-
KAIST Reads the Inside of Materials in 3D Using Everyday LED Light
<(From Left) Professor YongKeun Park, Professor Seung-Mo Hong, Professor Seokwoo Jeon, Ph.D candidate Juheon Lee>
KAIST announced on the 7th of May that a research team led by Professor YongKeun Park of the Department of Physics, in collaboration with Professor Seung-Mo Hong’s team at Asan Medical Center and Professor Seokwoo Jeon’s team at Korea University, has developed, for the first time in the world, “incoherent Dielectric Tensor Tomography (iDTT)*,” a technology that can read complex three-dimensional “optical fingerprints” inside materials using only everyday LED illumination.
*Incoherent Dielectric Tensor Tomography: an imaging technology that reconstructs, in three dimensions, the directional electrical properties inside a material (dielectric tensor) without relying on light interference (phase information).
<Interferometer-Free Optical System Design and Dielectric Tensor Reconstruction Algorithm>
Some materials possess an inherent property called “optical anisotropy,” in which the refractive index changes depending on the direction in which light passes through. This is a decisive “optical fingerprint” that reveals the internal structure and molecular arrangement of the material. There are two types of optical anisotropy. Uniaxial anisotropy refers to the case where only one direction is special, like a pencil, while biaxial anisotropy is a more general and complex case where all three directions differ, like a brick.
Professor YongKeun Park’s research team previously developed, for the first time in the world, “Dielectric Tensor Tomography (DTT),” a technology capable of measuring this optical fingerprint in three dimensions, opening a path for 3D dielectric tensor measurement that had not previously existed (Shin et al., Nature Materials, 2022). However, conventional DTT required a precise laser interferometer, which caused noise in images, reduced accuracy, and made the system highly sensitive to external vibrations. In particular, there were technical limitations in expanding it to large-area samples such as biological tissues.
The iDTT developed by the research team performs a total of 48 independent measurements by precisely controlling the polarization and angle of light used in hospitals. Through this, it reconstructs in three dimensions the “dielectric tensor,”* which fully describes how a material responds to light in all directions.
*Dielectric tensor: a 3×3 matrix that represents how a material responds to light, including refraction and absorption, in all directions. It mathematically describes the characteristics of materials whose optical properties vary depending on direction.
<Measurement Results of the Three-Dimensional Biaxial Anisotropy Orientation of Each Grain in a Polycrystalline Sample>
The core of iDTT lies in the introduction of an LED light source. By using LED illumination as an incoherent light source, iDTT fundamentally resolves these noise issues and greatly improves measurement stability and practicality. In fact, in a direct comparison using a sample with micrometer-scale periodic molecular alignment structures, the research team confirmed that iDTT clearly reconstructed fine structures that were almost invisible due to noise in conventional laser-based DTT.
The iDTT technology is expected to be applicable across materials science, semiconductors, pharmaceuticals, biomedicine, and displays.
The research team succeeded in making visible in three dimensions how molecules are arranged inside liquid crystal particles. They also precisely observed fibrosis, a phenomenon in which tissue hardens, in colon tissue after radiation therapy without any additional staining.
In addition, even when different crystalline materials such as quartz and calcium chloride were mixed together, the system automatically distinguished each material based solely on differences in their response to light (anisotropy), without chemical analysis.
Furthermore, in materials composed of multiple crystals, the technology non-destructively analyzed the orientation of each small crystal and whether the crystals were well aligned with each other (coherence) or misaligned (incoherence). Through this, the team confirmed that iDTT is a new analytical method capable of connecting microscopic internal structures with physical properties such as material strength.
<Research Concept Diagram (AI-Generated Image)>
Professor YongKeun Park stated, “This study suggests the possibility of replacing material anisotropy measurements that previously relied on large-scale facilities or destructive analysis with compact optical microscopy,” adding, “As stable dielectric tensor measurements are now possible using LEDs, this technology will become a new standard for non-destructive precision analysis used across various industrial fields.”
This study, with KAIST integrated master’s–PhD student Juheon Lee as the first author, was published in the world-renowned journal Nature Photonics on April 21, 2026.
※ Paper title: “Incoherent dielectric tensor tomography for quantitative three-dimensional measurement of biaxial anisotropy,” DOI: 10.1038/s41566-026-01897-0
This research was supported by the National Research Foundation of Korea’s Global Leader Research Program, the Korea Institute for Advancement of Technology’s International Collaborative R&D Program, and the Samsung Research Funding Center of Samsung Electronics.
2026.05.07 View 198 -
KAIST Solves Computer Problems That Would Take Thousands of Years Using Semiconductors
<(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.
2026.05.06 View 216 -
KAIST Develops New Concept Hologram Technology Where “Light Becomes the Key”… Enabling Hard-to-Copy Security
<(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.
2026.05.06 View 185 -
KAIST Uncovers the “Core Secret” of Energy Reactions—from Phone Charging to Hydrogen Production
<(From Left) Professor Hyungjun Kim, Ph.D candidate Dong Hyun Kim, Ph.D candidate Minho M. Kim, Ph.D candidate Junsic Cho, Professor Chang Hyuck Choi, Professor Seung-Jae Shin>
From smartphone charging to hydrogen production, the fundamental principles of energy technology have been revealed. Korean researchers have, for the first time, identified how molecular structures change within the ultra-small space called the “electric double layer” (a thin interface where the electrode and electrolyte meet; the electrode is a material through which electricity flows, and the electrolyte is a liquid through which ions move), where electrochemical reactions occur. This study opens a new path to simultaneously improve efficiency and performance in battery, hydrogen, and carbon-neutral technologies by reducing energy loss and selectively inducing desired reactions.
KAIST (President Kwang Hyung Lee) announced on the 3rd of May that a research team led by Professor Hyungjun Kim from the Department of Chemistry, in collaboration with Professor Chang Hyuck Choi from POSTECH (President Sung Keun Kim) and Professor Seung-Jae Shin from UNIST (President Jong Rae Park), has identified structural “phase transitions” (phenomena in which the state or arrangement of matter changes) occurring within the electric double layer. In particular, they revealed at the molecular level the cause of the phenomenon in which the pattern of electrical storage capacity (capacitance) changes from a “camel shape” to a “bell shape” depending on electrolyte concentration.
Electrochemical reactions occur within the ultra-small space called the “electric double layer,” where the electrode and electrolyte meet. In the field of electrochemistry, it has long been known that as electrolyte concentration increases, the capacitance curve changes from a “camel shape” with two peaks to a “bell shape” with a single peak, but the underlying cause had remained unexplained at the molecular level.
Through atomically precise simulations and experiments, the research team discovered that two key changes occur depending on the voltage applied to the electrode.
At the cathode, water molecules collectively realign in a uniform direction, while at the anode, anions (negatively charged particles) accumulate densely on the surface, forming a two-dimensional structure in a phenomenon known as “condensation.” These two processes each create peaks in the capacitance curve, and as electrolyte concentration increases, they merge into one, causing the curve to transition from a “camel” to a “bell” shape.
In simple terms, on one side, water molecules line up in an orderly fashion, while on the other side, ions gather densely. As the concentration increases, these two phenomena merge into one, and the graph changes from two peaks to a single peak.
In particular, the research team presented, for the first time in the world, a “phase diagram” that shows at a glance how the structure of the electric double layer changes depending on electrode potential (the voltage applied to the electrode) and electrolyte concentration. They also experimentally validated these theoretical predictions in real time using infrared spectroscopy (ATR-SEIRAS, an experimental technique that observes molecular movements in real time).
<Transition from a ‘camel-shaped’ to a ‘bell-shaped’ curve caused by changes in the electric double-layer structure>
In simple terms, they created a map that shows how structures change under different conditions and verified through experiments that the map is accurate.
Professor Hyungjun Kim stated, “This study is meaningful in that it provides the first understanding of the otherwise invisible, microscopic electrochemical reaction environment and opens the way to design it,” adding, “If we can precisely control phase transitions in the electric double layer, we will be able to accurately enhance the performance of energy technologies, such as increasing battery charging speed or maximizing hydrogen production efficiency.”
This study, with Minho Kim, a doctoral student in the Department of Chemistry at KAIST, and Dong Hyun Kim and Junsic Cho, doctoral students from the Department of Chemistry at POSTECH, as co-first authors, was published on March 7 in the international journal Nature Communications.
※ Paper title: “Electric double layer structure in concentrated aqueous solution,”
DOI: 10.1038/s41467-026-70322-5
This research was supported by the Samsung Future Technology Development Program, the InnoCore program of UNIST Hydro*Studio, and the National Research Foundation of Korea (NRF) through the Top-Tier Research Institution Collaboration Platform and Joint Research Support Program, as well as the Nano and Materials Technology Development Program.
2026.05.04 View 186 -
Professor Hyun Myung Selected for Research Grand Prize at ‘2026 Research Day’
< 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 >
2026.04.30 View 269 -
11 KAIST Professors, Including Professor Meeyoung Cha, Receive Government Awards on Science and ICT Day
<(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.
2026.04.30 View 240 -
Abandoned Fallen Leaves Transformed into ‘Biodegradable Agricultural Film’
<(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.
2026.04.30 View 295 -
KAIST Identifies Multiple Viruses and Variants Simultaneously by Controlling the “Speed” of CRISPR Gene Scissors
<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).
2026.04.30 View 206 -
KAIST: AI Learns to Say “I’m Not Sure” … Reducing Overconfidence and Improving Reliability
<Professor Se-Bum Paik, (Upper Right) M.S candidate Jeonghwan Cheon>
“AI should be able to say ‘I’m Not Sure’ on its own.”
A new approach has been proposed to address the problem of “overconfidence”—one of the most critical risks of artificial intelligence (AI) in areas such as autonomous driving and medical diagnosis, where AI shows high confidence in incorrect predictions. A KAIST research team has developed a training method that enables AI to recognize situations involving unfamiliar or unseen knowledge, laying the foundation for reducing overconfidence and improving reliability.
KAIST (President Kwang Hyung Lee) announced on the 27th of April that a research team led by Distinguished Professor Se-Bum Paik from the Department of Brain and Cognitive Sciences has identified that random initialization—widely used in deep learning (an AI technique that learns from data using artificial neural networks)—may be a fundamental cause of overconfidence in AI.
To address this, the research team proposed a “warm-up” strategy in which the neural network is briefly trained using random noise (meaningless arbitrary input data) before learning from real data.
<AI-generated images comparing a model with reliability calibration through pretraining and one without it>
The research team found that AI overconfidence already appears at the initialization stage, which can propagate and cause significant errors during subsequent training. In fact, when random data were input into a randomly initialized neural network, the model exhibited high confidence despite not having learned anything. This characteristic can lead to hallucination in generative AI, where false information is produced in a plausible manner.
The research team found clues for solving this issue in the biological brain. The human brain forms neural circuits through “spontaneous neural activity”—brain signals generated without external input—even before birth.
Applying this concept to artificial neural networks, the researchers introduced a “warm-up phase” in which the network undergoes brief pre-training with random noise inputs before actual learning. This corresponds to a process in which AI adjusts its own uncertainty before starting data learning. After the warm-up process, the AI model’s initial confidence is aligned to a low level close to chance, significantly reducing the overconfidence bias observed in conventional initialization.
In other words, before learning from real data, the model first learns the state of “I don’t know anything yet.”
As a result, the model’s accuracy (how often predictions are correct) and confidence (how strongly the model believes its predictions) naturally become aligned.
<A reliability test comparing the accuracy of responses and the model’s confidence levels in an artificial neural network>
A notable difference was also observed in responses to unseen data. While conventional models tend to give incorrect answers with high confidence even for data they have not encountered during training, models with warm-up training showed a clear improvement in their ability to lower confidence and recognize that they “do not know.”
This also led to strong performance in out-of-distribution detection, which refers to identifying data that differ from the training distribution.
<Random noise warm-up training that mimics the brain’s developmental process>
This study suggests the possibility that AI can go beyond simply producing correct answers and develop the ability to distinguish “what it knows” from “what it does not know”—that is, meta-cognition, the ability to recognize its own cognitive state.
Professor Se-Bum Paik stated, “This study demonstrates that by incorporating key principles of brain development, AI can recognize its own knowledge state in a way that is more similar to humans,” adding, “This is important because it helps AI understand when it is uncertain or might be mistaken, not just improve how often it gives the right answer.”
This technology is expected to be applied not only to fields requiring high reliability, such as autonomous driving, medical AI, and generative AI, but also to the initialization methods of nearly all deep learning models, making it a key technology for improving overall AI reliability.
This study, with Jeonghwan Cheon, a master’s student in the Department of Brain and Cognitive Sciences at KAIST (currently serving as a Private in the Republic of Korea Army), as the first author, was published online on April 9, 2026, in the international journal Nature Machine Intelligence, and was selected as a notable paper and featured in News & Views.
※ Paper title: “Brain-inspired warm-up training with random noise for uncertainty calibration,” DOI: 10.1038/s42256-026-01215-x
※ News & Views article: Learning to be uncertain before learning from data, DOI: 10.1038/s42256-026-01205-z
This research was supported by the Basic Science Research Program of the National Research Foundation of Korea and the KAIST Singularity Professor Research Program.
2026.04.30 View 219 -
AI Computation Enables Clearer Views of the Deep Brain, Bypassing the Need for Expensive Equipment
< 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)
2026.04.29 View 282 -
3D Stem Cell Culture Technology to Shift the Paradigm of Regenerative Medicine
< (From left) KAIST Dr. Changjin Seo, Professor Sangyong Jon >
A breakthrough technology has been developed to overcome the limitation where stem cells fail to survive for long periods in the body, even when administered in large quantities. Stem cells are vital for regenerating damaged tissues or recovering injured areas. A KAIST research team has successfully enhanced both the survival rate and therapeutic efficacy of these cells by developing a 3D culture technology that precisely designs the cellular microenvironment. This achievement is expected to transcend the current limits of stem cell therapy and reshape the landscape of regenerative medicine.
On April 29th, the research team—led by Professor Sangyong Jon from the Department of Biological Sciences and featuring researchers Changjin Seo, Dohyeon Kim, Junhyuk Song, Sun-Young Kim, Youngju Son, and Afia Tasnim Rahman—announced the development of a novel culture technology to grow healthier stem cells. The team implemented a 3D platform by applying a polymer matrix (an artificial structure coating the culture substrate) to an "artificial floor" that mimics the natural in vivo environment. On this platform, they cultured human adipose-derived stem cells (hADSCs) in three dimensions, confirming a dramatic improvement in cellular function and therapeutic impact.
Human adipose-derived stem cells have been favored for clinical use due to their ease of harvest, high proliferation, and low immune rejection. However, traditional 2D (planar) culture methods cause cells to age and lose function over time. Previous 3D methods, such as forming cell aggregates (spheroids), also faced hurdles in maintaining long-term survival and functionality within the body.
To solve this, the research team developed a densely cross-linked synthetic polymer material composed of siloxane (a biocompatible polymer of silicon and oxygen), named "poly-Z."
This material modifies the physicochemical properties of the culture substrate to promote the adsorption of albumin proteins found in the culture medium. As a result, cells do not adhere to the floor but instead self-assemble into 3D spheroid structures. These spheroids showed increased production of the extracellular matrix (ECM), creating an environment highly similar to the human body and demonstrating performance far superior to conventional methods.
Experimental results showed that stem cells cultured on the poly-Z platform exhibited enhanced differentiation potential and immunomodulatory functions, with a significantly increased survival time inside the body.
< Schematic of hADSC Spheroid Formation on the Synthetic Polymer Matrix, Poly-Z >
Notably, in animal models of acute colitis and acute liver injury, this method showed significantly higher therapeutic efficacy than conventional methods. This suggests that even with the same dosage, the cells live longer and act more vigorously. The team confirmed that the activation of integrin and FAK signaling pathways—the mechanisms through which cells sense and respond to their environment—strengthened the stem cells' functions, allowing them to better perceive their surroundings and perform more effectively after transplantation.
Professor Sangyong Jon stated, "This research proves that a precisely engineered synthetic polymer-based 3D environment can simultaneously enhance the function and therapeutic efficacy of stem cells. We expect this to be widely utilized in developing next-generation cell therapies for various incurable diseases, including inflammatory conditions."
The study, with Dr. Changjin Seo from the KAIST InnoCORE AI-Drug Discovery Center as the lead author, was published online on March 31 in the international journal Advanced Science (Impact Factor: 14.1).
Paper Title: Polymer Matrix-Based 3D Culture Significantly Enhances the Differentiation and Immunomodulatory Functions of Human Adipose-Derived Stem Cells
DOI: https://doi.org/10.1002/advs.202518704
This research was supported by the Korea Multi-Ministry Regenerative Medicine Project, the KAIST InnoCORE Program, and the Leader Research Grant of the National Research Foundation of Korea.
2026.04.29 View 238 -
Implementation of a DNA Molecular Computer Smaller Than 2nm Semiconductors… High Expectations for Bio-computing Applications
< (From left) KAIST Professor Yeongjae Choi, GIST MS/PhD Student Woojin Kim, KAIST Researcher Taehoon Kim, Researcher Sangeun Jeong, Researcher Sion Kim, GIST Master's Student Junho Sim >
Until now, molecular-level DNA circuits have mainly been used for simple tasks, such as detecting the presence of cancer-related substances. However, these systems have faced a key limitation: once a reaction occurs, the circuits cannot be reused. Overcoming this challenge, the research team has developed a DNA-based molecular computer that operates at a much smaller scale than conventional semiconductor devices, enabling both computation and memory within the same system. This advancement opens up new possibilities for future computing technologies in bio and medical applications, including disease diagnosis.
KAIST announced on April 22 that a research team led by Professor Yeongjae Choi from the Graduate School of Engineering Biology has developed a DNA-based bio-transistor—a molecular analogue of a key semiconductor component that receives signals and performs computations—and used it to implement a new molecular circuit capable of both information processing and storage.
As semiconductor technology approaches the 2-nanometer (nm) scale, widely considered to be nearing its physical limits, researchers are increasingly exploring alternative computing paradigms that operate beyond traditional silicon-based systems. DNA has emerged as a promising candidate due to its unique properties. By leveraging complementary base pairing, DNA can be precisely programmed to respond to specific inputs. Moreover, the distance between adjacent bases is only 0.34 nanometers, making DNA an attractive material for ultra-high-density information processing.
Despite this potential, conventional DNA circuits have been limited by their “one-time use” nature. Once a reaction occurs, the system is consumed, making it difficult to perform continuous or complex information processing.
To address this issue, the research team designed DNA molecules that change their binding configurations in response to input signals while maintaining those configurations over time. In this system, the resulting molecular configuration effectively stores information and influences subsequent operations. In other words, the researchers implemented a reset-free circuit capable of real-time information processing without requiring an external initialization step, while preserving previously processed information.
< Illustration of a DNA-based nanoscale bio-memory circuit capable of low-power operation >
This study is significant in that it demonstrates transistor-like functionality—the fundamental building block of semiconductor devices—at the level of DNA molecules. It provides a foundation for programmable molecular systems in which molecules can both process and store information, moving beyond simple chemical reactions.
Professor Yeongjae Choi stated, “This research advances the feasibility of implementing molecular computers using DNA,” adding, “It has the potential to open new directions in both bio-computing and medical technologies.”In this study, Professor Sung Sun Yim, Researcher Taehoon Kim, Researcher Sangeun Jeong, and Researcher Sion Kim from the KAIST Graduate School of Engineering Biology, and MS/PhD integrated student Woojin Kim and Master's student Junho Sim from GIST participated as co-authors, and Professor Yeongjae Choi served as the corresponding author.
Professor Sung Sun Yim, Researcher Taehoon Kim, Researcher Sangeun Jeong, and Researcher Sion Kim from the KAIST Graduate School of Engineering Biology, and MS/PhD integrated student Woojin Kim and Master's student Junho Sim from GIST participated as co-authors, and Professor Yeongjae Choi served as the corresponding author. The research results were published in the international academic journal ‘Science Advances’ on April 1, 2026.
※ Paper Title: Reset-free DNA logic circuits for real-time input processing and memory. DOI: 10.1126/sciadv.aeb1699
This research was conducted with support from the Future Promising Convergence Technology Pioneer Program supported by the Ministry of Science and ICT, the Basic Research Program supported by the Ministry of Education, and the KAIST Quantum+X Convergence R&D Project.
2026.04.26 View 531