Kaist

KAIST Develops Real-Time Diagnostic Smart Dressing..

<(From Left) Professor Inkyu Park, Dr. Seokjoo Cho, (Upper Right, From Left) Professor Ji-Hwan Ha, Researcher Junho Jeong , Professor Wei Gao> “Diabetic ulcers,” which occur in patients with diabetes, are dangerous complications that can lead to amputation if the treatment window is missed. A joint research team has developed a “smart dressing patch” that can monitor wound conditions in real time. KAIST (President Kwang Hyung Lee) announced on the 14th of May that a research team led by Distinguished Professor Inkyu Park of the Department of Mechanical Engineering, through joint research with Professor Ji-Hwan Ha of Hanbat National University (President Yongjun Oh), researcher Junho Jeong of the Korea Institute of Machinery & Materials (President Seog-Hyeon Ryu), and Professor Wei Gao of the California Institute of Technology (Caltech; President Thomas F. Rosenbaum) in the United States, has developed a “wireless, battery-free optoelectronic multi-modal sensor patch” for diabetic ulcer management. The patch developed by the research team combines an optoelectronic sensor, which can simultaneously measure multiple types of biological information, with a functional dressing. It can analyze glucose concentration, acidity (pH, an indicator of hydrogen ion concentration), and temperature changes at the wound site in real time, and patients can check their condition themselves using a smartphone. The research team fabricated a functional nanofiber dressing using electrospinning, a method that uses an electric field to create fibers much thinner than a human hair. This dressing changes color in response to increased glucose and changes in acidity that appear in diabetic foot wounds. In other words, if the wound condition worsens, the dressing color changes, allowing danger signals to be easily checked with the naked eye. Through this, abnormal signs that could lead to tissue necrosis can be detected and tracked over long periods in a non-invasive manner, meaning without cutting the skin or drawing blood. The research team combined this with an optoelectronic system to improve diagnostic accuracy. A light-emitting diode (LED, a semiconductor device that converts electricity into light) embedded in the patch and a photodiode, a semiconductor sensor that detects light, measure the color change of the dressing as light reflectance and then convert it into an electrical signal. This provides more accurate and stable data than ordinary camera-based imaging because it is less affected by changes in surrounding lighting. In particular, the patch operates without a separate battery by applying a flexible circuit based on near field communication (NFC), a wireless communication technology that exchanges data over short distances. When a smartphone is placed near the sensor, the patch receives power wirelessly and operates, transmitting the measured data in real time. In other words, patients and medical staff can immediately check and respond to wound conditions using only a smartphone app, without separate complex equipment. < Conceptual Diagram of a Multimodal Colorimetric Dressing and Optoelectronic Sensor for Diagnosing Diabetic Foot and Diabetic Diseases > The technology developed in this study has high clinical value because it provides both intuitive visual signals and quantitative electronic data while imposing no physical burden on patients. It is also expected to contribute to improving the quality of life of patients with diabetes by enabling continuous wound management without repeated blood sampling. Distinguished Professor Inkyu Park stated, “Research that began to reduce the pain of diabetic patients who have to prick their fingers with a needle every day has led to a technology for the preemptive diagnosis of complications,” adding, “This technology will become a core platform technology that can be expanded in the future to blood-free diagnostic technologies not only for diabetes but also for various chronic diseases.” In this study, KAIST Dr. Seokjoo Cho and Professor Ji-Hwan Ha of Hanbat National University participated as co-first authors. The research results were published on March 26, 2026, in the international materials science journal Advanced Functional Materials. The paper was also selected as a Front Cover article of the journal. ※ Paper title: “Wireless, Battery-Free, Optoelectronic, Multi-Modal Sensor Integrated With Colorimetric Dressing for Diabetic Ulcer Management,” DOI: 10.1002/adfm.202532167 < Front Cover Image > This research was supported by the Mid-Career Researcher Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT, the Alchemist Project of the Ministry of Trade, Industry and Energy, and the Daejeon RISE Center.

“Even Recognizing Puddles at Night”... KAIST Surpa..

< (From left) Ph.D. student Hanbin Cho, Postdoctoral Researcher Wenxuan Zhu, Professor Joonki Suh, and MS-PhD integrated student Changhwan Kim > A technology that surpasses the limitations of existing sensors, which failed to distinguish between water and asphalt on dark roads, has emerged to enhance the accuracy of autonomous driving and medical diagnostics. Our university's research team has developed a next-generation polarization sensor that can read the "direction" of light and change its own response. KAIST announced on May 12th that a research team led by Professor Joonki Suh from the Department of Chemical and Biomolecular Engineering has developed a "self-reconfigurable" polarization sensor array technology that regulates its operation by finding the optimal state using "polarization" information—the property of light vibrating in a specific direction. With the recent explosive increase in data and the rapid development of artificial intelligence technology, the need for next-generation vision systems that can efficiently process vast amounts of information with low energy is growing. However, existing image sensors only detect the intensity (brightness) of light, limiting their ability to precisely grasp the orientation or surface structure of objects. To overcome these limitations, the research team developed a polarization-based sensor technology capable of recognizing the vibration direction of light. In particular, by utilizing a "heterostructure" that combines two different materials—tellurium (Te) and rhenium disulfide (ReS₂)—they effectively implemented characteristics where the response to light varies depending on the crystal orientation. < Conceptual diagram of self-reconfigurable polarization sensor and in-sensor computing based on dual-anisotropy vdW heterostructures > To precisely stack the two materials so they cross each other, the research team applied "Epitaxial Atomic Layer Deposition," a process that controls crystal structures by stacking materials precisely at the atomic layer level. By ensuring the crystal structures of the two materials interlock accurately, they secured higher reproducibility and stable performance compared to previous methods. In this structure, when light is irradiated, interfacial carrier transfer and trapping (a phenomenon where electrons move or stay at specific locations) occur at the material boundary. As a result, a "bipolar photoresponse"—a light-induced reaction where the current direction flips depending on conditions such as light intensity, wavelength, and direction—appears. A key feature is that the sensor's operating state can be freely adjusted using only light, without external electrical signals. Furthermore, this technology can be applied to "in-sensor computing" structures where the sensor itself processes data, allowing for the efficient processing of multi-dimensional optical information that changes over time without complex calculation processes. In actual experiments, it recorded a high accuracy of over 95％ in recognizing moving objects, proving its potential for applications in various fields such as autonomous driving and medical diagnosis. < Experimental image of a polarization AI sensor platform capable of light-based operational reconfiguration (AI-generated image) > Professor Joonki Suh stated, "This research presents a new foundation for AI vision technology that can secure richer visual information by utilizing polarization information. It is expected to play an important role in implementing low-power, high-efficiency AI systems in the future." Wenxuan Zhu (Postdoctoral Researcher) and Changhwan Kim (Ph.D. student) participated as first authors in this study, with Professor Joonki Suh participating as the corresponding author. The research results were published on April 14 in the international academic journal Nature Sensors. Paper Title: Self-reconfigurable polarization perception in dual-anisotropy heterostructures for high-dimensional in-sensor computing Authors: Wenxuan Zhu, Changhwan Kim, Ruofan Zhang, Mingchun Lu, Namwook Hur, Hanbin Cho, Jihyun Kim, Jiacheng Sun, Joohoon Kang, Junchi Yan, Yuan Cheng & Joonki Suh DOI: https://doi.org/10.1038/s44460-026-00057-9 < Paper portfolio and QR code > Meanwhile, this research was conducted with the support of the PIM AI Semiconductor Core Technology Development (Device) Project and the Individual Basic Research Project of the National Research Foundation of Korea, funded by the Ministry of Science and ICT, and the Industrial Innovation Talent Growth Support Project of the Korea Institute for Advancement of Technology (KIAT).

KAIST demonstrates ultralow-noise microwave and mi..

<(From Left) Prof. Jungwon Kim, Dr. Changmin Ahn> Researchers at KAIST have demonstrated a chip-scale photonic approach for generating ultralow-noise and highly stable microwave and millimeter-wave signals based on optical frequency combs (microcombs), offering a potential pathway toward compact, high-performance frequency sources for next-generation technologies. High-frequency signals in the tens to hundreds of gigahertz range are essential for emerging applications such as 6G communications, radar, and precision sensing. However, achieving both low noise and high stability at these frequencies remains a fundamental challenge for conventional electronic signal sources. In the first study, the researchers addressed the long-standing challenge of transferring the stability of an optical reference to a microcomb. Direct stabilization is difficult due to the lack of carrier-envelope offset detection in high-repetition-rate microcombs. To overcome this, they used a mode-locked laser as a transfer oscillator and synchronized it to the microcomb using electro-optic sampling. This approach enabled direct and robust transfer of optical-reference stability to the microcomb repetition rate, achieving fractional frequency stability at the 10-18 level and a phase noise of -125 dBc/Hz at 100 Hz offset from a 22 GHz carrier, representing state-of-the-art performance and more than 80 dB improvement over the free-running microcomb in the low-offset-frequency regime. In the second study, the team addressed the degradation of noise performance typically observed when scaling microcombs to higher repetition rates. While microcombs with lower repetition rates (large resonators) exhibit better noise characteristics, increasing the repetition rate generally leads to performance degradation. The researchers showed that this limitation can be overcome using perfect soliton crystal (PSC) states, which enable repetition-rate multiplication while preserving the low-noise characteristics of the original comb. As a result, they generated millimeter-wave signals at 44 GHz and 66 GHz with timing jitter on the order of 3 femtoseconds, demonstrating that the low-noise performance of a microwave-rate microcomb can be preserved during scaling to millimeter-wave frequencies. <Ultra-compact optical resonator chip with noise suppression based on an optical reference signal and increased frequency via fully solitonic waves (AI-generated image)> Together, these results establish two key capabilities: (1) high-fidelity transfer of optical-reference stability to chip-scale microcombs, and (2) preservation of low-noise performance during frequency scaling to millimeter-wave regimes. This combined capability provides a practical route toward compact photonic signal sources that integrate optical-level stability with high-frequency operation. The research was led by Dr. Changmin Ahn and Prof. Jungwon Kim at KAIST, in collaboration with Prof. Hansuek Lee. The results were published in Laser & Photonics Reviews and Optica. · Optical-to-microcomb stability transfer for ultrastable timing and microwave/millimeter-wave generation (DOI: 10.1002/lpor.71135) · Preserving ultralow timing jitter in microcombs with repetition-rate multiplication via perfect soliton crystal formation (DOI: 10.1364/OPTICA.581054)

First Observation of the Moment Lithium Batteries ..

< (From left) Professor Seungbum Hong, Ph.D candidate Seonghyun Kim, Dr. Youngwoo Choi, and Dr. Yoonhan Cho > A crucial clue to simultaneously increasing electric vehicle (EV) driving range and battery lifespan has been discovered. A research team at our university has observed the exact moment of degradation in lithium metal batteries at the nanoscale (approximately 1/100,000th the thickness of a human hair) and identified the fundamental cause of performance decline. This is evaluated as a significant turning point in accelerating the commercialization of next-generation batteries. KAIST announced on May 10th that a research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering has identified the degradation mechanism of the lithium metal anode, a core component of next-generation batteries. Lithium metal is dubbed a "dream battery material" due to its significantly higher energy density compared to conventional batteries. However, the rapid decline in performance after repeated charge and discharge cycles has been the biggest obstacle to commercialization. In particular, when lithium is deposited or stripped irregularly, it can form "dead lithium"—lithium that is electrically disconnected—which leads to performance degradation and poses safety risks. The research team utilized in situ electrochemical atomic force microscopy (EC-AFM), which allows for real-time observation of the battery interior, to track the entire process of lithium deposition (plating) and removal (stripping). As a result, they confirmed that the lithium reaction does not occur uniformly across the entire surface but occurs selectively at specific locations. <Overview of the EC-AFM Measurement Process> Specifically, in porous regions with rough surfaces, voids were easily formed when lithium was stripped away, leading to the creation of "dead lithium" that becomes electrically isolated. This phenomenon acts as a direct cause of the sudden decline in battery performance. The significance of this study lies in experimentally identifying where and how lithium metal batteries are damaged. Furthermore, it proved that the "initial morphology," where lithium is first formed, is a key variable that determines the long-term lifespan of the battery. <Height Maps and Surface Slope Maps During the 1st–3rd Plating/Stripping Processes> Accordingly, it is expected that if the surface where lithium forms is controlled uniformly and precisely in the future, battery life and stability can be dramatically improved. This suggests a design direction that can simultaneously achieve increased EV driving range and the development of long-life batteries. Professor Seungbum Hong stated, "This research is highly significant as it directly confirmed the cause of battery performance degradation at the nanoscale. It will serve as an important foundation for developing safer and longer-lasting next-generation batteries." Seonghyun Kim, a PhD student in the Department of Materials Science and Engineering, participated as the lead author. The study was published on February 24, 2026, in ACS Energy Letters, a prestigious international academic journal in the fields of materials science, chemistry, and chemical engineering, and was selected as a cover article. ※ Paper Title: Spatially Selective Lithium Plating and Stripping in Lithium Metal Anodes, DOI: https://doi.org/10.1021/acsenergylett.6c00122 < Photo of Selection as ACS Energy Letters Cover Paper > Meanwhile, this research was conducted with support from LG Energy Solution and the Future Pioneering Convergence Science and Technology Development Program (RS-2023-00247245) of the National Research Foundation of Korea, funded by the Ministry of Science and ICT.

KAIST Unveils Complexity Paradox： Nanoparticles Gr..

<(From Left) Ph.D candidate Jeesoo Yoon, Dr. Jinwon Oh, Professor Hee-Tae Jung, Professor Matteo Cargnello> A KAIST and Stanford University joint research team revealed research results that overturn long-standing beliefs in the field of nanomaterials. Contrary to the conventional perception that mixing more metals complicates the system, this study revealed for the first time that complex compositions actually create more uniform nanoparticles, signaling a new turning point for next-generation energy and catalysis technology. KAIST (President Kwang Hyung Lee) announced that a joint research team led by distinguished professor Hee-Tae Jung from the Department of Chemical and Biomolecular Engineering and Professor Matteo Cargnello from Stanford University has identified a paradoxical phenomenon where mixing more metals leads to the formation of more uniform nanoparticles. Nanoparticles are core materials in various industries such as semiconductors, eco-friendly energy, and biotechnology. However, as the number of constituent elements increases, the different reaction rates of each element cause variations in particle size and shape, which has been considered a major challenge for precision control. The research team focused on composition-focusing, a phenomenon where the particle components converge in one direction and become more uniform as the number of metal elements increases. <Figure 1. (Top) Increasing number of possible product metal combinations (Bottom) Composition-focusing behavior>

KAIST Solves Computer Problems That Would Take Tho..

<(From Left) Professor Yang-Kyu Choi, Ph.D. candidate Seong-Yun Yun, (Upper Right) Professor Sanghyeon Kim, Dr. Joon Pyo Kim> In the era of big data and artificial intelligence, a new approach has emerged for solving combinatorial optimization problems, which involve finding the most efficient solution among many possible options and can otherwise take thousands of years to compute. A KAIST research team has developed computational hardware that can be implemented entirely using existing silicon processes, enabling deployment on existing fabrication lines without additional facilities. This is expected to enable faster and more accurate decision-making across various industries, including logistics, finance, and semiconductor design. KAIST (President Kwang-Hyung Lee) announced on the 6th of May that a joint research team led by Professor Yang-Kyu Choi and Professor Sanghyeon Kim from the School of Electrical Engineering has implemented an oscillatory Ising machine (a specialized-purpose computer in which multiple oscillating elements interact to find optimal solutions)—a next-generation specialized optimization hardware—using only conventional silicon semiconductor processes. The research team focused on oscillators that repeat electrical signals periodically. As multiple oscillators exchange signals and synchronize their rhythms, the system naturally reaches the most stable state, and in this process, it finds the optimal solution. Conventional oscillatory Ising machines have limitations in solving complex problems because it is difficult to precisely control slight frequency differences among oscillators, and the connectivity between elements is limited. <An aging machine using a silicon oscillator and coupler> To overcome this, the research team introduced a new approach in which both the oscillators and the couplers are implemented using single silicon transistors, which are the fundamental switching elements of semiconductors. Through this approach, they reduced frequency deviations among oscillators, enabling stable synchronization, and by using couplers, they implemented multi-level coupling, allowing more precise reflection of problem weights. As a result, both the ability to represent complex optimization problems and the performance of solution search were significantly improved. Using this technology, the research team successfully solved the representative combinatorial optimization problem known as the Max-Cut problem, which involves dividing a network into two groups to maximize connections. This problem can be directly applied to various industrial fields such as logistics route optimization, financial portfolio construction, and semiconductor circuit placement. A key advantage of this approach is that it uses the CMOS＊ process currently employed in the semiconductor industry without requiring special materials or non-standard processes. Therefore, the technology suitable for mass production and commercialization on existing semiconductor production lines without additional facility investment. ＊CMOS (Complementary Metal-Oxide-Semiconductor): the most standard process technology in modern semiconductor manufacturing, characterized by very low power consumption and low heat generation, and used to produce chips that serve as the “brains” of almost all digital devices, including smartphones and computer CPUs <(AI-generated image) Concept diagram of an AI-based silicon aging machine> Professor Yang-Kyu Choi stated, “This research presents an oscillatory Ising machine hardware that secures both scalability and precision by implementing both oscillators and couplers with silicon devices,” adding, “It is expected to be applied to various industrial fields requiring large-scale combinatorial optimization, such as semiconductor design automation, communication network optimization, and resource allocation.” He further noted that, as transistor miniaturization approaches its physical limits and increasingly requires atomic-level control, our group has spent the past decade exploring whether the future of transistors should extend beyond scaling toward the discovery of new functions. Futurist Alvin Toffler famously divided the development of society into three stages, describing the modern transition into a knowledge-based society as the “Third Wave.” In a similar way, the history of transistor technology, which now spans more than 80 years, may also be viewed in three waves. In 1935, Oskar Heil proposed the concept of controlling semiconductor current with an electric field in a British patent. In 1946, William Shockley developed the first solid-state transistor, an achievement that later led to the Nobel Prize. In 1961, Dawon Kahng invented the modern metal–oxide–semiconductor field-effect transistor, or MOSFET, which remains the foundation of today’s mass-produced semiconductor devices. From this perspective, the first wave of transistor technology can be defined as the “switch,” and the second wave as the “amplifier.” Our laboratory proposes a newly identified third wave: the transistor as an “oscillator.” For decades, semiconductor progress has largely been driven by improving the switching and amplification performance of transistors through miniaturization. However, as device fabrication now demands atomic-scale precision, the physical limits of scaling are becoming increasingly apparent. Future transistors therefore require a fundamental paradigm shift—from further miniaturization toward the realization of new functions. The greatest technological significance of this work lies in demonstrating the oscillator as a third fundamental function of the transistor. As a proof of this concept, we experimentally realized a physical Ising machine operating at room temperature. This research was led by KAIST Ph.D. candidate Seong-Yun Yun and Dr. Joon Pyo Kim as co-first authors, and was published in Science Advances, one of the world’s most prestigious scientific journals, on March 27. ※ Paper title: “Scalable Ising machine composed entirely of Si transistors,” DOI: 10.1126/sciadv.adz2384 This research was supported by the National Research Foundation of Korea through the Next-Generation Intelligent Semiconductor Technology Development Program, the National Semiconductor Research Laboratory Core Technology Development Program, and the PIM Artificial Intelligence Semiconductor Core Technology Development Program.

KAIST Develops New Concept Hologram Technology Whe..

<(From Left)Dr. Joonkyo Jung. Professor Jonghwa Shin> A new type of hologram technology has been developed that uses the motion of light as a “key,” revealing information only under specific conditions. This is gaining attention as a novel approach that can simultaneously overcome the limitations of existing optical communication and security technologies. KAIST (President Kwang Hyung Lee) announced on the 4th of May that a research team led by Professor Jonghwa Shin from the Department of Materials Science and Engineering has developed a next-generation vectorial hologram metasurface that uses the “total angular momentum (TAM)＊” of light as a key for information selection, enabling the realization of different vectorial images depending on the state of the incident light. ＊Total Angular Momentum (TAM): a physical quantity that represents both the vibration direction (polarization) and rotational (twisting) properties of light, enabling the creation of precise vectorial images whose intensity and polarization distribution vary depending on the state of light Previously, research utilizing either the vibration direction of light, known as “polarization,” or the property of light twisting in a helical form, known as “orbital angular momentum (OAM),” had been actively pursued. However, independently controlling these two properties within a single device had long been considered an unsolved challenge in the field of optics. To address this, the research team precisely designed nanoscale structures much smaller than the thickness of a human hair and implemented a “bi-layer metasurface” by stacking them in two layers. A metasurface is an optical device based on ultra-fine artificial structures designed to freely control the direction and properties of light. This device uses the “total angular momentum (TAM),” which combines the polarization and degree of twist of light, like a complex encryption key. In other words, the device responds and reconstructs hidden information only when light with a specific vibration pattern and a specific number of twists is incident. With this technology, even if light appears identical externally, the information cannot be read without the designated “light key,” ensuring high security. <Conceptual Diagram of the Study> In addition, the twisting state of light (OAM) can theoretically take on a very wide range of values, significantly increasing the amount of information that can be carried by a single light beam. This also enables expansion into ultra-high-capacity optical communication technologies capable of transmitting far more data simultaneously than before. In particular, this study is meaningful in that it goes beyond simple intensity-only image implementation and achieves a “vectorial hologram” that precisely controls the vibration direction (polarization) of light at each point in the image. A vectorial hologram is a high-dimensional holographic technology that represents not only the intensity of light but also its vibration direction information. <Vector hologram that generates independent intensity and polarization images depending on the conditions of the incident light> This achievement is the first demonstration that two key properties of light—polarization and twist—which had been difficult to separate physically, can be independently controlled within a single device. This is expected to enable applications not only in next-generation display technologies such as immersive holograms, smart glasses, augmented reality (AR), and virtual reality (VR) devices, but also in various fields including anti-counterfeiting security labels and ultra-high-speed optical communication. Professor Jonghwa Shin stated, “This study demonstrates that polarization and twist, which are fundamental properties of light, can be combined into a single independent information key and freely utilized,” adding, “It will evolve into a key platform for security systems that are difficult to replicate and for ultra-high-speed, ultra-high-capacity optical communication technologies.” This study, with Dr. Joonkyo Jung as the first author, was published online on March 12 in the international journal Advanced Materials. ※ Paper title: “Arbitrary Total Angular Momentum Vectorial Holography Using Bi-Layer Metasurfaces,” DOI: 10.1002/adma.202519106 This research was supported by the Ministry of Science and ICT through the “Nano Materials Technology Development Program” and the “Group Research Support Program,” as well as by the Ministry of Trade.

Abandoned Fallen Leaves Transformed into ‘Biodegra..

<(From left) (Top to bottom) Professor Jaewook Myung of the Department of Civil and Environmental Engineering, Dr. Shinhyeong Choe, Ph.D candidate Yongjun Cho, M.S candidate Hoseong Moon, (Center) Ph,D candidate Pham Thanh Trung Ninh> Fallen leaves, which were discarded every year, have been transformed into a resource that can replace waste plastics, a major nuisance in rural areas. A research team at our university has developed biodegradable agricultural vinyl made from fallen leaves, presenting a new way to solve the problem of conventional plastic vinyl, which has been pointed out as a cause of soil pollution. KAIST announced on April 30th that a research team led by Professor Jaewook Myung of the Department of Civil and Environmental Engineering developed an eco-friendly agricultural mulch film (an agricultural vinyl that covers the soil to suppress weeds and maintain moisture) that decomposes in the ground using fallen leaves collected from the campus and near the Gapcheon River in Daejeon. This research is significant in that it converted fallen leaves, which are non-edible biomass (plant resources not used for food) that were discarded as useless, into high-value functional materials. Mulch films, widely used in agricultural fields, are essential materials for suppressing weed growth and maintaining soil moisture. However, most films currently used are made of polyethylene (PE, a representative petroleum-based plastic), making them difficult to collect after use. Residuals left in the soil turn into microplastics (plastic particles so small they are invisible to the naked eye), causing environmental pollution. To extract key components from fallen leaves, the research team utilized a Hydrated Deep Eutectic Solvent (DES, a special eco-friendly solvent with low toxicity) that mixes citric acid and choline chloride. Through this, they extracted nanocellulose (plant-derived nanofibers with high strength and eco-friendliness) obtainable from plant cell walls and combined it with polyvinyl alcohol (PVA, a water-soluble and naturally degradable polymer material) to produce a composite film. In particular, the eco-friendliness was further enhanced by performing all manufacturing processes based on water instead of harmful organic solvents. The "fallen leaf film" developed in this way showed sufficient performance even in actual agricultural environments. As a result of the experiment, it effectively blocked ultraviolet rays (UVA and UVB) and exhibited moisturizing performance that suppressed soil moisture loss to a level of about 5％ for 14 days. In addition, ryegrass grown using this film showed better growth status than cases where no film was used. <Figure 1. An eco-friendly strategy that upcycles low-utilization fallen leaves into biodegradable mulching film for natural soil, along with the concept of applying sustainable plasticulture.> <Figure 2. A schematic diagram of the fabrication process and self-assembly mechanism by which a mulching film is formed through complex hydrogen-bonding interactions> Biodegradation performance was also confirmed. As a result of testing under soil conditions, the developed film decomposed by 34.4％ in about 115 days, showing a faster decomposition rate than conventional biodegradable films. Furthermore, it was confirmed that plant toxicity (harmful effects on plant germination or growth) did not occur during the decomposition process, thus not affecting the germination and early growth of ryegrass and bok choy. Professor Jaewook Myung said, “This research is meaningful in that it went beyond simply processing fallen leaves and converted them into functional materials that can protect the agricultural environment. Through the use of fallen leaves that do not compete with food resources and water-based processes, it can be utilized as a sustainable alternative technology for agricultural plastics.” This research was participated in by Pham Thanh Trung Ninh, a PhD student in the Department of Civil and Environmental Engineering, as the first author. The research results were published on February 6, 2026, in ‘Green Chemistry,’ an international academic journal in the fields of chemistry and environment, and were selected as the journal’s inside front cover. ※ Paper Title: All-water-based fabrication of biodegradable mulch films from dead leaves via complex hydrogen-bonded networks, DOI: 10.1039/d5gc06616f (Author Information: Pham Thanh Trung Ninh (KAIST, First Author), Shinhyeong Choe (KAIST), Yongjun Cho (KAIST), Hoseong Moon (KAIST), Jaewook Myung (KAIST, Corresponding Author) total of 5 persons) <Figure 3. The inside front cover page of the latest issue of the Green Chemistry journal> Meanwhile, this research was conducted with the support of the Excellent Young Researcher Program of the National Research Foundation of Korea under the Ministry of Science and ICT and the KAIST Grand Challenge 30 project funds.

Professor Hyun Myung Selected for Research Grand P..

< KAIST Research Day Group Photo > KAIST held the ‘2026 KAIST Research Day’ at the Chung Kunmo Conference Hall in the Academic Cultural Complex at the main Daejeon campus on the morning of the 28th starting at 10:00 AM. ‘Research Day’ is an annual festival for campus researchers that has been held since 2016. It serves as a platform to reward and encourage excellent researchers for their hard work and to exchange R&D information by introducing selected outstanding research achievements. Notably, this year’s award scale was expanded to further encourage researchers and foster an environment conducive to research immersion. The number of Research Award recipients increased from two to four, and Special Research Award recipients from one to two. During the event, Professor Hyun Myung (School of Electrical Engineering), who was selected as the recipient of the Research Grand Prize—the highest research honor—delivered a commemorative lecture titled “Spatial AI-based Autonomous Robot Navigation.” < Professor Hyun Myung Delivering His Lecture > Professor Hyun Myung developed proprietary autonomous robot navigation technology based on spatial AI and applied it to various robot platforms. Recently, he has also been pursuing commercialization through a startup venture. Since joining KAIST in 2008, he has been dedicated to researching autonomous mobile robot technology, applying it to various platforms such as wheeled robots, walking robots, and drones. Furthermore, he has proven his technical prowess by winning numerous international competitions. “By focusing on spatial AI and autonomous navigation technology—the core fields of robotics—for the past 17 years, I have been able to contribute to the localization and independence of mobile robot technology in Korea through industry-academic cooperation and startups,” Professor Myung stated in his acceptance speech. “I am grateful and pleased to have had the opportunity to nurture such excellent research talent.” < Professor Hyun Myung Receiving His Award > In addition, Professor Jae-Hung Han (Department of Aerospace Engineering), Professor Byung-Kwan Cho (Graduate School of Engineering Biology), Professor Joseph Searing (School of Computing), and Professor Hyun-Joo Lee (Department of Chemical and Biomolecular Engineering) were selected as recipients of the Research Award. The Special Research Award was presented to Professor Sun-Chang Kim (Graduate School of Engineering Biology) and Professor Woo-Young Cho (School of Electrical Engineering), while Professor Jae Kyoung Kim (Department of Mathematical Sciences) was selected as the recipient of the Innovation Award. Furthermore, Professor Himchan Cho (Department of Materials Science and Engineering) and Professor Jung-Yong Lee (School of Electrical Engineering) received the Convergence Research Award as a team. Professor Ji-Joon Song (Department of Biological Sciences) was selected for the International Collaborative Research Award, and Professor Bongjin Kim (School of Electrical Engineering) for the QAIST Creative Challenge Research Award. The ceremony also included awards for the ‘2025 Top 10 KAIST Research Achievements’ and the ‘KAIST 14 Future Leading Technologies,’ recognizing outstanding accomplishments in national strategic technology sectors with significant academic, social, and economic impact. President Kwong Hyoung Lee remarked, “Today’s Research Day is a meaningful occasion to share challenging and innovative ideas and to celebrate the achievements of our outstanding researchers. KAIST, which aims for the world’s first and best research, will continue to contribute to the development of the nation and human society through research and leap forward as a leading global institution in science and technology.” < 2026 Research Day Poster >

KAIST Identifies Multiple Viruses and Variants Sim..

<Professor Sungmin Son, (From Upper Left) Professor Dan Fletcher, Professor Melaine Ott> As the spread of infectious diseases accelerates, technologies that can accurately distinguish multiple viruses in a single test are becoming increasingly important. KAIST and an international research team have developed a new diagnostic technology that simultaneously identifies various viruses and variants by controlling the “speed” of gene scissors. This technology is expected to transform responses to emerging infectious diseases, as it can detect multiple infections at once while reducing the complexity of testing procedures. KAIST (President Kwang Hyung Lee) announced on the 26th of April that a research team led by Professor Sungmin Son from the Department of Bio and Brain Engineering, in collaboration with researchers from the University of California, Berkeley (UC Berkeley) and the Gladstone Institutes, has developed a new ribonucleic acid (RNA) diagnostic technology that can distinguish multiple viruses and variants simultaneously by utilizing the reaction speed of gene scissors. The tool used by the research team is a CRISPR-based protein called Cas13. Gene scissors are proteins that locate and cut specific genetic material, becoming activated when they recognize their target. Cas13 specifically targets RNA. When it finds its target, it becomes activated and cuts surrounding RNA, generating a fluorescent signal. Existing technologies require the use of different gene scissors or various fluorescent colors to detect multiple viruses simultaneously, making the system complex and difficult to apply in real-world settings. The research team took a different approach. They focused on the fact that when gene scissors bind to their target, the speed of “cutting” varies depending on the type of virus. By observing at the single-molecule level within tiny droplets, they confirmed that unique reaction speed patterns emerge depending on the combination of guide RNA and target RNA. Guide RNA is an RNA molecule that provides “positional information,” guiding the gene scissors to their target. < Conceptual diagram of kinetic barcoding using the reaction rate of the CRISPR Cas13 enzyme. The dashed area on the right represents the guide RNA region modified to control the reaction rate. > Based on this, the research team developed a “kinetic barcoding” technology that uses differences in reaction speed like a barcode. This method interprets reaction speeds as signal patterns to distinguish different viruses. Through this technology, it became possible to simultaneously identify multiple viruses and variants using only a single type of gene scissors. < Multiplex virus detection using microdroplet-based kinetic barcoding > In addition, by adjusting the design of guide RNA, the cutting speed of gene scissors can be tuned, enabling scalable and simultaneous detection of a wide range of viruses. The testing process has also been greatly simplified. In conventional methods, detecting RNA viruses requires a “reverse transcription” process that converts RNA into DNA, but this technology enables direct detection of RNA as it is. Reverse transcription is a step that increases testing time and complicates procedures. When tested on actual clinical samples, the technology successfully distinguished various respiratory viruses and SARS-CoV-2 variants in a single reaction. Professor Sungmin Son stated, “This study goes beyond simply determining whether a virus is present, and is the first case to use the reaction speed of gene scissors as a new form of diagnostic information,” adding, “It will become a next-generation platform capable of diagnosing various infectious diseases at once in the field.” This study was led by Professor Sungmin Son of KAIST as the first author and co-corresponding author, and was published on March 31, 2026, in the world-renowned journal in bioengineering, Nature Biomedical Engineering. ※ Paper title: “Programmable kinetic barcoding for multiplexed RNA detection with Cas13a,” DOI: 10.1038/s41551-026-01642-6 This research was supported by KAIST’s New Faculty Settlement Research Fund and by the U.S. National Institutes of Health (NIH/NIAID).

11 KAIST Professors, Including Professor Meeyoung ..

<(Top row from left) Professors Meeyoung Cha, Won Do Heo, Byungha Shin, Kyung Min Kim, Sue Moon, and Juyoung Kim (Bottom row from left) Professors Jinwoo Shin, Young Jae Jang, Song Chong, Inkyu Park, and Taek-Soo Kim> To mark Science and ICT Day, 11 faculty members from KAIST received government awards at the "2026 Science and ICT Day Ceremony" hosted by the Ministry of Science and ICT. Professor Meeyoung Cha (School of Computing) was awarded the Order of Science and Technological Merit (Innovation Medal/Hyeoksin-jang), Professor Won Do Heo (Department of Biological Sciences) received the Order of Science and Technological Merit (Ungbi Medal), and Professor Byungha Shin (Department of Materials Science and Engineering) was honored with the Order of Science and Technological Merit (Doyak Medal). Professors Jinwoo Shin (Kim Jaechul Graduate School of AI), Young Jae Jang (Department of Industrial and Systems Engineering), and Song Chong (Kim Jaechul Graduate School of AI) were awarded the Order of Service Merit (Red Stripes/Hongjo Geunjeong Medal) for their contributions to Information and Communications. In addition, Professor Kyung Min Kim (Department of Materials Science and Engineering) and Professor Sue Moon (School of Computing) received the Science and Technology Medal. Professor Juyoung Kim (School of Electrical Engineering), serving as the CEO of HyperAccel, was awarded the Industrial Service Medal for Information and Communications Merit. Professor Inkyu Park (Department of Mechanical Engineering) received the Presidential Citation, and Professor Taek-Soo Kim (Department of Mechanical Engineering) received the Prime Minister's Citation. In the category of Science and Technology Promotion, Professor Meeyoung Cha received the Order of Science and Technological Merit, Innovation Medal (2nd Class). Professor Cha has led research on solving social issues such as poverty detection based on big data. She was recognized for her contributions to creating academic and social value as the first Korean director at the Max Planck Institute. In the National R&D Performance Evaluation category, Professor Won Do Heo, who has led world-class research in biological sciences, received the Ungbi Medal. Professor Heo pioneered the field of molecular optogenetics in Korea and has contributed to the development of treatment technologies for brain diseases such as stroke, Parkinson's disease, and depression. Professor Byungha Shin received the Doyak Medal for his achievements accumulated over 20 years in the field of solar cells and optoelectronic materials/devices, specifically for developing high-efficiency devices. Professor Jinwoo Shin received the Red Stripes Order of Service Merit for his world-class research in AI and computer science, as well as his contributions to revitalizing the domestic physical AI industry through collaboration with robotics companies. Professor Young Jae Jang was also awarded the Red Stripes Order of Service Merit for establishing a manufacturing physical AI verification system based on cooperation between regions, universities, and research institutes, and for developing "KAIROS," the world's first robot operating platform, which contributed to manufacturing innovation and balanced regional development. Professor Song Chong received the Red Stripes Order of Service Merit for his role as the founding dean of Korea’s first Graduate School of AI, contributing to the cultivation of high-level AI talent and the establishment of an academic foundation. Furthermore, Professor Kyung Min Kim received the Science and Technology Medal for developing the world’s first high-dimensional brain-inspired computing technology that utilizes both heat and electricity, securing original technology for next-generation semiconductors. Professor Sue Moon received the Science and Technology Medal for her outstanding research in computer network performance measurement, online social network analysis, and ultra-high-performance network systems, as well as her efforts in promoting gender equality. Professor Juyoung Kim, as the CEO of the startup HyperAccel, received the Industrial Service Medal for developing "LPU," an AI semiconductor specialized for LLM inference, overcoming the limitations of GPU-centric AI infrastructure and contributing to high-efficiency, low-power AI systems. Professor Inkyu Park received the Presidential Citation for developing the world's first original technologies for ultra-low-power gas sensors and multi-sensors for smart healthcare. Professor Taek-Soo Kim was honored with the Prime Minister's Citation for leading global techniques in measuring and improving the mechanical properties of advanced thin-film materials, contributing to the development of the semiconductor and display industries. The ceremony was held on the 21st at the International Conference Hall of the Korea Federation of Science and Technology Societies. A total of 164 individuals were recognized for their contributions to Science, Technology, and ICT. Among them, 148 received their awards on-site, with a total scale of 36 Orders of Merit, 22 Medals, 47 Presidential Citations, and 59 Prime Minister's Citations.

AI Computation Enables Clearer Views of the Deep B..

< Professor Iksung Kang, KAIST > Observing the depths of a living brain with clarity has traditionally required expensive, high-end equipment. However, a KAIST research team has advanced neuroscience research by developing a physics-based AI computational algorithm that restores blurred images into sharp ones without the need for additional optical measurement hardware. KAIST (President Kwang Hyung Lee) announced on April 21st that Professor Iksung Kang (School of Electrical Engineering), in collaboration with Professor Na Ji's research team at UC Berkeley, has developed a technology that accurately corrects image aberrations in microscopes used for live biological imaging. Notably, the experimental design and algorithm development – the core components of this technology – were led by Professor Kang during his postdoctoral fellowship in Professor Na Ji’s group. This breakthrough was achieved using Neural Fields — a neural network-based technology that continuously represents 3D spatial structures to simultaneously reconstruct clear images and volumetric forms. The research team utilized Two-Photon Fluorescence Microscopy, a core technology for observing deep within living biological tissues by using two low-energy photons simultaneously to selectively illuminate specific points. However, as light passes through thick tissue, it bends and scatters, causing the image to become blurred — much like how objects appear distorted underwater. This phenomenon is known as optical aberration. Previously, correcting these distortions required adding complex and costly hardware, such as wavefront sensors, which measure exactly how much the light path has deviated. < Framework for Integrated Distortion Correction in Two-Photon Fluorescence Microscopy > In contrast, the research team developed an algorithm that inversely calculates how light was distorted using only the captured image data and corrects it. In other words, it is a method of restoring image clarity by analyzing blurred photos, without relying on any additional equipment. The core of this technology is a machine learning algorithm based on the Neural Fields model. This algorithm tracks the distortion process that occurs as light travels, implementing an integrated technology that compensates not only for optical aberrations caused by biological tissue but also for microscopic movements of the living specimen and alignment errors of the microscope itself. As a result, the team successfully and reliably obtained high-resolution, high-contrast images from deep within biological tissues, without any separate aberration measurement or correction devices. This research is particularly significant because it overcomes the conventional limitation that “better images require more expensive equipment” by solving the problem through a software-based approach. This is expected to lower the burden of research equipment costs and allow more researchers to perform precise brain observations. < Comparison of images using a framework that integrates correction for optical aberrations, sample motion, and microscope errors (AI-generated image) > Professor Iksung Kang stated, “This research opens the way to see more accurately inside living organisms by combining optics and artificial intelligence technology. Moving forward, we plan to develop this into an intelligent optical imaging system where the microscope itself finds the optimal image.” This study was published on April 13th in Nature Methods, a leading methodology journal in the field of life sciences. Paper Title: Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields DOI: 10.1038/s41592-026-03053-6 Authors: Iksung Kang (KAIST, Co-corresponding & First Author), Hyeonggeon Kim, Ryan Natan, Qinrong Zhang, Stella X. Yu, & Na Ji (UC Berkeley, Co-corresponding Author)