Kaist

Crude Oil Separates Without Boiling： KAIST and Ge..

< Simple synthetic membrane supports yield surprisingly selective separation of real crude oils at steady state. Credit: KAIST >   An international research team led by KAIST has developed a membrane technology that could significantly reduce the energy required for crude oil refining by replacing part of the century-old distillation process. KAIST(President Kwang Hyung Lee) announced that a team led by Professor Dong-Yeun Koh of KAIST, in collaboration with Professor Ryan Lively's group at Georgia Tech, demonstrated a simple and inexpensive membrane capable of separating crude oil at room temperature without heating. The research was published in Nature, one of the world's leading scientific journals. Crude oil underpins modern life by providing not only transportation fuels but also essential feedstocks for plastics, packaging materials, textiles, and countless consumer products. Because the cost of refining directly influences the price of these products, technologies that reduce refining energy consumption can generate substantial economic and environmental benefits. Traditionally, refineries separate crude oil through distillation, a process that heats crude oil above 350 °C to vaporize it and then cools the vapor to recover different fractions. Globally, crude oil distillation consumes approximately 1,100 terawatt-hours (TWh) of energy each year—equivalent to the annual output of about 130 nuclear power plants, each at gigawatt scale, operating continuously. As a result, distillation remains one of the largest sources of energy consumption and greenhouse gas emissions in the refining industry. At the same time, increasing cost pressures in global petrochemical markets have intensified the need for more energy-efficient separation technologies. Membrane-based crude oil fractionations have attracted increasing attention as a potential alternative. However, conventional wisdom has held that molecularly precise separation requires an ultrathin selective layer coated onto the membrane surface. While effective, such coatings increase manufacturing costs and are prone to defects when scaled to large areas, limiting industrial deployment. To overcome this challenge, the researchers took a radically different approach. Instead of relying on a specialized coating, they passed crude oil directly through a bare porous polyacrylonitrile (PAN) membrane—a chemically stable and inexpensive polymer commonly used as a support material in industrial membranes. As crude oil permeated through the membrane, heavy hydrocarbons selectively deposited on the pore walls, gradually narrowing the pores and creating self-assembled separation channels smaller than 2 nanometers. Rather than relying on a specially engineered coating, the crude oil itself created the nanoscale pathways needed for precise molecular separation. Through these self-formed channels, lighter fractions such as naphtha, gasoline, and kerosene permeated rapidly, while heavier components were effectively retained. In a surprising reversal, membrane fouling—normally regarded as a performance-degrading phenomenon—became the very mechanism that enabled highly selective separation. The bare PAN membrane delivered crude oil permeation rates approximately 23 times higher than those of previously reported state-of-the-art crude oil membranes while maintaining stable performance for 28 consecutive days. Professor Ryan Lively (Georgia Tech) commented “one of the key challenges facing membrane systems for crude oil separation was the low productivities of the membrane units – the PAN membranes with their surprising separation mechanism – dramatically increase the productivity of the membrane unit, to the point where industry should seriously consider adopting the technology.” Importantly, the technology can be integrated into existing refinery infrastructure as a modular filtration unit, avoiding major equipment replacement and reducing barriers to industrial adoption. Process simulations showed that using the membrane as a pretreatment step before conventional distillation could reduce energy consumption by 31.6％, carbon dioxide emissions by 37.6％, cooling water usage by 20.7％, and operating costs by 36％. If adopted throughout Korea's refining and petrochemical sector, the technology could reduce greenhouse-gas emissions by approximately 10 million tonnes annually—equivalent to the emissions of roughly four million internal combustion vehicles. Beyond crude oil refining, the membrane platform could be applied to a broad range of chemical separation processes, including the purification of pyrolysis oil derived from waste plastics, the recovery of solvents used in battery manufacturing, pharmaceutical purification, and biofuel production. The researchers believe the technology could serve as a versatile platform for next-generation molecular separations across multiple industries. Professor Dong-Yeun Koh of KAIST said, “This study reveals a new scientific principle in which a membrane interacts with a complex mixture and spontaneously forms its own separation channels. Working with real crude oil supplied by HD Hyundai Oilbank allowed us to validate the technology under conditions relevant to industrial operation.” Professor Jae W. Lee of KAIST, a co-corresponding author of the study, added, “By advancing large-area membrane modularization and long-term operational reliability, we hope to broaden the adoption of membrane-based processes throughout the refining and petrochemical industries.” Dr. Jihoon Choi and Dr. Hyeokjun Seo of KAIST, the study’s co-first authors, said, “Our goal is to precisely control this spontaneous pore-constriction phenomenon and develop it into a membrane platform applicable to the entire refining process. We also aim to expand the technology to plastic recycling, biofuel purification, and other sustainable chemical processes that support carbon neutrality.” The study was co-first-authored by Dr. Jihoon Choi and Dr. Hyeokjun Seo of KAIST and was published online in Nature on June 24, 2026. Paper Title: Crude Oil Fractionation by Means of Mesoporous Polyacrylonitrile Membranes DOI 10.1038/s41586-026-10677-3 https://www.nature.com/articles/s41586-026-10677-3 This research was supported by the Ministry of Science and ICT of Korea through the Basic Research Program for Outstanding Early-Career Researchers and the Engineering Research Center (ERC) Program.

KAIST Teams Win Both International Challenges at I..

  < From left: the ACDC-K Team, Prof. Hyun Myung, and the Curaytor Team. >     Two research teams from KAIST have claimed first place in international challenge competitions held at the world’s premier robotics and computer vision conferences. KAIST (President Kwang-Hyung Lee) announced that the ACDC-K Team and the Curaytor Team, both from the laboratory of Prof. Hyun Myung in the School of Electrical Engineering, won first place in international challenge competitions held in conjunction with the IEEE International Conference on Robotics and Automation (ICRA 2026) and the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026), respectively. The achievement highlights the global competitiveness of KAIST’s robotic perception and spatial intelligence technologies, with two teams from the same laboratory securing victories in leading international competitions across distinct research fields. The ACDC-K Team won first place among more than 60 participating teams in the SLAM (Simultaneous Localization And Mapping) category of the Hilti×Trimble SLAM Challenge 2026, held during the Open Challenges in Robotics for Asset Inspection (OCRAIM) Workshop at ICRA 2026 in Vienna, Austria, from June 1 to 5. < Example of SLAM (Simultaneous Localization and Mapping) results generated by the ACDC-K Team’s proposed technology in a 40 m × 30 m construction environment. >   Jointly organized by Hilti, Trimble, and the University of Oxford, the challenge evaluates robotic localization and mapping performance using sensor data collected from real construction sites. Participants were required to address practical challenges frequently encountered in construction environments, including non-overlapping front and rear fisheye camera configurations, low-texture indoor scenes, and rapid camera motion. To tackle these challenges, the ACDC-K Team developed a robust visual-inertial SLAM system that fuses front and rear fisheye camera data with inertial measurements. By integrating feature-point and feature-line observations with adaptive constraints and correction mechanisms, the team achieved highly reliable localization and mapping performance in complex construction environments. Meanwhile, the Curaytor Team won first place among eight participating teams in the Nothing Stands Still (NSS) Challenge 2026, held during the Computer Vision for the Built World (CV4AEC) Workshop at CVPR 2026 in Denver, Colorado, from June 3 to 7. < Example of multiway registration results generated by the Curaytor Team’s proposed technology in a large-scale environment covering approximately 4,960 m² >   Jointly organized by Stanford University, ETH Zurich, and Oregon State University, the NSS Challenge evaluates 3D point cloud registration technologies for construction and industrial environments that evolve over time. The Curaytor Team developed a novel multi-registration framework capable of aligning multiple LiDAR scans collected across different times and locations. The framework integrates feature extraction, correspondence estimation, robust global registration, registration confidence assessment, and change-aware refinement techniques. As a result, the team achieved highly accurate registration performance even in environments containing structural changes and dynamic objects. < . Curaytor Team receiving the award. Kim Daebeom (Team Leader, Ph.D. candidate). >   “This achievement demonstrates the robustness of our visual-inertial SLAM and 3D LiDAR registration technologies in complex and constantly changing real-world environments,” said Prof. Hyun Myung. “It is particularly meaningful that our students secured first-place finishes in highly competitive international challenges hosted at two of the world’s most prestigious conferences in robotics and computer vision.” Prof. Hyun Myung’s laboratory has consistently demonstrated excellence in spatial intelligence research. The laboratory previously won first place in the LiDAR track and ranked first among academic teams in the vision track of the Hilti SLAM Challenge in 2023. In addition, the Curaytor Team successfully defended its title in the NSS Challenge, securing back-to-back championships in 2025 and 2026. < . ACDC-K Team at the award ceremony. From left: Jinwoo Jeon (Ph.D. candidate, Team Leader) and Prof. Hyun Myung. >

KAIST Develops Next-Generation Self-Powered Wearab..

  < Photo of the KAIST research team. From left to right: Prof. Miso Kim, JungHun Park (Ph.D. student), Yong Jun Choi (Research Associate; first author), Jisoo Nam (Ph.D. student), and Prof. Gi-Dong Sim >   Wearable medical devices that monitor heart rate, respiration, and joint movements for long periods without battery concerns, electronic skins that sense external stimuli like human skin, and soft robots made of flexible materials that move freely have all come one step closer to reality. KAIST researchers have developed a self-powered sensor (a sensor that generates electricity on its own without a battery) that can stretch up to 668％ while producing stable electrical signals. KAIST announced on June 18th that a research team led by Professor Miso Kim from the Department of Mechanical Engineering has overcome the durability limitations of conventional piezoelectric fiber sensors (fiber-type sensors that convert pressure or movement into electrical signals) and successfully developed a highly stretchable piezoelectric fiber sensor that operates stably even under repeated deformation. < Conceptual illustration of a self-powered piezoelectric fiber coil sensor (AI-generated) >   The core material of the sensor, piezoelectric polymer, is a polymeric material that generates electricity when subjected to mechanical force. Although its lightweight and flexible nature makes it suitable for skin-attachable wearable sensors, conventional piezoelectric fiber sensors suffered from signal degradation during repeated stretching or bending, as the electrode layer collecting electrical signals and the piezoelectric layer generating electricity would become damaged. Furthermore, while coiling the fibers can increase stretchability to allow greater elongation, maintaining electrical stability remained a significant challenge. To resolve these issues, the research team developed a "Hierarchical Resilient Design" strategy, engineering the sensor to withstand deformation across multiple levels—from its constituent materials and electrodes to its overall structure. Simply put, just as a rubber band returns to its original shape after repeated stretching, the sensor is designed to self-maintain its performance after cyclical deformation. First, the research team embedded elastic polymer microparticles inside the piezoelectric nanofibers to create a closely interlocking structure. This creates a supportive effect similar to Velcro, helping the sensor recover its original shape even after being repeatedly stretched. Additionally, they designed the interface so that the electricity-collecting electrode and the electricity-generating piezoelectric layer connect seamlessly. By strongly bonding different materials together, they ensured they would not easily delaminate under impact or deformation, allowing the sensor to maintain a stable electrical signal even when significantly stretched or bent. Applying this design to a coil structure, the research team successfully stretched the sensor up to 668％—approximately 6.7 times its original length—while maintaining a stable output. The developed sensor generated consistent electrical signals under various movements, including stretching, bending, and pressing. Furthermore, the research team fabricated the sensor not only in coil forms but also in knot configurations, confirming its stable operation under repeated forces or sudden impacts. By leveraging artificial intelligence (AI) to analyze the sensor signals, they were also able to accurately distinguish between different movements, such as pressing, bending, and stretching. This study holds great significance as it presents a self-powered sensor platform that simultaneously achieves high stretchability and long-term stability without requiring a battery. In particular, because it enables stable signal measurement in environments undergoing repeated deformation, it is expected to be utilized in developing next-generation wearable medical devices for long-term monitoring of various biosignals, including heart rate, respiration, joint movement, and muscle activity. It is also projected to expand its range of applications to digital healthcare devices, electronic skins, and sensory sensors for soft robots by making devices lighter and more convenient to use. < Structure and operating principle of the self-powered piezoelectric fiber coil and knot sensor >     < Conceptual illustration of the proposed research (AI-generated) >   "The core achievement of this research is that it simultaneously secured mechanical resilience and electrical reliability by combining fiber structure design with electrode interface engineering (a technology that controls the boundary where different materials meet)," said Professor Miso Kim. She added, "In the future, we expect it to be applied to wearable medical devices requiring long-term wear, electronic skins, and sensory sensors for soft robots, enabling more accurate and continuous biosignal monitoring." The research findings, with researcher Yong Jun Choi as the first author, were published on March 10, 2026, in ACS Nano (Impact Factor 16.1), a world-renowned academic journal in the fields of nanotechnology and materials science. Paper Title: Mechanically and Functionally Resilient Piezoelectric Fiber Coils and Knots for Reliable Self-Powered Sensing DOI: doi/10.1021/acsnano.5c19628 Author Information: Yong Jun Choi 1 (KAIST, First Author), JungHun Park 1 (KAIST, Co-author), Jisoo Nam 1 (KAIST, Co-author), Gi-Dong Sim 1 (KAIST, Co-author), Myung-Gil Kim 2 (Sungkyunkwan University, Co-author), Miso Kim (KAIST, Corresponding Author) This research was conducted with support from the BRIDGE Convergence Research and Development Program (RS-2023-00254689), the Nano·Material Technology Development Program (RS-2024-00468995), and the Next-Generation Semiconductor-Compatible Micro-Substrate Technology Development Program (RS-2024-00433654) funded by the National Research Foundation of Korea under the Ministry of Science and ICT.      

KAIST Illuminates the Eyes of Humanoid Robots with..

  <CVPR 2026 poster session. From left to right: Minseok Seo (KAIST, first author), Mark Hamilton (MIT and Microsoft, second author), and Prof. Changick Kim (KAIST, corresponding author)>     From facial recognition on smartphones to humanoid robots, computer vision technology, which serves as the eyes of artificial intelligence (AI), is widely utilized in our daily lives. A joint research team from KAIST and international institutions has developed a technology that allows AI to see the world more clearly with minimal memory, increasing GPU (Graphics Processing Unit) memory efficiency by up to 16 times. This achievement is evaluated as a core technology that will accelerate the era of humanoid robots and on-device AI. <Overview of Upsample Anything. Given a high-resolution image, it is first downsampled to a low-resolution image and then reconstructed through test-time optimization (TTO). During this process, pixel-wise anisotropic kernel parameters are learned. The learned kernels are subsequently applied to low-resolution foundation feature maps to generate high-resolution feature maps. These feature maps are then used to perform pixel-wise anisotropic Joint Bilateral Upsampling, enabling high-quality reconstruction at high resolution>   KAIST announced on June 17th that a research team led by Professor Changick Kim from the School of Electrical Engineering, through joint research with researchers from MIT and Microsoft in the United States, has developed 'Upsample Anything,' a universal technology that can enhance the visual performance of AI even with limited GPU memory. Following its acceptance to 'CVPR 2026,' the world's most prestigious conference in the field of artificial intelligence and computer vision, this achievement was awarded the 'CVPR Compute Gold Star' in recognition of its efficient utilization of computational resources. It was also selected as the 'Transparency Champion,' ranking first overall in the category of research process transparency and reproducibility. This is an accomplishment that widely recognizes the core elements of responsible AI research, including research performance, computational resources used, code disclosure, and experimental reproducibility. Recently, humanoid robots, autonomous driving systems, and AI based on world models (AI models that learn and predict the physical environment and changes of the real world) have been compressing input images into low-resolution features (core information extracted from images by AI) to increase computational speed and reduce memory usage. However, during the compression process, a problem occurs where important visual information, such as small objects, thin structures, and minute defects, is lost. Conversely, processing all images at high resolution from the beginning requires massive GPU memory and computational resources, making real-time processing difficult. This has remained an unresolved challenge for a long time in situations where small devices like smartphones or robots, where mobility is crucial, must precisely perceive their surrounding environment. To overcome these limitations, the research team developed a training-free (requiring no additional data training) upsampling technology that restores low-resolution feature information into high resolution by utilizing the edge and structural information of the input image. Existing technologies required a separate retraining or complex optimization process to be applied to new environments or data. In contrast, 'Upsample Anything' developed by the research team can find the optimal restoration method using just a single input image, allowing it to be immediately applied to various environments. In addition, by compressing and utilizing only core information instead of storing and processing all visual information at high resolution, GPU memory usage was significantly reduced. Based on a 224×224 size image (approximately 50,000 pixels) widely used in AI research, the research team restored visual information close to the original with a short calculation of about 0.4 seconds, achieving a performance that improves GPU memory efficiency by up to 16 times. This means that artificial intelligence can perceive its surrounding environment more precisely even with limited computational resources. Therefore, this technology is expected to be widely used in various next-generation artificial intelligence fields, such as small devices like smartphones, as well as humanoid robots that need to accurately identify and manipulate small objects, autonomous driving systems, and on-device AI.   <Comparison image illustrating the performance gap with conventional methods (AI-generated). Conventional vision foundation models understand a scene by converting the input image into low-resolution features at a small patch level (left). Upsample Anything restores these low-resolution features to the original resolution level, enabling the AI to comprehend the scene's structure and boundaries with significantly higher precision (right)> Professor Changick Kim said, “This technology is an algorithm that can significantly increase the visual precision of artificial intelligence with fewer resources, and it is expected to accelerate the commercialization of humanoid robots and on-device AI.” He added, “It is even more meaningful because it was recognized at CVPR not only for its performance but also for its computational efficiency and research transparency.” This research was participated in by KAIST PhD student Minseok Seo as the first author, and this achievement was presented on June 7 at 'CVPR 2026,' the world's most prestigious conference in the field of artificial intelligence and computer vision. ※ Paper Title: Upsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling, DOI:10.48550/arXiv.2511.16301 ※ Author Information: Minseok Seo (KAIST, First Author), Mark Hamilton (MIT, Microsoft, Second Author), Changick Kim (KAIST, Corresponding Author)        

KAIST Breaks a Major AI Bottleneck with Liquid Coo..

  <(From Left) Professor Sung Jin Kim, Professor Ikjin Lee, Dr. Yong Jin Lee, Ph.D candidate Hansol Lee, Ph.D candidate ChulHyun Hwang>   AI data centers are often described as “power-hungry giants.” Not only do artificial intelligence computations consume enormous amounts of electricity, but a significant amount of energy is also required to cool the semiconductor chips that heat up during operation. As AI chips continue to deliver higher performance, the amount of heat they generate is increasing rapidly. As a result, conventional air cooling and external copper heat spreaders are approaching their practical limits. To address this challenge, KAIST research team has developed an ultra-high-efficiency liquid-cooling technology that cools semiconductor chips from within.   KAIST (President Kwang Hyung Lee) announced on the 16th that a joint research team led by Professor Sung Jin Kim of the Department of Mechanical Engineering and Professor Ikjin Lee of the School of AI and Computing has developed a highly efficient liquid-cooling technology that directly cools high-heat-flux semiconductor chips using room-temperature water. The researchers achieved this by embedding liquid-cooling channels, thinner than a human hair, directly inside a silicon semiconductor chip. The team successfully maintained the chip temperature below 100°C even under extreme heat-generation conditions exceeding 2,000 watts per square centimeter (W/cm²).   <Manifold Microchannel Cooling Device Structure for Cooling High-Heat-Generation Semiconductor Chips> The researchers focused on a Manifold MicroChannel (MMC hereafter) structure embedded directly inside a silicon chip. Microchannel cooling removes heat through microscopic fluid channels that are smaller than a human hair. In conventional designs, coolant must travel through numerous microchannels from one end of the chip to the other. This long flow path increases flow resistance and requires greater pumping power to circulate the coolant.   The manifold structure developed by the research team distributes coolant through multiple inlet channels and collects it through multiple outlets. An analogy can be drawn to a logistics network: instead of shipping all goods from a single origin to a distant destination, multiple distribution centers are strategically placed to shorten transportation distances. Because the coolant travels only a short distance within each channel, flow resistance is significantly reduced, and the required pumping pressure becomes much lower.   Previous MMC designs often suffered from uneven coolant distribution, with some channels receiving too much coolant while others received too little. The research team overcame this challenge by optimizing the structure so that coolant could flow evenly through all microchannels. Through extensive design analysis and advanced simulations, they identified an optimized cooling architecture that delivers high cooling performance while minimizing energy loss.   The optimized structure was then fabricated in an actual silicon semiconductor chip and experimentally validated. Under the same temperature-rise condition, the cooling system achieved a coefficient of performance (COP) of 106,000. This is approximately ten times higher than the previous world-leading result of around 10,000 reported in Nature (van Erp et al.) in 2020. In practical terms, it means that only about one-tenth of the pumping power is required to remove the same amount of heat.   Notably, this performance was achieved without relying on phase-change cooling, nanoscale surface modifications, or expensive materials such as diamond. Ordinary room-temperature water was used as the coolant. In addition, the device was fabricated using a low-temperature process below 350°C that is compatible with conventional semiconductor manufacturing. This means the technology could be implemented in existing semiconductor foundries without requiring major additional equipment investments.     <Fabricated Cooling Device, Experimental Results, and Applications> The technology is expected to help address thermal management challenges in a wide range of high-heat-flux electronic systems, including AI accelerators, high-performance computing (HPC) systems, three-dimensional semiconductor packaging, power electronics, and defense electronics. In particular, data centers are increasingly constrained not only by computing performance but also by cooling power consumption and cooling infrastructure requirements. Technologies that reduce pumping power at the chip level could therefore play an important role in improving the energy efficiency of next-generation data centers and alleviating thermal bottlenecks.       <Research Image(AI-generated)> Professor Sung Jin Kim said, “As the performance of AI semiconductors and advanced electronic packaging becomes increasingly limited by heat, we expect this technology to serve as a foundational cooling solution for future high-performance computing systems.”   The study was co-first-authored by Young Jin Lee, ChulHyun Hwang, and Hansol Lee from the Department of Mechanical Engineering at KAIST. The research was published on June 15 in the international journal Energy Conversion and Management.   Paper title: Highly energy-efficient manifold microchannel for cooling electronics with a coefficient of performance over 100,000 DOI: 10.1016/j.enconman.2026.121422     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 (Grant No. 2021R1A2C3011944), and by the Specialized Research Laboratory Program for Ultra-High-Heat-Flux Cooling Systems of the Korea Research Institute for Defense Technology Planning and Advancement (KRIT), funded by the Defense Acquisition Program Administration (Grant No. KRIT-CT-22-022).      

A Breakthrough Concept in Nano-Printing Technology..

  <(From Left) Professor Inkyu Park, Ph.D candidate Byung-Ho Kang, Researcher Jun-Ho Jeong, Professor Junseong Ahn>   A breakthrough technology has been developed that allows metal circuits floating on water to be transferred directly onto any desired surface. A South Korean research team has introduced a novel technique capable of transferring ultra-fine nano-circuits onto plant leaves and fruits, as well as curved automotive surfaces and robot exteriors, all without causing any damage. This technology is expected to be widely utilized across various cutting-edge industries, including smart agriculture, wearable healthcare, and bioelectronics. KAIST announced on June 15th that a joint research team led by Distinguished Professor Inkyu Park from the Department of Mechanical Engineering, Dr. Jun-Ho Jeong from the Korea Institute of Machinery and Materials and Professor Junseong Ahn from Korea University has successfully developed "Water-Floating Nano-Transfer Printing (WF-nTP)." This technology enables precision metal thin films floated on water to be transferred onto various 3D surfaces. Conventional nano-transfer printing (nTP), which is widely used to manufacture electronic devices and sensors, typically requires high heat, intense pressure, adhesives, or toxic chemical solvents. Consequently, applying this method to biological tissues or complex curved surfaces that are sensitive to heat and pressure has proven highly challenging. To overcome these limitations, the research team proposed a completely new approach: floating metal circuits on the surface of water. Fabrication: The team deposited an ultra-thin layer of metals such as gold (Au), platinum (Pt), palladium (Pd), or nickel (Ni) onto a polymer mold. Separation: They then used plasma gas which has a high-energy state of ionized gas to selectively remove (etch) a part of the mold. Floating: When this structure is placed in water, water infiltrates through the microscopic gaps, causing the 20-nanometer (nm) thick metal film to float to the surface on its own while perfectly maintaining its original shape. <Overview of the water-floated nano mesh and versatile transfer technique> The research team transferred the metal circuits using a "scooping" method, which involves submerging the target object beneath the floating film and slowly lifting it upward. As the water evaporates, capillary force (the force that moves liquid through narrow spaces) tightly adheres the circuit to the surface. Once the water completely dries, intermolecular forces come into play, securing the circuit firmly in place without the need for any adhesive. Notably, the team also succeeded in transferring circuits onto hydrophobic (water-repellent) surfaces, such as lotus leaves. By adding a small amount of ethanol to the water to lower its surface tension (the property of a liquid surface that causes it to shrink), they effectively overcame a major limitation of conventional technologies. This technology holds immense potential for widespread application because it can adapt to diverse surfaces while flawlessly preserving the nano-patterns. Using this method, the research team fabricated Surface-Enhanced Raman Scattering (SERS) sensors which are used for high-sensitivity detection of trace chemical substances and attached them directly to plant leaves and fruit surfaces. Through this, they successfully detected thiram, a pesticide component, on the surfaces of leaves and fruits. Furthermore, they successfully implemented a wearable, high-performance hydrogen gas sensor by transferring a palladium (Pd) mesh onto highly flexible thermoplastic polyurethane (TPU) fibers. <Research Image(AI-generated)> Distinguished Professor Inkyu Park stated:  "This technology is highly significant as it shatters the substrate limitations of conventional nano-transfer printing, allowing nano-patterns to be transferred onto sensitive surfaces like living plant leaves or human skin without heat or adhesives. We expect it to evolve into a core platform technology for wearable sensors and bioelectronics, finding applications in smart agriculture for pesticide detection without damaging crops, wearable health monitoring devices, bioelectronic devices, and electronic skins for next-generation robots." This research, with PhD student Byung-Ho Kang from the KAIST Department of Mechanical Engineering participating as the first author, was published online on March 30, 2026, in the prestigious international academic journal Nature Communications. The study authors include Byung-Ho Kang, Ji-Hwan Ha, Yeongjae Kwon, Sohee Jeon, Donho Lee, Byeongmin Kang, Soon Hyoung Hwang, Junseong Ahn, Jun-Ho Jeong, and Inkyu Park. Meanwhile, this research was conducted with funding from the Ministry of Science and ICT through the National Research Foundation of Korea (NRF) Mid-Career Researcher Support Program, and the Ministry of Trade, Industry and Energy through the Korea Evaluation Institute of Industrial Technology (KEIT) Alchemist Project.    

How Small Can Semiconductors Get？ KAIST Develops A..

  <(From Left)  Dr. Tae Hyung Kim, Dr. Juho Lee, (Upper Left) Professor Yong-Hoon Kim>     As the global semiconductor industry enters the so-called "2 nm (nanometer, one-billionth of a meter) process" era, the actual size of transistors — the core components of semiconductor chips — still remains above 10 nm. How much smaller, then, can transistors actually get? KAIST researchers have developed a technology to predict that limit through quantum mechanical atom-level calculations. KAIST (President Kwang Hyung Lee) announced on the 14th that a research team led by Professor Yong-Hoon Kim of the School of Electrical Engineering has developed a computational design technology that utilizes computer simulations to analyze and predict the scaling limits of transistors, a key challenge in developing next-generation semiconductor devices. <Research Image(AI-generated)> Transistors are ultra-small switches that turn electrical currents on and off, serving as key components that determine the performance and power efficiency of semiconductor chips that power smartphones, artificial intelligence computers, and more. The semiconductor industry has continuously downsized transistors to achieve higher performance and lower power consumption. However, when the size becomes excessively small, quantum tunneling occurs—a quantum mechanical phenomenon where electrons pass through energy barriers they normally cannot cross—making current control difficult. For this reason, identifying how much smaller transistors can be made within the boundaries of quantum tunneling is a critical task in next-generation semiconductor development. However, it is virtually impossible to experimentally confirm the scaling limits of transistors directly. With current technology, it is difficult to precisely control and quantitatively analyze the contact area where the metal electrode and the semiconductor channel (the pathway through which current flows inside a transistor) meet at the atomic level. The research team resolved this issue by utilizing ab initio or first-principles calculations, a method that computes material properties based solely on fundamental physics laws without relying on experimental data. The research team had previously developed and reported a new theoretical-computational framework called multi-space constrained-search density functional theory (MS-DFT), which extends the scope of first-principles calculations from materials to devices by precisely analyzing the complex quantum phenomena occurring at the interface where metal electrodes and semiconductors meet and across which electrons flow. In this study, the team built on this framework to perform computational transfer length method (TLM) experiments, the gold standard experimental technique for extracting contact resistance (the resistance to current flow occurring at the metal electrode-semiconductor interface). Based on the atomic-level TLM calculations results, they identified the quantum tunneling limit (the length at which electrons stop leaking and begin to allow transistor current control). The research team applied this technology to a monolayer MoS₂ (molybdenum disulfide) device, a representative two-dimensional semiconductor material that can be made as thin as an atomic layer and is a candidate material for next-generation transistor channels. As a result, they were able to quantitatively analyze how deeply electrons penetrate into the channel and how much this hinders current flow control depending on the type of metal electrode and the contact atomic geometry. In other words, they clarified that the limit to how small a transistor can be made varies depending on which metal and contact structure are selected. This implies that the performance and limits of a device can now be predicted in advance solely through computer simulations before the actual transistor fabrication. < Analysis of Contact Resistance and Critical Tunneling Length in Two-Dimensional Semiconductors Using the First-Principles Transfer Length Method > According to the research results, the critical tunneling length—the maximum length at which electrons penetrate into the channel and begin to affect transistor operation—was found not to be a single fixed value. This length emerged as a design variable that changes depending on the work function of the metal (the minimum energy required to remove an electron from a metal) and the contact structure of the interface where the metal and semiconductor meet. This signifies that the extent to which a transistor can be downsized depends on the combination of materials and structural design. In particular, among the candidate metal types and contact structures considered, the research team confirmed that the length where electrons stop leaking could be reduced to less than 4 nm. This result demonstrates the possibility of making transistors even smaller than the levels achieved today. Furthermore, the research team proposed a design strategy for next-generation semiconductor chips that reduce power consumption by combining two-dimensional semiconductors with different properties. This study is significant because it establishes a platform for predicting scaling limits and designing optimal device configurations before actually fabricating semiconductor chips. Through this, it is expected to reduce trial and error and shorten the development period in the process of developing next-generation ultra-small AI semiconductor devices. Professor Yong-Hoon Kim said, "This study is significant because it presents a new physical criterion for defining how small next-generation transistors can become. By computationally analyzing quantum mechanical phenomena in the sub-10 nm regime, which are difficult to probe experimentally, we have opened a path toward utilizing these findings in next-generation transistor design." The study, in which Dr. Tae Hyung Kim participated as the first author, was published online on May 28th in the prestigious computational journal 'npj Computational Materials,  a prestigious journal in the field of computational materials science ※ Title of the paper: Ab initio transfer length method simulations of tunneling limits in 2D semiconductors, DOI: https://doi.org/10.1038/s41524-026-02101-1 This research was conducted with support from programs such as the Mid-Career Researcher Program and EDISON 2.0 Program of the National Research Foundation of Korea.    

˝What if there is no one to farm？ KAIST reveals a ..

  <(From Left) Professor  Nicklas Forsell, Professor Hyungjun Kim, Ph.D candidate Hongtak Lee, Professor Haewon Chon>     The cause of future food shortages may not be a lack of farmland, but a shortage of agricultural workforce." Amid the reality of low birth rates and rural extinction, a joint international research team from KAIST has developed a new data-driven model that reflects the decline in the agricultural workforce into the analysis of future food security (the ability to stably produce and supply food required by the public). The research findings show that in the future, a shortage of agricultural workforce could act as a key constraint on farmland utilization in most regions of the world. KAIST announced on June 12th that a research team led by Professor Hyungjun Kim from the Department of AI Future (adjunct at the Moon Soul Graduate School of Future Strategy), in joint research with Professor Haewon Chon from the KI Institute for Climate, Environment, and Energy (Graduate School of Green Growth and Sustainability), Professor Nicklas Forsell, and Professor Taikan Oki from the University of Tokyo in Japan, analyzed the impact of the agricultural workforce decline on future food production. < Regional Farmland Supply, Demand, and Shortage Outlook for 2030 and 2100 > Until now, food security and climate change research have mainly focused on "how much farmland can be secured." The approach was to predict the future by calculating whether the climate and soil are suitable for farming and how much food demand will increase in the future. However, the research team asked a different question: "What if there is farmland, but no one to farm it?" In fact, as low birth rates and urban concentration manifest in many countries, the rural population is declining. As economies develop, there is also a stronger tendency for people to move from agriculture to the manufacturing or service sectors. The research team determined that these changes could have a significant impact on future food production. The research team performed the analysis using five future scenarios that combine SSP (Shared Socioeconomic Pathways) and RCP (Representative Concentration Pathways), which are representative international scenario frameworks that predict how future society and climate change will unfold. SSP is a scenario that assumes the direction of societal changes such as population growth, economic growth, and technological development, while RCP is a scenario that shows how the future climate will change depending on greenhouse gas emissions. The research team newly reflected the agricultural workforce variable into these future outlooks. While previous predictions were mainly based on the land available for farming and food demand, this study simultaneously considered the actual number of people who will farm. In other words, the reality that food production can be limited if the agricultural workforce is insufficient, even if farmland and climate conditions are adequate, was reflected in the model. The results of the analysis were even clearer than expected. In the future, it was shown that the farmland area that can actually be utilized will decrease due to the shortage of agricultural workforce in most regions of the world. In some regions, the lack of agricultural workforce was analyzed to act as a greater limiting factor than climate or soil. The research team explained that the agricultural workforce problem may not be easily resolved even in a future where technological development occurs rapidly. Technological development increases the cultivable area per person. However, as industries grow, more people move to the manufacturing and service sectors, which conversely accelerates the decline of the rural population, leading to a reduced workforce and a phenomenon where farmland utilization becomes more restricted. These results suggest the importance of a sustainable development model. In addition, it was confirmed that if international migration is restricted, developed countries will experience a shortage of agricultural workforce, while conversely, the agricultural population in some low-income countries may increase excessively. This shows that migration policies are also closely linked to food security. Professor Hyungjun Kim explained, "This study analyzed future food issues by considering not only climate and land, but also changes in people. It is a study showing that realistic social problems such as low birth rates and the avoidance of rural areas can have a significant impact on future food security and climate change responses." This study, in which Ph.D. student Hongtak Lee from the Moon Soul Graduate School of Future Strategy participated as the first author and Professor Hyungjun Kim from the Department of AI Future conducted as the corresponding author, was published on June 1 in the international academic journal 'Nature Sustainability'. Furthermore, in recognition of its academic importance, the study was prominently highlighted in a separate commentary titled "Farming needs more hands" (News & Views; https://doi.org/10.1038/s41893-026-01841-8) in the same journal. The commentary evaluated this research as "a first step that shifted the conventional question of 'how much land is there' to whether there are enough people and productivity per worker to cultivate that land." ※ Title of Paper: Agricultural Workforce as a Potential Bottleneck of Future Cropland Availability, DOI: https://doi.org/10.1038/s41893-026-01824-9 ※ Main Authors: Hongtak Lee (KAIST, First Author), Nicklas Forsell (KAIST), Taikan Oki (University of Tokyo), Haewon Chon (KAIST), Hyungjun Kim (KAIST, Corresponding Author) <Research Image(AI-generated)> This research was conducted with the support of the AI-based Future Climate Technology Development Framework Program, the Brain Pool Program, and the Plus Project (Ministry of Science and ICT) through the National Research Foundation of Korea.  

˝KAIST to Produce ＇Janus-Faced＇ Nanomaterials... P..

  <(From Left) Professor Ho Jin Ryu, Dr. Hyun Woo Seong, Dr. Minseok Lee>     The way has been paved for the development of multi-functional materials for applications such as removing radioactive pollutants and shielding electromagnetic waves. A KAIST research team has succeeded, for the first time in the world, in synthesizing the core raw material for fabricating asymmetric MXene, a so-called "Janus-faced" nanomaterial that can implement distinct functions due to differing atomic compositions on its two sides. <AI-Generated Research Image> KAIST announced on June 11th that a research team led by Professor Ho Jin Ryu from the Department of Nuclear and Quantum Engineering has successfully synthesized experimentally an asymmetric layered ceramic (a ceramic with an asymmetric structure where atomic layers are stacked on top of each other), which is a required precursor for fabricating asymmetric MXene (a two-dimensional nanomaterial with different atomic compositions on its two sides). MXene is a two-dimensional nanomaterial with excellent electrical conductivity and high surface reactivity, drawing significant attention in various advanced technology sectors including energy storage devices and sensors. However, the MXenes developed so far possess a symmetric structure with identical atomic compositions on both sides, which has limited the functions they can implement. In contrast, asymmetric MXenes have different atomic compositions on their two sides, allowing each side to perform distinct functions. This asymmetry enables the emergence of new properties that are difficult to achieve with conventional symmetric-structured materials. In particular, it is expected to be utilized in developing next-generation functional materials, such as adsorption filters for removing radionuclides and materials for absorbing and shielding electromagnetic waves. Until now, however, the existence of asymmetric MXene had mostly been suggested only through computer simulations, and its actual implementation remained difficult because the raw materials required for manufacturing had not been secured. To solve this problem, the research team applied a high-entropy material design strategy (a material design approach that mixes multiple elements to achieve new properties). By simultaneously mixing six elements—titanium (Ti), zirconium (Zr), hafnium (Hf), tantalum (Ta), aluminum (Al), and tin (Sn)—they discovered that a stable asymmetric structure, in which the composition of the outer metal atomic layers is arranged differently due to differences in atomic size, forms naturally. This is evaluated as a new structure-forming mechanism that has never been reported in conventional MXene raw materials. The asymmetric layered ceramic synthesized by the research team acts as a precursor (a raw material for making the final material) that can be converted into asymmetric MXene with different atomic compositions on its two sides when subjected to chemical etching (a process that selectively removes only specific atomic layers). < Experimental Observations of the Asymmetric Ceramic Structure Synthesized in This Study > This achievement holds great significance as it establishes the foundation for actually implementing asymmetric MXene, which had previously remained confined to theory. In particular, it presents the possibility of expanding into various advanced technology fields that were difficult to achieve with existing symmetric structures, such as radionuclide capturing, electromagnetic wave shielding, sensors, and piezoelectric devices (devices that convert pressure or vibration into electrical energy). The research team has currently filed patent applications in South Korea, the United States, and Japan for the asymmetric layered ceramic and the asymmetric MXene utilizing it. They plan to verify the actual radioactive ion removal performance and electromagnetic wave shielding performance through follow-up studies. Professor Ho Jin Ryu said, "This study is an instance of realizing an asymmetric atomic structure, which was difficult to achieve using conventional crystallography, through a high-entropy material design strategy. We expect that it can be developed into a core original technology in the fields of safety and the environment, such as radionuclide capturing and electromagnetic wave shielding, in the future." Dr. Minseok Lee of KAIST (currently at the Korea Atomic Energy Research Institute) participated as the first author, and Dr. Hyun Woo Seong of KAIST (currently at the Korea Atomic Energy Research Institute) participated as a co-author. The study was published in the world-renowned scientific journal 'Nature Communications' on April 30. ※ Paper Title: An Asymmetrically Out-of-Plane Ordered MAX Phase as a Precursor for Janus MXenes, DOI : 10.1038/s41467-026-72561-y Meanwhile, this research was conducted with support from the Nuclear Energy Basic Research Support Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT.      

˝Age of Robots Making Human-Like Judgments, KAIST ..

  < (From left) Professor Chang D. Yoo, Tung M. Luu (PhD candidate, first author) at the back center, and Hwanhee Kim (M.S candidate, second author) at the front right >      “Robots that make judgments like humans are coming faster than we think.” A core technology that will accelerate the era where robots understand human intentions and choose the correct actions on their own has been developed in South Korea. KAIST researchers solved a key challenge in the commercialization of physical AI by developing a technology where AI learns human judgment criteria on its own with just a few videos. KAIST announced on June 10th that a research team led by Professor Chang D. Yoo from the School of Electrical Engineering has developed 'VOTP (Video-based Optimal TransPort Preference)' for the first time in the world, a new technology that allows AI to learn human intentions and judgment criteria using just a few preference videos instead of thousands to tens of thousands of human evaluation data points. < VOTP Overview Diagram >  The research team's paper has been accepted to ICML (International Conference on Machine Learning) 2026, the world's most prestigious AI conference, which will be held at COEX in Seoul this July. It was selected for an Oral presentation, an honor given to only the top 0.7％ (168 papers) out of all submitted papers (23,918 papers), recognizing the excellence of the research. ICML is considered one of the most influential international conferences in the fields of AI and machine learning. Recently, AI technology is rapidly evolving beyond generative AI that writes text and draws pictures into the era of 'Physical AI,' which moves actual machines and acts in the real world. Representative examples include robots that perform dangerous tasks in factories instead of humans, autonomous vehicles that judge road situations on their own, and medical robots that perform delicate surgeries. However, there was a barrier that had to be overcome for the practical application of physical AI. It is the problem of learning human-level evaluation criteria to judge whether the actions performed by a machine match human intentions and which actions are more desirable. For example, when a surgical robot performs suturing or an autonomous vehicle passes through a complex intersection, the AI must choose the most appropriate action among numerous options. To achieve this, a 'Reward Function' that reflects human preferences and judgment criteria is required. However, until now, humans had to directly evaluate thousands to tens of thousands of action data points to build this, which required an enormous amount of time and cost. The research team focused on the way humans learn new tasks after seeing just a few demonstrations. VOTP, developed by the research team, helps AI understand human-preferred action patterns on its own with just a few videos of good and bad examples. Even without humans evaluating a vast amount of data one by one as before, AI can understand human judgment criteria and expand its learning to various situations. The core idea of this research is that intelligent machines such as robots or autonomous vehicles can quickly grasp human intents with only a small number of videos containing human preferences. The algorithm developed for this purpose proved its effectiveness and generalization performance through extensive experiments across various environments and tasks. This method can significantly reduce human feedback and data construction costs required for physical AI development. Since robots, autonomous vehicles, and industrial machinery can learn actions that meet human expectations with only a small number of examples, it is expected to drastically shorten development time and costs. The technology can be widely applied not only to robot arm control, humanoid robots, autonomous vehicles, smart factories, drones, and surgical robots, but also to AI agents that directly operate computers. In particular, it is expected to be utilized as a core foundational technology for all physical AI systems that need to learn human intention and satisfaction. < VOTP Research Image (AI Generated) >  Professor Chang D. Yoo said, "The core of physical AI is making machines understand human intentions and choose the correct actions," and added, "Since VOTP can learn human judgment criteria with only a small number of videos, it is a core technology that will accelerate the era of robots making human-like judgments." This research, in which PhD student Tung M. Luu from the School of Electrical Engineering participated as the first author, was selected as an Oral presentation paper at ICML (International Conference on Machine Learning) 2026, the world's most prestigious AI conference. ※ Paper Title: Video-Based Optimal Transport for Feedback-Efficient Offline Preference-Based Reinforcement Learning, Paper File: https://sanctusfactory.com/data/file/publications/202606091714078906.pdf This research was conducted with support from the Institute for Information & Communication Technology Planning & Evaluation (IITP) and the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT."          

Humanoid Robot Pilot PIBOT Wins Best Paper Award a..

  < (From left of the award recipients) Ph. D candidate Sungjae Min, Ph. D candidate Gyuree Kang, Professor David Hyunchul Shim,  Ph.D candidate Hyungjoo Kim >      KAIST announced on June 5th that a paper proposing an aircraft autonomous piloting framework based on the humanoid robot pilot ‘PIBOT,’ developed by a research team led by Professor David Hyunchul Shim of the School of Electrical Engineering, was selected as the Best Paper Award among the papers published in the IEEE Robotics & Automation Magazine (IEEE RAM) in 2025. < The proposed PIBOT system framework capable of piloting based on aviation manuals and voice communication without modifying existing aircraft >  This award is highly meaningful as it signifies that grassroots research based entirely on domestic, independent initiatives has been recognized as a world-class achievement in robotics. The award ceremony took place in Vienna, Austria, on June 4, 2026 (local time) during the International Conference on Robotics and Automation (ICRA 2026). IEEE Robotics & Automation Magazine (IEEE RAM) is a prestigious academic magazine published by the IEEE Robotics and Automation Society (RAS), under the umbrella of IEEE, the world's largest technical professional organization. It is well known for delivering the latest research achievements, industry trends, and tutorials in the fields of robotics and automation, widely conveying robot technologies applicable to actual industrial sites to researchers in both industry and academia. As of 2025, IEEE RAM recorded an Impact Factor (IF) of 7.1, holding the second highest impact among IEEE publications in the field of robotics. In particular, it presents the Best Paper Award to research that has a significant academic and industrial impact among the papers published after undergoing rigorous peer review. This study was selected as a Future Challenge Defense Technology Research and Development Project by the Agency for Defense Development (ADD) in 2021 and was conducted based purely on domestic technology with support of approximately 5.7 billion won over five years. The research team received high praise for implementing Physical AI technology at an exceptionally high level, enabling a humanoid robot to systematically and adaptively perform complex tasks such as piloting aircraft based on artificial intelligence, going beyond simple walking or carrying items. Recently, humanoid robot technology has been developing rapidly in terms of athletic performance, such as tumbling or implementing complex movements. However, in the industrial sector, the applicability to actual industrial sites is drawing attention as a more critical factor. The pilot robot ‘PIBOT’ being developed by Professor David Hyunchul Shim's research team is designed to acquire specialized knowledge required for aircraft operation and to recognize and respond to actual flight situations in real time, going beyond simple repetitive tasks or logistics processing. Accordingly, it is evaluated as presenting a new direction for the utilization of humanoid robot technology, termed as Expert Physical AI. < The research team's PIBOT sitting in an actual aircraft (KLA-100) and operating the instruments and control stick >  The research team has successfully completed Phase 1 of the research since the project launched in 2021, and since 2024, they have been developing Phase 2 of the pilot robot, which features a human-like physique and joint structure suitable for actual aircraft piloting. In addition, they are pursuing collaborative research with relevant organizations to expand and apply this technology to various mobile vehicle piloting fields, such as ground vehicles and ships, as well as aircraft. < PIBOT performing piloting in an aircraft simulator device >  Professor David Hyunchul Shim said, “It is very meaningful that the pilot robot technology, proposed for the first time in the world by Korean researchers, has been recognized as a world-class research achievement thanks to the support of a large-scale national project. We will further develop our research in a direction where humanoid robots can help humans in real-world environments and safely operate complex systems.” In this study, PhD students Sungjae Min, Gyuree Kang, and Hyungjoo Kim participated as co-first authors, and Professor David Hyunchul Shim served as the corresponding author. The paper can be found through IEEE Xplore. ※ Paper Title: “Toward Fully Autonomous Aviation: PIBOT, a Humanoid Robot Pilot for Human-Centric Aircraft Cockpits”, Paper Links: https://doi.org/10.1109/MRA.2024.3505774, https://ieeexplore.ieee.org/document/10798973/ Meanwhile, this research was conducted with support from the Agency for Defense Development's Future Challenge Defense Technology Research and Development Project.    

KAIST Turns DNA from Genetic Information Carrier i..

  <Professor Jimin Park(Center), Ph.D candidate Tae Kyoung Lee, Ph. D candidate Sang Yeon Oh>   The fixed idea that DNA is only a molecule that stores genetic information is being challenged. KAIST researchers have developed a technology that controls the chemical environment around catalysts at the nanometer scale by designing DNA sequences, the arrangement of A, T, G, and C that make up genetic information. The team has presented a new catalyst platform that can improve hydrogen production efficiency and increase the yield of desired chemical products by designing DNA much like writing a computer program. KAIST (President Kwang Hyung Lee) announced on the 21st of May that a research team led by Professor Jimin Park of the Department of Chemical and Biomolecular Engineering has developed a core technology that precisely controls the microscopic chemical environment around catalysts by coating the surface of gold nanoparticles, ultrafine gold particles measuring 1–100 nm, with “single-stranded DNA,”a flexible DNA molecule composed of a single strand that can be designed with a desired length and structure and serves as a nano-coating material for controlling the reaction environment. In electrochemical reactions, which use electricity to drive chemical reactions and are used for hydrogen production or the manufacture of eco-friendly chemicals, performance is determined not only by the catalyst itself but also by the local reaction environment around the catalyst, such as acidity (pH) and ion distribution. However, conventional approaches have relied on special polymer coating materials, plastic-like materials made of long molecular chains, and have faced limitations in precisely designing internal structures at the nanometer scale. To solve this problem, the research team focused on “single-stranded DNA,” DNA composed of a single strand. DNA carries a negative charge, meaning it can influence the movement of surrounding ions, atoms or molecules with electric charge, and it has the advantage that its length and base sequence can be freely designed. In particular, changing the base sequence allows the internal network structure of DNA to be precisely controlled, making it possible to create a customized nano-coating layer on the catalyst surface. < Schematic illustration of catalytic interfacial microenvironment regulation using a single-stranded DNA layer > The research team attached DNA with various base sequences to the surface of gold nanoparticles and analyzed the electrochemical reactions. As a result, they found that the key factor determining catalyst performance was not simply the thickness of the coating layer, but the internal network structure formed according to the DNA base sequence. This means that even coating layers of the same thickness can create different pathways for the movement of ions needed for reactions, depending on how the internal DNA structure is organized. It is the same principle as traffic flow changing depending on how a road network is designed, even when the roads are the same width. The team also used real-time surface-enhanced Raman spectroscopy, a technology that uses lasers to analyze the chemical state of molecules in real time, to observe the reaction process. Through this, they directly confirmed that the DNA layer functions as an interfacial layer, a layer that performs a special function at the boundary where two materials meet, by regulating the movement of hydroxide ions (OH⁻) and changing the local pH around the catalyst. In simple terms, the DNA layer acts like a “traffic control center” around the catalyst, guiding the movement of ions. It helps some ions move more quickly while restricting the movement of others, thereby changing the reaction environment in the desired direction. By observing this process in real time, the researchers proved that DNA is not merely a protective film, but actively regulates the reaction environment. The team applied this technology to the hydrogen evolution reaction and the glycerol oxidation reaction, which converts glycerol, a byproduct of biodiesel production, into high-value chemicals. As a result, hydrogen production efficiency varied significantly depending on the DNA base sequence, and the selectivity, the proportion of a specific product formed, for glycerate, a material used in cosmetics and pharmaceuticals, also improved. This means that desired reaction outcomes can be achieved simply by adjusting the DNA sequence, without newly creating complex catalyst structures. < Research image (AI-generated image) > Professor Jimin Park said, “This study shows that DNA can be used not as a genetic information storage medium, but as a precise nanomaterial that controls electrochemical reactions,” adding, “By designing DNA sequences to control acidity and ion movement on catalyst surfaces, we expect this technology to be broadly applied across carbon-neutral technologies, including hydrogen production and biomass conversion.” This study was conducted with KAIST Department of Chemical and Biomolecular Engineering doctoral students Sang Yeon Oh and Tae Kyoung Lee as co-first authors, and Professor Jimin Park as the corresponding author. The research was published on May 5 in the internationally renowned Journal of the American Chemical Society.  ※ Paper title: “Programmable Single-Stranded DNA Layers as Modulators of Nanoscale pH at Electrocatalytic Interfaces,” DOI: 10.1021/jacs.6c02995  ※ Author information: Sang Yeon Oh and Tae Kyoung Lee (KAIST, co-first authors), Jaeyeon Jun, Jinse Woo, Changho Lee, Yongha Kim (KAIST, co-authors), and Jimin Park (KAIST, corresponding author) This research was supported by the National Research Foundation of Korea’s Outstanding Young Scientist Program , Global Matching Program , and Young Researcher Infrastructure Support Program.