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KAIST Reveals Principle Behind Ultra-Fast DNA Repair, Like “Finding a Needle in Seoul”
<(From Upper Left) Professor Ja Yil Lee, Professor Gwangrog Lee, Professor Jejoong Yoo, (From Bottom Left) Ph.D candidate Subin Kim, Dr. Donghun Lee, Ph.D candidate Gyeongpil Jo>
DNA is the blueprint of the human body. However, tens of thousands of DNA lesions occur in our bodies every day. In particular, if “apurinic/apyrimidinic sites” (AP sites, damaged sites where one letter of DNA information has been erased) are not properly repaired, they can lead to cancer and aging. Finding these tiny damaged sites within the vast genome is as difficult as “finding a single needle in Seoul.” Korean researchers have uncovered the secret of how a DNA repair enzyme rapidly searches for damaged sites by sliding along DNA.
KAIST (President Kwang Hyung Lee) announced on the 4th of June that a research team led by Professor Gwangrog Lee of the Department of Biological Sciences, together with Professor Ja Yil Lee’s team at UNIST (President Jong Rae Park) and Professor Jejoong Yoo’s team at Sungkyunkwan University (President Jibeom Yoo), has identified the precise molecular mechanism by which the DNA repair enzyme “APE1” (apurinic/apyrimidinic endonuclease 1, an enzyme that recognizes DNA damage sites and initiates repair) detects damaged DNA.
The research team tracked the movement of APE1 in real time by combining single-molecule FRET (smFRET, an analytical technique that observes the movement and structural changes of single biomolecules in real time), DNA curtain technology (a technique that aligns multiple strands of DNA to observe their interactions with proteins), and molecular dynamics (MD, a simulation method that calculates molecular movement using computers).
As a result, the team found that APE1 does not search DNA randomly, but instead uses a “one-dimensional diffusion” strategy (a method of searching by moving along the DNA strand), sliding along the DNA to find damaged sites.
This is similar to an intelligent inspection robot moving through a maze-like network of underground pipes beneath a huge city to detect a tiny leak. Instead of searching aimlessly from place to place, the enzyme moves efficiently along the “genomic highway” of DNA to quickly locate damaged sites.
In particular, the research team also found that the enzyme’s flexible end region, known as an “intrinsically disordered region” (IDR, a protein segment that moves freely without a fixed structure), plays a key role in the DNA search process.
This intrinsically disordered region acts like a hook that holds onto DNA, helping APE1 remain on the DNA and move along it for a long time without falling off. In fact, when the research team removed this region, the enzyme’s ability to find damaged sites decreased by more than fivefold.
The researchers also confirmed that magnesium ions (Mg²⁺, metal ions that assist various enzymatic reactions inside cells) are not merely auxiliary factors, but key elements that increase the efficiency of DNA search. Magnesium ions were found to stabilize the binding between APE1 and DNA, helping the enzyme move more effectively along DNA.
< Research Image (AI-Generated Image) >
Professor Gwangrog Lee of KAIST explained, “This study identified the mechanism by which a biomolecule rapidly searches for DNA damage through an intrinsically disordered region (IDR), and then operates precisely through a structured region,” adding, “This principle could provide a key clue for developing next-generation anticancer drugs that disable DNA repair functions in cancer cells, as well as for research on suppressing aging.” Professor Ja Yil Lee of UNIST emphasized, “This study is significant in that it revealed that an intrinsically disordered region, which moves flexibly without a fixed structure and interacts with various molecules, plays a key role in finding DNA damage sites.”
This study, with KAIST Dr. Donghun Lee, UNIST doctoral student Subin Kim, and Sungkyunkwan University doctoral student Gyeongpil Jo as co-first authors, was published on May 14 in the world-renowned international journal Nucleic Acids Research.
※ Paper title: “APE1 Coordinates Its Disordered Region and Metal Cofactors to Drive Genome Surveillance,” DOI: org/10.1093/nar/gkag479
This research was supported by the KAIST Grand Challenge 30 Project (KC30), the National Research Foundation of Korea’s Core Synthetic Biology Technology Development Program, Mid-Career Researcher Program, Basic Research Laboratory Program, the Korea Drug Development Fund’s Drug Development Foundation Expansion Research Program, the Institute for Basic Science (IBS), and the Institute of Information & Communications Technology Planning & Evaluation (IITP)’s Advanced AI Source Technology Development Program.
2026.06.04 View 619 -
Fatty Acid in Body Acts as Natural Brake Suppressing Cancer Cell Growth
< (Left) Professor Seyun Kim from KAIST, (Right) Professor Young-Joo Byun from Korea University >
'mTOR', a protein in our body, becomes excessively activated in cancer cells, promoting cell growth and metastasis. Korean researchers have discovered for the first time in the world that '13-HODE'—a substance produced when fatty acids, which are abundant in vegetable oils, are metabolized in the body—binds directly to mTOR and acts as a 'natural brake' that suppresses cancer cell growth. This research presents the possibility of developing next-generation anticancer treatment strategies. Our university announced on the 2nd that a joint research team led by Professor Seyun Kim from the Department of Biological Sciences and Professor Young-Joo Byun from the College of Pharmacy at Korea University (President Dong-One Kim) has discovered that the lipid metabolite '13-HODE' (a lipid metabolite produced when fatty acids are metabolized) suppresses the activity of mTOR, a key regulatory factor in cancer cell growth. In addition, this research involved joint participation from Professor Mi Young Kim from the Department of Biological Sciences at KAIST, Professor Byung-Chul Oh from the College of Medicine at Gachon University (President Gil-ya Lee), and Professor Patrick L. Wintrode and Professor Daniel Deredge from the School of Pharmacy at the University of Maryland, USA. mTOR is an important enzyme (a protein that helps biological reactions) that regulates cell growth and energy usage. However, in cancer cells, mTOR activity is known to increase abnormally, promoting cell proliferation and metastasis. For this reason, anticancer research aimed at controlling mTOR is being actively conducted worldwide. The research team focused on substances capable of binding to the mTOR protein, particularly natural metabolites produced by the body itself. Through extensive metabolite screening (a technology that analyzes large quantities of metabolites in vivo), they discovered that a lipid metabolite called '13-HODE', which is formed as fat changes in the body, attaches directly to the active site of the mTOR protein and stops its operation in cancer cells.
< (AI Image) Cancer cell growth suppression effect based on direct inhibition of mTOR by linoleic acid-derived 13-HODE >
The 13-HODE (13-Hydroxyoctadecadienoic acid) molecule is produced in our body during the process of metabolizing linoleic acid (an essential unsaturated fatty acid), which is abundant in vegetable oils. In this process, 'ALOX15 (an enzyme that induces a fatty acid oxidation reaction)' oxidizes linoleic acid to produce 13-HODE. The core of this research goes beyond the simple level of showing that 13-HODE has anticancer efficacy; it clarifies the molecular mechanism (the biological principle of operation) by which 13-HODE physically binds directly to the mTOR protein to fundamentally block its function. The research team verified this through molecular docking simulations (computer-based analysis of molecular interactions) and mass spectrometry (a technology that analyzes the mass and structure of molecules). The research team also confirmed that 13-HODE concentrations are extremely low in breast and colorectal cancer cells. This was found to be due to a decrease in the expression (the process by which genetic information is actually made into protein) of the ALOX15 enzyme required for 13-HODE generation. The research team proved that increasing the production of ALOX15 and 13-HODE reduces mTOR activity and suppresses cancer cell growth. Professor Seyun Kim said, "This research is significant in that it revealed that lipid metabolites generated within the human body can directly inhibit mTOR, a core protein for cancer growth. It can be utilized not only for new anticancer treatment strategies leveraging lipid metabolism but also for developing treatments that regulate mTOR overactivation observed during inflammation and aging processes."
Professor Young-Joo Byun from the College of Pharmacy at Korea University, who co-led the joint research, said, "This research is a study that clarified the interaction between proteins and fatty acid metabolites at the molecular level through the convergence of biological sciences and pharmacy. It will serve as an important foundation for the development of innovative new drugs in the future." Professor Jie Chen from the University of Illinois, USA, a world-renowned authority in the field of mTOR research, evaluated it in a journal preview as "an outstanding discovery that presents a new breakthrough in cancer cell control." This research, with Dr. Seung Ju Park and Ph.D. student Sera Kim from the Department of Biological Sciences at KAIST participating as co-first authors, and Professor Young-Joo Byun from the College of Pharmacy at Korea University and Professor Seyun Kim from the Department of Biological Sciences at KAIST participating as co-corresponding authors, was published on May 21st in the international academic journal in the field of chemical biology, Cell Chemical Biology. Furthermore, in recognition of its importance, it was selected as the cover article for the May issue of the journal. ※ Paper Title: Mechanism by which a linoleic acid metabolite suppresses cancer cell growth by inhibiting mTOR, DOI: https://doi.org/10.1016/j.chembiol.2026.04.004 ※ Author Information: Seung Ju Park, Sera Kim, Hongmok Kwon, Jiyeon Choi, Ji Kwang Kim, Inhong Jung, Seol-Wa Lim, Young Ran Kim, A-Yeong Yang, Boah Lee, Haein Lee, Seung Eun Park, Seulgi Lee, Myeongsu Shin, Bernie Byunghoon Park, YunHye Kim, Jinwook Lee, Byung-Chul Oh, Daniel Deredge, Patrick L. Wintrode, … Seyun Kim
< Cover Article of the Journal Cell Chemical Biology May Issue >
Meanwhile, this research was conducted with support from the Samsung Science and Technology Foundation, the Mid-Career Research Program, the Basic Research Laboratory of the National Research Foundation of Korea, the Leading Research Center, the KAIST Quantum+X Interdisciplinary Convergence Technology Development Project, the KAIST Grand Challenge Project, and the Ministry of Education's Core Research Institute Program.
2026.06.04 View 644 -
KAIST Develops New Catalyst Design Technology to Improve Battery and Hydrogen Fuel Cell Performance
<(From Left) Professor Seung Jun Hwang, Professor Jaeyune Ryu>
Korean researchers have developed a new catalyst design technology that can improve the performance of batteries and hydrogen fuel cells while reducing energy loss.
KAISTannounced on the 1st of June that a research team led by Professor Seung Jun Hwang of the Department of Chemistry, through joint research with Professor Jaeyune Ryu’s team from the Department of Chemical and Biological Engineering at Seoul National University , has proposed a new catalyst design strategy that can improve the efficiency of key reactions that generate electricity inside batteries and fuel cells.
A catalyst is a material that helps chemical reactions occur faster and more efficiently. In batteries or fuel cells, it plays a role in facilitating the reactions that generate electricity. Catalysts usually consist of a central metal and a molecular structure surrounding it.
In previous studies, methods mainly involved changing the type of metal from iron (Fe) to cobalt (Co) or nickel (Ni), or newly designing the molecular structure around the metal, known as the ligand, to improve reaction performance. In simple terms, this approach changes the material or shape of the catalyst itself to make it react better. By contrast, this study is differentiated by showing that performance can be improved simply by adjusting the electrical environment around the catalyst, without greatly changing the catalyst itself.
<(AI Image) Visualization of Enhanced Fe Porphyrin Catalyst Reactivity Induced by the Electric Field of Metal Cations>
To use a simple analogy, this study can be compared to “making cooking work better by adjusting the kitchen environment instead of changing the cooking tool itself.” Previous catalyst research was closer to changing the material of a frying pan or redesigning its shape. By contrast, this study keeps the frying pan the same and precisely adjusts the surrounding temperature and airflow so that the food cooks better. In other words, the core of this research is that the team made the reaction occur more efficiently by adjusting the electrical environment around the catalyst, rather than creating an entirely new catalyst.
The research team confirmed that placing “cations (+)” around the catalyst to create a very small electric field can induce the reaction needed to generate electricity to occur more stably. In particular, the proportion of the desired reaction increased from the previous level of about 12% to as high as 52%.
Through this, the research team confirmed that the desired reaction can be efficiently induced with less energy than before. This is expected to contribute to improving the efficiency, lifespan, and stability of batteries and hydrogen fuel cells.
The oxygen reduction reaction (ORR, a key reaction in which oxygen receives electrons to generate electricity) examined in this study is a core reaction that generates electricity in next-generation energy devices such as fuel cells for hydrogen vehicles (Fuel Cell, a device that produces electricity through a chemical reaction between hydrogen and oxygen) and metal-air batteries (Metal-Air Battery, a next-generation battery that stores and produces electricity using metal and oxygen in the air).
The research team also believes that this principle can be applied to catalyst technologies that convert carbon dioxide (CO₂) or hydrogen into other useful substances, and that it can therefore be used in the development of various next-generation energy catalysts, including carbon dioxide reduction technologies and eco-friendly hydrogen production technologies.
<Schematic Illustration of Cation-Mediated Regulation of ORR Catalyst Activity>
<(AI Image) Schematic Illustration of Cation-Mediated Regulation of ORR Catalyst Activity>
Professor Seung Jun Hwang stated, “This study demonstrates that reaction properties can be precisely controlled solely through the surrounding electrical environment, without changing the structure of the catalyst itself,” adding, “We expect it to present a new direction for developing next-generation batteries, fuel cells, and eco-friendly energy catalyst technologies.”
This research, with POSTECH chemistry doctoral students Hwi Yul Jo and Vom Kang and KAIST postdoctoral researcher Dongyoung Kim as co-first authors, was published online on April 12 in the Journal of the American Chemical Society (JACS).
※ Paper title: “Localized Cation Unlocks Unique Activity–Selectivity Trends in Molecular Oxygen Reduction Catalysis,” DOI: pubs.acs.org/doi/10.1021/jacs.5c18246
Lead author information: Hwi Yul Jo (doctoral student, POSTECH), Vom Kang (integrated master’s–PhD student, POSTECH), Dongyoung Kim (postdoctoral researcher, KAIST)
This research was supported by the Samsung Science and Technology Foundation, the National Research Foundation of Korea’s “Hanwoomul” Basic Research Program, and the Nano and Material Technology Development Program.
2026.06.01 View 750 -
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 1050 -
"Development of 'ADvisor', an AI that Predicts Instagram Advertising Performance in Advance"
<(Bottom from left) M.S candidate Gyurim Hwang, M.S candidate Yeongho Kim, Ph.D. candidate Kyungho Kim, Ph.D.candidate Jongha Lee, M.S candidate Yeonje Choi (Top from left) Undergraduate student Sejin Chung, Researcher Hongseok Lee, Researcher Myeong Ho Song, Ph.D. candidate Sunwoo Kim, M.S candidate Juyeon Kim, Professor Kijung Shin>
Social media advertising usually requires running multiple ad drafts in practice before determining which ad is effective. Because of this, testing advertisements demands significant time and costs. Furthermore, the criteria for an effective advertisement vary greatly by brand. While some brands prefer person-centered advertisements, others receive better responses from advertisements that emphasize actual usage scenes. However, these effective advertising strategies for each brand are often not clearly defined in the field, which has limited the technology to systematically reflect them and predict advertising performance.
To solve this problem, a research team led by Professor Kijung Shin at KAIST, in collaboration with the AI marketing company MADUP, developed 'ADvisor', an AI technology that predicts advertising performance for each brand.
ADvisor utilizes a generative vision-language model that understands images and text simultaneously to find different advertising success criteria for each brand and predict advertising effectiveness based on them. To achieve this, it not only analyzes the characteristics of the brand but also considers advertising data from other brands with similar tendencies for new brands that do not have sufficient advertising data to derive advertising strategies. Through this process, it can identify distinct advertising success criteria for each brand; for instance, a "strong headline phrase" is analyzed as an important criterion for a specific fashion brand, while "logo exposure" acts as a key element for another brand. Afterward, ADvisor evaluates the advertisement based on the derived criteria for each brand, reviews the evaluation results on its own, and repeatedly compensates for deficiencies to make the final prediction.
The research team verified the technology's performance using data from 10 brands in the beauty, fashion, and platform sectors collected through actual marketing campaigns. As a result, ADvisor recorded up to 7.2% higher performance compared to existing AI advertising prediction models. In particular, in an online A/B test conducted in a real Instagram advertising environment, it achieved an average of 27% better performance in key indicators such as click-through rate (CTR), cost per click (CPC), and return on ad spend (ROAS) than advertisements selected by field marketing experts, proving that it can be utilized in actual marketing decision-making.
Professor Kijung Shin stated, "Predicting advertising performance in advance is the first step for effective advertisement production," adding, "In the future, we will develop our research in a direction where AI directly generates and optimizes advertisements tailored to brand characteristics."
The study, in which Ph.D candidate Kyungho Kim and M.S candidate Yeonje Choi from the KAIST Kim Jaechul Graduate School of AI participated as co-first authors, was published online on April 18 in the Industry Track of ACL 2026, one of the most prestigious international academic conferences in the field of natural language processing. It has been accepted as an oral presentation paper and is scheduled to be presented in the United States this coming July.
※ Paper Title: Pre-Deployment Advertisement Ranking under Data Scarcity via Context-Aware Criteria Generation with VLMs ※ Paper Link: https://openreview.net/forum?id=il84gAzAxx
Meanwhile, this research is an achievement of the project 'EntireDB2AI: Deep Representation Learning and Prediction Source Technology and Software Development Utilizing Entire Relational Databases Comprehensively', supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP).
2026.06.01 View 412 -
Development of a Virtual AI Testbed Capable of Performance Verification Before Building Massive AI Servers
< From left: Professor Jongse Park , M.S candidate Jaehong Cho, M.S candidate Hyunmin Choi, Professor Brandon Reagen ISPASS >
Operating Large Language Model (LLM) services like ChatGPT requires a server infrastructure on the scale of tens of thousands of units. However, constructing actual equipment every time a new AI semiconductor or system architecture needs to be verified incurs massive costs and time. A research team at our university has developed a ‘virtual testbed’ that can pre-verify performance and efficiency inside a computer before building an actual large-scale AI server.
KAIST announced on May 29th that the research on a Large Language Model (LLM) serving infrastructure simulator (virtual testing software) developed by Professor Jongse Park’s research team in the School of Computing won the Best Paper Award at ‘ISPASS 2026 (IEEE International Symposium on Performance Analysis of Systems and Software),’ a world-renowned conference in the field of computer system performance analysis.
‘LLMServingSim 2.0,’ developed by the research team, is a simulation platform capable of virtually analyzing various hardware and software combinations in complex AI service environments. Researchers and developers can freely experiment with various design options and verify performance without having to directly build expensive, large-scale server infrastructures.
< LLMServingSim 2.0 is workload >
In particular, this technology is drawing attention because it goes beyond the existing Graphics Processing Unit (GPU)-centric environment to support diverse hardware environments, including Neural Processing Units (NPUs), which are rising as next-generation AI semiconductors, and Processing-In-Memory (PIM, a semiconductor technology that performs operations inside the memory).
In other words, it is a technology that allows future-oriented AI semiconductors that have not yet been commercialized to be tested in advance within a virtual datacenter environment. Through this, it is possible to replicate and analyze inside a computer how much the service speed improves, how much power consumption is reduced, and whether it operates stably even in a server environment scaled to tens of thousands of units when a specific semiconductor is applied.
In addition, it reproduces complex operations that occur during actual AI service operations—such as data processing, request distribution, and memory utilization—at the system level, enabling performance evaluations that are close to reality. Notably, it can even analyze disaggregated infrastructure environments where multiple server resources are separated and connected for use, showing great potential for utilization in next-generation AI datacenter research.
This simulator is expected to be widely utilized not only by researchers but also by LLM service companies and AI semiconductor startups to design and optimize next-generation AI infrastructures. This is because it can rapidly verify new AI semiconductors or service architectures prior to actual construction, thereby significantly reducing the cost and time of AI infrastructure development.
< Research Image (AI-generated image) >
Professor Jongse Park said, “The competitiveness of AI services is determined not only by the model itself but also by the infrastructure technology that operates it stably and efficiently.” He added, “We hope this simulator will serve as an important foundation for researchers and the industry to develop next-generation AI infrastructures faster and more efficiently.”
This research was led by M.S candidate Jaehong Cho and Hyunmin Choi in the School of Computing as co-first authors. Following their Best Paper Award at the 2024 IISWC (IEEE International Symposium on Workload Characterization), the research team won the Best Paper Award again at this ISPASS 2026, proving their research competitiveness in the field of AI infrastructure once more.
※ Paper Title: LLMServingSim 2.0: A Unified Simulator for Heterogeneous and Disaggregated LLM Serving Infrastructure, DOI: 10.1109/ISPASS69572.2026.00012 (Authors: Jaehong Cho, Hyunmin Choi, Guseul Heo, Jongse Park) ※ Open Source Link: https://llmservingsim.ai/ Meanwhile, this research was conducted with support from the Ministry of Science and ICT (MSIT), the Institute for Information & Communications Technology Planning & Evaluation (IITP, No. RS-2024-00396013), the Electronics and Telecommunications Research Institute (ETRI, No. RS-2025-02305453), and SK hynix.
2026.05.29 View 630 -
Clearing the Expressway for Bubble Blockages to Achieve High-Efficiency Green Hydrogen Production
< (From left) Ph.D candidate Jaeho Byeon, Ph.D candidate Minkyeong Ban, Professor Jinwoo Lee, Dr. Sungjun Kim, Professor Jang Yong Lee>
As the global transition toward carbon neutrality accelerates, "water electrolysis"—a technology that splits water electrically to produce clean hydrogen—is drawing significant attention. However, a major limitation has been the decline in efficiency caused by bubbles formed during the electrolysis process that block the pathways. A domestic research team has resolved this challenge by developing an innovative technology that rapidly discharges bubbles and boosts hydrogen production efficiency, much like clearing an expressway through a heavily congested road.
KAIST announced on May 28th that a research team led by Professor Jinwoo Lee from the Department of Chemical and Biomolecular Engineering, in collaboration with a research team led by Dr. Sungjun Kim from KRICT (President Suk-min Shin) and a research team led by Professor Jang Yong Lee from Konkuk University (President Jong-phil Won), has departed from the conventional method of simply increasing catalytic activity itself. Instead, they have successfully secured both water electrolysis performance and stability simultaneously by newly designing a "pathway" inside the catalyst layer through which water and gas pass.
< Development of World-Class Anion Exchange Membrane Water Electrolysis via Carbon-Induced Ru-C Bonds and Catalyst Layer Structural Design >
Using paper-thin 2D mesoporous carbon (a thin carbon structure with numerous nanoscale pores) nanosheets, the research team created a low-tortuosity structure where materials can move without obstruction. Simply put, they implemented a "highway-like pathway" inside the catalyst layer through which water and gas can pass rapidly, instead of a narrow and complex alleyway.
Furthermore, ruthenium (Ru) nanoclusters (ultrafine metal particles several nanometers in size) were stably anchored onto the defect-introduced carbon surface to accelerate the hydrogen evolution reaction rate. Simultaneously, the interface structure was controlled to prevent catalyst degradation even during long-term operation.
Through this technology, it was confirmed that bubbles generated during the water electrolysis process were rapidly discharged without accumulating inside the catalyst layer, and a stable reaction was maintained even under extreme environments with high current density.
As a result, the technology recorded a world-class performance of 17.1 A cm⁻² at 80°C, vastly exceeding the 2026 target set by the U.S. Department of Energy (DOE). This figure represents the amount of current flowing per unit area; a higher value signifies that more hydrogen can be produced faster.
In addition, it demonstrated practical industrial applicability by operating stably for over 1,000 hours even under a low noble metal loading condition (0.09 mgRu cm⁻²). This means that the amount of ruthenium, a precious metal used in the catalyst, has been significantly reduced, which can also enhance the economic viability of water electrolysis systems.
The core of this research lies not simply in making a "good catalyst," but in newly designing the pathway itself through which hydrogen is formed. In conventional water electrolysis devices, bubbles generated during the reaction process accumulate inside, blocking the flow of water and electricity, which leads to a degradation in performance. The research team solved this problem by changing the structure of the catalyst layer so that bubbles can exit rapidly.
This technology holds great significance as it opens the way to produce eco-friendly hydrogen more affordably and efficiently in the future. Hydrogen is currently attracting attention as a core clean energy source for the carbon-neutral era, but it has faced limitations due to high production costs and low system efficiency. In particular, conventional high-performance water electrolysis devices required large amounts of expensive noble metals, making large-scale commercialization difficult.
The research team explained that this technology demonstrates the potential to achieve high performance and stability with only a small amount of noble metals. It is expected to expand into various fields in the future, including large-scale green hydrogen production, eco-friendly power generation systems, hydrogen vehicles/eco-friendly mobility, and carbon-neutral industrial processes.
< 2D Mesoporous Catalyst Layer-Based Green Hydrogen Production Technology (AI-Generated Image) >
Professor Jinwoo Lee stated, "This research is a technology that improves water electrolysis efficiency by designing not only the catalyst itself but also the path through which energy flows. Since high-efficiency green hydrogen production is possible with only a small amount of noble metals, we expect to accelerate the commercialization of eco-friendly hydrogen production in the future."
In this study, PhD students Jaeho Byeon and Minkyeong Ban from the KAIST Department of Chemical and Biomolecular Engineering participated as co-first authors. The research findings were published online on May 22, 2026, in Joule, the world's leading academic journal in the energy field, and will be featured in the formal issue of Joule on September 16.
※ Paper Title: Outperforming water electrolysis through catalyst layer structuring with defective 2D mesoporous carbon, DOI: 10.1016/j.joule.2026.102478
※ Author Information: A total of 18 authors including Jaeho Byeon (KAIST, co-first author), Minkyeong Ban (KAIST, co-first author), Liangliang Xu (co-first author), Seunggeon Lee, Seongbeen Kim, Seonggyu Lee, Seongmin Shin, Donghyeok Son, Wonchul Park, Jinkyu Park, Hoyoung Kim, Dongyoon Woo, Seongseop Kim, Dong Young Chung, Jaewook Nam, Jang Yong Lee (Konkuk University, corresponding author), Sungjun Kim (KRICT, corresponding author), and Jinwoo Lee (KAIST, corresponding author).
This research was conducted with support from the National Research Foundation of Korea’s "AEM Water Electrolysis Technology Development" (RS-2024-00467234), the "Nano-Future Materials Source Technology Development" (RS-2023-00235596), the Ministry of Education’s "Ph.D. Student Research Support Project" (RS-2025-25424765), the Korea Research Institute of Chemical Technology (KS2522-10), and the Lotte Chemical Carbon Neutral Center.
2026.05.28 View 656 -
KAIST Develops AI Technology That Automatically Generates Sounds as If a “Jurassic Park” Dinosaur Were Actually Walking Toward You
<(From Left) Hyun-Bin Oh, Takida Yuhta, Uesaka Toshimitsu, Tae-Hyun Oh, Mitsufuji Yuki>
When people watch a scene in the film Jurassic Park where a giant dinosaur walks toward them, they naturally imagine a heavy, rumbling sound, as if the ground were shaking. This is because humans predict sound by considering not only the shape of an object, but also physical properties such as its size, weight, and speed of movement. However, existing video-to-audio generation AI mainly generates sound based on the category of objects or scene information in the video, and has not sufficiently reflected physical properties that vary depending on weight or speed.
KAIST (President Kwang Hyung Lee) announced on the 26th of May that a collaborative research team involving Professor Tae-Hyun Oh of the School of Computing, KAIST, together with joint researchers from POSTECH (President Sung Keun Kim) and Sony AI, has developed “PAVAS (Physics-Aware Video-to-Audio Synthesis),” an artificial intelligence (AI) technology that understands the physical situation in a video and generates more realistic sound.
<Concept Diagram of PAVAS (Physics-Aware Video-to-Audio Synthesis) Technology>
The key feature of this technology is that it is designed so that AI can infer invisible physical information such as the mass and velocity of objects in a video on its own. Ordinary videos do not provide exact numerical values for an object’s weight or speed, but the research team enabled AI to estimate them by analyzing the surrounding environment and movement context, and to reflect the results in the sound generation process.
In other words, the AI was designed to go beyond simply recognizing “what is visible” and to understand the physical cause of “why this sound should occur.”
As a result of technical validation, the research team’s AI generated sounds very similar to real-world environments in scenes involving physical interactions such as collisions or impacts between objects. In particular, it produced more realistic audio in which loudness and tone naturally changed when the mass and velocity of objects varied.
Recently, generative AI technologies that simultaneously generate video and audio have been advancing rapidly. Representative examples include Google’s “Veo 3” and ByteDance’s “Seedance 2.0.” However, in actual film, advertising, and game production sites, there is far greater demand for post-production work that adds sound effects suited to existing video scenes or supplements audio than for generating entirely new videos.
While existing commercial AI models have focused on generating video and audio together, PAVAS is differentiated by its ability to analyze the movement and collision characteristics of objects in a video and generate realistic sound effects that precisely match the scene.
<Comparison of Spectrograms Generated by Conventional Video-to-Audio Models and PAVAS>
The research team explained that this technology presents new possibilities in the field of “Physical AI,” or physically consistent generative AI. Physically consistent generative AI refers to AI that goes beyond simply producing plausible results and understands the laws of physics and causal relationships in the real world.
In the future, this technology is expected to provide more immersive user experiences in a wide range of fields, including the automation of content sound production, augmented reality (AR) and virtual reality (VR) content, the metaverse, and robotics simulation.
Professor Tae-Hyun Oh stated, “While existing generative AI has developed by increasing the scale of data and models, this research is meaningful in that it was designed so that AI directly understands physical quantities and causal relationships,” adding, “In the future, it can be expanded into a core foundational technology for next-generation multimodal AI that simultaneously understands and processes diverse types of information, including text, video, and speech.”
This study was led by POSTECH integrated M.S.-Ph.D. student Hyun-Bin Oh as the first author, with KAIST Professor Tae-Hyun Oh and Sony AI researchers Yuhta Takida, Toshimitsu Uesaka, and Yuki Mitsufuji participating as co-authors. This research was selected as an Oral presentation paper at CVPR 2026 (Computer Vision and Pattern Recognition 2026), the world’s most prestigious academic conference in the field of computer vision (image-based artificial intelligence technology), where only the top 0.88% of all papers are selected for oral presentation, recognizing the excellence of the work. The presentation is scheduled to take place on June 6.
※ Paper title: “PAVAS: Physics-Aware Video-to-Audio Synthesis,” DOI: https://arxiv.org/abs/2512.08282
This research was supported by the Mid-Career Research Program under the Basic Research Program of the Ministry of Science and ICT, the Pioneer Research Program for Future Converging Technology of the Ministry of Science, ICT and Future Planning, the AGI Program of the Ministry of Science and ICT, and the KAIST InnoCORE Program.
2026.05.27 View 1122 -
Talking to AI Before Seeing a Doctor… KAIST Develops Technology to Support Initial Psychiatric Interviews
<(Front row, from right)Professor Uichin Lee, Professor Eunjoo Kim, Professor Tak Yeon Lee, (Back row, from left) M.S candidate Gyeongmin Na, Ph.D candidate Yugyeong Jung, Researcher Hyangkyeong Oh, M.S candidate Jae Young Choi, Ph.D candidate Hyun Seung Moon>
People often say that seeking psychiatric care can feel intimidating. Patients may feel burdened when they first open up about their emotional distress, while medical staff must accurately understand a patient’s extensive history and symptoms within limited consultation time. Korean researchers have developed artificial intelligence (AI) technology that supports the initial psychiatric interview process, the first step in psychiatric care.
KAIST (President Kwang Hyung Lee) announced on the 24th of May that a joint research team led by Professor Uichin Lee of the School of Computing and Professor Tak Yeon Lee of the Department of Industrial Design, together with Professor Eunjoo Kim’s team from the Department of Psychiatry at Gangnam Severance Hospital (President Yong-Wook Kim), has developed a large language model (LLM)-based technology to support initial psychiatric interviews.
This study was conducted in a way that allows patients to first talk with AI before meeting a doctor, helping them organize their symptoms and condition in advance.
<AI Interviewer System Overview Diagram>
<Ask-Evaluate-Check-Plan Conversation Flow>
The research team designed the system so that AI can adjust the flow of conversation according to patient responses. The AI analyzes patients’ answers in real time by comparing them with specialized medical knowledge in psychiatry and generates the key questions that should be asked next. In particular, this system goes beyond simple question-and-answer interaction by applying real counseling techniques such as expressions of empathy, restating the patient’s words in an organized way, and clarifying ambiguous content. This is intended to help patients talk about their condition more comfortably.
As a result of experiments conducted with 1,440 virtual patients to verify performance, the team confirmed that in most cases, the system effectively obtained key clinical information needed for treatment within just 30 minutes.
Based on the collected conversation, the AI generates a clinical dashboard that shows symptoms and potential conditions at a glance and provides it to medical staff. Through this, doctors can understand the patient’s condition more systematically before the patient enters the consultation room, allowing them to focus more on in-depth counseling with the patient during the actual consultation.
The core of this research is that AI is defined not as a replacement for doctors, but as a “coachable apprentice ” It is a collaborative model in which AI handles repetitive and structured information collection, while doctors make the final diagnosis and prescription based on that information.
The research team made clear that AI still has limitations in understanding subtle emotional changes or handling sensitive topics, and emphasized that final judgment must always be carried out by trained medical professionals.
Professor Uichin Lee stated, “If AI reduces the burden of the initial consultation stage, medical staff can focus more on deeper counseling with patients,” adding, “This shows the possibility of developing a new model of care in which humans and AI collaborate in medical settings.”
This study, with doctoral student Yugyeong Jung as the first author, was presented on April 13 at ACM CHI 2026 (ACM Conference on Human Factors in Computing Systems), the most prestigious conference in the field of human-computer interaction.
※ Paper title: “Toward Flexible Psychiatric History-Taking and Visualization: Exploring Clinician Perspectives with Large Language Models,” DOI: https://dl.acm.org/doi/10.1145/3772318.3790970 ※ Author information: Yugyeong Jung (KAIST, first author), Thu Hoang Anh Vo (KAIST, second author), Hyun Seung Moon (KAIST, third author), Jae Young Choi (KAIST, fourth author), Hyangkyeong Oh (Gangnam Severance Hospital, fifth author), Ujin Lee (Gangnam Severance Hospital, sixth author), Eunjoo Kim (Gangnam Severance Hospital, seventh author), Tak Yeon Lee (KAIST, corresponding author), Uichin Lee (KAIST, corresponding author)
This research was supported by the Digital Columbus Project of the Institute of Information & Communications Technology Planning & Evaluation (project title: Development of Digital Innovation Element Technologies for Predicting Complex Diseases in Advance and Expanding Non-Face-to-Face Care).
2026.05.26 View 570 -
“Why Only Copper?”… KAIST Reveals Key Limitation of Catalysts That Convert Carbon into Fuel
<(From left) Professor Jihun Oh, Ph.D candidate Suneon Wang, (Starting from the left circle) Dr. Beomil Kim, Ph.D candidate Seungchang Han, Professor Stefan Ringe>
Technology that converts carbon dioxide (CO₂) into fuels and plastic feedstocks using electricity is gaining attention as a core technology in the era of carbon neutrality. In particular, ethylene and ethanol are high-value materials widely used in the production of plastics, fuels, and chemical products, but until now, the only metal that has effectively produced them has essentially been copper (Cu). Through this study, Korean researchers have revealed the limitations of existing catalyst theories that have explained this principle.
KAIST (President Kwang Hyung Lee) announced on the 21st of May that a research team led by Professor Jihun Oh of the Department of Materials Science and Engineering, through joint research with Professor Stefan Ringe’s team from the Department of Chemistry at Korea University (President Dongwon Kim), has identified a new operating principle of the electrochemical CO₂ reduction reaction (CO₂ reduction reaction, a reaction that uses electricity to convert carbon dioxide into other chemical substances).
The research team fabricated alloy catalysts made by mixing gold (Au), silver (Ag), and palladium (Pd), and analyzed what substances these catalysts convert CO₂ into.
Existing catalyst theories have predicted that if the “d-band center” (an indicator of the electronic reactivity of a catalyst) and “work function” (the energy required for a metal to release electrons outward), which indicate the reactivity of electrons on the catalyst surface, are similar to those of copper, then the catalyst should be able to produce multi-carbon (C2+) compounds such as ethylene and ethanol like copper does.
Using a co-sputtering process (a technique that simultaneously deposits multiple metals as thin films to create a new alloy with a desired ratio), the research team precisely fabricated a ternary alloy (AuAgPd, an alloy made by mixing three metals: gold, silver, and palladium) with electronic properties very similar to those of copper.
However, the actual experimental results were different. This alloy produced simple products such as carbon monoxide (CO), but it did not produce complex multi-carbon compounds such as ethylene or ethanol at all. This means that complex CO₂ conversion reactions are difficult to explain using only the electronic properties of catalysts. In other words, the study confirmed that how atoms are arranged on the catalyst surface also has an important effect on reaction performance.
<Catalytic reactions that produce different products from the same carbon dioxide (AI image)>
The research team expects that this study will provide important clues for developing next-generation high-efficiency catalysts that can replace copper in the future. In particular, the study is significant in that it presents a new direction showing the need for precise catalyst design strategies that go beyond existing designs centered only on simple electronic structure and also consider atomic arrangement.
Professor Jihun Oh stated, “This study shows that existing catalyst theories alone are insufficient to fully explain complex multistep carbon conversion reactions,” adding, “In the future, a new catalyst design strategy that considers both electronic properties and local atomic arrangement, meaning how atoms are arranged on the catalyst surface, will be necessary.”
This paper, with KAIST Dr. Beomil Kim, doctoral student Suneon Wang, and Korea University Dr. Seungchang Han as first authors, was published in the May 2026 issue of the international journal Nature Catalysis.
※ Paper title: “Peaks and pitfalls of electrocatalytic CO₂ reduction descriptor models,” DOI: 10.1038/s41929-026-01526-7
※ Lead authors: Beomil Kim (KAIST, first author), Seungchang Han (Korea University, first author), Suneon Wang (KAIST, first author), Jihun Oh (KAIST, corresponding author), Stefan Ringe (Korea University, corresponding author)
This research was supported by the Nano and Material Technology Development Program, the Top-Tier Research Institution Collaboration Platform and Joint Research Support Program, and the Individual Research Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT, as well as by the National Supercomputing Center at the Korea Institute of Science and Technology Information (KISTI).
2026.05.21 View 695 -
Overcoming the Limits of Hydrogen Storage and Transport… KAIST Develops Next-Generation Ammonia Protonic Ceramic Fuel Cell
<(Top row, from left) Professor Kang Taek Lee, Professor Joongmyeon Bae, Dr. Tae Ho Shin, Dr. Ki-Min Roh, (Bottom row, from left) Dr. Dongyeon Kim, Researcher Dong Jae Park, Dr. Incheol Jeong>
As ammonia gains attention as a next-generation energy source capable of overcoming the limits of hydrogen storage and transport, KAIST and a joint research team have developed fuel cell technology that directly uses ammonia as fuel while achieving world-class performance and stability. This achievement is regarded as a core technology that will accelerate the commercialization of the next-generation hydrogen economy and carbon-free power generation.
KAIST (President Kwang Hyung Lee) announced on the 20th of May that Professor Kang Taek Lee and Professor Joongmyeon Bae of the Department of Mechanical Engineering, together with a joint research team including Dr. Tae Ho Shin of the Korea Institute of Ceramic Engineering and Technology (KICET, President Jong-Suk Yoon) and Dr. Ki-Min Roh of the Korea Institute of Geoscience and Mineral Resources (KIGAM, President Kwon Yi Kyun), have developed catalyst technology that dramatically improves the performance and durability of ammonia-based protonic ceramic fuel cells (PCFCs, next-generation high-efficiency fuel cells that generate electricity by transporting hydrogen ions).
<AI image: A next-generation fuel cell that generates electricity using ammonia (NH₃) as fuel>
Ammonia is attracting attention as a next-generation hydrogen carrier (Energy Carrier, a medium that stores and transports hydrogen) because it is easy to store and transport in liquid form. It is also regarded as a representative carbon-free fuel because it consists only of nitrogen (N) and hydrogen (H), producing almost no carbon dioxide (CO₂) during power generation. However, inside fuel cells, ammonia has caused problems by damaging nickel-based materials and slowing reaction rates, leading to performance degradation and shortened lifespan.
To solve this problem, the research team designed a new catalyst structure combining a “high-entropy” oxide catalyst (High-Entropy, a design method that enhances material stability and performance by mixing multiple elements) that improves structural stability by mixing multiple elements, with metal nanoparticles (Nano Particle, ultrafine metal particles on the nanometer scale) that form spontaneously on the surface during operation.
This catalyst was found not only to resist structural collapse even in an ammonia environment, but also to effectively promote the reaction that decomposes ammonia into hydrogen. Through density functional theory (DFT, Density Functional Theory, a simulation method that calculates reaction mechanisms at the atomic level) analysis, the research team identified that the high-entropy oxide structure lowers the energy barrier required for ammonia decomposition and promotes the formation of metal particles.
<AI-generated image of a high-entropy catalyst made by mixing multiple metallic elements>
In particular, the metal alloy nanoparticles that formed spontaneously on the catalyst surface showed much higher catalytic activity than single-metal catalysts. A fuel cell applying this catalyst recorded a maximum power density of 2.04 W per unit area (1 cm²) at 700°C. This means that high power can be produced from an area the size of a fingernail, representing world-class performance in the field of ammonia-based protonic ceramic fuel cells that generate electricity by transporting hydrogen ions (protons).
In addition, the cell operated stably for more than 255 hours even under harsh conditions of 600°C, significantly improving the problem of performance degradation (a phenomenon in which performance decreases over time) seen in existing catalysts.
<Schematic of an ammonia-fueled PCFC incorporating a high-entropy catalyst>
<Microstructure and elemental distribution results of the high-entropy catalyst>
Professor Kang Taek Lee stated, “Through the synergistic structure of high-entropy oxides and alloy nanoparticles, we improved both the performance and durability of ammonia fuel cells,” adding, “This study will serve as a catalyst for accelerating the commercialization of ammonia-based carbon-free power generation technology and next-generation hydrogen energy systems.”
This research, with Dr. Dongyeon Kim of the Department of Mechanical Engineering at KAIST, researcher Dong Jae Park of the Korea Institute of Ceramic Engineering and Technology, and Dr. Incheol Jeong of the Korea Institute of Geoscience and Mineral Resources as co-first authors, was published on April 17 in Nano-Micro Letters (IF: 36.3), an international journal in the fields of energy and materials.
※ Paper title: “Entropy-Modulated Oxide–Metal Catalyst Architectures for Direct Ammonia Protonic Ceramic Fuel Cells,” DOI: https://link.springer.com/article/10.1007/s40820-026-02194-9
This research was supported by the Mid-Career Researcher Program of the Ministry of Science and ICT, the Global Basic Research Laboratory Program, the InnoCORE Program of the Institutes of Science and Technology, and the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources.
2026.05.20 View 883 -
KAIST-Hanwha Solutions Establishes ‘Eco-Friendly Bio-Platform’ to Replace Petroleum-Derived Naphtha
<(From Left) Hyun Bae Bang, Cheon Woo Moon, Cindy Pricilia Surya Prabowo, Minjung Ki, Sang Yup Lee, Changhee Cho, (Upper Left) Jae Sung Cho, Namjin Jang>
KAIST announced on May 19th that the KAIST-Hanwha Solutions Future Technology Research Institute, has secured bio-technology capable of mass-producing eco-friendly raw materials for plastics and textiles using waste resources, offering an alternative to petroleum-derived naphtha.
Naphtha, an essential feedstock for the petrochemical industry, has faced sharp price increases and supply instability in recent years, driving demand for sustainable alternatives. The new technology, addresses both resource supply stability and environmental concerns simultaneously.
A study led by Distinguished Professor Sang-yup Lee of the Department of Chemical and Biomolecular Engineering was published on May 12th in the journal Nature Chemical Engineering and has been selected as the cover paper for the May issue, a designation reserved for research achievements that represent the corresponding issue.
This platform uses ‘glycerol,’ a byproduct discarded during the biodiesel production process, as a raw material. The team engineered high-efficiency microorganisms to convert this waste into 1,3-propanediol (1,3-PDO), a key material for plastics and cosmetics, and optimized the fermentation process for industrial application. The research team succeeded in maintaining high production level even in a 300L pilot process, which serves as a test production stage before application in large-scale plant facilities, moving beyond the laboratory scale.
This study also used computer simulations to predict which genes to engineer, which resulted in improved production levels. The team also developed the fermentation system without antibiotic supplementation — a significant advance, as antibiotic use in industrial fermentation raises concerns about antimicrobial resistance and regulatory hurdles for food, cosmetic, and pharmaceutical applications.
< (AI Image) Microbial-based process for 1,3-propanediol (1,3-PDO) production >
The achievement reflects a 10-year partnership between KAIST and Hanwha Solutions that began in November 2015, with researchers from both sides working together directly on the experiments. Through the KAIST-Hanwha Solutions Future Technology Research Institute, the collaboration has produced 6 patent applications and 13 published papers, standing as a representative model of industry-academic cooperation in South Korea.
< Schematic diagram of microbial-based metabolic engineering strategies for 1,3-PDO production >
※ Paper Title: High-titer, antibiotic-free, pilot-scale production of 1,3-propanediol by engineered Corynebacterium, DOI: 10.1038/s44286-026-00389-w
※ Authors: Jae Sung Cho (KAIST, First Author), Cindy Pricilia Surya Prabowo (KAIST, First Author), Taehee Han (KAIST), Cheon Woo Moon (KAIST), Yoo-Sung Ko (KAIST), Changhee Cho (Hanwha Solutions), Je Woong Kim (KAIST), Won Jun Kim (Hanwha Solutions), Hyun Bae Bang (Hanwha Solutions), Jae Eun Lee (KAIST), Minjung Ki (KAIST), Namjin Jang (Hanwha Solutions), Sang Yup Lee (KAIST, Corresponding Author)
Jung-dae Kim, Head of the Research Institute at Hanwha Solutions, said, “This research is highly significant in that it confirmed the possibility of replacing existing petrochemical processes using bio-based raw materials. We expect it to be an important foundation for sustainable chemical material production and industrial application in the future.”
KAIST Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering stated, “This research is a case showing that microorganism-based chemical production can be sufficiently expanded to an actual industrial scale beyond the laboratory. It will contribute to producing various chemical materials in a more eco-friendly way in the future.”
2026.05.19 View 2106