Kaist

KAIST develops world’s most sensitive light-powere..

  <(From left) Ph.D candidate Jaeha Hwang, Ph.D candidate Jungi Song ,Professor Kayoung Lee from Electrical Engineering>   Silicon semiconductors used in existing photodetectors have low light responsivity, and the two-dimensional semiconductor MoS₂ (molybdenum disulfide) is so thin that doping processes to control its electrical properties are difficult, limiting the realization of high-performance photodetectors. The KAIST research team has overcome this technical limitation and developed the world’s highest-performing self-powered photodetector, which operates without electricity in environments with a light source. This paves the way for an era where precise sensing is possible without batteries in wearable devices, biosignal monitoring, IoT devices, autonomous vehicles, and robots, as long as a light source is present.   KAIST (President Kwang Hyung Lee) announced on the 14th of August that Professor Kayoung Lee’s research team from the School of Electrical Engineering has developed a self-powered photodetector that operates without external power supply. This sensor demonstrated a sensitivity up to 20 times higher than existing products, marking the highest performance level among comparable technologies reported to date.   Professor Kayoung Lee’s team fabricated a “PN junction structure” photodetector capable of generating electrical signals on its own in environments with light, even without an electrical energy supply, by introducing a “van der Waals bottom electrode” that makes semiconductors extremely sensitive to electrical signals without doping.   First, a “PN junction” is a structure formed by joining p-type (hole-rich) and n-type (electron-rich) materials in a semiconductor. This structure causes current to flow in one direction when exposed to light, making it a key component in photodetectors and solar cells.   Normally, to create a proper PN junction, a process called “doping” is required, which involves deliberately introducing impurities into the semiconductor to alter its electrical properties. However, two-dimensional semiconductors such as MoS₂ are only a few atoms thick, so doping in the conventional way can damage the structure or reduce performance, making it difficult to create an ideal PN junction.   To overcome these limitations and maximize device performance, the research team designed a new device structure incorporating two key technologies: the “van der Waals electrode” and the “partial gate.”       The “partial gate” structure applies an electrical signal only to part of the two-dimensional semiconductor, controlling one side to behave like p-type and the other like n-type. This allows the device to function electrically like a PN junction without doping.   Furthermore, considering that conventional metal electrodes can chemically bond strongly to the semiconductor and damage its lattice structure, the “van der Waals bottom electrode” was attached gently using van der Waals forces. This preserved the original structure of the two-dimensional semiconductor while ensuring effective electrical signal transfer.   This innovative approach secured both structural stability and electrical performance, enabling the realization of a PN junction in thin two-dimensional semiconductors without damaging their structure.   Thanks to this innovation, the team succeeded in implementing a high-performance PN junction without doping. The device can generate electrical signals with extreme sensitivity as long as there is light, even without an external power source. Its light detection sensitivity (responsivity) exceeds 21 A/W, more than 20 times higher than powered conventional sensors, 10 times higher than silicon-based self-powered sensors, and over twice as high as existing MoS₂ sensors. This level of sensitivity means it can be applied immediately to high-precision sensors capable of detecting biosignals or operating in dark environments.       Professor Kayoung Lee stated that they “have achieved a level of sensitivity unimaginable in silicon sensors, and although two-dimensional semiconductors are too thin for conventional doping processes, [they] succeeded in implementing a PN junction that controls electrical flow without doping.” She added, “This technology can be used not only in sensors but also in key components that control electricity inside smartphones and electronic devices, providing a foundation for miniaturization and self-powered operation of next-generation electronics.”       <Jaeha Hwang, Jungi Song, Experimnet in Porgress>     This research, with doctoral students Jaeha Hwang and Jungi Song as co-first authors, was published online on July 26 in Advanced Functional Materials (IF 19), a leading journal in materials science. ※ Paper title: Gated PN Junction in Ambipolar MoS₂ for Superior Self-Powered Photodetection ※ DOI: https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202510113   Meanwhile, this work was supported by the National Research Foundation of Korea, the Korea Basic Science Institute, Samsung Electronics, and the Korea Institute for Advancement of Technology.  

KAIST develops “FlexGNN,” a graph analysis AI 95 t..

  <(From Left) Donghyoung Han, CTO of GraphAI Co, Ph.D candidate Jeongmin Bae from KAIST, Professor Min-soo Kim from KAIST>   Alongside text-based large language models (LLMs) including ChatGPT, in industrial fields, GNN (Graph Neural Network)-based graph AI models that analyze unstructured data such as financial transactions, stocks, social media, and patient records in graph form are being actively used. However, there is a limitation in that full graph learning—training the entire graph at once—requires massive memory and GPU servers. A KAIST research team has succeeded in developing the world’s highest-performance software technology that can train large-scale GNN models at maximum speed using only a single GPU server.   KAIST (President Kwang Hyung Lee) announced on the 13th that the research team led by Professor Min-Soo Kim of the School of Computing has developed “FlexGNN,” a GNN system that, unlike existing methods using multiple GPU servers, can quickly train and infer large-scale full-graph AI models on a single GPU server. FlexGNN improves training speed by up to 95 times compared to existing technologies.   Recently, in various fields such as climate, finance, medicine, pharmaceuticals, manufacturing, and distribution, there has been a growing number of cases where data is converted into graph form, consisting of nodes and edges, for analysis and prediction.   While the full graph approach, which uses the entire graph for training, achieves higher accuracy, it has the drawback of frequently running out of memory due to the generation of massive intermediate data during training, as well as prolonged training times caused by data communication between multiple servers.   To overcome these problems, FlexGNN performs optimal AI model training on a single GPU server by utilizing SSDs (solid-state drives) and main memory instead of multiple GPU servers.       <Figure (a): This illustrates the typical execution flow of a conventional full-graph GNN training system. All intermediate data generated during training are retained in GPU memory, and computations are performed sequentially without data movement or memory optimization. Consequently, if the GPU memory capacity is exceeded, training becomes infeasible. Additionally, inter-GPU data exchange relies solely on a fixed method (X_rigid), limiting performance and scalability. Figure (b): This depicts an example of the execution flow based on the optimized training execution plan generated by FlexGNN. For each intermediate data, strategies such as retention, offloading, or recomputation are selectively applied. Depending on resource constraints and data size, an appropriate inter-GPU exchange method—either GPU-to-GPU (G2G) or GPU-to-Host (G2H)—is adaptively chosen by the exchange operator (X_adapt). Furthermore, offloading and reloading operations are scheduled to overlap as much as possible with computation, maximizing compute-data movement parallelism. The adaptive exchange operator and various data offloading and reloading operators (R, O) within the figure demonstrate FlexGNN's ability to flexibly control intermediate data management and inter-GPU exchange strategies based on the training execution plan.>   Particularly, through AI query optimization training—which optimizes the quality of database systems—the team developed a new training optimization technology that calculates model parameters, training data, and intermediate data between GPU, main memory, and SSD layers at the optimal timing and method.   As a result, FlexGNN flexibly generates optimal training execution plans according to available resources such as data size, model scale, and GPU memory, thereby achieving high resource efficiency and training speed.   Consequently, it became possible to train GNN models on data far exceeding main memory capacity, and training could be up to 95 times faster even on a single GPU server. In particular, the realization of full-graph AI, capable of more precise analysis than supercomputers in applications such as climate prediction, has become a reality.   Professor Min-Soo Kim of KAIST stated, “As full-graph GNN models are actively used to solve complex problems such as weather prediction and new material discovery, the importance of related technologies is increasing.” He added that “since FlexGNN has dramatically solved the longstanding problems of training scale and speed in graph AI models, we expect it to be widely used in various industries.”   In this research, Jeongmin Bae, a doctoral student in the School of Computing at KAIST, participated as the first author, Donghyoung Han, CTO of GraphAI Co. (founded by Professor Kim) participated as the second author, and Professor Kim served as the corresponding author.   The research results were presented on August 5 at ACM KDD, a world-renowned data mining conference. The FlexGNN technology is also planned to be applied to Grapheye’s graph database solution, GraphOn.   ● Paper title: FlexGNN: A High-Performance, Large-Scale Full-Graph GNN System with Best-Effort Training Plan Optimization   ● DOI: https://doi.org/10.1145/3711896.3736964   This research was supported by the IITP SW Star Lab and IITP-ITRC of the Ministry of Science and ICT, as well as the mid-level project program of the National Research Foundation of Korea.

KAIST Develops World’s First Wireless OLED Contact..

  <ID-style photograph against a laboratory background featuring an OLED contact lens sample (center), flanked by the principal authors (left: Professor Seunghyup Yoo ; right: Dr. Jee Hoon Sim). Above them (from top to bottom) are: Professor Se Joon Woo, Professor Sei Kwang Hahn, Dr. Su-Bon Kim, and Dr. Hyeonwook Chae>   Electroretinography (ERG) is an ophthalmic diagnostic method used to determine whether the retina is functioning normally. It is widely employed for diagnosing hereditary retinal diseases or assessing retinal function decline.   A team of Korean researchers has developed a next-generation wireless ophthalmic diagnostic technology that replaces the existing stationary, darkroom-based retinal testing method by incorporating an “ultrathin OLED” into a contact lens. This breakthrough is expected to have applications in diverse fields such as myopia treatment, ocular biosignal analysis, augmented-reality (AR) visual information delivery, and light-based neurostimulation.   On the 12th, KAIST (President Kwang Hyung Lee) announced that a research team led by Professor Seunghyup Yoo from the School of Electrical Engineering, in collaboration with Professor Se Joon Woo of Seoul National University Bundang Hospital (Director Jeong-Han Song), Professor Sei Kwang Hahn of POSTECH (President Sung-Keun Kim) and CEO of PHI Biomed Co., and the Electronics and Telecommunications Research Institute (ETRI, President Seungchan Bang) under the National Research Council of Science & Technology (NST, Chairman Youngshik Kim), has developed the world’s first wireless contact lens-based wearable retinal diagnostic platform using organic light-emitting diodes (OLEDs).       <Figure 1. Schematic and photograph of the wireless OLED contact lens>   This technology enables ERG simply by wearing the lens, eliminating the need for large specialized light sources and dramatically simplifying the conventional, complex ophthalmic diagnostic environment.   Traditionally, ERG requires the use of a stationary Ganzfeld device in a dark room, where patients must keep their eyes open and remain still during the test. This setup imposes spatial constraints and can lead to patient fatigue and compliances challenges.   To overcome these limitations, the joint research team integrated an ultrathin flexible OLED —approximately 12.5 μm thick, or 6–8 times thinner than a human hair— into a contact lens electrode for ERG. They also equipped it with a wireless power receiving antenna and a control chip, completing a system capable of independent operation.   For power transmission, the team adopted a wireless power transfer method using a 433 MHz resonant frequency suitable for stable wireless communication. This was also demonstrated in the form of a wireless controller embedded in a sleep mask, which can be linked to a smartphone —further enhancing practical usability.       <Figure 2. Schematic of the electroretinography (ERG) testing system using a wireless OLED contact lens and an example of an actual test in progress>   While most smart contact lens–type light sources developed for ocular illumination have used inorganic LEDs, these rigid devices emit light almost from a single point, which can lead to excessive heat accumulation and thus usable light intensity. In contrast, OLEDs are areal light sources and were shown to induce retinal responses even under low luminance conditions. In this study, under a relatively low luminance＊ of 126 nits, the OLED contact lens successfully induced stable ERG signals, producing diagnostic results equivalent to those obtained with existing commercial light sources. ＊Luminance: A value indicating how brightly a surface or screen emits light; for reference, the luminance of a smartphone screen is about 300–600 nits (can exceed 1000 nits at maximum).   Animal tests confirmed that the surface temperature of a rabbit’s eye wearing the OLED contact lens remained below 27°C, avoiding corneal heat damage, and that the light-emitting performance was maintained even in humid environments—demonstrating its effectiveness and safety as an ERG diagnostic tool in real clinical settings.   Professor Seunghyup Yoo stated that “integrating the flexibility and diffusive light characteristics of ultrathin OLEDs into a contact lens is a world-first attempt,” and that “this research can help expand smart contact lens technology into on-eye optical diagnostic and phototherapeutic platforms, contributing to the advancement of digital healthcare technology.”       < Wireless operation of the OLED contact lens >   Jee Hoon Sim, Hyeonwook Chae, and Su-Bon Kim, PhD researchers at KAIST, played a key role as co-first authors alongside Dr. Sangbaie Shin of PHI Biomed Co.. Corresponding authors are Professor Seunghyup Yoo (School of Electrical Engineering, KAIST), Professor Sei Kwang Hahn (Department of Materials Science and Engineering, POSTECH), and Professor Se Joon Woo (Seoul National University Bundang Hospital). The results were published online in the internationally renowned journal ACS Nano on May 1st.       ● Paper title: Wireless Organic Light-Emitting Diode Contact Lenses for On-Eye Wearable Light Sources and Their Application to Personalized Health Monitoring   ● DOI: https://doi.org/10.1021/acsnano.4c18563   ● Related video clip: http://bit.ly/3UGg6R8       < Close-up of the OLED contact lens sample >

KAIST Develops Bioelectrosynthesis Platform for Sw..

  <(From left)Professor Jimin Park, Ph.D candidate Myeongeun Lee, Ph.D cadidate Jaewoong Lee,Professor Jihan Kim>     Cells use various signaling molecules to regulate the nervous, immune, and vascular systems. Among these, nitric oxide (NO) and ammonia (NH₃) play important roles, but their chemical instability and gaseous nature make them difficult to generate or control externally. A KAIST research team has developed a platform that generates specific signaling molecules in situ from a single precursor under an applied electrical signal, enabling switch-like, precise spatiotemporal control of cellular responses. This approach could provide a foundation for future medical technologies such as electroceuticals, electrogenetics, and personalized cell therapies.   KAIST (President Kwang Hyung Lee) announced on August 11 that a research team led by Professor Jimin Park from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor Jihan Kim's group, has developed a 'Bioelectrosynthesis Platform' capable of producing either nitric oxide or ammonia on demand using only an electrical signal. The platform allows control over the timing, spatial range, and duration of cell responses.   Inspired by enzymes involved in nitrite reduction, the researchers implemented an electrochemical strategy that selectively produces nitric oxide or ammonia from a single precursor, nitrite (NO₂⁻). By changing the catalyst, the team generated ammonia or nitric oxide from nitrite using a copper-molybdenum-sulfur catalyst (Cu2MoS4) and an iron-incorporated catalyst (FeCuMS4), respectively.   Through electrochemical measurements and computer simulations, the team revealed that Fe sites in the FeCuMoS4 catalyst bind nitric oxide intermediates more strongly, shifting product selectivity toward nitric oxide. Under the same electrical conditions, the Fe-containing catalyst preferentially produces nitric oxide, whereas the Cu2MoS4 catalyst favors ammonia production.       <Figure 1. Schematic diagram of a bio-electrosynthesis platform that synthesizes a desired signaling substance with an electrical signal (left) and the results of precise cell control using it (right)>   The research team demonstrated biological functionality by using the platform to activate ion channels in human cells. Specifically, electrochemically produced nitric oxide activated TRPV1 channels (responsive to heat and chemical stimuli), while electrochemically produced ammonia induced intracellular alkalinization and activated OTOP1 proton channels. By tuning the applied voltage and electrolysis duration, the team modulated the onset time, spatial extent, and termination of cellular responses, which effectively turned cellular signaling on and off like a switch.       <Figure 2. Experimental results showing the change in the production ratio of nitric oxide and ammonia signaling substances according to the type of catalyst (left) and computational simulation results showing the strong bond between iron and nitric oxide (right)>   Professor Jimin Park said, "This work is significant because it enables precise cellular control by selectively producing signaling molecules with electricity. We believe it has strong potential for applications in electroceutical technologies targeting the nervous system or metabolic disorders."   Myeongeun Lee and Jaewoong Lee, Ph.D. students in the Department of Chemical and Biomolecular Engineering at KAIST, served as the co-first authors. Professor Jihan Kim is a co-author. The paper was published online in 'Angewandte Chemie International Edition' on July 8, 2025 (DOI: 10.1002/ange.202508192).   Reference: https://doi.org/10.1002/ange.202508192   Authors: Myeongeun Lee†, Jaewoong Lee†, Yongha Kim, Changho Lee, Sang Yeon Oh, Prof. Jihan Kim, Prof. Jimin Park＊   †These authors contributed equally. ＊Corresponding author.

＇Team Atlanta＇, in which KAIST Professor Insu Yun ..

  <Photo1. Group Photo of Team Atlanta>   Team Atlanta, led by Professor Insu Yun of the Department of Electrical and Electronic Engineering at KAIST and Tae-soo Kim, an executive from Samsung Research, along with researchers from POSTECH and Georgia Tech, won the final championship at the AI Cyber Challenge (AIxCC) hosted by the Defense Advanced Research Projects Agency (DARPA). The final was held at the world's largest hacking conference, DEF CON 33, in Las Vegas on August 8 (local time).   With this achievement, the team won a prize of ＄4 million (approximately 5.5 billion KRW), demonstrating the excellence of their AI-based autonomous cyber defense technology on the global stage.       <Photo2.Championship Commemorative:On the left and right are tournament officials. From the second person, Professor Tae-soo Kim(Samsung Research / Georgia Tech), Researcher Hyeong-seok Han (Samsung Research America), and Professor Insu Yun (KAIST)>   The AI Cyber Challenge is a two-year global competition co-hosted by DARPA and the Advanced Research Projects Agency for Health (ARPA-H). It challenges contestants to automatically analyze, detect, and fix software vulnerabilities using AI-based Cyber Reasoning Systems (CRS). The total prize money for the competition is ＄29.5 million, with the winning team receiving ＄4 million.   In the final, Team Atlanta scored a total of 392.76 points, a difference of over 170 points from the second-place team, Trail of Bits, securing a dominant victory. The CRS developed by Team Atlanta successfully and automatically detected various types of vulnerabilities and patched a significant number of them in real time.   Among the 7 finalist teams, an average of 77％ of the 70 intentionally injected vulnerabilities were found, and 61％ of them were patched. The teams also found 18 additional unknown vulnerabilities in real software, proving the potential of AI security technology.   All CRS technologies, including those of the winning team, will be provided as open-source and are expected to be used to strengthen the security of core infrastructure such as hospitals, water, and power systems.       <Photo3. Final Scoreboard: An overwhelming victory with over 170 points>   Professor Insu Yun of KAIST, a member of Team Atlanta, stated, "I am very happy to have achieved such a great result. This is a remarkable achievement that shows Korea's cyber security research has reached the highest level in the world, and it was meaningful to show the capabilities of Korean researchers on the world stage. I will continue to conduct research to protect the digital safety of the nation and global society through the fusion of AI and security technology."   KAIST President Kwang-hyung Lee stated, "This victory is another example that proves KAIST is a world-leading institution in the field of future cyber security and AI convergence. We will continue to provide full support to our researchers so they can compete and produce results on the world stage."       <Photo4. Results Announcement>

KAIST’s Wearable Robot Design Wins ‘2025 Red Dot A..

  < Professor Hyunjoon Park, M.S candidate Eun-ju Kang, Prospective M.S candidate Jae-seong Kim, undergraduate student Min-su Kim >   A team led by Professor Hyunjoon Park from the Department of Industrial Design won the ‘Best of the Best’ award at the 2025 Red Dot Design Awards, one of the world's top three design awards, for their 'Angel Robotics WSF1 VISION Concept.'   The design for the next-generation wearable robot for people with paraplegia successfully implements functionality, aesthetics, and social inclusion. This latest achievement follows the team's iF Design Award win for the WalkON Suit F1 prototype, which also won a gold medal at the Cybathlon last year. This marks consecutive wins at top-tier international design awards.   KAIST (President Kwang-hyung Lee) announced on the 8th of August that Move Lab, a research team led by Professor Hyunjoon Park from the Department of Industrial Design, won the 'Best of the Best' award in the Design Concept-Professional category at the prestigious '2025 Red Dot Design Awards' for their next-generation wearable robot design, the ‘Angel Robotics WSF1 VISION Concept.’   The German 'Red Dot Design Awards' is one of the world's most well-known design competitions. It is considered one of the world's top three design awards along with Germany’s iF Design Awards and America’s IDEA. The ‘Best of the Best’ award is given to the best design in a category and is awarded only to a very select few of the top designs (within the top 1％) among all Red Dot Award winners.   Professor Hyunjoon Park’s team was honored with the ‘Best of the Best’ award for a user-friendly follow-up development of the ‘WalkON Suit F1 prototype,’ which won a gold medal at the 2024 Cybathlon and an iF Design Award in 2025.       <Figure 1. WSF1 Vision Concept Main Image>   This award-winning design is the result of industry-academic cooperation with Angel Robotics Inc., founded by Professor Kyoungchul Kong from the KAIST Department of Mechanical Engineering. It is a concept design that proposes a next-generation wearable robot (an ultra-personal mobility device) that can be used by people with paraplegia in their daily lives.   The research team focused on transforming Angel Robotics Inc.'s advanced engineering platform into an intuitive and emotional, user-centric experience, implementing a design solution that simultaneously possesses functionality, aesthetics, and social inclusion.       <Figure 2. WSF1 Vision Concept Full Exterior (Front View)>   The WSF1 VISION Concept includes innovative features implemented in Professor Kyoungchul Kong’s Exo Lab, such as:   An autonomous access function where the robot finds the user on its own. A front-loading mechanism designed for the user to put it on alone while seated. Multi-directional walking functionality realized through 12 powerful torque actuators and the latest control algorithms. AI vision technology, along with a multi-visual display system that provides navigation and omnidirectional vision. This provides users with a safer and more convenient mobility experience.   The strong yet elegant silhouette was achieved through a design process that pursued perfection in proportion, surfaces, and details not seen in existing wearable robots. In particular, the fabric cover that wraps around the entire thigh from the robot's hip joint is a stylish element that respects the wearer's self-esteem and individuality, like fashionable athletic wear. It also acts as a device for the wearer to psychologically feel safe in interacting with the robot and blending in with the general public. This presents a new aesthetic for wearable robots where function and form are harmonized.       <Figure 3. WSF1 Vision Concept's Operating Principle. It walks autonomously and is worn from the front while the user is seated.>   KAIST Professor Hyunjoon Park said of the award, "We are focusing on using technology, aesthetics, and human-centered innovation to present advanced technical solutions as easy, enjoyable, and cool experiences for users. Based on Angel Robotics Inc.'s vision of 'recreating human ability with technology,' the WSF1 VISION Concept aimed to break away from the traditional framework of wearable robots and deliver a design experience that adds dignity, independence, and new style to the user's life."       <Figure 4. WSF1 Vision Concept Detail Image>   A physical model of the WSF1 VISION Concept is scheduled to be unveiled in the Future Hall of the 2025 Gwangju Design Biennale from August 30 to November 2. The theme is 'Po-yong-ji-deok' (the virtue of inclusion), and it will showcase the role of design language in creating an inclusive future society.       <Figure 5. WSF1 Vision Concept: Image of a Person Wearing and Walking>

Unlocking New Potential for Natural Gas–Based Biop..

  < (From Left)Jaewook Myung from KAIST, Sunho Park from KAIST, Dr. Chungheon Shin from Stanford University, Prof. Craig S. Criddle from Stanford University >   KAIST announced that a research team led by Professor Jaewook Myung from the Department of Civil and Environmental Engineering, in collaboration with Stanford University, has identified how ethane (C2H6)—a major constituent of natural gas—affects the core metabolic pathways of the obligate methanotroph Methylosinus trichosporium OB3b.   Methane (CH4), a greenhouse gas with roughly 25 times the global warming potential of carbon dioxide, is rarely emitted alone into the environment. It is typically released in mixtures with other gases. In the case of natural gas, ethane can comprise up to 15％ of the total composition.   Methanotrophs are aerobic bacteria that can utilize methane as their sole source of carbon and energy. Obligate methanotrophs, in particular, strictly utilize only C1 compounds such as methane or methanol. Until now, little was known about how these organisms respond to C2 compounds like ethane, which they cannot use for growth.       < Figure 1. Conceptual overview of obligate methanotroph metabolism and PHB biosynthesis under mixed-substrate conditions of methane and ethane >   This study reveals that although ethane cannot serve as a growth substrate, its presence significantly affects key metabolic functions in M. trichosporium OB3b—including methane oxidation, cell proliferation, and the intracellular synthesis of polyhydroxybutyrate (PHB), a biodegradable polymer.   Under varying methane and oxygen conditions, the team observed that ethane addition consistently resulted in three metabolic effects: reduced cell growth, lower methane consumption, and increased PHB accumulation. These effects intensified with rising ethane concentrations. Notably, ethane oxidation occurred only when methane was present, confirming that it is co-oxidized via particulate methane monooxygenase (pMMO), the key enzyme responsible for methane oxidation.       < Figure2. Effects of increasing ethane concentrations on methane and ethane consumption, cell growth, and PHB production in Methylosinus trichosporium OB3b >   Further analysis showed that acetate, an intermediate formed during ethane oxidation, played a pivotal role in this response. Higher acetate levels inhibited growth but enhanced PHB production, suggesting that ethane-derived acetate drives contrasting carbon assimilation patterns depending on nutrient conditions—nutrient-balanced growth phase and nutrient-imbalanced PHB accumulation phas.   In addition, when external reducing power was supplemented (via methanol or formate), ethane consumption was enhanced significantly, while methane oxidation remained largely unaffected. This finding suggests that ethane, despite not supporting growth, actively competes for intracellular resources such as reducing equivalents. It offers new insights into substrate prioritization and resource allocation in methanotrophs under mixed-substrate conditions.   Interestingly, while methane uptake declined in the presence of ethane, the expression of pmoA, the gene encoding pMMO, remained unchanged. This suggests that ethane’s impact occurs beyond the transcriptional level—likely via post-transcriptional or enzymatic regulation.       < Figure2. Effects of increasing ethane concentrations on methane and ethane consumption, cell growth, and PHB production in Methylosinus trichosporium OB3b >   “This is the first study to systematically investigate how obligate methanotrophs respond to complex gas mixtures involving ethane,” said Professor Jaewook Myung. “Our findings show that even non-growth substrates can meaningfully influence microbial metabolism and biopolymer synthesis, opening new possibilities for methane-based biotechnologies and bioplastic production.”   The study was supported by the National Research Foundation of Korea, the Ministry of Land, Infrastructure and Transport, and the Ministry of Oceans and Fisheries. The results were published in Applied and Environmental Microbiology, a journal of the American Society for Microbiology.

Prof. Seungbum Koo’s Team Receives Clinical Biomec..

  < (From Left) Ph.D candidate Jeongseok Oh from KAIST, Dr. Seungwoo Yoon from KAIST, Prof.Joon-Ho Wang from Samsung Medical Center, Prof.Seungbum Koo from KAIST >   Professor Seungbum Koo’s research team received the Clinical Biomechanics Award at the 30th International Society of Biomechanics (ISB) Conference, held in July 2025 in Stockholm, Sweden. The Plenary Lecture was delivered by first author and Ph.D. candidate Jeongseok Oh. This research was conducted in collaboration with Professor Joon-Ho Wang’s team at Samsung Medical Center.   Residual Translational and Rotational Kinematics After Combined ACL and Anterolateral Ligament Reconstruction During Walking Jeongseok Oh, Seungwoo Yoon, Joon-Ho Wang, Seungbum Koo   The study analyzed gait-related knee joint motion using high-speed biplane X-ray imaging and three-dimensional kinematic reconstruction in 10 healthy individuals and 10 patients who underwent ACL reconstruction with ALL augmentation. The patient group showed excessive anterior translation and internal rotation, suggesting incomplete restoration of normal joint kinematics post-surgery. These findings provide mechanistic insight into the early onset of knee osteoarthritis often reported in this population.'       The ISB conference, held biennially for over 60 years, is the largest international biomechanics meeting. This year, it hosted 1,600 researchers from 46 countries and featured over 1,400 presentations. The Clinical Biomechanics Award is given to one outstanding study selected from five top-rated abstracts invited for full manuscript review. The winning paper is published in Clinical Biomechanics, and the award includes a monetary prize and a Plenary Lecture opportunity.   From 2019 to 2023, Koo and Wang’s teams developed a system with support from the Samsung Future Technology Development Program to track knee motion in real time during treadmill walking, using high-speed biplane X-rays and custom three-dimensional reconstruction software. This system, along with proprietary software that precisely reconstructs the three-dimensional motion of joints, was approved for clinical trials by the Ministry of Food and Drug Safety and installed at Samsung Medical Center. It is being used to quantitatively analyze abnormal joint motion patterns in patients with knee ligament injuries and those who have undergone knee surgery.   Additionally, Jeongseok Oh was named one of five finalists for the David Winter Young Investigator Award, presenting his work during the award session. This award recognizes promising young researchers in biomechanics worldwide.

KAIST Develops ‘Real-Time Programmable Robotic She..

  <(From left) Prof. Inkyu Park from KAIST, Prof. Yongrok Jeong from Kyungpook National University, Dr. Hyunkyu Park from KAIST and Prof.Jung Kim from KAIST>   Folding structures are widely used in robot design as an intuitive and efficient shape-morphing mechanism, with applications explored in space and aerospace robots, soft robots, and foldable grippers (hands). However, existing folding mechanisms have fixed hinges and folding directions, requiring redesign and reconstruction every time the environment or task changes. A Korean research team has now developed a “field-programmable robotic folding sheet” that can be programmed in real time according to its surroundings, significantly enhancing robots’ shape-morphing capabilities and opening new possibilities in robotics.   KAIST (President Kwang Hyung Lee) announced on the 6th that Professors Jung Kim and Inkyu Park of the Department of Mechanical Engineering have developed the foundational technology for a “field-programmable robotic folding sheet” that enables real-time shape programming.   This technology is a successful application of the “field-programmability” concept to foldable structures. It proposes an integrated material technology and programming methodology that can instantly reflect user commands—such as “where to fold, in which direction, and by how much”—onto the material's shape in real time.   The robotic sheet consists of a thin and flexible polymer substrate embedded with a micro metal resistor network. These metal resistors simultaneously serve as heaters and temperature sensors, allowing the system to sense and control its folding state without any external devices.     Furthermore, using software that combines genetic algorithms and deep neural networks, the user can input desired folding locations, directions, and intensities. The sheet then autonomously repeats heating and cooling cycles to create the precise desired shape.   In particular, closed-loop control of the temperature distribution enhances real-time folding precision and compensates for environmental changes. It also improves the traditionally slow response time of heat-based folding technologies.   The ability to program shapes in real time enables a wide variety of robotic functions to be implemented on the fly, without the need for complex hardware redesign.   In fact, the research team demonstrated an adaptive robotic hand (gripper) that can change its grasping strategy to suit various object shapes using a single material. They also placed the same robotic sheet on the ground to allow it to walk or crawl, showcasing bioinspired locomotion strategies. This presents potential for expanding into environmentally adaptive autonomous robots that can alter their form in response to surroundings.   Professor Jung Kim stated, “This study brings us a step closer to realizing ‘morphological intelligence,’ a concept where shape itself embodies intelligence and enables smart motion. In the future, we plan to evolve this into a next-generation physical AI platform with applications in disaster-response robots, customized medical assistive devices, and space exploration tools—by improving materials and structures for greater load support and faster cooling, and expanding to electrode-free, fully integrated designs of various forms and sizes.”   This research, co-led by Dr. Hyunkyu Park (currently at Samsung Advanced Institute of Technology, Samsung Electronics) and Professor Yongrok Jeong (currently at Kyungpook National University), was published in the August 2025 online edition of the international journal Nature Communications. ※ Paper title: Field-programmable robotic folding sheet ※ DOI: 10.1038/s41467-025-61838-3   This research was supported by the National Research Foundation of Korea (Ministry of Science and ICT). (RS-2021-NR059641, 2021R1A2C3008742)   Video file: https://drive.google.com/file/d/18R0oW7SJVYH-gd1Er_S-9Myar8dm8Fzp/view?usp=sharing

KAIST Develops AI ‘MARIOH’ to Uncover and Reconstr..

  <(From Left) Professor Kijung Shin, Ph.D candidate Kyuhan Lee, and Ph.D candidate Geon Lee>   Just like when multiple people gather simultaneously in a meeting room, higher-order interactions—where many entities interact at once—occur across various fields and reflect the complexity of real-world relationships. However, due to technical limitations, in many fields, only low-order pairwise interactions between entities can be observed and collected, which results in the loss of full context and restricts practical use. KAIST researchers have developed the AI model “MARIOH,” which can accurately reconstruct＊ higher-order interactions from such low-order information, opening up innovative analytical possibilities in fields like social network analysis, neuroscience, and life sciences. ＊Reconstruction: Estimating/reconstructing the original structure that has disappeared or was not observed.   KAIST (President Kwang Hyung Lee) announced on the 5th that Professor Kijung Shin’s research team at the Kim Jaechul Graduate School of AI has developed an AI technology called “MARIOH” (Multiplicity-Aware Hypergraph Reconstruction), which can reconstruct higher-order interaction structures with high accuracy using only low-order interaction data.   Reconstructing higher-order interactions is challenging because a vast number of higher-order interactions can arise from the same low-order structure.   The key idea behind MARIOH, developed by the research team, is to utilize multiplicity information of low-order interactions to drastically reduce the number of candidate higher-order interactions that could stem from a given structure.   In addition, by employing efficient search techniques, MARIOH quickly identifies promising interaction candidates and uses multiplicity-based deep learning to accurately predict the likelihood that each candidate represents an actual higher-order interaction.       <Figure 1. An example of recovering high-dimensional relationships (right) from low-dimensional paper co-authorship relationships (left) with 100％ accuracy, using MARIOH technology.>   Through experiments on ten diverse real-world datasets, the research team showed that MARIOH reconstructed higher-order interactions with up to 74％ greater accuracy compared to existing methods.   For instance, in a dataset on co-authorship relations (source: DBLP), MARIOH achieved a reconstruction accuracy of over 98％, significantly outperforming existing methods, which reached only about 86％. Furthermore, leveraging the reconstructed higher-order structures led to improved performance in downstream tasks, including prediction and classification.   According to Kijung, “MARIOH moves beyond existing approaches that rely solely on simplified connection information, enabling precise analysis of the complex interconnections found in the real world.” Furthermore, “it has broad potential applications in fields such as social network analysis for group chats or collaborative networks, life sciences for studying protein complexes or gene interactions, and neuroscience for tracking simultaneous activity across multiple brain regions.”   The research was conducted by Kyuhan Lee (Integrated M.S.–Ph.D. program at the Kim Jaechul Graduate School of AI at KAIST; currently a software engineer at GraphAI), Geon Lee (Integrated M.S.–Ph.D. program at KAIST), and Professor Kijung Shin. It was presented at the 41st IEEE International Conference on Data Engineering (IEEE ICDE), held in Hong Kong this past May. ※ Paper title: MARIOH: Multiplicity-Aware Hypergraph Reconstruction ※ DOI: https://doi.ieeecomputersociety.org/10.1109/ICDE65448.2025.00233       <Figure 2. An example of the process of recovering high-dimensional relationships using MARIOH technology>   This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) through the project “EntireDB2AI: Foundational technologies and software for deep representation learning and prediction using complete relational databases,” as well as by the National Research Foundation of Korea through the project “Graph Foundation Model: Graph-based machine learning applicable across various modalities and domains.”

Material Innovation Realized with Robotic Arms and..

  <(From Left) M.S candidate Dongwoo Kim from KAIST, Ph.D candidate Hyun-Gi Lee from KAIST, Intern Yeham Kang from KAIST, M.S candidate Seongjae Bae from KAIST, Professor Dong-Hwa Seo from KAIST, (From top right, from left) Senior Researcher Inchul Park from POSCO Holdings, Senior Researcher Jung Woo Park, senior researcher from POSCO Holdings>   A joint research team from industry and academia in Korea has successfully developed an autonomous lab that uses AI and automation to create new cathode materials for secondary batteries. This system operates without human intervention, drastically reducing researcher labor and cutting the material discovery period by 93％.   ＊ Autonomous Lab: A platform that autonomously designs, conducts, and analyzes experiments to find the optimal material.   KAIST (President Kwang Hyung Lee) announced on the 3rd of August that the research team led by Professor Dong-Hwa Seo of the Department of Materials Science and Engineering, in collaboration with the team of LIB Materials Research Center in Energy Materials R&D Laboratories at POSCO Holdings' POSCO N.EX.T Hub (Director Ki Soo Kim), built the lab to explore cathode materials using AI and automation technology.   Developing secondary battery cathode materials is a labor-intensive and time-consuming process for skilled researchers. It involves extensive exploration of various compositions and experimental variables through weighing, transporting, mixing, sintering＊, and analyzing samples.   ＊ Sintering: A process in which powder particles are heated to form a single solid mass through thermal activation.   The research team's autonomous lab combines an automated system with an AI model. The system handles all experimental steps—weighing, mixing, pelletizing, sintering, and analysis—without human interference. The AI model then interprets the data, learns from it, and selects the best candidates for the next experiment.       <Figure 1. Outline of the Anode Material Autonomous Exploration Laboratory>   To increase efficiency, the team designed the automation system with separate modules for each process, which are managed by a central robotic arm. This modular approach reduces the system's reliance on the robotic arm.   The team also significantly improved the synthesis speed by using a new high-speed sintering method, which is 50 times faster than the conventional low-speed method. This allows the autonomous lab to acquire 12 times more material data compared to traditional, researcher-led experiments.       <Figure 2. Synthesis of Cathode Material Using a High-Speed Sintering Device>   The vast amount of data collected is automatically interpreted by the AI model to extract information such as synthesized phases and impurity ratios. This data is systematically stored to create a high-quality database, which then serves as training data for an optimization AI model. This creates a closed-loop experimental system that recommends the next cathode composition and synthesis conditions for the automated system.   ＊ Closed-loop experimental system: A system that independently performs all experimental processes without researcher intervention.   Operating this intelligent automation system 24 hours a day can secure more than 12 times the experimental data and shorten material discovery time by 93％. For a project requiring 500 experiments, the system can complete the work in about 6 days, whereas a traditional researcher-led approach would take 84 days.   During development, POSCO Holdings team managed the overall project planning, reviewed the platform design, and co-developed the partial module design and AI-based experimental model. The KAIST team, led by Professor Dong-hwa Seo, was responsible for the actual system implementation and operation, including platform design, module fabrication, algorithm creation, and system verification and improvement.   Professor Dong-Hwa Seo of KAIST stated that this system is a solution to the decrease in research personnel due to the low birth rate in Korea. He expects it will enhance global competitiveness by accelerating secondary battery material development through the acquisition of high-quality data.       <Figure 3. Exterior View (Side) of the Cathode Material Autonomous Exploration Laboratory>   POSCO N.EX.T Hub plans to apply an upgraded version of this autonomous lab to its own research facilities after 2026 to dramatically speed up next-generation secondary battery material development. They are planning further developments to enhance the system's stability and scalability, and hope this industry-academia collaboration will serve as a model for using innovative technology in real-world R&D.       <Figure 4. Exterior View (Front) of the Cathode Material Autonomous Exploration Laboratory>   The research was spearheaded by Ph.D. student Hyun-Gi Lee, along with master's students Seongjae Bae and Dongwoo Kim from Professor Dong-Hwa Seo’s lab at KAIST. Senior researchers Jung Woo Park and Inchul Park from LIB Materials Research Center of POSCO N.EX.T Hub's Energy Materials R&D Laboratories (Director Jeongjin Hong) also participated.

Is 24-hour health monitoring possible with ambient..

  <(From left) Ph.D candidate Youngmin Sim, Ph.D candidate Do Yun Park, Dr. Chanho Park, Professor Kyeongha Kwon>     Miniaturization and weight reduction of medical wearable devices for continuous health monitoring such as heart rate, blood oxygen saturation, and sweat component analysis remain major challenges. In particular, optical sensors consume a significant amount of power for LED operation and wireless transmission, requiring heavy and bulky batteries. To overcome these limitations, KAIST researchers have developed a next-generation wearable platform that enables 24-hour continuous measurement by using ambient light as an energy source and optimizing power management according to the power environment.   KAIST (President Kwang Hyung Lee) announced on the 30th that Professor Kyeongha Kwon's team from the School of Electrical Engineering, in collaboration with Dr. Chanho Park’s team at Northwestern University in the U.S., has developed an adaptive wireless wearable platform that reduces battery load by utilizing ambient light.   To address the battery issue of medical wearable devices, Professor Kyeongha Kwon’s research team developed an innovative platform that utilizes ambient natural light as an energy source. This platform integrates three complementary light energy technologies.       <Figure1.The wireless wearable platform minimizes the energy required for light sources through i) Photometric system that directly utilizes ambient light passing through windows for measurements, ii) Photovoltaic system that receives power from high-efficiency photovoltaic cells and wireless power receiver coils, and iii) Photoluminescent system that stores light using photoluminescent materials and emits light in dark conditions to support the two aforementioned systems. In-sensor computing minimizes power consumption by wirelessly transmitting only essential data. The adaptive power management system efficiently manages power by automatically selecting the optimal mode among 11 different power modes through a power selector based on the power supply level from the photovoltaic system and battery charge status.>   The first core technology, the Photometric Method, is a technique that adaptively adjusts LED brightness depending on the intensity of the ambient light source. By combining ambient natural light with LED light to maintain a constant total illumination level, it automatically dims the LED when natural light is strong and brightens it when natural light is weak.   Whereas conventional sensors had to keep the LED on at a fixed brightness regardless of the environment, this technology optimizes LED power in real time according to the surrounding environment. Experimental results showed that it reduced power consumption by as much as 86.22％ under sufficient lighting conditions.   The second is the Photovoltaic Method using high-efficiency multijunction solar cells. This goes beyond simple solar power generation to convert light in both indoor and outdoor environments into electricity. In particular, the adaptive power management system automatically switches among 11 different power configurations based on ambient conditions and battery status to achieve optimal energy efficiency.   The third innovative technology is the Photoluminescent Method. By mixing strontium aluminate microparticles＊ into the sensor’s silicone encapsulation structure, light from the surroundings is absorbed and stored during the day and slowly released in the dark. As a result, after being exposed to 500W/m² of sunlight for 10 minutes, continuous measurement is possible for 2.5 minutes even in complete darkness. ＊Strontium aluminate microparticles: A photoluminescent material used in glow-in-the-dark paint or safety signs, which absorbs light and emits it in the dark for an extended time.   These three technologies work complementarily—during bright conditions, the first and second methods are active, and in dark conditions, the third method provides additional support—enabling 24-hour continuous operation.   The research team applied this platform to various medical sensors to verify its practicality. The photoplethysmography sensor monitors heart rate and blood oxygen saturation in real time, allowing early detection of cardiovascular diseases. The blue light dosimeter accurately measures blue light, which causes skin aging and damage, and provides personalized skin protection guidance. The sweat analysis sensor uses microfluidic technology to simultaneously analyze salt, glucose, and pH in sweat, enabling real-time detection of dehydration and electrolyte imbalances.   Additionally, introducing in-sensor data computing significantly reduced wireless communication power consumption. Previously, all raw data had to be transmitted externally, but now only the necessary results are calculated and transmitted within the sensor, reducing data transmission requirements from 400B/s to 4B/s—a 100-fold decrease.   To validate performance, the research tested the device on healthy adult subjects in four different environments: bright indoor lighting, dim lighting, infrared lighting, and complete darkness. The results showed measurement accuracy equivalent to that of commercial medical devices in all conditions A mouse model experiment confirmed accurate blood oxygen saturation measurement in hypoxic conditions.       <Frigure2.The multimodal device applying the energy harvesting and power management platform consists of i) photoplethysmography (PPG) sensor, ii) blue light dosimeter, iii) photoluminescent microfluidic channel for sweat analysis and biomarker sensors (chloride ion, glucose, and pH), and iv) temperature sensor. This device was implemented with flexible printed circuit board (fPCB) to enable attachment to the skin. A silicon substrate with a window that allows ambient light and measurement light to pass through, along with photoluminescent encapsulation layer, encapsulates the PPG, blue light dosimeter, and temperature sensors, while the photoluminescent microfluidic channel is attached below the photoluminescent encapsulation layer to collect sweat>   Professor Kyeongha Kwon of KAIST, who led the research, stated, “This technology will enable 24-hour continuous health monitoring, shifting the medical paradigm from treatment-centered to prevention-centered shifting the medical paradigm from treatment-centered to prevention-centered,” further stating that “cost savings through early diagnosis as well as strengthened technological competitiveness in the next-generation wearable healthcare market are anticipated.”   This research was published on July 1 in the international journal Nature Communications, with Do Yun Park, a doctoral student in the AI Semiconductor Graduate Program, as co–first author. ※ Paper title: Adaptive Electronics for Photovoltaic, Photoluminescent and Photometric Methods in Power Harvesting for Wireless and Wearable Sensors ※ DOI: https://doi.org/10.1038/s41467-025-60911-1 ※ URL: https://www.nature.com/articles/s41467-025-60911-1   This research was supported by the National Research Foundation of Korea (Outstanding Young Researcher Program and Regional Innovation Leading Research Center Project), the Ministry of Science and ICT and Institute of Information & Communications Technology Planning & Evaluation (IITP) AI Semiconductor Graduate Program, and the BK FOUR Program (Connected AI Education & Research Program for Industry and Society Innovation, KAIST EE).