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KAIST Develops ‘Real-Time Programmable Robotic Sheet’ That Can Grasp and Walk on Its Own
<(From left) Prof. Inkyu Park from KAIST, Prof. Yongrok Jeong from Kyungpook National University, Dr. Hyunkyu Park from KAIST and Prof.Jung Kim from KAIST>
Folding structures are widely used in robot design as an intuitive and efficient shape-morphing mechanism, with applications explored in space and aerospace robots, soft robots, and foldable grippers (hands). However, existing folding mechanisms have fixed hinges and folding directions, requiring redesign and reconstruction every time the environment or task changes. A Korean research team has now developed a “field-programmable robotic folding sheet” that can be programmed in real time according to its surroundings, significantly enhancing robots’ shape-morphing capabilities and opening new possibilities in robotics.
KAIST (President Kwang Hyung Lee) announced on the 6th that Professors Jung Kim and Inkyu Park of the Department of Mechanical Engineering have developed the foundational technology for a “field-programmable robotic folding sheet” that enables real-time shape programming.
This technology is a successful application of the “field-programmability” concept to foldable structures. It proposes an integrated material technology and programming methodology that can instantly reflect user commands—such as “where to fold, in which direction, and by how much”—onto the material's shape in real time.
The robotic sheet consists of a thin and flexible polymer substrate embedded with a micro metal resistor network. These metal resistors simultaneously serve as heaters and temperature sensors, allowing the system to sense and control its folding state without any external devices.
Furthermore, using software that combines genetic algorithms and deep neural networks, the user can input desired folding locations, directions, and intensities. The sheet then autonomously repeats heating and cooling cycles to create the precise desired shape.
In particular, closed-loop control of the temperature distribution enhances real-time folding precision and compensates for environmental changes. It also improves the traditionally slow response time of heat-based folding technologies.
The ability to program shapes in real time enables a wide variety of robotic functions to be implemented on the fly, without the need for complex hardware redesign.
In fact, the research team demonstrated an adaptive robotic hand (gripper) that can change its grasping strategy to suit various object shapes using a single material. They also placed the same robotic sheet on the ground to allow it to walk or crawl, showcasing bioinspired locomotion strategies. This presents potential for expanding into environmentally adaptive autonomous robots that can alter their form in response to surroundings.
Professor Jung Kim stated, “This study brings us a step closer to realizing ‘morphological intelligence,’ a concept where shape itself embodies intelligence and enables smart motion. In the future, we plan to evolve this into a next-generation physical AI platform with applications in disaster-response robots, customized medical assistive devices, and space exploration tools—by improving materials and structures for greater load support and faster cooling, and expanding to electrode-free, fully integrated designs of various forms and sizes.”
This research, co-led by Dr. Hyunkyu Park (currently at Samsung Advanced Institute of Technology, Samsung Electronics) and Professor Yongrok Jeong (currently at Kyungpook National University), was published in the August 2025 online edition of the international journal Nature Communications.
※ Paper title: Field-programmable robotic folding sheet ※ DOI: 10.1038/s41467-025-61838-3
This research was supported by the National Research Foundation of Korea (Ministry of Science and ICT). (RS-2021-NR059641, 2021R1A2C3008742)
Video file: https://drive.google.com/file/d/18R0oW7SJVYH-gd1Er_S-9Myar8dm8Fzp/view?usp=sharing
2025.08.06 View 399 -
KAIST Reveals Placental Inflammation as the Cause of Allergies such as Pediatric Asthma
<(From left)Professor Heung-kyu Lee from the Department of Biological Sciences, Dr.Myeong Seung Kwon from the Graduate School of Medical Science>
It is already well-known that when a mother experiences inflammation during pregnancy, her child is more likely to develop allergic diseases. Recently, a KAIST research team became the first in the world to discover that inflammation within the placenta affects the fetus's immune system, leading to the child exhibiting excessive allergic reactions after birth. This study presents a new possibility for the early prediction and prevention of allergic diseases such as pediatric asthma.
KAIST (President Kwang Hyung Lee) announced on the 4th of August that a research team led by Professor Heung-kyu Lee from the Department of Biological Sciences found that inflammation occurring during pregnancy affects the fetus's stress response regulation system through the placenta. As a result, the survival and memory differentiation of T cells (key cells in the adaptive immune system) increase, which can lead to stronger allergic reactions in the child after birth.
The research team proved this through experiments on mice that had excessive inflammation induced during pregnancy. First, they injected the toxin component 'LPS (lipopolysaccharide),' a substance known to be a representative material that induces an inflammatory response in the immune system, into the mice to cause an inflammatory response in their bodies, which also caused inflammation in the placenta.
It was confirmed that the placental tissue, due to the inflammatory response, increased a signaling substance called 'Tumor Necrosis Factor-alpha (TNF-α),' and this substance activated immune cells called 'neutrophils*', causing inflammatory damage to the placenta. *Neutrophils: The most abundant type of white blood cells in our bodies (40-75%), playing an important role in innate immunity and killing invading bacteria and fungi.
This damage modulated postnatal offspring stress response, leading to a large secretion of stress hormone (glucocorticoid). As a result, the offspring's T cells, which are responsible for immune memory, survived longer and had stronger memory functions.
In particular, the memory T cells created through this process caused excessive allergic reactions when repeatedly exposed to antigens after birth. Specifically, when house dust mite 'allergens' were exposed to the airways of mice, a strong eosinophilic inflammatory response and excessive immune activation were observed, with an increase in immune cells important for allergy and asthma reactions.
Professor Heung Kyu Lee stated, "This study is the first in the world to identify how a mother's inflammatory response during pregnancy affects the fetus's allergic immune system through the placenta." He added, "This will be an important scientific basis for developing biomarkers for early prediction and establishing prevention strategies for pediatric allergic diseases."
The first author of this study is Dr. Myeong Seung Kwon from the KAIST Graduate School of Medical Science (currently a clinical fellow of gynecological oncology at Konyang University Hospital's Department of Obstetrics and Gynecology), and the research results were published in the authoritative journal in the field of mucosal immunology, 'Mucosal Immunology,' on July 1st. ※ Paper Title: Placental inflammation-driven T cell memory formation promotes allergic responses in offspring via endogenous glucocorticoids ※ DOI: https://doi.org/10.1016/j.mucimm.2025.06.006
This research was conducted as part of the Basic Science Research Program and the Bio-Medical Technology Development Program supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.08.03 View 224 -
KAIST Enables On-Site Disease Diagnosis in Just 3 Minutes... Nanozyme Reaction Selectivity Improved 38-Fold
<(From Left) Professor Jinwoo Lee, Ph.D candidate Seonhye Park and Ph.D candidate Daeeun Choi from Chemical & Biomolecular Engineering>
To enable early diagnosis of acute illnesses and effective management of chronic conditions, point-of-care testing (POCT) technology—diagnostics conducted near the patient—is drawing global attention. The key to POCT lies in enzymes that recognize and react precisely with specific substances. However, traditional natural enzymes are expensive and unstable, and nanozymes (enzyme-mimicking catalysts) have suffered from low reaction selectivity. Now, a Korean research team has developed a high-sensitivity sensor platform that achieves 38 times higher selectivity than existing nanozymes and allows disease diagnostics visible to the naked eye within just 3 minutes.
On the 28th, KAIST (President Kwang Hyung Lee) announced that Professor Jinwoo Lee’s research team from the Department of Chemical & Biomolecular Engineering, in collaboration with teams led by Professor Jeong Woo Han at Seoul National University and Professor Moon Il Kim at Gachon University, has developed a new single-atom catalyst that selectively performs only peroxidase-like reactions while maintaining high reaction efficiency.
Using bodily fluids such as blood, urine, or saliva, this diagnostic platform enables test results to be read within minutes even outside hospital settings—greatly improving medical accessibility and ensuring timely treatment. The key lies in the visual detection of biomarkers (disease indicators) through color changes triggered by enzyme reactions. However, natural enzymes are expensive and easily degraded in diagnostic environments, limiting their storage and distribution.
To address this, inorganic nanozyme materials have been developed as substitutes. Yet, they typically lack selectivity—when hydrogen peroxide is used as a substrate, the same catalyst triggers both peroxidase-like reactions (which cause color change) and catalase-like reactions (which remove the substrate), reducing diagnostic signal accuracy.
To control catalyst selectivity at the atomic level, the researchers used an innovative structural design: attaching chlorine (Cl) ligands in a three-dimensional configuration to the central ruthenium (Ru) atom to fine-tune its chemical properties. This enabled them to isolate only the desired diagnostic signal.
<Figure1. The catalyst in this study (ruthenium single-atom catalyst) exhibits peroxidase-like activity with selectivity akin to natural enzymes through three-dimensional directional ligand coordination. Due to the absence of competing catalase activity, selective peroxidase-like reactions proceed under biomimetic conditions. In contrast, conventional single-atom catalysts with active sites arranged on planar surfaces exhibit dual functionality depending on pH. Under neutral conditions, their catalase activity leads to hydrogen peroxide depletion, hindering accurate detection. The catalyst in this study eliminates such interference, enabling direct detection of biomarkers through coupled reactions with oxidases without the need for cumbersome steps like buffer replacement. The ability to simultaneously detect multiple target substances under biomimetic conditions demonstrates the practicality of ruthenium single-atom catalysts for on-site diagnostics>
Experimental results showed that the new catalyst achieved over 38-fold improvement in selectivity compared to existing nanozymes, with significantly increased sensitivity and speed in detecting hydrogen peroxide. Even in near-physiological conditions (pH 6.0), the catalyst maintained its performance, proving its applicability in real-world diagnostics.
By incorporating the catalyst and oxidase into a paper-based sensor, the team created a system that could simultaneously detect four key biomarkers related to health: glucose, lactate, cholesterol, and choline—all with a simple color change.
This platform is broadly applicable across various disease diagnostics and can deliver results within 3 minutes without complex instruments or pH adjustments. The findings show that diagnostic performance can be dramatically improved without changing the platform itself, but rather by engineering the catalyst structure.
<Figure 2.(a) Schematic diagram of the paper sensor (Zone 1: glucose oxidase immobilized; Zone 2: lactate oxidase immobilized; Zone 3: choline oxidase immobilized; Zone 4: cholesterol oxidase immobilized; Zone 5: no oxidase enzyme). (b) Single biomarker (single disease indicator) detection using the ruthenium single‑atom catalyst–based paper sensor.(c) Multiple biomarker (multiple disease indicator) detection using the ruthenium single‑atom catalyst–based paper sensor>
Professor Jinwoo Lee of KAIST commented, “This study is significant in that it simultaneously achieves enzyme-level selectivity and reactivity by structurally designing single-atom catalysts.” He added that “the structure–function-based catalyst design strategy can be extended to the development of various metal-based catalysts and other reaction domains where selectivity is critical.”
Seonhye Park and Daeeun Choi, both Ph.D. candidates at KAIST, are co-first authors. The research was published on July 6, 2025, in the prestigious journal Advanced Materials
-Title: Breaking the Selectivity Barrier of Single-Atom Nanozymes Through Out-of-Plane Ligand Coordinatio
- Authors: Seonhye Park (KAIST, co–first author), Daeeun Choi (KAIST, co–first author), Kyu In Shim (SNU, co–first author), Phuong Thy Nguyen (Gachon Univ., co–first author), Seongbeen Kim (KAIST), Seung Yeop Yi (KAIST), Moon Il Kim (Gachon Univ., corresponding author), Jeong Woo Han (SNU, corresponding author), Jinwoo Lee (KAIST, corresponding author
-DOI: https://doi.org/10.1002/adma.202506480
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea (NRF).
2025.07.29 View 471 -
KAIST Team Develops Optogenetic Platform for Spatiotemporal Control of Protein and mRNA Storage and Release
<Dr. Chaeyeon Lee, Professor Won Do Heo from Department of Biological Sciences>
A KAIST research team led by Professor Won Do Heo (Department of Biological Sciences) has developed an optogenetic platform, RELISR (REversible LIght-induced Store and Release), that enables precise spatiotemporal control over the storage and release of proteins and mRNAs in living cells and animals.
Traditional optogenetic condensate systems have been limited by their reliance on non-specific multivalent interactions, which can lead to unintended sequestration or release of endogenous molecules. RELISR overcomes these limitations by employing highly specific protein–protein (nanobody–antigen) and protein–RNA (MCP–MS2) interactions, enabling the selective and reversible compartmentalization of target proteins or mRNAs within engineered, membrane-less condensates.
In the dark, RELISR stably sequesters target molecules within condensates, physically isolating them from the cellular environment. Upon blue light stimulation, the condensates rapidly dissolve, releasing the stored proteins or mRNAs, which immediately regain their cellular functions or translational competency. This allows for reversible and rapid modulation of molecular activities in response to optical cues.
< Figure 1. Overview of the Artificial Condensate System (RELISR). The artificial condensate system, RELISR, includes "Protein-RELISR" for storing proteins and "mRNA-RELISR" for storing mRNA. These artificial condensates can be disassembled by blue light irradiation and reassembled in a dark state>
The research team demonstrated that RELISR enables temporal and spatial regulation of protein activity and mRNA translation in various cell types, including cultured neurons and mouse liver tissue. Comparative studies showed that RELISR provides more robust and reversible control of translation than previous systems based on spatial translocation.
While previous optogenetic systems such as LARIAT (Lee et al., Nature Methods, 2014) and mRNA-LARIAT (Kim et al., Nat. Cell Biol., 2019) enabled the selective sequestration of proteins or mRNAs into membrane-less condensates in response to light, they were primarily limited to the trapping phase. The RELISR platform introduced in this study establishes a new paradigm by enabling both the targeted storage of proteins and mRNAs and their rapid, light-triggered release. This approach allows researchers to not only confine molecular function on demand, but also to restore activity with precise temporal control.
< Figure 2. Cell shape change using the artificial condensate system (RELISR). A target protein, Vav2, which contributes to cell shape, was stored within the artificial condensate and then released after light irradiation. This release activated the target protein Vav2, causing a change in cell shape. It was confirmed that the storage, release, and activation of various proteins were effectively achieved>
Professor Heo stated, “RELISR is a versatile optogenetic tool that enables the precise control of protein and mRNA function at defined times and locations in living systems. We anticipate this platform will be broadly applicable for studies of cell signaling, neural circuits, and therapeutic development. Furthermore, the combination of RELISR with genome editing or tissue-targeted delivery could further expand its utility for molecular medicine.”
< Figure 3. Expression of a target mRNA using the artificial condensate system (RELISR) in mice. The genetic material for the artificial condensate system, RELISR, was injected into a living mouse. Using this system, a target mRNA was stored within the mouse's liver. Upon light irradiation, the mRNA was released, which induced the translation of a luminescent protein>
This research was conducted by first author Dr. Chaeyeon Lee, under the supervision of Professor Heo, with contributions from Dr. Daseuli Yu (co-corresponding author) and Professor YongKeun Park (co-corresponding author, Department of Physics), whose group performed quantitative imaging analyses of biophysical changes induced by RELISR in cells.
The findings were published in Nature Communications (July 7, 2025; DOI: 10.1038/s41467-025-61322-y). This work was supported by the Samsung Future Technology Foundation and the National Research Foundation of Korea.
2025.07.23 View 375 -
Why Do Plants Attack Themselves? The Secret of Genetic Conflict Revealed
<Professor Ji-Joon Song of the KAIST Department of Biological Sciences>
Plants, with their unique immune systems, sometimes launch 'autoimmune responses' by mistakenly identifying their own protein structures as pathogens. In particular, 'hybrid necrosis,' a phenomenon where descendant plants fail to grow healthily and perish after cross-breeding different varieties, has long been a difficult challenge for botanists and agricultural researchers. In response, an international research team has successfully elucidated the mechanism inducing plant autoimmune responses and proposed a novel strategy for cultivar improvement that can predict and avoid these reactions.
Professor Ji-Joon Song's research team at KAIST, in collaboration with teams from the National University of Singapore (NUS) and the University of Oxford, announced on the 21st of July that they have elucidated the structure and function of the 'DM3' protein complex, which triggers plant autoimmune responses, using cryo-electron microscopy (Cryo-EM) technology.
This research is drawing attention because it identifies defects in protein structure as the cause of hybrid necrosis, which occurs due to an abnormal reaction of immune receptors during cross-breeding between plant hybrids.
This protein (DM3) is originally an enzyme involved in the plant's immune response, but problems arise when the structure of the DM3 protein is damaged in a specific protein combination called 'DANGEROUS MIX (DM)'.
Notably, one variant of DM3, the 'DM3Col-0' variant, forms a stable complex with six proteins and is recognized as normal, thus not triggering an immune response. In contrast, another 'DM3Hh-0' variant has improper binding between its six proteins, causing the plant to recognize it as an 'abnormal state' and trigger an immune alarm, leading to autoimmunity.
The research team visualized this structure using atomic-resolution cryo-electron microscopy (Cryo-EM) and revealed that the immune-inducing ability is not due to the enzymatic function of the DM3 protein, but rather to 'differences in protein binding affinity.'
<Figure 1. Mechanism of Plant Autoimmunity Triggered by the Collapse of the DM3 Protein Complex>
This demonstrates that plants can initiate an immune response by recognizing not only 'external pathogens' but also 'internal protein structures' when they undergo abnormal changes, treating them as if they were pathogens.
The study shows how sensitively the plant immune system changes and triggers autoimmune responses when genes are mixed and protein structures change during the cross-breeding of different plant varieties. It significantly advanced the understanding of genetic incompatibility that can occur during natural cross-breeding and cultivar improvement processes.
Dr. Gijeong Kim, the co-first author, stated, "Through international research collaboration, we presented a new perspective on understanding the plant immune system by leveraging the autoimmune phenomenon, completing a high-quality study that encompasses structural biochemistry, genetics, and cell biological experiments."
Professor Ji-Joon Song of the KAIST Department of Biological Sciences, who led the research, said, "The fact that the immune system can detect not only external pathogens but also structural abnormalities in its own proteins will set a new standard for plant biotechnology and crop breeding strategies. Cryo-electron microscopy-based structural analysis will be an important tool for understanding the essence of gene interactions."
This research, with Professor Ji-Joon Song and Professor Eunyoung Chae of the University of Oxford as co-corresponding authors, Dr. Gijeong Kim (currently a postdoctoral researcher at the University of Zurich) and Dr. Wei-Lin Wan of the National University of Singapore as co-first authors, and Ph.D candidate Nayun Kim, as the second author, was published on July 17th in Molecular Cell, a sister journal of the international academic journal Cell.
This research was supported by the KAIST Grand Challenge 30 project.
Article Title: Structural determinants of DANGEROUS MIX 3, an alpha/beta hydrolase that triggers NLR-mediated genetic incompatibility in plants DOI: https://doi.org/10.1016/j.molcel.2025.06.021
2025.07.21 View 540 -
KAIST Develops New AI Inference-Scaling Method for Planning
<(From Left) Professor Sungjin Ahn, Ph.D candidate Jaesik Yoon, M.S candidate Hyeonseo Cho, M.S candidate Doojin Baek, Professor Yoshua Bengio>
<Ph.D candidate Jaesik Yoon from professor Ahn's research team>
Diffusion models are widely used in many AI applications, but research on efficient inference-time scalability*, particularly for reasoning and planning (known as System 2 abilities) has been lacking. In response, the research team has developed a new technology that enables high-performance and efficient inference for planning based on diffusion models. This technology demonstrated its performance by achieving a 100% success rate on an giant maze-solving task that no existing model had succeeded in. The results are expected to serve as core technology in various fields requiring real-time decision-making, such as intelligent robotics and real-time generative AI.
*Inference-time scalability: Refers to an AI model’s ability to flexibly adjust performance based on the computational resources available during inference.
KAIST (President Kwang Hyung Lee) announced on the 20th that a research team led by Professor Sungjin Ahn in the School of Computing has developed a new technology that significantly improves the inference-time scalability of diffusion-based reasoning through joint research with Professor Yoshua Bengio of the University of Montreal, a world-renowned scholar in deep learning. This study was carried out as part of a collaboration between KAIST and Mila (Quebec AI Institute) through the Prefrontal AI Joint Research Center.
This technology is gaining attention as a core AI technology that, after training, allows the AI to efficiently utilize more computational resources during inference to solve complex reasoning and planning problems that cannot be addressed merely by scaling up data or model size. However, current diffusion models used across various applications lack effective methodologies for implementing such scalability particularly for reasoning and planning.
To address this, Professor Ahn’s research team collaborated with Professor Bengio to propose a novel diffusion model inference technique based on Monte Carlo Tree Search. This method explores diverse generation paths during the diffusion process in a tree structure and is designed to efficiently identify high-quality outputs even with limited computational resources. As a result, it achieved a 100% success rate on the "giant-scale maze-solving" task, where previous methods had a 0% success rate.
In the follow-up research, the team also succeeded in significantly improving the major drawback of the proposed method—its slow speed. By efficiently parallelizing the tree search and optimizing computational cost, they achieved results of equal or superior quality up to 100 times faster than the previous version. This is highly meaningful as it demonstrates the method’s inference capabilities and real-time applicability simultaneously.
Professor Sungjin Ahn stated, “This research fundamentally overcomes the limitations of existing planning method based on diffusion models, which required high computational cost,” adding, “It can serve as core technology in various areas such as intelligent robotics, simulation-based decision-making, and real-time generative AI.”
The research results were presented as Spotlight papers (top 2.6% of all accepted papers) by doctoral student Jaesik Yoon of the School of Computing at the 42nd International Conference on Machine Learning (ICML 2025), held in Vancouver, Canada, from July 13 to 19.
※ Paper titles: Monte Carlo Tree Diffusion for System 2 Planning (Jaesik Yoon, Hyeonseo Cho, Doojin Baek, Yoshua Bengio, Sungjin Ahn, ICML 25), Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning (Jaesik Yoon, Hyeonseo Cho, Yoshua Bengio, Sungjin Ahn)
※ DOI: https://doi.org/10.48550/arXiv.2502.07202, https://doi.org/10.48550/arXiv.2506.09498
This research was supported by the National Research Foundation of Korea.
2025.07.21 View 470 -
KAIST Holds '2025 KAIST Science Frontier Camp' for Multicultural Youth
<2025 KAIST Science Frontier Camp Activities>
KAIST (President Kwang Hyung Lee) announced on the 18th of July that it hosted the '2025 KAIST Science Frontier Camp' for multicultural youth from the 15th for three days and two nights at the Creative Learning Building on its main campus in Daejeon.
This event was organized in accordance with the 'Multicultural Talent Nurturing Agreement' signed by KAIST and GS Caltex in 2024. It marks the first year of a mid-to-long-term project in which 100 million KRW in development funds will be contributed annually for four years. The Global Institute for Talented Education organized the camp, and approximately 30 middle school students from multicultural families affiliated with the 'Hanmaum Educational Volunteer Group' (Director, Honorary Professor Byung Kyu Choi), a mentoring and volunteer organization for multicultural students, participated.
The camp participants enjoyed developing their scientific thinking skills and problem-solving abilities, and broadening their understanding of STEM (Science, Technology, Engineering, and Mathematics) career paths through a variety of science activity programs, including: △'Black Box: Record the Egg's Last Moment!' △'Find the Best Strategy! Heuristic Algorithm Challenge' △'Future Society and AI, Finding Career Directions' △'Distance Dominates the World!' and △'Career Talk Concert.'
During the opening ceremony, Director Byung Kyu Choi delivered a congratulatory speech. Additionally, Yong Hyun Kim, Dean of Admissions at KAIST, gave a special lecture titled 'La La Land KAIST – A Story of Chasing the Dream of a Young Scientist,' sharing honest stories about careers and dreams as a scientist.
Gi Jung Yoo, a freshman from the Division of Undeclared Majors who participated in the camp as a student mentor, shared that he had a very meaningful time mentoring the participating students, who are future STEM hopefuls, sharing vivid experiences as well as insights on metric functions. He added his hope that more students would be given such opportunities.
< Students Actively Taking Part in the Camp Activities>
Si Jong Kwak, Director of the Global Institute for Talented Education, stated, "We hope this will be a practical way to help students foster their interest in science, learn the joy of discussion and communication, and design their future."
KAIST President Kwang Hyung Lee remarked, "This camp was a valuable opportunity for students from diverse cultural backgrounds to gain confidence through science and envision their future." He added, "KAIST will continue to dedicate efforts to nurturing multicultural talent and contribute to creating a sustainable society."
Since 2024, KAIST has introduced and selected multicultural students through its Equal Opportunity Admission track. Utilizing the development funds from GS Caltex, KAIST also established the 'GS Caltex Multicultural Excellence Scholarship Program.' Through this scholarship program, undergraduate students from multicultural families receive living expenses each semester, allowing them to focus more stably on their studies. As the number of applicants for the Equal Opportunity Admission track is increasing every year, more multicultural students are expected to benefit from scholarships in the future.
Additionally, in May, both organizations invited Ms. Si Si Wu Fong, a foreign employee at GS Caltex, to give a special lecture titled 'Working Life for Foreigners in Korea' to support foreign students' career exploration. Foreign students who attended the lecture reported positive feedback, stating that they gained practical career information and were motivated to pursue employment in STEM fields in Korea.
KAIST plans to continue strengthening its efforts to nurture multicultural talent, increase understanding of the upcoming multicultural society, and help spread social values.
<At the 2025 KAIST Science Frontier Camp>
2025.07.18 View 538 -
KAIST reveals for the first time the mechanism by which alcohol triggers liver inflammation
<(From left)Dr. Keungmo Yang, Professor Won-Il Jeong, Ph.D candidate Kyurae Kim>
Excessive alcohol consumption causes alcoholic liver disease, and about 20% of these cases progress to alcohol-associated steatohepatitis (ASH), which can lead to liver cirrhosis and liver failure. Early diagnosis and treatment are therefore extremely important. A KAIST research team has identified a new molecular mechanism in which alcohol-damaged liver cells increase reactive oxygen species (ROS), leading to cell death and inflammatory responses. In addition, they discovered that Kupffer cells, immune cells residing in the liver, act as a “dual-function regulator” that can either promote or suppress inflammation through interactions with liver cells.
KAIST (President Kwang-Hyung Lee) announced on the 17th that a research team led by Professor Won-Il Jeong from the Graduate School of Medical Science and Engineering, in collaboration with Professor Won Kim’s team at Seoul National University Boramae Medical Center, has uncovered the molecular pathway of liver damage and inflammation caused by alcohol consumption. This finding offers new clues for the diagnosis and treatment of alcohol-associated liver disease (ALD).
Professor Won-Il Jeong’s research team found that during chronic alcohol intake, expression of the vesicular glutamate transporter VGLUT3 increases, leading to glutamate accumulation in hepatocytes. Subsequent binge drinking causes rapid changes in intracellular calcium levels, which then triggers glutamate* secretion. The secreted glutamate stimulates the glutamate receptor mGluR5 on liver-resident macrophages (Kupffer cells), which induces ROS production and activates a pathological pathway resulting in hepatocyte death and inflammation.
*Glutamate: A type of amino acid involved in intercellular signaling, protein synthesis, and energy metabolism in various tissues including the brain and liver. In excess, it can cause overexcitation and death of nerve cells.
Glutamate accumulation in perivenous hepatocytes through vesicular glutamate transporter 3 after 2-week EtOH intake and its release by binge drinking>
A particularly groundbreaking aspect of this study is that damaged hepatocytes and Kupffer cells can form a "pseudosynapse"—a structure similar to a synapse which is previously thought to occur only in the brain—enabling them to exchange signals. This is the first time such a phenomenon has been identified in the liver.
This pseudosynapse forms when hepatocytes expand (ballooning) due to alcohol, becoming physically attached to Kupffer cells. Simply put, the damaged hepatocytes don’t just die—they send distress signals to nearby immune cells, prompting a response.
This discovery proposes a new paradigm: even in peripheral organs, direct structural contact between cells can allow signal transmission. It also shows that damaged hepatocytes can actively stimulate macrophages and induce regeneration through cell death, revealing the liver’s “autonomous recovery function.”
The team also confirmed in animal models that genetic or pharmacological inhibition of VGLUT3, mGluR5, or the ROS-producing enzyme NOX2 reduces alcohol-induced liver damage. They also confirmed that the same mechanism observed in animal models was present in human patients with ALD by analyzing blood and liver tissue samples.
Professor Won-Il Jeong of KAIST said, “These findings may serve as new molecular targets for early diagnosis and treatment of ASH in the future.”
This study was jointly led by Dr. Keungmo Yang (now at Yeouido St. Mary’s Hospital) and Kyurae Kim, a doctoral candidate at KAIST, who served as co–first authors. It was conducted in collaboration with Professor Won Kim’s team at Seoul National University Boramae Medical Center and was published in the journal Nature Communications on July 1.
※ Article Title: Binge drinking triggers VGLUT3-mediated glutamate secretion and subsequent hepatic inflammation by activating mGluR5/NOX2 in Kupffer cells ※ DOI: https://doi.org/10.1038/s41467-025-60820-3
This study was supported by the Ministry of Science and ICT through the National Research Foundation of Korea's Global Leader Program, Mid-Career Researcher Program, and the Bio & Medical Technology Development Program.
2025.07.17 View 658 -
KAIST Successfully Implements 3D Brain-Mimicking Platform with 6x Higher Precision
<(From left) Dr. Dongjo Yoon, Professor Je-Kyun Park from the Department of Bio and Brain Engineering, (upper right) Professor Yoonkey Nam, Dr. Soo Jee Kim>
Existing three-dimensional (3D) neuronal culture technology has limitations in brain research due to the difficulty of precisely replicating the brain's complex multilayered structure and the lack of a platform that can simultaneously analyze both structure and function. A KAIST research team has successfully developed an integrated platform that can implement brain-like layered neuronal structures using 3D printing technology and precisely measure neuronal activity within them.
KAIST (President Kwang Hyung Lee) announced on the 16th of July that a joint research team led by Professors Je-Kyun Park and Yoonkey Nam from the Department of Bio and Brain Engineering has developed an integrated platform capable of fabricating high-resolution 3D multilayer neuronal networks using low-viscosity natural hydrogels with mechanical properties similar to brain tissue, and simultaneously analyzing their structural and functional connectivity.
Conventional bioprinting technology uses high-viscosity bioinks for structural stability, but this limits neuronal proliferation and neurite growth. Conversely, neural cell-friendly low-viscosity hydrogels are difficult to precisely pattern, leading to a fundamental trade-off between structural stability and biological function. The research team completed a sophisticated and stable brain-mimicking platform by combining three key technologies that enable the precise creation of brain structure with dilute gels, accurate alignment between layers, and simultaneous observation of neuronal activity.
The three core technologies are: ▲ 'Capillary Pinning Effect' technology, which enables the dilute gel (hydrogel) to adhere firmly to a stainless steel mesh (micromesh) to prevent it from flowing, thereby reproducing brain structures with six times greater precision (resolution of 500 μm or less) than conventional methods; ▲ the '3D Printing Aligner,' a cylindrical design that ensures the printed layers are precisely stacked without misalignment, guaranteeing the accurate assembly of multilayer structures and stable integration with microelectrode chips; and ▲ 'Dual-mode Analysis System' technology, which simultaneously measures electrical signals from below and observes cell activity with light (calcium imaging) from above, allowing for the simultaneous verification of the functional operation of interlayer connections through multiple methods.
< Figure 1. Platform integrating brain-structure-mimicking neural network model construction and functional measurement technology>
The research team successfully implemented a three-layered mini-brain structure using 3D printing with a fibrin hydrogel, which has elastic properties similar to those of the brain, and experimentally verified the process of actual neural cells transmitting and receiving signals within it.
Cortical neurons were placed in the upper and lower layers, while the middle layer was left empty but designed to allow neurons to penetrate and connect through it. Electrical signals were measured from the lower layer using a microsensor (electrode chip), and cell activity was observed from the upper layer using light (calcium imaging). The results showed that when electrical stimulation was applied, neural cells in both upper and lower layers responded simultaneously. When a synapse-blocking agent (synaptic blocker) was introduced, the response decreased, proving that the neural cells were genuinely connected and transmitting signals.
Professor Je-Kyun Park of KAIST explained, "This research is a joint development achievement of an integrated platform that can simultaneously reproduce the complex multilayered structure and function of brain tissue. Compared to existing technologies where signal measurement was impossible for more than 14 days, this platform maintains a stable microelectrode chip interface for over 27 days, allowing the real-time analysis of structure-function relationships. It can be utilized in various brain research fields such as neurological disease modeling, brain function research, neurotoxicity assessment, and neuroprotective drug screening in the future."
The research, in which Dr. Soo Jee Kim and Dr. Dongjo Yoon from KAIST's Department of Bio and Brain Engineering participated as co-first authors, was published online in the international journal 'Biosensors and Bioelectronics' on June 11, 2025.
※Paper: Hybrid biofabrication of multilayered 3D neuronal networks with structural and functional interlayer connectivity
※DOI: https://doi.org/10.1016/j.bios.2025.117688
2025.07.16 View 587 -
KAIST Develops Robots That React to Danger Like Humans
<(From left) Ph.D candidate See-On Park, Professor Jongwon Lee, and Professor Shinhyun Choi>
In the midst of the co-development of artificial intelligence and robotic advancements, developing technologies that enable robots to efficiently perceive and respond to their surroundings like humans has become a crucial task. In this context, Korean researchers are gaining attention for newly implementing an artificial sensory nervous system that mimics the sensory nervous system of living organisms without the need for separate complex software or circuitry. This breakthrough technology is expected to be applied in fields such as in ultra-small robots and robotic prosthetics, where intelligent and energy-efficient responses to external stimuli are essential.
KAIST (President Kwang Hyung Lee) announced on July15th that a joint research team led by Endowed Chair Professor Shinhyun Choi of the School of Electrical Engineering at KAIST and Professor Jongwon Lee of the Department of Semiconductor Convergence at Chungnam National University (President Jung Kyum Kim) developed a next-generation neuromorphic semiconductor-based artificial sensory nervous system. This system mimics the functions of a living organism's sensory nervous system, and enables a new type of robotic system that can efficiently responds to external stimuli.
In nature, animals — including humans — ignore safe or familiar stimuli and selectively react sensitively to important or dangerous ones. This selective response helps prevent unnecessary energy consumption while maintaining rapid awareness of critical signals. For instance, the sound of an air conditioner or the feel of clothing against the skin soon become familiar and are disregarded. However, if someone calls your name or a sharp object touches your skin, a rapid focus and response occur. These behaviors are regulated by the 'habituation' and 'sensitization' functions in the sensory nervous system. Attempts have been consistently made to apply these sensory nervous system functions of living organisms in order to create robots that efficiently respond to external environments like humans.
However, implementing complex neural characteristics such as habituation and sensitization in robots has faced difficulties in miniaturization and energy efficiency due to the need for separate software or complex circuitry. In particular, there have been attempts to utilize memristors, a neuromorphic semiconductor. A memristor is a next-generation electrical device, which has been widely utilized as an artificial synapse due to its ability to store analog value in the form of device resistance. However, existing memristors had limitations in mimicking the complex characteristics of the nervous system because they only allowed simple monotonic changes in conductivity.
To overcome these limitations, the research team developed a new memristor capable of reproducing complex neural response patterns such as habituation and sensitization within a single device. By introducing additional layer inside the memristor that alter conductivity in opposite directions, the device can more realistically emulate the dynamic synaptic behaviors of a real nervous system — for example, decreasing its response to repeated safe stimuli but quickly regaining sensitivity when a danger signal is detected.
<New memristor mimicking functions of sensory nervous system such as habituation/sensitization>
Using this new memristor, the research team built an artificial sensory nervous system capable of recognizing touch and pain, an applied it to a robotic hand to test its performance. When safe tactile stimuli were repeatedly applied, the robot hand, which initially reacted sensitively to unfamiliar tactile stimuli, gradually showed habituation characteristics by ignoring the stimuli. Later, when stimuli were applied along with an electric shock, it recognized this as a danger signal and showed sensitization characteristics by reacting sensitively again. Through this, it was experimentally proven that robots can efficiently respond to stimuli like humans without separate complex software or processors, verifying the possibility of developing energy-efficient neuro-inspired robots.
<Robot arm with memristor-based artificial sensory nervous system>
See-On Park, researcher at KAIST, stated, "By mimicking the human sensory nervous system with next-generation semiconductors, we have opened up the possibility of implementing a new concept of robots that are smarter and more energy-efficient in responding to external environments." He added, "This technology is expected to be utilized in various fusion fields of next-generation semiconductors and robotics, such as ultra-small robots, military robots, and medical robots like robotic prosthetics".
This research was published online on July 1st in the international journal 'Nature Communications,' with Ph.D candidate See-On Park as the first author.
Paper Title: Experimental demonstration of third-order memristor-based artificial sensory nervous system for neuro-inspired robotics
DOI: https://doi.org/10.1038/s41467-025-60818-x
This research was supported by the Korea National Research Foundation's Next-Generation Intelligent Semiconductor Technology Development Project, the Mid-Career Researcher Program, the PIM Artificial Intelligence Semiconductor Core Technology Development Project, the Excellent New Researcher Program, and the Nano Convergence Technology Division, National Nanofab Center's (NNFC) Nano-Medical Device Project.
2025.07.16 View 751 -
KAIST Ushers in Era of Predicting ‘Optimal Alloys’ Using AI, Without High-Temperature Experiments
<Picture1.(From Left) Prof. Seungbum Hong, Ph.D candidate Youngwoo Choi>
Steel alloys used in automobiles and machinery parts are typically manufactured through a melting process at high temperatures. The phenomenon where the components remain unchanged during melting is called “congruent melting.” KAIST researchers have now addressed this process—traditionally only possible through high-temperature experiments—using artificial intelligence (AI). This study draws attention as it proposes a new direction for future alloy development by predicting in advance how well alloy components will mix during melting, a long-standing challenge in the field.
KAIST (President Kwang Hyung Lee) announced on the 14th of July that Professor Seungbum Hong’s research team from the Department of Materials Science and Engineering, in international collaboration with Professor Chris Wolverton’s group at Northwestern University, has developed a high-accuracy machine learning model that predicts whether alloy components will remain stable during melting. This was achieved using formation energy data derived from Density Functional Theory (DFT)* calculations.
*Density Functional Theory (DFT): A computational quantum mechanical method used to investigate the electronic structure of many-body systems, especially atoms, molecules, and solids, based on electron density.
The research team combined formation energy values obtained via DFT with experimental melting reaction data to train a machine learning model on 4,536 binary compounds.
Among the various machine learning algorithms tested, the XGBoost-based classification model demonstrated the highest accuracy in predicting whether alloys would mix well, achieving a prediction accuracy of approximately 82.5%. The team also applied the Shapley value method* to analyze the key features of the model. One major finding was that sharp changes in the slope of the formation energy curve (referred to as “convex hull sharpness”) were the most significant factor. A steep slope indicates a composition with energetically favorable (i.e., stable) formation.
*Shapley value: An explainability method in AI used to determine how much each feature contributed to a prediction.
The most notable significance of this study is that it predicts alloy melting behavior without performing high-temperature experiments. This is especially useful for materials such as high-entropy alloys or ultra-heat-resistant alloys, which are difficult to handle experimentally. The approach could also be extended to the design of complex multi-component alloy systems in the future.
Furthermore, the physical indicators identified by the AI model showed high consistency with actual experimental results on how well alloys mix and remain stable. This suggests that the model could be broadly applied to the development of various metal materials and the prediction of structural stability.
Professor Seungbum Hong of KAIST stated, “This research demonstrates how data-driven predictive materials development is possible by integrating computational methods, experimental data, and machine learning—departing from the traditional experience-based alloy design.” He added, “In the future, by incorporating state-of-the-art AI techniques such as generative models and reinforcement learning, we could enter an era where completely new alloys are designed automatically.”
<Model performance and feature importance analysis for predicting melting congruency. (a) SHAP summary plot showing the impact of individual features on model predictions. (b) Confusion matrix illustrating the model’s classification performance. (c) Receiver operating characteristic (ROC) curve with an AUC (area under the curve) score of 0.87, indicating a strong classification performance.>
Ph.D. candidate Youngwoo Choi, from the Department of Materials Science and Engineering at KAIST, participated as the first author. The study was published in the May issue of APL Machine Learning, a prestigious journal in the field of machine learning published by the American Institute of Physics, and was selected as a “Featured Article.”
※ Paper title: Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy ※ DOI: 10.1063/5.0247514
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.07.14 View 486 -
KAIST Develops Novel Candidiasis Treatment Overcoming Side Effects and Resistance
<(From left) Ph. D Candidate Ju Yeon Chung, Prof.Hyun Jung Chung, Ph.D candidate Seungju Yang, Ph.D candidate Ayoung Park, Dr. Yoon-Kyoung Hong from Asan Medical Center, Prof. Yong Pil Chong, Dr. Eunhee Jeon>
Candida, a type of fungus, which can spread throughout the body via the bloodstream, leading to organ damage and sepsis. Recently, the incidence of candidiasis has surged due to the increase in immunosuppressive therapies, medical implants, and transplantation. Korean researchers have successfully developed a next-generation treatment that, unlike existing antifungals, selectively acts only on Candida, achieving both high therapeutic efficacy and low side effects simultaneously.
KAIST (President Kwang Hyung Lee) announced on the 8th that a research team led by Professor Hyun-Jung Chung of the Department of Biological Sciences, in collaboration with Professor Yong Pil Jeong's team at Asan Medical Center, developed a gene-based nanotherapy (FTNx) that simultaneously inhibits two key enzymes in the Candida cell wall.
Current antifungal drugs for Candida have low target selectivity, which can affect human cells. Furthermore, their therapeutic efficacy is gradually decreasing due to the emergence of new resistant strains. Especially for immunocompromised patients, the infection progresses rapidly and has a poor prognosis, making the development of new treatments to overcome the limitations of existing therapies urgent.
The developed treatment can be administered systemically, and by combining gene suppression technology with nanomaterial technology, it effectively overcomes the structural limitations of existing compound-based drugs and successfully achieves selective treatment against only Candida.
The research team created a gold nanoparticle-based complex loaded with short DNA fragments called antisense oligonucleotides (ASO), which simultaneously target two crucial enzymes—β-1,3-glucan synthase (FKS1) and chitin synthase (CHS3)—important for forming the cell wall of the Candida fungus.
By applying a surface coating technology that binds to a specific glycolipid structure (a structure combining sugar and fat) on the Candida cell wall, a targeted delivery device was implemented. This successfully achieved a precise targeting effect, ensuring the complex is not delivered to human cells at all but acts selectively only on Candida.
<Figure 1: Overview of antifungal therapy design and experimental approach>
This complex, after entering Candida cells, cleaves the mRNA produced by the FKS1 and CHS3 genes, thereby inhibiting translation and simultaneously blocking the synthesis of cell wall components β-1,3-glucan and chitin. As a result, the
Candida cell wall loses its structural stability and collapses, suppressing bacterial survival and proliferation.
In fact, experiments using a systemic candidiasis model in mice confirmed the therapeutic effect: a significant reduction in
Candida count in the organs, normalization of immune responses, and a notable increase in survival rates were observed in the treated group.
Professor Hyun-Jung Chung, who led the research, stated, "This study presents a method to overcome the issues of human toxicity and drug resistance spread with existing treatments, marking an important turning point by demonstrating the applicability of gene therapy for systemic infections". She added, "We plan to continue research on optimizing administration methods and verifying toxicity for future clinical application."
This research involved Ju Yeon Chung and Yoon-Kyoung Hong as co-first authors , and was published in the international journal 'Nature Communications' on July 1st.
Paper Title: Effective treatment of systemic candidiasis by synergistic targeting of cell wall synthesis
DOI: 10.1038/s41467-025-60684-7
This research was supported by the Ministry of Health and Welfare and the National Research Foundation of Korea.
2025.07.08 View 725