Kaist

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."

The World＇s Smallest Fully Wireless Neural Implant..

< (From left) Sunwoo Lee, KAIST Joint Professor, Alyosha Molnar, Cornell University Professor > The human brain contains about 100 billion brain cells, and the chemical and electrical signals they exchange create most mental functions. Neural implant technology for precisely reading these signals is essential for the research and treatment of neurodegenerative diseases. A research team from KAIST and international collaborators has successfully implemented a fully wireless, ultra-small implant, which was previously only a theoretical possibility, going beyond simple miniaturization and weight reduction of neural implants. KAIST announced on the November 27th that a joint research team led by Professor Sunwoo Lee (Joint Professor in Materials Science and Engineering at KAIST and from the School of Electrical and Electronic Engineering at Nanyang Technological University, NTU) and Professor Alyosha Molnar's team from Cornell University in the US has developed 'MOTE (Micro-Scale Opto-Electronic Tetherless Electrode)', an ultra-small wireless neural implant less than 100 micrometers (µm) — smaller than a grain of salt. The team successfully implanted this device into the brains of laboratory mice and stably measured brain waves for one year. In the brain, invisible, minute electrical signals constantly move, creating our various mental activities such as memory, judgment, and emotion. The technology to directly measure these signals outside the body without connecting wires has been highlighted as key for brain research and the treatment of neurological disorders like dementia and Parkinson's disease. However, existing implants have limitations: their thick wired structure causes movement in the brain, leading to inflammation and signal degradation over time, and their size and heat generation restrict long-term use. To overcome these limitations, the research team created an ultra-small circuit based on the existing semiconductor process (CMOS) and combined it with their self-developed ultra-fine Micro-LEDs (µLEDs) to drastically miniaturize the device. They also applied a special surface coating to significantly enhance durability, allowing it to withstand the biological environment for a long time. The resulting MOTE is less than 100 µm thick and has a volume of less than 1 nanoliter, making it thinner than a human hair and smaller than a grain of salt, the world's smallest level among currently reported wireless neural implants. Another key feature of MOTE is that it is a fully wireless system that requires no battery. The device is structured to receive external light to generate power, detect brain waves, and then transmit the information back outside embedded in the light signal using Pulse Position Modulation (PPM). This method drastically reduces energy consumption, minimizes the risk of heat generation, and eliminates the need for battery replacement, enabling long-term use. The research team conducted a one-year long-term experiment by implanting the ultra-small MOTE into the brains of mice. The results showed normal brain wave measurement over the extended period, with almost no inflammation observed around the implant and no degradation in device performance. This is considered the first clear demonstration that an ultra-small wireless implant can maintain normal function for a prolonged time inside a living body. < MOTE neural implant on a salt crystal (left), MOTE neural implants after 296 days of implantation in a laboratory mouse (right) > Professor Sunwoo Lee stated, "The greatest significance of the newly developed neural implant lies in its actual implementation of a fully wireless, ultra-small implant that was previously only anticipated as a possibility, going beyond simple miniaturization and weight reduction." He added, "This proves the technological possibility of resolving not only the known unknowns raised during the development and use of wireless neural implants, but also the unknown unknowns that newly emerge during the actual development process." He further added, "This technology will be broadly applicable not only to brain science research but also to nervous system disease monitoring and the development of long-term recording-based treatment technologies." The research results were published online in the prestigious journal Nature Electronics on November 3rd. ※ Paper Title: A subnanolitre tetherless optoelectronic microsystem for chronic neural recording in awake mice, DOI: https://doi.org/10.1038/s41928-025-01484-1 This research was supported by the US National Institutes of Health (NIH), Nanyang Technological University (Singapore), the Singapore National Research Foundation, the Singapore Ministry of Education, and the ASPIRE League Partnership Seed Fund 2024. The specialized fabrication processes were conducted at the Cornell NanoScale Facility (part of the US National Nanotechnology Coordinated Infrastructure, NNCI) and NTU's Nanyang NanoFabrication Centre.

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 Professor and Alumni Who Won AIxCC Donate 15..

<(From Left) Professor Insu Yun from KAIST School of Electrical Engineering, Researcher HyungSeok Han from Samsung Research America> KAIST (President Kwang Hyung Lee) announced on the 23rd of November that HyungSeok Han (Ph.D. alumnus from the School of Computing) and Insu Yun (B.S. alumnus, currently Associate Professor in the School of Electrical Engineering) donated 150 million KRW from the prize money won by Team Atlanta, which took first place in the world’s largest AI security competition, the “AI Cyber Challenge (AIxCC),” organized by the U.S. Defense Advanced Research Projects Agency (DARPA). The AIxCC final round was held this August in Las Vegas, where Team Atlanta—a joint team consisting of researchers from Samsung Research, KAIST, POSTECH, and Georgia Tech—secured the top prize. AIxCC is the world’s largest AI security competition, with a total prize pool of 29.5 million USD (approx. 41 billion KRW). Over the past two years, security companies and research teams worldwide have competed with AI-based security technologies, showcasing state-of-the-art capabilities. A total of 91 teams registered for the competition, 31 teams participated in the qualifiers, and 7 teams advanced to the finals. Team Atlanta won the first-place prize of 4 million USD (approx. 5.8 billion KRW), securing victory with an overwhelming margin comparable to the combined scores of the second- and third-place teams. The team also swept major titles such as “Most Vulnerabilities Identified” and “Highest Scoring Team,” demonstrating exceptional technical superiority. HyungSeok Han earned his B.S. (2017) and Ph.D. (2023) from the KAIST School of Computing, then worked as a postdoctoral researcher at Georgia Tech before joining Samsung Research America where he currently works. In the competition, he served as the team leader for the development of the automatic vulnerability detection system and oversaw system integration and infrastructure, making major contributions. Insu Yun received his B.S. (2015) from the KAIST School of Computing and his Ph.D. (2020) from Georgia Tech. Since 2021, he has been a faculty member in the KAIST School of Electrical Engineering. In this competition, he led the patch development team and played a central role in enhancing overall system completeness. The two researchers decided to donate 150 million KRW of their prize money to the School of Computing and the School of Electrical Engineering. The School of Computing will use the donation as a scholarship fund, while the School of Electrical Engineering will apply it toward student education and research support, in line with the spirit of the donation. Alumnus HyungSeok Han remarked, “Building a system in which AI autonomously discovers vulnerabilities and even generates patches has long been a dream of mine and an important milestone in the security field. I’m grateful to have achieved meaningful results together with KAIST alumni, and I hope KAIST will continue to exert a positive influence on global technological advancement.” <Final Scoreboard> Professor Insu Yun stated, “I’m truly grateful to every member of Team Atlanta. In particular, I want to thank Professor Taesoo Kim, our overall team leader and advisor, the students in our lab who worked tirelessly, and Dr. HyungSeok Han, who joined me in making this meaningful contribution.” KAIST President Kwang Hyung Lee commented, “I deeply thank our alumni for achieving outstanding results on the world stage of technological competition and for generously giving back to their alma mater. This achievement demonstrates KAIST’s educational and research excellence and stands as meaningful evidence of the global competitiveness of Korea’s AI and security technologies. KAIST will continue to lead advanced AI and security innovation and do its utmost to nurture creative talent who will contribute to humanity and society.” To encourage further alumni contributions, the KAIST Development Foundation is operating the Team KAIST (https://giving.kaist.ac.kr/ko/sub01/sub0103_1.php) campaign to promote alumni participation.

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)

Professor Youngjin Kwon＇s Team Wins Google Award ＇..

< Professor Youngjin Kwon > Modern CPUs have complex structures, and in the process of handling multiple tasks simultaneously, an order-scrambling error known as a 'concurrency bug' can occur. Although this can lead to security issues, these bugs were extremely difficult to detect using conventional methods. Our university's research team has developed a world-first-level technology to automatically detect these bugs by precisely reproducing the internal operation of the CPU in a virtual environment without needing a physical chip. Through this, they successfully found and fixed 11 new bugs in the latest Linux kernel. Our university announced on the 21st that the research team led by Professor Youngjin Kwon of the School of Computing has won the 'Research Scholar Award' (Systems category) presented by Google. The Google Research Scholar Award is a global research support program, implemented since 2020, to support Early-Career Professors conducting innovative research in various fields such as AI, Systems, Security, and Data Management. It is known as a highly competitive program, with the selection process conducted directly by Google Research scientists, and only a tiny fraction of the hundreds of applicants worldwide are chosen. In particular, this award is recognized as one of the most prestigious industry research support programs globally in the field of AI and Computer Systems, and domestic recipients are rare. ■ Technology Developed to Detect Concurrency Bugs in the Latest Apple M3 and ARM Servers Professor Kwon's team developed a technology that automatically detects concurrency bugs in the latest ARM (a CPU design method that uses less power and is highly efficient) based servers, such as the Apple M3 (Apple's latest-generation computer processor chip). A concurrency bug is an error that occurs when the order of operations gets mixed up while the CPU handles multiple tasks simultaneously. This is a severe security vulnerability that can cause the computer to suddenly freeze or become a pathway for hackers to attack the system. However, these errors were extremely difficult to find with existing testing methods alone. ■ Automatically Detects Bugs by Reproducing CPU Internal Operations Without a Real CPU The core achievement of Professor Kwon's team is the 'technology to reproduce the internal operation of the CPU exactly in a virtual environment without a physical chip.' Using this technology, it is possible to precisely analyze the order in which instructions are executed and where problems occur using only software, without having to disassemble the CPU or use the actual chip. By running the Linux operating system based on this system to automatically detect bugs, the research team discovered 11 new bugs in the latest Linux kernel＊ and reported them to the developer community, where they were all fixed. ＊Linux kernel: The core operating system engine that forms the basis of servers, supercomputers, and smartphones (Android) worldwide. It acts as the 'heart' of the system, managing the CPU, memory, and storage devices. Google recognized this technology as 'very important for its own infrastructure' and conferred the Award. < Google Scholar Award Recipient Page > This technology is evaluated to have general applicability, not only to Linux but also to various operating systems such as Android and Windows. The research team has released the software as open-source (GitHub) so that anyone in academia or industry can utilize it. Professor Youngjin Kwon stated, "This award validates the international competitiveness of KAIST's systems research," and "We will continue our research to establish a safe and highly reliable computing environment." ※ Google Scholar Award Recipient Page: https://research.google/programs-and-events/research-scholar-program/recipients/ GitHub (Technology Open-Source): https://github.com/casys-kaist/ozz

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 >

AI Opens a New Era in Medical Science and Bio​

< (From left) KAIST Professors Yoonjae Choi, Tae-Kyun Kim, Jong Chul Ye, Hyunwoo Kim, Seunghoon Hong, Sang Yup Lee > KAIST announced on the 14th of November that it has been selected as a major participating institution in the 'Lunit Consortium' for the 'AI Specialized Foundation Model Development Project' supervised by the Ministry of Science and ICT, and has officially started developing an AI foundation model for the medical science and bio fields. Through this project, KAIST plans to develop an 'AI Foundation Model Specialized for Medical Science' that encompasses the entire lifecycle of bio and medical data, and lead the creation of an AI based life science innovation ecosystem. The 'Lunit Consortium' includes 7 companies-Lunit, Trillion Labs, Kakao Healthcare, Igenscience, SK Biopharm, and Rebellion-along with 9 medical and research institutions, including KAIST, Seoul National University, NYU, National Health Insurance Service Ilsan Hospital, and Yonsei Severance Hospital. This consortium will be supported by 256 state of the art B200 GPUs to build and demonstrate a 'Chain of Evidence-Based Full-Cycle Medical Science AI Model', an AI system that connects and analyzes medical data from beginning to end, and a 'Multi-Agent Service', a system where multiple AIs collaborate to perform diagnosis and prediction. KAIST's participation in this project involves a joint research team formed by professors from the School of Computing and the Kim Jaechul Graduate School of AI. Professors Yoonjae Choi, Tae-Kyun Kim, Jong Chul Ye, Hyunwoo Kim, and Seunghoon Hong will serve as the research team, and Vice President for Research Sang Yup Lee will take on an advisory role. The research team is not merely collecting data but they are establishing a strategy (L1~L7 stages) to precisely process and systematically manage medical and life science data so that the AI can actually learn and utilize it. Through this, they plan to develop and verify an AI model that connects and analyzes diverse life science data, including medical information, gene/protein data, and new drug candidates. The data the research team aims to integrate includes a wide range from language to actual patient treatment information. Specifically, L1 represents language data, L2 is the structure of molecules, L3 is proteins and antibodies, L4 is omics data encompassing genetic and protein information, L5 is drug information, L6 is medical science research and clinical data, and L7 is real-world clinical data obtained from actual hospitals. In essence, the data handled by the AI connects everything from speech and text to molecules, proteins, drugs, clinical research, and actual patient treatment information. < The process of training AI by viewing X ray images and doctor's interpretation (text) together (MedViLL from Professor Jae-Yoon Choi' s lab) > Vice President Sang Yup Lee is a world-renowned scholar in the fields of synthetic biology and systems metabolic engineering, leading the establishment of a bio manufacturing platform and policy advice through the convergence of life science, engineering, and AI. He advises on the analysis of life information (omics) such as genes and proteins and designs a feedback system for verifying experimental results, supporting the Korean-developed medical AI model to secure international reliability and competitiveness. Vice President Lee stated, "AI technology is breaking down the boundaries of life science and engineering, creating a new paradigm for knowledge creation," adding, "KAIST will utilize full cycle medical science data to accelerate the era where AI uncovers the causes of diseases and predicts treatments." KAIST President Kwang Hyung Lee said, "KAIST will contribute to creating an AI-based life science innovation ecosystem, lead the innovation of national strategic industries through world-class AI-bio convergence research, and drive the progress of human health and science and technology." The model developed in the Lunit Consortium will be released as an Open License for commercial use, and is expected to expand into various medical and healthcare services such as national health chatbots. With this participation, KAIST plans to strengthen research on AI-based life science data infrastructure establishment, medical AI standardization, and AI ethics and policy advice, leading the AI transition of national bio and medical science research.

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.