candidate Hyeonggeun Kim>     As power demand surges in the AI era, the “protonic ceramic electrochemical cell (PCEC),” which can simultaneously produce electricity and hydrogen, is gaining attention as a next-generation energy technology. However, this cell has faced the technical limitation of requiring an ultra-high production temperature of 1,500°C. A KAIST research team has succeeded in establishing a new manufacturing process that lowers this limit by more than 500°C for the first time in the world. KAIST (President Kwang Hyung Lee) announced on the 4th of December that Professor Kang Taek Lee’s research team in the Department of Mechanical Engineering developed a new process that enables the fabrication of high-performance protonic ceramic electrochemical cells at temperatures more than 500°C lower than before, using “microwave ＋ vapor control technology” that leverages microwave heating principles and the diffusion environment of chemical vapor generated from specific chemical components. The electrolyte—the key material of protonic ceramic electrochemical cells—contains barium (Ba), and barium easily evaporates at temperatures above 1,500°C, which has been the main cause of performance degradation. Therefore, the ability to harden the ceramic electrolyte at a lower temperature has been the core issue that determines cell performance. As power demand surges in the AI era, the “protonic ceramic electrochemical cell (PCEC),” which can simultaneously produce electricity and hydrogen, is gaining attention as a next-generation energy technology. However, this cell has faced the technical limitation of requiring an ultra-high production temperature of 1,500°C. A KAIST research team has succeeded in establishing a new manufacturing process that lowers this limit by more than 500°C for the first time in the world. KAIST (President Kwang Hyung Lee) announced on the 4th of December that Professor Kang Taek Lee’s research team in the Department of Mechanical Engineering developed a new process that enables the fabrication of high-performance protonic ceramic electrochemical cells at temperatures more than 500°C lower than before, using “microwave ＋ vapor control technology” that leverages microwave heating principles and the diffusion environment of chemical vapor generated from specific chemical components. The electrolyte—the key material of protonic ceramic electrochemical cells—contains barium (Ba), and barium easily evaporates at temperatures above 1,500°C, which has been the main cause of performance degradation. Therefore, the ability to harden the ceramic electrolyte at a lower temperature has been the core issue that determines cell performance. To solve this, the research team devised a new heat-treatment method called “vapor-phase diffusion.” This technique places a special auxiliary material (a vapor source) next to the cell and irradiates it with microwaves to quickly diffuse vapor. When the temperature reaches approximately 800°C, the vapor released from the auxiliary material moves toward the electrolyte and tightly bonds the ceramic particles. Thanks to this technology, a process that previously required 1,500°C can now be completed at just 980°C. In other words, a world-first ceramic electrochemical cell fabrication technology has been created that produces high-performance electricity at a “low temperature” without damaging the electrolyte. A cell fabricated with this process produced 2 W of power stably from a 1 cm² cell (roughly the size of a fingernail) at 600°C and generated 205 mL of hydrogen per hour at 600°C (about the volume of a small paper cup, among the highest in the industry). It also maintained stability without performance degradation during 500 hours of continuous operation. In other words, this technology reduces the production temperature (−500°C), lowers the operating temperature (600°C), doubles performance (2 W/cm²), and extends the lifespan (500-hour stability), achieving world-class performance in ceramic cell technology. The research team also enhanced the reliability of the technology by using digital twins (virtual simulations) to analyze gas-transport phenomena occurring in the microscopic internal structure of the cell − phenomena that are difficult to observe in actual experiments. <Figure 1. (a) Schematic of the vapor-diffusion-based process; (b) Surface microstructure of the electrolyte; (c) Internal barium composition ratio of the electrolyte according to processing conditions; (d) Comparison of power-generation performance with previous studies> < Figure 2. (a) Three-dimensional reconstructed image of the protonic ceramic electrochemical cell fuel electrode according to processing conditions (b) Pore structure (c) Gas-transport simulation results > Professor Kang Taek Lee emphasized, “This study is the world’s first case of using vapor to lower the heat-treatment temperature by more than 500°C while still producing a high-performance, high-stability cell.” He added, “It is expected to become a key manufacturing technology that addresses the power challenges of the AI era and accelerates the hydrogen society.” Dongyeon Kim (KAIST PhD) and Yejin Kang (KAIST PhD candidate) participated as co–first authors. The research results were published in Advanced Materials (IF: 26.8), one of the world’s leading journals in energy and materials science, and were selected as the Inside Front Cover article on October 29.  (Paper title: “Sub-1000°C Sintering of Protonic Ceramic Electrochemical Cells via Microwave-Driven Vapor Phase Diffusion,” DOI: https://doi.org/10.1002/adma.202506905) This research was supported by the MSIT’s Mid-career Researcher Program and the H2 Next Round Program.      

KAIST Unveils Cause of Performance Degradation in ..

  <(From left in the front row) Professor Nam-Soon Choi, Professor Dong-Hwa Seo, (back row, from left) Ph.D candidate Gihoon Lee, Ph.D candidate Seung Hee Han, Ph.D candidate Jae-Seung Kim, (top) M.S candidate Junyoung Kim>   High-nickel batteries, which are high-energy lithium-ion batteries primarily used in electric vehicles, offer high energy density but suffer from rapid performance degradation. A research team from KAIST has, for the first time globally, identified the fundamental cause of the rapid deterioration (degradation) of high-nickel batteries and proposed a new approach to solve it. KAIST announced on December 3rd  that a research team led by Professor Nam-Soon Choi of the Department of Chemical and Biomolecular Engineering, in collaboration with a research team led by Professor Dong-Hwa Seo of the Department of Materials Science and Engineering, has revealed that the electrolyte additive 'succinonitrile (CN4), which has been used to improve battery stability and lifespan, is actually the key culprit causing performance degradation in high-nickel batteries. In a battery, electricity is generated as lithium ions travel between the cathode and the anode. A small amount of CN4 is included in the electrolyte to facilitate the movement of lithium. The research team confirmed through computer calculations that CN4, which has two nitrile (-CN) structures, attaches excessively strongly to the nickel ions on the surface of the high-nickel cathode. The nitrile structure is a 'hook-like' structure, where carbon and nitrogen are bound by a triple bond, making it adhere well to metal ions. This strong bonding destroys the protective electrical double layer (EDL) that should form on the cathode surface. During the charging and discharging process, the cathode structure is distorted (Jahn-Teller distortion), and even electrons from the cathode are drawn out to the CN4, leading to rapid damage of the cathode. Nickel ions that leak out during this process migrate through the electrolyte to the anode surface, where they accumulate. This nickel acts as a 'bad catalyst' that accelerates electrolyte decomposition and wastes lithium, further speeding up battery degradation. Various analyses confirmed that CN4 transforms the high-nickel cathode surface into an abnormal layer deficient in nickel, and changes the normally stable structure into an abnormal 'rock-salt structure'. This proves the dual nature of CN4: while useful in LCO batteries (lithium cobalt oxide), it actually causes the structural collapse in high-nickel batteries with a high nickel ratio. This research holds significant meaning as a precise analysis that goes beyond simple control of charging/discharging conditions, to even elucidating the actual electron transfer occurring between metal ions and electrolyte molecules. Based on this achievement, the research team plans to develop a new electrolyte additive optimized for high-nickel cathodes.   <Schematic diagram of the ligand coordination between CN₄ molecules and Ni³⁺ on the high-nickel cathode surface and the cathode structural degradation process> Professor Nam-Soon Choi stated, "A precise, molecular-level understanding is essential to enhance battery lifespan and stability. This research will pave the way for the development of new additives that do not excessively bond with nickel, significantly contributing to the commercialization of next-generation high-capacity batteries." This research, jointly led by Professor Nam-Soon Choi, Seung Hee Han, Junyoung Kim, and Gihoon Lee of the Department of Chemical and Biomolecular Engineering, and Professor Dong-Hwa Seo and Jae-Seung Kim of the Department of Materials Science and Engineering as co-first authors, was published online on November 14th in the prestigious international journal 'ACS Energy Letters' and was selected as the cover article. ※ Paper Title: Unveiling Bidentate Nitrile-Driven Structural Degradation in Ultra-High-Nickel Cathodes, https://doi.org/10.1021/acsenergylett.5c02845 <Cover Page of International Journal(ACS Energy Letters)>   The research was supported by Samsung SDI.  

AI Technology World No. 1 in Finding the Exact Mom..

  < (From left) Professor Joon Hyuk Noh (Assistant Professor, Department of Artificial Intelligence, Ewha Womans University), Seojin Hwan, Yoonki Cho (Ph.D. Candidate), Professor Sung-Eui Yoon (School of Computing, KAIST) >      When faced with a complex question like 'What object disappeared while the camera was pointing elsewhere?', a common problem is that AI often relies on language patterns to guess a 'plausible answer,' instead of actually observing the real situation in the video. To overcome this limitation, our university's research team developed a technology that enables the AI to autonomously identify the 'exact critical moment (Trigger moment)' within the video, and the team’s excellence was proven by winning an international AI competition with this technology. The university announced on the 28th that the research team led by Professor Sung-Eui Yoon from the School of Computing, in collaboration with Professor Joon Hyuk Noh's team from Ewha Womans University, took 1st place in the Grounded Video Question Answering track of the Perception Test Challenge held at ICCV 2025, a world-renowned computer vision conference. The Perception Test Challenge held at ICCV 2025 was organized by Google DeepMind with a total prize pool of 50,000 Euros (approximately 83 million KRW). It assesses the cognitive and reasoning abilities of multimodal AI, which must comprehensively understand various data, including video, audio, and text. Crucially, the core evaluation factor is the ability to make judgments based on actual video evidence, moving beyond language-centric bias. Unlike conventional methods that analyze the entire video indiscriminately, our university's research team developed a new technology that instructs the AI to first locate the core scene (Trigger moment) essential for finding the correct answer. Simply put, this technology is designed to make the AI autonomously determine: “This scene is decisive for answering this question！” The research team calls this framework CORTEX (Chain-of-Reasoning for Trigger Moment Extraction). The research team's system consists of a three-stage structure where three models performing different functions operate sequentially. First, the Reasoning AI (Gemini 2.5 Pro) reasons about which moment is required to answer the question and finds candidate Trigger moments. Next, the Object Location Finding Model (Grounding Model, Molmo-7B) accurately identifies the exact location (coordinates) of people, cars, and objects on the screen during the selected moment. Finally, the Tracking Model (SAM2) precisely tracks the movement of objects in the time frame before and after the selected scene, using that scene as a reference, thereby reducing errors. In short, the 'method of accurately pinpointing a key scene and tracking the evidence for the answer centered on that scene' significantly reduced problems like initial misjudgment or occlusion in the video. In the Grounded Video Question Answering (Grounded VideoQA) track, which saw 23 participating teams, the KAIST team SGVR Lab (Scalable Graphics, Vision & Robotics Lab) recorded 0.4968 points in the HOTA (Higher Order Tracking Accuracy) metric, overwhelmingly surpassing the 2nd place score of 0.4304 from Columbia University, USA, to secure 1st place. This achievement is nearly double the previous year's winning score of 0.2704 points. This technology has wide-ranging applications in real-life settings. Autonomous driving vehicles can accurately identify moments of potential accident risk, robots can understand the surrounding environment smarter, security and surveillance systems can rapidly locate critical scenes, and media analysis can precisely track the actions of people or objects in chronological order. This is a core technology that enables AI to judge based on "actual evidence in the video." The ability to accurately pinpoint how objects behave over time in a video is expected to greatly expand the application of AI in real-world scenarios in the future. < Pipeline image of the grounding framework for video question answering proposed by the research team >  This research was presented on October 19th at ICCV 2025, the 3rd Perception Test Challenge conference. The achievement was supported by the Ministry of Science and ICT's Basic Research Program (Mid-Career Researcher), the SW Star Lab Project's 'Development of Perception, Action, and Interaction Algorithms for Open-World Robot Services,' and the AGI Project's 'Reality Construction and Bi-directional Capability Approach based on Cognitive Agents for Embodied AGI' tasks."        

KAIST K HERO Rides Nuri Rocket, Next Generation Mi..

  < (From left) Ph.D candidate Jaehong Park, COSMOVY researcher Yoonsoo Kim, Professor Wonho Choe, Ph.D candidate Dongha Park, M.S candidate Seungbeom Heo >   KAIST announced on the November 26th that the CubeSat 'K-HERO (KAIST Hall Effect Rocket Orbiter)', developed by the research team of Professor Wonho Choe from the Department of Nuclear and Quantum Engineering, is scheduled to launch into space aboard the 4th Nuri rocket launch vehicle on November 27th from the Naro Space Center in Goheung, Jeollanam-do. This 4th Nuri launch is the first to be managed by the private company Hanwha Aerospace, which received technology transfer from the Korea Aerospace Research Institute (KARI), marking a significant milestone in the transformation of the domestic space industry. Along with the main payload, the Next-Generation Medium Satellite 3, twelve CubeSats developed by industry, academia, and research institutions will be onboard, with K-HERO being one of them. The development of K-HERO was officially initiated when Professor Wonho Choe's research team was selected as the basic satellite development team in the '2022 CubeSat Competition' organized by KARI. The basic satellite is a technology verification satellite designed to confirm whether the design and core components operate normally in the space environment before proceeding with the flight model (FM) production. K-HERO is a 3U standard CubeSat with dimensions of ＄10＼text{ cm}＄ (width) ＄＼times＄ ＄10＼text{ cm}＄ (length) ＄＼times＄ ＄30＼text{ cm}＄ (height) and a weight of ＄3.9＼text{ kg}＄. It was designed to satisfy all stability, electrical specifications, and interface conditions with the launch vehicle. The core mission of K-HERO is to directly verify the in-space operation of the 150 W class micro-satellite Hall thruster developed by the research team. The Hall thruster can be simply described as a 'space engine powered by electricity'. It is an electric propulsion engine that moves the satellite slowly but very efficiently using electricity. Instead of burning a lot of fuel to generate instantaneous thrust, like a rocket, it works by using electricity to turn gas (Xenon) into a plasma state and rapidly accelerating it backward to push the satellite forward. Hall thrusters are considered a core technology for the era of small and constellation satellites due to their high fuel efficiency. < Image of plasma generation in the micro-satellite Hall thruster mounted on the K-HERO CubeSat > Hall thrusters are already a proven technology, having been used in large satellites and deep-space probes for over 20-30 years. However, their size and power requirements were large, so in the past, they were mainly operated on large geostationary (GEO) communication/broadcasting satellites and used by NASA and ESA deep-space probes for long-distance flights. Recently, the emergence of the SpaceX Starlink satellite constellation has led to a surge in demand for small and micro electric thrusters. As the global space industry shifts towards satellite constellations, 'small and efficient thrusters' have become essential technology. K-HERO is the first case of direct in-space demonstration of a micro Hall thruster made with domestic technology, and it is expected to be an important milestone in enhancing domestic technological competitiveness. Professor Wonho Choe's research team began research on Hall thrusters in Korea in 2003, securing original technology based on plasma physics. In 2013, they successfully mounted a 200 W class Hall thruster on the 'KAIST Science and Technology Satellite 3,' proving its practical utility. This time, they have improved the design to operate even at a lower power of 30 W, developing a next-generation model aimed at micro-satellites. COSMOVY Inc, a laboratory startup founded by Professor Wonho Choe's research team, also participated in the development of K-HERO, further strengthening the foundation for technology commercialization. < K-HERO CubeSat being loaded into the Nuri rocket's CubeSat dispenser (Photo source: Korea Aerospace Research Institute) > Professor Wonho Choe stated, "Starting with K-HERO, the number of small satellites equipped with electric thrusters will increase significantly in Korea. The Hall thruster being verified this time can be utilized for various missions, including low-Earth orbit constellation surveillance and reconnaissance satellites, 6G communication satellites, very-low-Earth orbit high-resolution satellites, and asteroid probes." President Kwang Hyung Lee stated, "The launch of K-HERO is a significant opportunity to directly verify KAIST's electric propulsion technology on a micro-satellite platform once again in space, and it will be an important turning point that will further enhance the technological competitiveness of small satellites in Korea. KAIST will continue to contribute to the development of our country's space technology.    

How Does AI Think？ KAIST Achieves First Visualizat..

  <(From Left) Ph.D candidate Daehee Kwon, Ph.D candidate Sehyun lee, Professor Jaesik Choi>   Although deep learning–based image recognition technology is rapidly advancing, it still remains difficult to clearly explain the criteria AI uses internally to observe and judge images. In particular, technologies that analyze how large-scale models combine various concepts (e.g., cat ears, car wheels) to reach a conclusion have long been recognized as a major unsolved challenge. KAIST (President Kwang Hyung Lee) announced on the 26th of November that Professor Jaesik Choi’s research team at the Kim Jaechul Graduate School of AI has developed a new explainable AI (XAI) technology that visualizes the concept-formation process inside a model at the level of circuits, enabling humans to understand the basis on which AI makes decisions. The study is evaluated as a significant step forward that allows researchers to structurally examine “how AI thinks.” Inside deep learning models, there exist basic computational units called neurons, which function similarly to those in the human brain. Neurons detect small features within an image—such as the shape of an ear, a specific color, or an outline—and compute a value (signal) that is transmitted to the next layer. In contrast, a circuit refers to a structure in which multiple neurons are connected to jointly recognize a single meaning (concept). For example, to recognize the concept of cat ear, neurons detecting outline shapes, neurons detecting triangular forms, and neurons detecting fur-color patterns must activate in sequence, forming a functional unit (circuit). Up until now, most explanation techniques have taken a neuron-centric approach based on the idea that “a specific neuron detects a specific concept.” However, in reality, deep learning models form concepts through cooperative circuit structures involving many neurons. Based on this observation, the KAIST research team proposed a technique that expands the unit of concept representation from “neuron → circuit.” The research team’s newly developed technology, Granular Concept Circuits (GCC), is a novel method that analyzes and visualizes how an image-classification model internally forms concepts at the circuit level. GCC automatically traces circuits by computing Neuron Sensitivity and Semantic Flow. Neuron Sensitivity indicates how strongly a neuron responds to a particular feature, while Semantic Flow measures how strongly that feature is passed on to the next concept. Using these metrics, the system can visualize, step-by-step, how basic features such as color and texture are assembled into higher-level concepts. The team conducted experiments in which specific circuits were temporarily disabled (ablation). As a result, when the circuit responsible for a concept was deactivated, the AI’s predictions actually changed. In other words, the experiment directly demonstrated that the corresponding circuit indeed performs the function of recognizing that concept. This study is regarded as the first to reveal, at a fine-grained circuit level, the actual structural process by which concepts are formed inside complex deep learning models. Through this, the research suggests practical applicability across the entire explainable AI (XAI) domain—including strengthening transparency in AI decision-making, analyzing the causes of misclassification, detecting bias, improving model debugging and architecture, and enhancing safety and accountability. The research team stated, “This technology shows the concept structures that AI forms internally in a way that humans can understand,” adding that “this study provides a scientific starting point for researching how AI thinks.” Professor Jaesik Choi emphasized, “Unlike previous approaches that simplified complex models for explanation, this is the first approach to precisely interpret the model’s interior at the level of fine-grained circuits,” and added, “We demonstrated that the concepts learned by AI can be automatically traced and visualized.” < external_image >   < Overview of the Conceptual Circuit Proposed by the Research Team > This study, with Ph.D. candidates Dahee Kwon and Sehyun Lee from KAIST Kim Jaechul Graduate School of AI as co–first authors, was presented on October 21 at the International Conference on Computer Vision (ICCV). Paper title: Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations Paper link: https://openaccess.thecvf.com/content/ICCV2025/papers/Kwon_Granular_Concept_Circuits_Toward_a_Fine-Grained_Circuit_Discovery_for_Concept_ICCV_2025_paper.pdf This research was supported by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP) under the “Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation” project, the AI Research Hub Project, and the KAIST AI Graduate School Program, and was carried out with support from the Defense Acquisition Program Administration (DAPA) and the Agency for Defense Development (ADD) at the KAIST Center for Applied Research in Artificial Intelligence.        

KAIST Confirms Reduction of Amyloid-β Using Red OL..

  <Professor Kyung Cheol Choi, Dr. Byeongju Noh, Ph.D candidate Young-Hun Jung, Ph.D candidate Minwoo Park, Dr.Ja Wook Koo, Researcher Jiyun Lee, Researcher Ji-Eun Lee, Dr. Hyang Sook Hoe, Dr.  Hyun-Ju Lee, Dr. Sora Kang, Researcher Seokjun Oh>     A Korean research team, raising the question “Which OLED light color can actually improve memory and pathological markers in Alzheimer’s patients?”, has identified the most effective OLED color capable of enhancing cognitive function using only light—with no drugs involved. The OLED platform developed for this study can precisely control color, brightness, flicker frequency, and exposure duration, suggesting potential future development into personalized OLED-based electroceuticals. On the 24th, KAIST (President Kwang Hyung Lee) announced that a joint research team led by Professor Kyung Cheol Choi from the School of Electrical Engineering at KAIST and Dr. Ja Wook Koo and Dr. Hyang Sook Hoe from the Korea Brain Research Institute (KBRI) developed a uniform-illuminance, three-color OLED photostimulation technology and confirmed that “red 40-Hz light” was the most effective among blue, green, and red in improving Alzheimer's pathology and memory function. To overcome the structural limitations of conventional LEDs—such as brightness imbalance, heat generation risk, and variability caused by animal movement—the researchers developed an OLED-based photostimulation platform that emits light uniformly. Using this platform, they compared white, red, green, and blue light under identical conditions (40-Hz frequency, brightness, and exposure time) and found that red 40-Hz light produced the most significant improvement. In an early-stage (3-month-old) Alzheimer’s animal model, improvement in pathology and memory was observed after only two days of stimulation. When early Alzheimer’s model mice were exposed to one hour of light per day for two days, both white and red light improved long-term memory. Additionally, the amount of amyloid-β (Aβ) plaques—protein aggregates known as a major factor in Alzheimer’s disease—was reduced in key brain regions such as the hippocampus, and levels of the plaque-clearing enzyme ADAM17 increased. This indicates that even very short periods of light stimulation can reduce harmful proteins in the brain and improve memory function. In particular, with red light, the inflammatory cytokine IL-1β, known to exacerbate inflammation and contribute to Alzheimer’s progression, decreased significantly, demonstrating an anti-inflammatory effect. Moreover, the more plaque was reduced, the greater the improvement in memory—direct evidence that pathological improvement leads to cognitive enhancement. In the mid-stage (6-month-old) Alzheimer’s model, statistically significant pathological improvement was seen only with red light. In a two-week long-term stimulation experiment under the same conditions, both white and red light improved memory, but a statistically meaningful reduction in plaques appeared only under red light. < The mechanism by which red OLED stimulation of neurons reduces amyloid-β in Alzheimer’s model mice > Differences at the molecular level were also clear. Under red light, levels of ADAM17 (which helps remove plaques) increased, while levels of BACE1, an enzyme responsible for producing plaques, decreased—demonstrating a dual effect of both inhibiting plaque formation and promoting plaque removal. In contrast, white light only lowered BACE1, showing more limited therapeutic effects compared to red light. This scientifically identifies that the color of light is a key factor determining therapeutic efficacy. To determine which neural circuits were activated by light stimulation, the team analyzed the expression of c-Fos, an immediate-early gene that is activated when neurons fire. They found activation throughout the visual–memory circuit, extending from the visual cortex → thalamus → hippocampus, providing direct neurological evidence that light stimulation awakens the visual pathway, enhancing hippocampal function and memory. Thanks to the uniform-illuminance OLED platform, light was evenly delivered regardless of animal movement, ensuring stable experimental results and high reproducibility across repeated tests. This study is the first to demonstrate that cognitive function can be improved using only light, without drugs, and that Alzheimer’s pathological markers can be regulated through combinations of light color, frequency, and duration. The OLED platform developed in this study allows fine control over color, brightness, flicker ratio, and exposure time, making it suitable for personalized stimulation design in future human clinical research. The research team plans to expand conditions such as stimulation intensity, energy, duration, and combined visual–auditory stimulation, aiming toward clinical-stage development. < Graphical abstract for the journal ACS Biomaterials Science & Engineering – Illustration of the mechanism by which red OLED stimulation reduces amyloid-β > Dr. Byeongju Noh (from Professor Kyung Cheol Choi’s research team) said, “This study experimentally demonstrates the importance of color standardization and confirms that red OLED is the key color that activates ADAM17 and suppresses BACE1 across disease stages.” Professor Kyung Cheol Choi emphasized, “Our uniform-illuminance OLED platform overcomes the structural limitations of traditional LEDs and enables high reproducibility and safe evaluation. We expect wearable RED OLED electroceuticals for everyday use to present a new therapeutic paradigm for Alzheimer’s disease.” The research findings were published online on October 25 in ACS Biomaterials Science & Engineering, a leading international journal in biomedical and materials science. Paper Title: Color Dependence of OLED Phototherapy for Cognitive Function and Beta-Amyloid Reduction through ADAM17 and BACE1 DOI: https://pubs.acs.org/doi/full/10.1021/acsbiomaterials.5c01162 Co-authors: Byeongju Noh, Hyun-Ju Lee, Jiyun Lee, Jiyun Lee, Ji-Eun Lee, Bitna Joo, Young-Hun Jung, Minwoo Park, Sora Kang, Seokjun Oh, Jeong-Woo Hwang, Dae-Si Kang, Yongmin Jeon, So-Min Lee, Hyang Sook Hoe, Ja Wook Koo, Kyung Cheol Choi This research was supported by the National Research Foundation of Korea and the National IT Industry Promotion Agency under the Ministry of Science and ICT, and the Korea Brain Research Institute Basic Research Program. (2017R1A5A1014708, 2022M3E5E9018226, H0501-25-1001, 25-BR-02-02, 25-BR-02-04)    

A KAIST team develops the world＇s first modular co..

  <(From Left) Distinguished Professor Sang Yup Lee, Ph.D candidate Pingxin Lin, Ph.D candiate Zhou Hengrui>       The integration of systems metabolic engineering with co-culture strategies that couples bacterial cellulose production with natural colorant biosynthesis enabled the one-pot generation of rainbow-colored bacterial cellulose, establishing a sustainable biomanufacturing platform that can replace petroleum-based textiles and eliminate chemical dyeing processes.   A research group at KAIST has successfully developed a modular co-culture platform for the one-pot production of rainbow-colored bacterial cellulose. The team, led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, engineered Komagataeibacter  xylinus for bacterial cellulose synthesis and Escherichia coli for natural colorants overproduction. A co-culture of these engineered strains enabled the in situ coloration of bacterial cellulose. This research offers a versatile platform for producing living materials in multiple colors, and provides new opportunities for sustainable textiles, wearable biomaterials, and functional living materials that combine optical and structural properties beyond the reach of conventional textile technologies.   Bacterial cellulose is an attractive and biodegradable alternative to petroleum-derived fabrics due to its high purity, mechanical strength, and water-retention properties. However, the limited color range of bacterial cellulose, which is typically white, has limited its broader application in the textile industry, where more vibrant colored fabrics are increasingly desired. Conventional dyeing methods rely on petroleum-based colorants and toxic reagents, creating environmental and processing challenges. These challenges have driven the demand for alternative production methods.   To address these issues, KAIST researchers, including Ph.D. Candidate Hengrui Zhou, Ph.D. Candidate Pingxin Lin, Professor Ki Jun Jeong, and Distinguished Professor Sang Yup Lee, combined systems metabolic engineering with co-culture strategies to develop a bio-based route that integrates bacterial cellulose formation with natural pigment synthesis, enabling the production of colored living materials in a single step without additional chemical processing.   The team’s work, entitled “One-pot production of colored bacterial cellulose,” was published in Trends in Biotechnology on November 12,2025. This research details the one-pot production of multicolored bacterial cellulose using a modular co-culture platform that integrates a bacterial cellulose-overproducing K. xylinus strain with natural colorant-producing E. coli strains. The team focused on addressing the limitations in bacterial cellulose coloration caused by environmental challenges and complex processing requirements. By employing vesicle engineering and optimizing co-culture parameters, the researchers achieved one-pot production of red, orange, yellow, green, blue, navy, and purple bacterial cellulose, eliminating the need for external dyes and toxic chemical treatments.   To enhance dyeing efficiency, E. coli strains were engineered for the overproduction and secretion of natural colorants. It was determined that the intracellular accumulation of these pigments disrupts cellular metabolism and physiology, thereby inhibiting their production. To overcome this limitation, vesicle engineering has emerged as a key strategy to mitigate these cytotoxic effects, including the induction of inner- and outer-membrane vesicles and the modulation of cell morphology, enabling the more efficient secretion of colorants and increased overall production. The engineered E. coli strains were optimized in fed-batch fermentation, achieving record-breaking production of 16.92 ± 0.10 g/L of deoxyviolacein, 8.09 ± 0.17 g/L of violacein, 1.82 ± 0.07 g/L of proviolacein, and 936.25 ± 9.70 mg/L of prodeoxyviolacein, the highest reported titers to date for all four violacein derivatives. < Figure 1. Rainbow-colored bacterial cellulose (microbial fiber) with applied color >   A co-culture platform combining the K. xylinus with E. coli strains was further developed and optimized, enabling the in situ one-pot coloration of bacterial cellulose in vibrant green, blue, navy, and purple. Fed-batch fermentation further improved the performance of the platform, achieving the world-first one-pot production of multicolored bacterial cellulose on a larger scale. To expand the bacterial cellulose color palette, engineered carotenoid-producing E. coli strains were incorporated, enabling the successful synthesis of red, orange, and yellow bacterial cellulose. This milestone demonstrates the potential of microbial fermentation as a sustainable alternative to petroleum-based textile processes.   “We can anticipate that this microbial cell factory-based one-pot production of rainbow-colored bacterial cellulose has the potential to replace current petroleum-based textile processes,” said Ph.D. Candidate Hengrui Zhou. “The systems metabolic engineering strategies developed in this study could be broadly applied for the production of diverse sustainable textiles, wearable biomaterials, and functional living materials that combine optical and structural properties beyond the capabilities of conventional textile technologies.” He added, “This platform reduces the environmental impact while greatly expanding design possibilities. Beyond serving as a proof-of-concept, this technology offers a promising route toward scalable, eco-friendly fabrics with in situ coloration. Its modular design allows the incorporation of diverse natural colorant pathways, enabling the creation of living materials in multiple colors.”   < Figure 2. Schematic of a microbe-based platform for one-step production of rainbow-colored bacterial cellulose > “As demand for sustainable textiles and living materials continues to grow, we expect that the integrated biomanufacturing platform developed here will play a pivotal role in producing diverse functional biomaterials with additional design possibilities in a single step, without additional chemical processing,” explained Distinguished Professor Sang Yup Lee.   This work was supported by the Development of Next-generation Biorefinery Platform Technologies for Leading Bio-based Chemicals Industry project (2022M3J5A1056072) and the Development of Platform Technologies of Microbial Cell Factories for the Next-generation Biorefineries project (2022M3J5A1056117) from the National Research Foundation supported by the Korean Ministry of Science and ICT.     Source: Hengrui Zhou (1st), Pingxin Lin (2nd), Ki Jun Jeong (3rd), and Sang Yup Lee (Corresponding). “One-pot production of colored bacterial cellulose”. Trends in Biotechnology (Published) doi: 10.1016/j.tibtech.2025.09.019      

Makes Summer Cooler and Winter Warmer Without Powe..

<(Front row from left)Professor Young Min Song, Ph.D candidate Hyung Rae Kim, M.S candidate Hyunkyu Kwak, (Back row from left)Ph.D candidate Hyo Eun Jeong, Dr. Sehui Chang, Ph.D candidate Do Hyeon Kim, (Circle from left) Professor Dae-Hyeong Kim, Dr. Yoonsoo Shin, Dr. Se-Yeon Heo>     The poplar (Populus alba) has a unique survival strategy: when exposed to hot and dry conditions, it curls its leaves to expose the ventral surface, reflecting sunlight, and at night, the moisture condensed on the leaf surface releases latent heat to prevent frost damage. Plants have evolved such intricate mechanisms in response to dynamic environmental fluctuations in diurnal and seasonal temperature cycles, light intensity, and humidity, but there have been few instances of realizing such a sophisticated thermal management system with artificial materials. Through this research, the KAIST research team has developed an artificial material that mimics the thermal management strategy of the poplar leaf, significantly increasing the applicability of power-free, self-regulating thermal management technology in applications such as building facades, roofs, and temporary shelters. KAIST announced on November 18 that the research team led by Professor Young Min Song of the School of Electrical Engineering, in collaboration with Professor Dae-Hyeong Kim’s team at Seoul National University, has developed a flexible hydrogel-based ‘Latent-Radiative Thermostat (LRT)’ that mimics the natural heat regulation strategy of the poplar leaf. The LRT developed by the research team is a bio-inspired thermal regulator that autonomously switches between cooling and heating modes. This technology is a new thermal management technique that can simultaneously realize latent heat regulation through the evaporation and condensation of water, and radiative heat regulation using light reflection and transmission, all within a single device. The primary functional material is a composite that integrates lithium ions (Li＋) and hydroxypropyl cellulose (HPC) within a polyacrylamide (PAAm) hydrogel. Li＋ maintains warmth by condensing and absorbing moisture to regulate latent heat, and HPC changes between transparent and opaque states according to temperature changes, regulating the reflection and absorption of sunlight to switch between cooling and heating modes. When the temperature rises, HPC molecules aggregate, causing the hydrogel to become opaque, which reflects sunlight and strengthens the natural cooling effect. The resulting LRT automatically switches among four thermal management modes based on the surrounding temperature, humidity, and sunlight. < external_image >  

KAIST Develops Wearable Ultrasound Sensor Enabling..

<(From Left) Professor Hyunjoo Jenny Lee, Dr.Sang-Mok Lee, Ph.D candidate Xiaojia Liang>       Conventional wearable ultrasound sensors have been limited by low power output and poor structural stability, making them unsuitable for high-resolution imaging or therapeutic applications. A KAIST research team has now overcome these challenges by developing a flexible ultrasound sensor with statically adjustable curvature. This breakthrough opens new possibilities for wearable medical devices that can capture precise, body-conforming images and perform noninvasive treatments using ultrasound energy. KAIST (President Kwang Hyung Lee) announced on November 12 that a research team led by Professor Hyunjoo Jenny Lee from the School of Electrical Engineering developed a “flex-to-rigid (FTR)” capacitive micromachined ultrasonic transducer (CMUT) capable of transitioning freely between flexibility and rigidity using a semiconductor wafer process (MEMS). The team incorporated a low-melting-point alloy (LMPA) inside the device. When an electric current is applied, the metal melts, allowing the structure to deform freely; upon cooling, it solidifies again, fixing the sensor into the desired curved shape. Conventional polymer-membrane-based CMUTs have suffered from a low elastic modulus, resulting in insufficient acoustic power and blurred focal points during vibration. They have also lacked curvature control, limiting precise focusing on target regions. Professor Lee’s team designed an FTR structure that combines a rigid silicon substrate with a flexible elastomer bridge, achieving both high output performance and mechanical flexibility. The embedded LMPA enables dynamic adjustment and fixation of the transducer’s shape by toggling between solid and liquid states through electrical control. As a result, the new sensor can automatically focus ultrasound on a specific region according to its curvature—without requiring separate beamforming electronics—and maintains stable electrical and acoustic performance even after repeated bending. The device’s acoustic output reaches the level of low-intensity focused ultrasound (LIFU), which can gently stimulate tissues to induce therapeutic effects without causing damage. Experiments on animal models demonstrated that noninvasive spleen stimulation reduced inflammation and improved mobility in arthritis models. In the future, the team plans to extend this technology to a two-dimensional (2D) array structure—arranging multiple sensors in a grid—to enable simultaneous high-resolution ultrasound imaging and therapeutic applications, paving the way for a new generation of smart medical systems. Because the technology is compatible with semiconductor fabrication processes, it can be mass-produced and adapted for wearable and home-use ultrasound systems. This study was conducted by Sang-Mok Lee, Xiaojia Liang (co–first authors), and their collaborators under the supervision of Professor Hyunjoo Jenny Lee. The results were published online on October 23 in npj Flexible Electronics (Impact Factor: 15.5). Paper title: “Flexible ultrasound transducer array with statically adjustable curvature for anti-inflammatory treatment” DOI: [10.1038/s41528-025-00484-7] The research was supported by the Bio & Medical Technology Development Program (Brain Science Convergence Research Program) of the Ministry of Science and ICT (MSIT) and the Korea Medical Device Development Fund, a multi-ministerial R&D initiative.