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KAIST Opens the Era of “Space Sensors” with Optical Functions Reconfigurable by Electrical Signals Alone
Until now, satellites and space payloads have required new optical filters and sensors to be designed whenever their missions changed. A future is now on the horizon in which a single ultra-compact optical chip can perform a variety of roles—including those of a thermal imaging sensor, spectrometer, and infrared camera—using electrical signals alone.
KAIST (President Choongsik Bae) announced on 14th of July that a research team led by Professor Hyun Jung Kim from the Department of Aerospace Engineering, in collaboration with a research team led by Professor Juejun Hu at the Massachusetts Institute of Technology (MIT), has demonstrated the first transmissive mid-infrared amplitude-only spatial light modulator based on a scalable two-dimensional, electrically addressable metasurface architecture.
The key achievement of this research is that a single optical chip can perform a variety of sensor functions using electrical signals alone. Previously, new optical filters and sensors had to be fabricated for each new mission. In the future, the technology is expected to enable the realization of “software-defined sensors,” whose functions can be changed without replacing the hardware.
The device developed by the research team is a transmissive mid-infrared spatial light modulator, or SLM, based on a metasurface. A metasurface is an ultrathin optical structure that uses microscopic patterns much smaller than the width of a human hair to freely control the intensity, direction, and wavelength of light.
A spatial light modulator controls the spatial distribution of light on a pixel-by-pixel basis. In the present device, each pixel switches the intensity of transmitted mid-infrared light between two programmed states. The research team succeeded, for the first time in the world, in electrically and independently controlling each individual pixel.
Conventional spatial light modulators face significant limitations in the mid-infrared. Liquid-crystal-based devices suffer from material absorption and relatively slow response, while digital micromirror devices operate in reflection. Transmissive mid-infrared SLMs have therefore remained largely unexplored. This has limited their application to satellite sensors, ultra-compact spectrometers—which analyze light according to wavelength—and adaptive optical systems, which automatically adjust their optical performance in response to changes in the surrounding environment.
To address these limitations, the researchers used GSST—Ge₂Sb₂Se₄Te, or germanium-antimony-selenium-tellurium—an optical phase-change material (PCM) whose light transmittance changes when it receives an electrical signal.
Once GSST receives an electrical signal, it retains its state and continues to maintain the same optical performance even after the power is turned off. This nonvolatile characteristic eliminates the need for a continuous power supply, making the material suitable for satellites and space payloads, where the available electrical power is limited.
As the number of pixels on an optical chip increases, electrical current can flow into pixels other than the selected pixel, causing unintended pixels to operate as well. This is known as the “sneak-path” problem.
The research team solved this problem by integrating a silicon PIN diode into each pixel. A PIN diode is a semiconductor device that allows electrical current to flow only to the intended pixel. This enabled the researchers to accurately select and control only the desired pixels.
Using this approach, the team independently controlled all the pixels in a 6 × 6 pixel array and successfully produced desired optical patterns. The device also maintained stable performance after more than 16,700 switching cycles, demonstrating approximately 13 times greater endurance than previous technology.
The device was fabricated using silicon photonics, a technology that produces optical devices through standard semiconductor manufacturing processes. This makes it relatively easy to scale the technology to larger optical chips containing hundreds, thousands, or even more pixels.
The current device controls only the amount of transmitted light. In the future, however, more sophisticated metasurface designs are expected to enable the technology to develop into “universal reconfigurable optics,” capable of freely controlling the direction and polarization of light as well.
The greatest significance of this research is that it presents a new concept in which “optics, too, can be changed like software.” In other words, the study provides a foundation for programmable optical hardware that could support different sensing functions through reconfiguration rather than hardware replacement. In the future, this is expected to usher in an era of software-defined sensors, in which a single optical chip can perform different functions depending on the situation, serving as a thermal imaging sensor, spectrometer, infrared camera, or optical communication device.
Once commercialized, the technology is expected to make it possible to implement a wide range of optical systems on a single platform. Potential applications include satellites and space payloads, launch-vehicle health diagnostics, thermal monitoring of space stations, measurement of in-space manufacturing processes, infrared imaging, and optical communications.
This research is an achievement that further advances MIT–NASA collaborative research initiated in 2018, when Professor Hyun Jung Kim was working as a researcher at the National Aeronautics and Space Administration (NASA), and subsequently continued at KAIST.
Building on this foundation, KAIST’s STAR Lab and Professor Juejun Hu’s research team at MIT are currently conducting joint research on active meta-optics, silicon photonics, and space sensor systems, with the goal of applying the technology in actual space environments.
The two teams have established a full-cycle international collaborative research framework encompassing material development, chip design and fabrication, sensor-system integration, space-environment verification, and future flight demonstrations.
Professor Kim’s research team is now developing the technology into an operational space sensor. Under the Ministry of Science and ICT’s Young Researcher Program, the team is developing an ultra-precise system for measuring the surface temperature of launch vehicles.
The research is also being expanded through the “Space Services and Manufacturing Research Center” under the Innovation Research Center Program. The team is conducting research to develop the technology into a common optical platform that can be used for space-station thermal monitoring, anomaly diagnosis, measurement of in-space manufacturing processes, and optical communications.
“This research is not simply about creating one more new optical device,” said Professor Kim. “It presents the foundation for an era of software-defined sensors, in which a single optical chip performs a variety of functions depending on the mission.”
“By combining MIT’s nanophotonics technology—which uses nanostructures to control light—with KAIST’s space sensor technology, we plan to develop this technology into an actual space system,” she added. The research was published online in the international journal Nature Communications on July 7.
Paper title: “Two-Dimensional Pixel-Level Addressable Mid-Infrared Metasurface Spatial Light Modulator”
DOI: 10.1038/s41467-026-75346-5
This work was funded by the Air Force SBIR Program under contract FA2394-23-C-5076, the National Science Foundation under awards 2329088 and 2132929, and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2025-00515651 and RS-2025-02213804).
2026.07.14 View 3430 -
KAIST Develops AI Technology to Detect Early Warning Signs of Cerebrovascular Disease at Home
Cerebrovascular disease can lead to serious aftereffects if treatment is delayed, but it is difficult to detect before symptoms appear. KAIST researchers have developed an AI technology that analyzes real-life daily activity and environmental data from older adults to identify digital behavioral markers of cerebrovascular disease risk based on subtle changes at home.
KAIST (President Choongsik Bae) announced on the 12th of July that a research team led by Professor Lisa Lim from the Department of Civil and Environmental Engineering, in collaboration with Professor Jo Woon Chong from the School of Electronic and Electrical Engineering at Sungkyunkwan University (President Ji-Beom Yoo) and Professor Kyung-Hee Cho from the Department of Neurology at Korea University Anam Hospital (President Dongwon Kim), has developed an AI framework that uses long-term lifelog data collected in the homes of older adults to identify the prodromal phase of cerebrovascular disease and assess imminent diagnostic risk.
The study was based on lifelog data from 1,224 older adults collected by LivOn Care Co., Ltd. in real residential environments. The research team analyzed a total of 13,362 two-week lifelog samples, demonstrating the possibility of detecting early warning signs through subtle changes in daily life, rather than relying only on the conventional approach of treating the disease after it has already occurred.
The research team developed AI technology that identifies cerebrovascular disease risk stages by analyzing daily activity, sleep, circadian rhythm, and indoor environmental information, together with age and chronic disease data. This shows that changes in everyday living patterns, which are difficult to capture through hospital examinations alone, can serve as important clues for detecting early risk signals of cerebrovascular disease.
The team also succeeded in assessing whether a cerebrovascular disease diagnosis was approaching by analyzing changes in lifestyle patterns over time. When lifelog data from within four weeks before diagnosis were classified as the “imminent diagnostic risk period” and data from 12 weeks before diagnosis were classified as the “non-imminent period,” the AI distinguished between the two periods with a high accuracy of 96.53%. This result suggests that even before a hospital visit, small changes in daily life may help identify whether the risk of cerebrovascular disease has increased.
Another key feature of this study is that the AI does not simply determine whether a risk exist, but also applies explainable AI to identify the lifestyle patterns and environmental factors behind its judgment.
The analysis showed that older adults in the prodromal phase of cerebrovascular disease tended to show frequent continuous activity between 10 p.m. and 2 a.m., a time when the body would normally be preparing for sleep. In other words, irregular daily rhythms, such as delayed sleep onset and a reduced distinction between day and night activity, were closely associated with prodromal signals of cerebrovascular disease.
The researchers also found that as the time of diagnosis approached, the frequency of continuous activity during the evening period from 6 p.m. to 10 p.m. noticeably decreased, while inactive time increased. Low indoor humidity, indicating a dry indoor environment, also emerged as an important factor in identifying an imminent diagnostic risk.
The research team expects this technology to be used as a digital healthcare tool that can objectively monitor the health status of older adults who may have difficulty clearly describing their own condition, while providing useful early warning indicators to medical professionals and caregivers.
However, the team explained that this study does not predict the exact onset of cerebrovascular disease or replace clinical diagnosis. Rather, it is a supportive technology intended to aid prevention and early medical consultation, and prospective validation in larger patient groups will be necessary before actual clinical application.
Professor Lisa Lim said, “The key point of this study is not that AI should replace a hospital diagnosis, but that it can first detect risk signals in small lifestyle changes at home and help connect patients to medical care at the right time,” adding, “We expect this technology to contribute to a shift from a healthcare system that treats disease after it occurs to one that supports prevention and early intervention.”
This study, with KAIST Dr. Jeongyeop Baek as the first author, was published on June 2 in npj Digital Medicine, a leading international journal in digital healthcare published by Nature Portfolio, with an impact factor of 15.1 and ranked in the top 0.3% of JCR journals.
※ Paper title: AI home monitoring for behavioral markers of cerebrovascular disease
DOI: https://doi.org/10.1038/s41746-026-02836-7
This work was also supported by the National Research Foundation (NRF) grant funded by the Korea government (Ministry of Science and ICT) (RS-2025-16068234).
2026.07.13 View 3051 -
KAIST Automates the Search for “Dream Semiconductor” 2D Semiconductors
The era of researchers manually searching for two-dimensional semiconductors, which are drawing attention as next-generation AI semiconductors, is coming to an end. KAIST researchers have automated semiconductor screening and device fabrication, analyzed thousands of devices, and revealed the relationship between thickness and performance that had long been difficult to identify. This achievement is expected to shift next-generation semiconductor research toward a data-driven approach and accelerate the commercialization of AI semiconductors and ultra-low-power semiconductors.
KAIST (President Choongsik Bae) announced on the 9th that a research team led by Professor Jimin Kwon of the School of Electrical Engineering and the Department of AI System has developed a technology that automatically identifies two-dimensional semiconductors from optical microscope images alone and connects the process to transistor fabrication, through joint research with UNIST, Hanbat National University, Hanyang University, and Washington University in St. Louis in the United States.
Two-dimensional semiconductors are ultrathin semiconductors only a few atomic layers thick. They are called “dream semiconductors” because they can enable smaller semiconductors that consume less electricity than conventional silicon semiconductors. Today’s silicon semiconductors are approaching physical limits, as continued miniaturization of circuits leads to greater power loss and heat generation. Two-dimensional semiconductors, which are attracting attention as next-generation materials to overcome these limits, are expected to be used in a wide range of future technologies, including AI semiconductors, smartphones, data centers, wearable devices, foldable or stretchable electronics, and ultra-small medical sensors.
However, in two-dimensional semiconductors made through solution processing, the position, size, and thickness of each small semiconductor flake all differ, requiring researchers to find the desired samples one by one under a microscope. They then had to manually design electrodes according to the identified positions, requiring substantial time and effort, and making it practically difficult to analyze thousands or more devices at once.
The research team used molybdenum disulfide (MoS₂), a representative two-dimensional semiconductor material. By using the fact that the RGB red, green, and blue brightness values seen under a microscope change depending on thickness, the team enabled a computer to automatically identify the desired semiconductor and automatically design the electrodes. Verification using atomic force microscopy (AFM) confirmed that even subtle thickness differences of three to eight layers could be accurately distinguished.
Through this approach, the team successfully selected suitable samples automatically from more than 120,000 semiconductor flakes and fabricated and analyzed 1,615 transistors.
The large-scale analysis also produced meaningful results. The team statistically clarified for the first time that as the semiconductor becomes thicker, current flows more easily, but the ability to switch electricity on and off actually decreases. This characteristic had been difficult to confirm previously because only a small number of samples could be analyzed, but the team revealed it through large-scale data.
The greatest significance of this study is that it did not simply automate the fabrication process, but transformed two-dimensional semiconductor research, which had relied on human experience, into data-driven research. Going forward, the technology is expected to enable researchers to fabricate and analyze more semiconductors more quickly, identify high-performance materials, and ultimately expand into research in which AI designs new semiconductors.
This study was conducted with Professor Jimin Kwon, Dr. Haksoon Jung, and Dr. Yongwoo Lee of KAIST as co-corresponding authors, and Sanghyun Lee of UNIST as the first author. The research results were published on April 3 in Advanced Functional Materials, a leading international journal in materials science, and were also selected as an Inside Back Cover article in the field of 2D Materials & Electronics.
※ Paper title: Statistically Resolving Thickness-Dependent Electrical Characteristics in Multilayer-MoS₂ Transistors, DOI: 10.1002/adfm.202532204
※ Author information: Professor Jimin Kwon (KAIST, corresponding author), Dr. Haksoon Jung (KAIST, corresponding author), Dr. Yongwoo Lee (KAIST, corresponding author), Sanghyun Lee (UNIST, first author), and participating researchers from partner institutions: Sumin Hong (UNIST), Minho Park (UNIST), Professor Seongju Kim (Hanbat National University), Professor Sang-Hoon Baek (Hanyang University), Professor Joonki Suh (KAIST), Seonguk Yang (KAIST), Professor Sang-Hoon Bae (Washington University in St. Louis), and Dr. Chang-Soo Lee (TDS)
This research was supported by the Individual Basic Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT), and by the Advanced Strategic Industry Super-Gap Technology Development Program of the Korea Planning & Evaluation Institute of Industrial Technology (KEIT), funded by the Ministry of Trade, Industry and Energy (MOTIE).
2026.07.09 View 3998 -
KAIST Develops Core Display Technology That Prevents Image Distortion Even When Stretched
Beyond bendable and foldable displays, the era of stretchable displays, whose screens can expand freely like rubber, is now emerging. KAIST researchers have developed a core technology that allows text, images, and other on-screen information to retain their original shape even when the screen is stretched by up to 15%. The achievement is expected to help solve the problem of image distortion and accelerate the commercialization of next-generation high-quality stretchable displays.
KAIST (President Choongsik Bae) announced on the July 8 that a research team led by Professor Seunghyup Yoo of the School of Electrical Engineering, in collaboration with Professor Hanul Moon’s team at Dong-A University (President Hae Woo Lee), has successfully implemented an auxetic-based stretchable display platform. Auxetic structures expand in both width and length when pulled, allowing the display to stretch uniformly at the same ratio in all directions without distorting the image on the screen.
Conventional stretchable displays are generally made by forming light-emitting devices on a stretchable substrate, which serves as the base layer of the display. However, when such a substrate is stretched in one direction, it tends to shrink in the opposite direction, causing letters and images on the screen to become flattened or distorted. Auxetic structures have been used to address this problem, but most previous approaches were limited to maintaining the overall horizontal-to-vertical ratio of the screen, while the letters and images within the screen still remained vulnerable to distortion.
Instead of bonding the auxetic structure and the stretchable substrate across the entire surface, as in conventional methods, the research team proposed a new design approach that uses computational analysis to selectively connect only the necessary points that ensure isotropic expansion throughout the substrate.
In the conventional approach, the twisting deformation that occurs as the auxetic structure stretches is directly transferred to the substrate, distorting the image inside the screen. In contrast, the platform developed by the research team was designed so that each region moves evenly outward from its original position. This allows not only the entire screen but also small areas such as letters and images to expand together while maintaining their original shapes.
The research team verified the platform’s performance by repeatedly stretching a substrate patterned with letters and images in both the horizontal and vertical directions. In the conventional method, the patterns underwent local deformation, whereas in the new platform, the shapes of the letters and images remained intact. This demonstrates that not only the whole screen but also fine images on-screen can expand uniformly without distortion.
The team also integrated an LED array, a structure in which multiple LEDs are arranged at regular intervals, onto the platform to verify its performance as an working stretchable display. Even when stretched by up to 15% in both the horizontal and vertical directions, stable electrical operation and the screen brightness were maintained. After repeated stretching to 15%, the decrease in brightness remained below 2%, confirming the platform’s potential for practical display applications.
This technology is expected to serve as a core platform for next-generation electronics with freely changeable shapes, including wearable electronic devices, electronic skin, or e-skin, which refers to electronic devices that stretch like skin while sensing and displaying information, medical biosensors, soft robots, and curved displays for automobiles and aircraft.
Professor Seunghyup Yoo of KAIST said, “For stretchable displays to be used as actual information display devices, they must not only stretch well, but also preserve on-screen information accurately during stretching,” adding, “This platform enables uniform expansion from small areas of the screen to the entire display, and will serve as a key foundational technology for accelerating the commercialization of high-quality stretchable displays.”
This study was led by KAIST Dr. Su-Bon Kim and Dr. Junho Kim as co-first authors, with Professor Hanul Moon of Dong-A University and Professor Seunghyup Yoo of KAIST as co-corresponding authors. The research was published in the international journal Nature Communications on June 10.
※ Paper title: Hybrid auxetic metamaterial platforms enabling multiscale isotropic expansion for distortion-free stretchable displays, DOI: 10.1038/s41467-026-74141-6
This research was supported by the National Research Foundation of Korea (NRF) Mid-Career Researcher Program, the Future Display Strategic Research Laboratory Program, the Korea Planning & Evaluation Institute of Industrial Technology (KEIT), and the Korea Institute for Advancement of Technology (KIAT) HRD Program.
2026.07.08 View 3700 -
KAIST Enables DNA Synthesis Using Only Temperature Instead of Chemical Reagents
"Complex chemical processes are essential for making DNA." This long-held assumption in the field of biotechnology has been overturned by a Korean research team. A KAIST research team has developed the world's first foundational technology that enables the synthesis of desired DNA using only temperature. Using this technology, the team also demonstrated a "DNA temperature black box" that records temperature changes during shipping without electricity.
KAIST announced on the 7th of July that a research team led by Professor Yeongjae Choi of the Graduate School of Engineering Biology, in collaboration with ATG Lifetech Inc. (CEO Taehoon Ryu) and a research team led by Professor Hansol Choi from the Department of Life Science at Ewha Womans University, has developed this platform technology that synthesizes desired DNA sequences by controlling only temperature.
DNA is the "blueprint" that contains the genetic information of humans and all other living organisms. Scientists use custom-made DNA in various biotechnology applications, such as diagnosing diseases, developing new drugs, and creating microorganisms with new functions. Until now, however, each time one of the four bases that make up DNA—A, T, G, and C—was connected, chemical reagents had to be added and washed out repeatedly. As a result, costly automated DNA synthesis equipment and specialized research facilities were essential.
To overcome these limitations, the research team developed "hairpin DNA that reacts only at specific temperatures." This hairpin DNA is a special DNA structure that remains folded like a hairpin and unfolds only at a certain temperature. The team placed multiple types of hairpin DNA that operate at different temperatures into a single test tube and succeeded in synthesizing desired DNA step by step by changing only the temperature in the sequence.
This opens the way for synthesizing DNA with only a general temperature control device, without the need for complex reagent replacement or large-scale equipment.
As the technology advances, it is expected to greatly reduce the cost and time required to make DNA, lowering the entry barriers not only for synthetic biology and genetic research, but also for various bioindustries such as drug development and precision medicine.
To demonstrate the practical applicability of the technology, the research team also implemented a power-free "DNA temperature black box." This device is normally stored in a freeze-dried state and begins operating when a single drop of water is added just before use. It then automatically records—directly into a DNA sequence—when, how long, and in what order the temperature changes during shipping. In addition, when exposed to temperatures above a certain level, the device changes color, allowing abnormalities to be checked visually on the spot. It is expected to be used for the quality control of products for which cold-chain distribution is important, such as vaccines, biopharmaceuticals, cell therapies, and fresh foods.
KAIST researcher Jangho Choi and GIST doctoral student Jinho Kim participated in this research as co-first authors, and the research results were published in the international journal Nature Communications on July 2.
※ Paper title: Programmable one-pot polymerase-mediated DNA synthesis via temperature control
※ DOI: https://doi.org/10.1038/s41467-026-74890-4
※ Related Video: https://drive.google.com/file/d/1bUtzC83qIm1k-hNFKTb09yFPhfsD4iU-/view?usp=drive_lin
※ Authors: Jangho Choi (KAIST, co-first author), Jinho Kim (GIST, co-first author), Hansol Choi (Ewha Womans University, corresponding author), Yeongjae Choi (KAIST, corresponding author)
This research was supported by the Ministry of Science and ICT through the Future Promising Convergence Technology Pioneer Program, the Biofoundry-Based Technology Development Program, the Young Researcher Program, and the Global Basic Research Laboratory Program.
2026.07.07 View 5240 -
KAIST Begins Developing the World’s First Brain-to-Robot Technology, Moving Robots by Thought and Sending Sensation Back to the Brain
KAIST researchers have begun developing a next-generation brain-robot interface platform that uses human brain signals to control an exoskeleton in real time and sends the tactile and force information sensed by the robot back to the brain.
KAIST, led by President Kwang-Hyung Lee, announced on the 25th that research teams led by Professors Kyoungchul Kong and Jung Kim of its Department of Mechanical Engineering, together with Angel Robotics Co., Ltd., have launched the world’s first bidirectional “Brain-to-Robot” system as a flagship initiative of the Korea Medical Device Development Fund (KMDF). The project runs from April 2026 to December 2032.
Professor Kyoungchul Kong is a world-renowned wearable-robotics researcher who founded Angel Robotics, a developer of walking-assist exoskeletons, and led his team to back-to-back gold medals at Cybathlon, the international competition for assistive technologies for people with disabilities. Professor Jung Kim is a globally recognized researcher who received the Scientist and Engineer of the Month Award for his work on robotic skin. Together, the two teams have formed a consortium to develop a Brain-to-Robot platform that merges neural interfaces with exoskeleton robotics.
Brain interface technologies that let users move a cursor or operate a smartphone with brain signals have already reached the stage of human clinical trials, and U.S. companies such as Neuralink and Synchron are accelerating their development. Existing approaches, however, have struggled to link actual movement and sensory feedback at the same time. They have also concentrated largely on advancing signal decoding itself, without clearly defining the target of control, namely what the brain signals actually drive and what kind of sensory information is returned.
Brain-to-Robot is designed to overcome these limitations head-on. It sets the exoskeleton itself as the control target: brain signals read the user’s movement intentions to drive the robot, and at the same time the robot’s sensory readings are delivered back to the brain. These readings include ground reaction force (the force the floor exerts on the foot), joint torque (rotational force at the joints), and tactile information. The aim is a complete bidirectional interface.
According to the research team, no fully bidirectional Brain-to-Robot system that combines exoskeleton control with sensory feedback has yet been reported anywhere in the world, and the project is expected to mark a turning point in brain interface technology.
Within this system, the KAIST teams are responsible for the core technologies. Professor Kong’s team will develop wearable-robot control and AI-based interpretation of movement intention, and will design a somatosensory interface, a system for transmitting bodily sensory information, that delivers the robot’s sensory data accurately to the Brain Chip, the semiconductor that processes brain signals.
Professor Kim’s team will develop robotic skin that senses in place of impaired sensation for people with disabilities, along with AI-based interpretation of somatosensory information.
The teams will also develop AI-based encoding and decoding algorithms that turn brain signals into robot commands and send the robot’s sensory information back to the brain. A key challenge is processing hundreds of channels of cortical signals, the neural signals generated in the cerebral cortex, while stably maintaining an ultra-low-latency closed loop, a control cycle in which signals are exchanged continuously in real time.
Commercialization of the flagship project will be led by Angel Robotics (KOSDAQ: 455900), the company founded by Professor Kong. The team plans to pursue full-cycle commercialization, from regulatory approval by the Ministry of Food and Drug Safety through to real-world deployment.
“If this technology succeeds, it will open a new rehabilitation paradigm in which people with quadriplegia can move beyond the hospital to walk on their own, pick up objects, and even feel sensation at their fingertips in everyday life,” Professor Kong said.
The research team stressed that, because this is an unprecedented and highly complex convergence technology never attempted at home or abroad, long-term safety, clinical validation, and a regulatory approval framework must advance in parallel with the technology itself. To reach the global market, they added, safety and efficacy testing, the accumulation of clinical evidence, a risk-management system, protection of brain-signal data, cybersecurity, and ethical review must all be addressed in an integrated way.
Meanwhile, KAIST is conducting a wide range of fundamental research in the field of brain interfaces. A research team led by Professor Hyung-Soon Park of the Department of Mechanical Engineering is studying wearable rehabilitation robot technologies based on neural intention-recognition interfaces, which identify users’ movement intentions from brain signals, for the effective treatment of neurological disorders. A research team led by Professor Sungho Cho of the School of Computing is developing AI-based brain-signal interpretation technologies.
A research team led by Professor Jihoon Lee of the Department of Brain and Cognitive Sciences is conducting next-generation brain–machine interface research focused on ultra-low-power bio/neural interface circuits, which connect and process biological and neural signals with low power consumption; wireless neural signal measurement technologies, which measure neural signals without wires; and on-device AI-based closed-loop neuromodulation technologies, which use cyclical control structures to exchange signals in real time.
In addition, a research team led by Professor Hyunjoo Lee of the School of Electrical Engineering is conducting research on high-resolution neural signal measurement and precision brain stimulation based on ultra-miniaturized multimodal neural electrodes, which can simultaneously measure and stimulate multiple types of neural signals. A research team led by Professor Minkyu Je of the Department of AI Semiconductor Systems is studying AI-based semiconductor integrated circuits and system technologies for next-generation neural interfaces. A research team led by Professor Jae-Woong Jeong of the School of Electrical Engineering is conducting research on high-precision brain-signal measurement, which precisely measures neural signals generated in the brain, and neuroengineering based on neural stimulation.
“This Brain-to-Robot flagship project is a world-class, highly challenging convergence research initiative led by the teams of Professors Kyoungchul Kong and Jung Kim,” said KAIST President Kwang-Hyung Lee. “KAIST has a wide range of researchers studying fundamental technologies in brain interfaces, AI, semiconductors, and robotics, and based on this foundation, we will lead innovation in next-generation Brain-to-Robot technologies.”
2026.06.25 View 6212 -
KAIST Develops Next-Generation Self-Powered Wearable Sensor Resilient to 668% Elongation
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.
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.
"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.
2026.06.18 View 7207 -
KAIST Illuminates the Eyes of Humanoid Robots with Minimal Memory
<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)
2026.06.17 View 4550 -
How Small Can Semiconductors Get? KAIST Develops Atomic-Level Prediction Technology
<(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.
2026.06.15 View 4764 -
Professor Hoon Sohn of the Department of Civil and Environmental Engineering Selected as the June Winner of the 'Korea Scientist and Engineer Award'
Professor Hoon Sohn from KAIST Department of Civil and Environmental Engineering has been selected as the June winner of the 'Korea Scientist and Engineer Award.'
The Korea Scientist and Engineer Award is presented monthly by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF) to a researcher who has made significant contributions to the advancement of science and technology through original research achievements over the past three years. The award includes a commendation from the Deputy Prime Minister and Minister of Science and ICT, along with a cash prize of 10 million KRW.
Professor Hoon Sohn was recognized for his contributions to developing an affordable, high-precision displacement sensor technology capable of detecting disaster and hazard risks in small-to-medium-sized infrastructure in real-time.
With the rapid aging of infrastructure such as bridges and buildings in recent years, the importance of technology that continuously monitors the structural safety of facilities has been growing. However, small-to-medium-sized structures—which make up the vast majority of infrastructure worldwide—exhibit very subtle movements on a millimeter scale, requiring highly precise measurement. Moreover, existing equipment is prohibitively expensive, making widespread adoption difficult.
To overcome these limitations, Professor Sohn combined millimeter-wave (mmWave) radar with Micro-Electro-Mechanical Systems (MEMS) accelerometers and applied signal processing algorithms. Through this, he successfully developed a technology that can simultaneously measure a structure's vibration, tilt, and displacement with a single sensor.
The production cost of this sensor is under 1 million KRW, which is approximately 1/40th the cost of conventional equipment, yet it boasts a high precision of 0.026 mm. Its power consumption has also been reduced to 1/100th of existing systems. Furthermore, it incorporates energy harvesting technology that utilizes ambient wasted energy, allowing for completely wireless operation.
The reliability of this technology has been proven through field demonstrations at more than 13 domestic and international sites, including a parking garage at Stanford University (USA), a highway in San Jose (USA), a bridge in Weifang (China), and the Geumgang Pedestrian Bridge in Sejong (South Korea).
Professor Sohn stated, "The significance of this research lies in establishing a technological foundation to precisely manage small-to-medium-sized structures that have previously been excluded from continuous, routine monitoring." He added, "Moving forward, I will continue my research on AI-based digital twins to lead the automation, unmanned operation, and intelligent advancement of the safety diagnosis market, thereby contributing to public safety and disaster prevention."
2026.06.04 View 3862 -
KAIST Produces Eco-Friendly Core Nylon Precursors Used from Clothing to Automobiles with Microbes
<(From Left) Dr. Da-Hee Ahn, Distinguished Professor Sang Yup Lee>
Nylon is a representative plastic material used throughout our daily lives, from clothing to automobiles. However, most of its raw materials have been produced through petrochemical processes, resulting in large carbon emissions. KAIST researchers have developed a technology that can produce key nylon precursors in an eco-friendly way using microbes.
KAIST (President Kwang Hyung Lee) announced on the 31st of May that a research team led by Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering has developed an Escherichia coli-based modular platform capable of producing three key monomers (basic molecular units that make up polymers) of “nylon 6,6” and “nylon 6” — adipic acid, hexamethylenediamine, and epsilon-caprolactam — from “glycerol (an eco-friendly bio-based byproduct generated during biodiesel production),” a renewable carbon source, using systems metabolic engineering (a technology that designs and optimizes microbial metabolic pathways to maximize the production of desired substances).
“Nylon 6” is highly flexible and is used in clothing and films, while “nylon 6,6” has excellent strength and heat resistance and is used in automobiles and machinery parts. The numbers after the nylon name indicate the number of carbon atoms contained in the raw material molecules.
The core of this study is that the biosynthetic pathway was divided into upstream and downstream modules, with E. coli strains assigned different roles. The upstream strain was designed to produce adipic acid from glycerol, while the downstream strain was designed to convert it into hexamethylenediamine or epsilon-caprolactam, respectively. Through this, the research team succeeded in producing adipic acid and hexamethylenediamine, the key raw materials of nylon 6,6, and epsilon-caprolactam, the key raw material of nylon 6, within a single integrated platform.
To improve production efficiency, the researchers compared and validated various enzymes (proteins that promote chemical reactions in living organisms), including carboxylic acid reductases and transaminases, and applied the optimal combination, thereby improving hexamethylenediamine titer. In addition, in the epsilon-caprolactam production process, they designed a flexible-linker fusion enzyme that enhances reaction efficiency through efficient cofactor regeneration.In the upstream module, the team reconstructed the biosynthetic pathway (a series of reaction processes through which compounds are produced in living organisms) and improved the performance of key enzymes using artificial intelligence (AI), increasing production titer. As a result, they succeeded in producing adipic acid at a level of 6 grams per liter (g/L) in a fed-batch fermentation process.
The research team also applied a “delayed inoculation” strategy (time-staggered co-culture), in which the second strain is introduced later after sufficient adipic acid has first been produced, rather than adding the two types of E. coli simultaneously. This is a method of sequentially introducing microbes with different roles at different times.
When this strategy was applied to a fed-batch fermentation process (a fermentation method that increases productivity by supplying nutrients step by step), the team produced 230 milligrams per liter (mg/L) of hexamethylenediamine and 808 micrograms per liter (μg/L) of epsilon-caprolactam using only glycerol. Although the production amounts are not yet high, the research team explained that these results represent world-class performance among cases of direct production from glycerol.
<Schematic Diagram>
This technology is significant in that it presents the possibility of producing nylon raw materials, which have relied on petrochemical processes, through bio-based methods.
The research team plans to further improve titer by combining AI-based enzyme design with additional systems metabolic engineering, and to expand the platform to produce various polymer raw materials (substances formed by the repeated bonding of multiple monomers).
Distinguished Professor Sang Yup Lee stated, “This study is meaningful in that it presents a modular microbial platform capable of producing key monomers required for nylon 6 and nylon 6,6 production from renewable carbon sources,” adding, “We will continue to advance enzyme and metabolic flux engineering to improve titer and develop this into a core platform for sustainably producing various bio-based polymer raw materials.”
The results of this study were published on May 4 in the Proceedings of the National Academy of Sciences (PNAS), with Dr. Da-Hee Ahn of the Department of Chemical and Biomolecular Engineering as the first author.
※ Paper title: “Metabolic engineering of Escherichia coli for the biosynthesis of nylon 6 and nylon 6,6 monomers”
Authors: Sang Yup Lee (KAIST, corresponding author), Da-Hee Ahn (KAIST, first author), Tong Un Chae (KAIST, second author), total of 3 authors
DOI: https://doi.org/10.1073/pnas.2535786123
This research was supported by the “Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries” project under the Petroleum Replacement Eco-Friendly Chemical Technology Development Program supported by the Ministry of Science and ICT, and by the “Development of Advanced Synthetic Biology Source Technologies for Leading the Biomanufacturing Industry” project under the Core Synthetic Biology Technology Development Program.
2026.06.01 View 3795 -
KAIST Develops Self-Regenerating Catalyst That Restores Its Own Performance, Opening a Breakthrough for CO₂ Conversion Technology
<(From Left) Professor Dong Young Chung, Ph.D Candidate Hongmin An, Hanjoo Kim>
Technologies that convert carbon dioxide (CO₂) emitted from factories and power plants into useful chemical feedstocks are considered key to achieving carbon neutrality. However, rapid degradation of catalyst performance has long hindered commercialization. KAIST researchers have now developed a “self-regenerating” catalyst that restores its activity during operation, offering a potential solution to this challenge.
KAIST (President Kwang Hyung Lee) announced on the 11th of March that a research team led by Professor Dong Young Chung from the Department of Chemical and Biomolecular Engineering has identified the fundamental cause of catalyst degradation in electrochemical reactions that convert CO₂ into useful materials and has developed a new design strategy that allows catalysts to maintain their active state during the reaction.
<Schematic Illustration of Copper Catalyst Reconstruction>
The research team focused particularly on copper (Cu) catalysts, which are widely used in CO₂ conversion reactions. Copper catalysts are known not to simply degrade during reactions but instead undergo a process called surface reconstruction, in which their surface structure continuously changes. The study revealed that the performance and lifetime of the catalyst vary significantly depending on how this reconstruction occurs.
The researchers discovered that copper catalyst reconstruction occurs mainly through two different mechanisms. The first involves formation and reduction of oxide layers on the catalyst surface. While this temporarily increases catalytic activity, it ultimately leads to long-term degradation of catalyst performance.
The second mechanism involves partial dissolution of the catalyst metal into the electrolyte followed by redeposition onto the catalyst surface. During this process, new reactive sites—known as active sites—are continuously created on the catalyst surface.
Based on this mechanism, the team proposed a method that allows the catalyst to maintain its active state during the reaction. By introducing a trace amount of copper ions into the electrolyte, dissolution and redeposition of copper occur in a balanced cycle on the catalyst surface. This continuous cycle generates new active sites, enabling the catalyst to maintain stable performance over extended periods.
Importantly, this technology can be implemented without complex additional processes or high-voltage conditions, significantly reducing energy consumption while enabling stable production of high-value C₂ compounds such as ethylene and ethanol. C₂ compounds are molecules containing two carbon atoms and are industrially important chemicals used as feedstocks for plastics, fuels, and other materials.
This research is significant because it proposes a new design concept in which catalysts are not merely optimized at the initial stage but are engineered to maintain their optimal state throughout the reaction process. The concept is expected to be applicable not only to CO₂ conversion technologies but also to a wide range of electrochemical energy conversion systems.
Professor Dong Young Chung stated, “This research approached catalyst degradation not as an inevitable phenomenon but as a controllable process,” adding, “We proposed a new strategy that allows catalysts to continuously maintain optimal activity during the reaction.”
The study was led by Hanjoo Kim, a doctoral student at KAIST, and Hongmin An, a combined master’s-doctoral student, as co-first authors. The research was published online on February 5 in the Journal of the American Chemical Society (JACS), one of the world’s most prestigious journals in chemistry.
※ Paper title: “Dynamic Interface Engineering via Mechanistic Understanding of Copper Reconstruction in Electrochemical CO₂ Reduction Reaction” DOI: 10.1021/jacs.5c16244
This research was supported by the Global Young Connect Program for Materials and the National Strategic Materials Technology Development Program funded through the National Research Foundation of Korea.
2026.03.11 View 4555