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KAIST Develops Electrode Technology Achieving 86% Efficiency for Converting CO₂ into Plastic Precursors
<(From Left) Dr. Jonghyeok Park, Ph.D candidate Yunkyoung Han, Professor Hyunjoon Song, Dr. Sungjoo Kim>
KAIST Develops Electrode Technology Achieving 86% Efficiency for Converting CO₂ into Plastic Precursors
In the process of converting carbon dioxide into useful chemicals such as ethylene—a key precursor for plastics—a major challenge has been the flooding of electrodes, where electrolyte penetrates the electrode structure and reduces performance. KAIST researchers have developed a new electrode design that blocks water while maintaining efficient electrical conduction and catalytic reactions, thereby improving both efficiency and stability.
KAIST (President Kwang Hyung Lee) announced on the 6th of April that a research team led by Professor Hyunjoon Song from the Department of Chemistry has developed a novel electrode structure utilizing silver nanowire networks—ultrafine silver wires arranged like a spiderweb—to significantly enhance the efficiency of electrochemical CO₂ conversion to useful chemical products.
In electrochemical CO₂ conversion processes, a long-standing issue has been flooding, where the electrode becomes saturated with electrolyte, reducing the space available for CO₂ to react. While hydrophobic materials can prevent water intrusion, they typically suffer from low electrical conductivity, requiring additional components and complicating the system.
To overcome this, the research team designed a three-layer electrode architecture that simultaneously repels water and enables efficient charge transport. The structure consists of a hydrophobic substrate, a catalyst layer, and an overlaid silver nanowire (Ag NW) network, which acts as an efficient current collector while preventing electrolyte flooding.
< Schematic diagram of a porous polymer–copper oxide catalyst silver nanowire network electrode structure >
A key finding of this study is that the silver nanowires do more than just conduct electricity—they actively participate in the chemical reaction. During CO₂ reduction, the silver nanowires generate carbon monoxide (CO), which is then transferred to adjacent copper-based catalysts, where further reactions occur. This creates a tandem catalytic system, in which two catalysts cooperate sequentially, significantly enhancing the production of multi-carbon compounds such as ethylene.
The electrode demonstrated outstanding performance. It achieved 79% selectivity toward C₂₊ products in alkaline electrolytes and 86% selectivity in neutral electrolytes, representing a world-leading level. It also maintained stable operation for more than 50 hours without performance degradation. These results indicate that most of the converted products are the desired chemicals, while also overcoming the durability limitations of conventional systems.
< Conceptual diagram of a CO₂ electrolysis electrode utilizing a stacked silver nanowire structure (AI-generated image) >
Professor Hyunjoon Song stated, “This study is significant in showing that silver nanowires not only serve as electrical conductors but also directly participate in chemical reactions,” adding, “This approach provides a new design strategy that can be extended to converting CO₂ into a wide range of valuable products such as ethanol and fuels.”
This research, led by Jonghyeok Park (KAIST, first author), was published on March 24, 2026, in the international journal Advanced Science.
※ Paper title: “Overlaid Conductive Silver Nanowire Networks on Gas Diffusion Electrodes for High-Performance Electrochemical CO₂-to-C₂₊ Conversion,” DOI: https://doi.org/10.1002/advs.75003
2026.04.07 View 357 -
Playground for Future Quantum Technology: KAIST-MIT Quantum Information Winter School Successfully Concluded
< Group photo of the KAIST-MIT Quantum Information Winter School >
“Through the KAIST-MIT Quantum Information Winter School, I was able to view research from a broader perspective. The experience of collaborating with students from various universities and majors to complete a project was very refreshing,” said Jun-hyeong Cho, a student at the KAIST School of Electrical Engineering.
KAIST announced on the 16th that the Graduate School of Quantum Science and Technology successfully concluded the ‘KAIST-MIT Quantum Information Winter School,’ held jointly with the Massachusetts Institute of Technology (MIT) from January 5th to 16th at the KAIST main campus in Daejeon.
For this year’s Winter School, 50 junior and senior undergraduate students from Korea and abroad were selected to receive intensive training to grow into next-generation quantum talent. Eight world-renowned scholars from KAIST and MIT participated in the program, providing a multi-dimensional curriculum that spanned theory and practice—ranging from theoretical lectures and introductions to cutting-edge quantum experiments to visits to government-funded research institutes and student poster presentations.
Celebrating its third anniversary since its inception in 2024, the Winter School is now evaluated as a premier quantum information education program in Korea. Alongside KAIST faculty, world-class scholars from MIT participated directly in lectures and field training, operating an intensive curriculum that covered the entirety of quantum information science.
The lecturing faculty included world authorities in quantum computing, quantum devices, quantum machine learning, and quantum simulation, such as MIT professors Pablo Jarillo-Herrero, Seth Lloyd, Kevin P. O’Brien, and William D. Oliver, as well as KAIST scholars Jaewook Ahn, Joonwoo Bae, Gil-Young Cho, and Jae-yoon Choi.
Going beyond theoretical lectures, participants gained a broad understanding of research trends, technical limitations, and future development directions of state-of-the-art quantum technology through experimental training in core areas such as quantum computing, communication, sensing, and simulation.
< Scene from a Winter School lecture >
Furthermore, students visited the Korea Research Institute of Standards and Science (KRISS) and the Electronics and Telecommunications Research Institute (ETRI) to experience actual research sites, engaging in field-oriented education that bridges quantum theory and practice. The poster presentation session, where students shared their own research ideas, received enthusiastic responses as a forum for deep academic exchange, allowing students to receive direct feedback from MIT faculty.
Tae-hee Kim, a student from Pusan National University, remarked, “I was greatly inspired by the passion of the MIT faculty and the high level of questions from the students. It served as a motivation for me to pursue deeper studies independently.” Byung-jin Hwang, a student from Yonsei University, added, “I expected lectures from world-class scholars to be difficult, but I was impressed by the explanations tailored to the undergraduate level. The poster presentation session was particularly memorable.”
Eun-seong Kim, Dean of the KAIST Graduate School of Quantum Science and Technology, stated, “The KAIST-MIT Quantum Information Winter School is a special educational program where students can learn directly from world-renowned quantum researchers and experience cutting-edge research. We look forward to the active participation of future talents who will lead the quantum industry.”
Participants for this Winter School were selected through a document review process, and the program was operated entirely free of charge. KAIST covered all educational expenses and provided dormitory accommodations and lunch. Detailed information about the event can be found on the KAIST Graduate School of Quantum Science and Technology website (https://quantumschool.kaist.ac.kr/).
< Poster for the KAIST-MIT Quantum Information Winter School >
2026.01.16 View 1922 -
Where did this fish come from? Securing World-Class Seafood Traceability Technology
< (From left) KAIST Ph.D. candidate Hyeontaek Hwang, Research Professor Yalew Kidane, Senior Researcher Young-jong Lee, Researcher Geon-woo Park, and (Top) Professor Daeyoung Kim >
When buying seafood at a supermarket, you may have wondered where the fish was caught and what process it went through to reach your dinner table. However, due to complex distribution processes, it has been difficult to transparently track that path. KAIST’s research team has developed a digital technology that solves this problem, allowing the movement path of seafood to be checked at a glance based on international standards recognized worldwide.
KAIST announced on December 19th that "OLIOPASS," a GS1 international standard-based digital transformation solution developed by Director Daeyoung Kim (Professor, School of Computing) of the KAIST Auto-ID Labs Busan Innovation Center, has passed the rigorous performance verification of the GDST (Global Dialogue on Seafood Traceability). It is the first in Korea to obtain the "GDST Capable Solution" certification.
< (Left) GDST Global Certification Logo, (Right) KAIST OLIOPASS Platform Logo >
Only 13 technologies worldwide have received this GDST certification. Among them, only 7 entities, including KAIST, support "Full Chain" traceability technology, which manages the entire process from production and processing to distribution and sales.
The GDST is an international organization established in 2015 at the suggestion of the World Economic Forum (WEF). It helps record and share information on all seafood movement processes digitally, according to the GS1 international standard agreed upon by the global community. This can be compared to creating a "common language for the supply chain" used worldwide.
The GDST is a global standard system that increases the reliability of seafood history information by defining Key Data Elements (KDEs) that must be recorded during the movement of seafood and Critical Tracking Events (CTEs) that define when, where, and what moved, based on international standards.
As major food distribution companies in the United States and Europe have recently begun requiring GDST compliance, this standard is becoming a de facto essential requirement for entering the global market. Since 2019, KAIST has participated as a founding member of GDST and has played a key role in designing seafood traceability models and system-to-system information interoperability.
In particular, with the U.S. Food and Drug Administration (FDA) announcing the mandatory enforcement of food traceability (FSMA 204) starting in July 2028, this certification is significant as it secures a technical solution for domestic companies to meet global market regulations.
OLIOPASS, which received certification on November 5th, is a digital traceability platform that combines KAIST's IoT technology with international standards (GS1 EPCIS 2.0, GS1 Digital Link). It records and shares movement information of various products and assets in a standardized language and utilizes blockchain technology to fundamentally prevent forgery or alteration. Even if systems differ between companies, history data is seamlessly linked.
Furthermore, OLIOPASS is designed as an "AI-ready data" infrastructure, allowing for the easy application of next-generation AI technologies such as Large Multimodal Models (LMM), AI agents, knowledge graphs, and ontologies. This allows it to serve as a platform that supports both digital and AI transformation beyond simple history management.
Daeyoung Kim, Director of the KAIST Auto-ID Labs Busan Innovation Center, stated, "This certification is an international recognition of our capability in reliable data technology across the global supply chain. We will expand OLIOPASS beyond seafood and food into various fields such as pharmaceuticals, logistics, defense, and smart cities, ensuring KAIST’s technology grows into a platform used by the world."
※ Related Link: https://thegdst.org/verified-gdst-capable-solutions/
< List of Certified Organizations >
2025.12.22 View 1176 -
Harry Potter–Style ‘Moving Invisibility Cloak’ Technology Developed
<(Top row, left) Ph.D candidate Hyeonseung Lee, Professor Wonho Choe, (Second row, left) Professor Hyoungsoo Kim, Professor Sanghoo Park,(Top) First author Dr. Jeongsu Pyeon>
What do Harry Potter’s invisibility cloak and stealth fighter jets that evade radar have in common? They both make objects invisible despite their physical presence. Building upon this concept, our research team has taken it one step further by developing a “smart invisibility cloak” like technology that hides electromagnetic waves even better as it stretches and moves. This technology is expected to open new possibilities for moving robots, body-mounted wearable devices, and next-generation stealth technologies.
On December 16th, research teams led by Professor Hyoungsoo Kim of the Department of Mechanical Engineering and Professor Sanghoo Park of the Department of Nuclear and Quantum Engineering from KAIST announced that they have developed a core enabling technology for next-generation stretchable cloaking* based on Liquid Metal Composite Ink (LMCP), which can absorb, modulate, and shield electromagnetic waves.
* Cloaking: A technology that makes an object appear as if it does not exist to detection equipment such as radar or sensors, even though it is physically present.
To realize cloaking technology, it is necessary to freely control light or electromagnetic waves on the surface of an object. However, conventional metallic materials are rigid and do not stretch well, and when forcibly stretched, they easily break. For this reason, there have been significant difficulties in applying such materials to body-conforming electronic devices or robots that freely change shape.
The liquid metal composite ink developed by the research team maintains electrical conductivity even when stretched up to 12 times its original length (1200%), and it demonstrated high stability with little oxidation or performance degradation even after being left in air for nearly a year. Unlike conventional metals, this ink is rubber-like and soft while fully retaining metallic functionality.
These properties are possible because, during the drying process, liquid metal particles inside the ink spontaneously connect with one another to form a mesh-like metallic network structure. This structure functions as a “metamaterial”—an artificial structure in which extremely small patterns are repeatedly printed using ink so that electromagnetic waves interact with the structure in a designed manner. As a result, the material simultaneously exhibits liquid-like flexibility and metal-like robustness.
The fabrication process is also simple. Without complex procedures such as high-temperature sintering or laser processing, the ink can be printed using a printer or applied with a brush and then simply dried. In addition, common drying issues such as stains or cracking do not occur, enabling smooth and uniform metal patterns.
To verify the performance of the ink, the research team became the first in the world to fabricate a “stretchable metamaterial absorber” whose electromagnetic wave absorption characteristics change depending on the degree of stretching.
Simply stretching the rubber-like substrate after printing patterns with the ink changes the type (frequency band) of electromagnetic waves that are absorbed. This demonstrates the potential for cloaking technology that can more effectively hide objects from radar or communication signals depending on the situation.
<Figure. Comparison of LMCP ink properties, printing process applicability, mechanical/electrical performance, and versatility on various substrates.
(a) Comparison results regarding surface tension, viscosity, wettability, and post-processing requirements between conventional liquid metal-based inks and the LMCP ink in this study. The results demonstrate that LMCP ink possesses the advantage of requiring no post-processing while maintaining relatively high viscosity and excellent wettability. (Right radar chart: Qualitative comparison of key performance indicators, including electrical conductivity, surface tension, viscosity, wettability, and post-processing requirements).
(b) Various printing methods based on the self-sintering characteristics of LMCP ink: nozzle-based direct writing, brushing, patterning using shadow masks and doctor blade processes, and large-area electrode fabrication via the roll-to-roll method.
(c) Stretchability and electrical stability of LMCP electrodes. Results show resistance changes when samples are stretched from 0% to 1200%, and stable operation is confirmed under 0%–500% strain through a 3 V LED driving experiment.
(d) Examples of various patterns and devices fabricated using LMCP ink. Applicable structures are presented, including large-area uniform coating, precise grid patterns, crack-free metal paths, LED circuits operating under tension, and stretchable spiral electrodes>
(e) Examples demonstrating stable printing of LMCP ink on various substrates (SIR, NBR, PVC, PET, WPU, PDMS, Latex), indicating excellent pattern reproducibility and adhesion regardless of the substrate type>
This technology is evaluated as a groundbreaking electronic material technology that simultaneously satisfies stretchability, electrical conductivity, long-term stability, process simplicity, and electromagnetic wave control functionality.
Professor Hyoungsoo Kim stated, “We have made it possible to implement electromagnetic wave functionality using only printing processes without complex equipment,” adding, “This technology is expected to be utilized in various future technologies such as robotic skin, body-mounted wearable devices, and radar stealth technologies in the defense sector.”
This research was recognized as an important fundamental technology in the field of next-generation electronic materials and was published in the October 2025 issue of the international Wiley journal Small on October 16, where it was selected as a cover article.
Paper title:
J. Pyeon, H. Lee, W. Choe, S. Park, H. Kim,
“Versatile Liquid Metal Composite Inks for Printable, Durable, and Ultra-Stretchable Electronics,”
Small 2501829 (2025)
DOI: https://doi.org/10.1002/smll.202501829
Author information:
First author: Dr. Jeongsu Pyeon
Co-authors: Doctoral candidate Hyeonseung Lee, Professor Wonho ChoeCorresponding authors: Professor Hyoungsoo Kim, Professor Sanghoo Park
This work was supported by the National Research Foundation of Korea’s Mid-Career Research Program (MSIT: 2021R1A2C2007835) and the KAIST UP Program.
< Selected as the cover article of the October 2025 issue of the international journal Small >
< Invisibility cloak technology image (AI-generated image) >
2025.12.16 View 3339 -
KAIST Removes 99.9% of Ultrafine Dust Using Nano Water Droplet Technology
<(From Left) Ph.D candidate Sungyoon Woo, Professor Il-Doo Kim, Professor Seung S.Lee, Ph.D candiate Jihwan Chae, Researcher Jiyeon Yu, (Upper Right) Dr. Yujang Cho>
A KAIST research team has drawn attention by developing a new water-based air purification technology that combines “nano water droplets that capture dust” with a “nano sponge structure that autonomously draws up water,” enabling dust removal using nano water droplets without filters, self-supplied water operation, and long-term, quiet, and safe performance.
KAIST (President Kwang Hyung Lee) announced on the December 8 that a joint research team led by Professor Il-Doo Kim of the Department of Materials Science and Engineering and Professor Seung S. Lee of the Department of Mechanical Engineering developed a new water electrospray–based air purification device that rapidly removes ultrafine dust without filters, generates no ozone, and operates with ultra-low power consumption.
The research team confirmed that this device overcomes the limitations of conventional air purifiers by eliminating the need for filter replacement, producing no ozone, and removing even extremely fine ultrafine dust as small as PM0.3 (diameter 0.3 μm), which is about 1/200 the thickness of a human hair, within a short time. In addition, it demonstrated high stability and durability without performance degradation even during long-term use.
This device was created by combining Professor Seung S. Lee’s “ozone-free water electrospray” technology with Professor Il-Doo Kim’s “hygroscopic nanofiber Emitter” technology.
Inside the device are a high-voltage electrode, a nanofiber absorber that autonomously draws up water, and polymer microchannels that transport water via capillary action. Thanks to this structure, a self-pumped configuration is achieved in which water is automatically supplied without a pump, enabling stable long-term water electrospray operation.
Tests conducted by the research team in a 0.1 m3 experimental chamber showed that the device removed 99.9% of various particles in the PM0.3–PM10 range within 20 minutes. In particular, it exhibited outstanding performance by removing 97% of PM0.3 ultrafine dust, which is difficult to eliminate using conventional filter-based air purifiers, within just 5 minutes.
Even after 30 consecutive tests and 50 hours of continuous operation, the device operated stably without performance degradation, and its power consumption was approximately 1.3 W, which is lower than that of a smartphone charger and only about 1/20 that of conventional HEPA (High Efficiency Particulate Air) filter–based air purifiers.
In addition, because there is no filter, there is no pressure loss in airflow and almost no noise is generated.
This technology maintains high-efficiency purification performance while generating no ozone at all, presenting the potential for a next-generation eco-friendly air purification platform.
In particular, with advantages such as elimination of filter replacement costs, ultra-low power operation, and secured long-term stability, it is expected to expand into various fields including indoor environments as well as automotive, cleanroom, portable, and wearable air purification modules.
Commercialization of this technology is currently underway through A2US Co., Ltd., a university spin-off company from Professor Seung S. Lee’s laboratory.
A2US Co., Ltd. won a CES 2025 Innovation Award and plans to launch a portable air purifier product in 2026. The product is equipped not only with fine dust removal using nano water droplets but also with odor removal and pathogen sterilization functions.
<Figure1.Design and Operating Mechanism of a Miniature Air-Purification Device Based on Cone-Jet Water Electrospray Using a Self-Pumping Hygroscopic (PVA–PAA–MMT) Nanofiber Membrane (PPM-NFM) Emitter.>
<Figure 2. (a) Schematic of the Self-Pumping Hygroscopic Nanofiber Membrane (PPM-NFM) Emitter, and (b) Corresponding Photograph and Surface Scanning Microscopy Images.>
This research was conducted with Jihwan Chae (Ph.D. candidate, Department of Mechanical Engineering, KAIST) and Yujang Cho (Ph.D., Department of Materials Science and Engineering, KAIST) as co–first authors, and with Professor Seung S. Lee (Department of Mechanical Engineering) and Professor Il-Doo Kim (Department of Materials Science and Engineering) as corresponding authors. The research results were published on November 14 in the international journal Advanced Functional Materials (AFM), published by Wiley, a world-renowned publisher in materials science and nanotechnology.
※ Paper title: “Self-Pumped Hygroscopic Nanofiber Emitter for Ozone-Free Water Electrospray-Based Air Purification,” DOI: 10.1002/adfm.202523456
This research was supported by the National Research Foundation of Korea, the Ministry of Science and ICT, and the KAIST–MIT Future Energy Frontier Research Center (AI-robotics–based energy materials innovation) program.
2025.12.08 View 2404 -
KAIST, Production Temperature ↓ by 500°C, Power Output ↑ 2x… Next-Generation Ceramic Electrochemical Cell Reborn
<(Top row, from left) Professor Kang Taek Lee, Ph.D candidate Yejin Kang, Dr. Dongyeon Kim, (Bottom row, from left) M.S candidate Mincheol Lee, Ph.D candidate Seeun Oh, Ph.D candidate Seungsoo Jang, Ph.D candidate Hyeonggeun Kim>
As power demand surges in the AI era, the “protonic ceramic electrochemical cell (PCEC),” which can simultaneously produce electricity and hydrogen, is gaining attention as a next-generation energy technology. However, this cell has faced the technical limitation of requiring an ultra-high production temperature of 1,500°C. A KAIST research team has succeeded in establishing a new manufacturing process that lowers this limit by more than 500°C for the first time in the world.
KAIST (President Kwang Hyung Lee) announced on the 4th of December that Professor Kang Taek Lee’s research team in the Department of Mechanical Engineering developed a new process that enables the fabrication of high-performance protonic ceramic electrochemical cells at temperatures more than 500°C lower than before, using “microwave + vapor control technology” that leverages microwave heating principles and the diffusion environment of chemical vapor generated from specific chemical components.
The electrolyte—the key material of protonic ceramic electrochemical cells—contains barium (Ba), and barium easily evaporates at temperatures above 1,500°C, which has been the main cause of performance degradation. Therefore, the ability to harden the ceramic electrolyte at a lower temperature has been the core issue that determines cell performance.
As power demand surges in the AI era, the “protonic ceramic electrochemical cell (PCEC),” which can simultaneously produce electricity and hydrogen, is gaining attention as a next-generation energy technology. However, this cell has faced the technical limitation of requiring an ultra-high production temperature of 1,500°C. A KAIST research team has succeeded in establishing a new manufacturing process that lowers this limit by more than 500°C for the first time in the world.
KAIST (President Kwang Hyung Lee) announced on the 4th of December that Professor Kang Taek Lee’s research team in the Department of Mechanical Engineering developed a new process that enables the fabrication of high-performance protonic ceramic electrochemical cells at temperatures more than 500°C lower than before, using “microwave + vapor control technology” that leverages microwave heating principles and the diffusion environment of chemical vapor generated from specific chemical components.
The electrolyte—the key material of protonic ceramic electrochemical cells—contains barium (Ba), and barium easily evaporates at temperatures above 1,500°C, which has been the main cause of performance degradation. Therefore, the ability to harden the ceramic electrolyte at a lower temperature has been the core issue that determines cell performance.
To solve this, the research team devised a new heat-treatment method called “vapor-phase diffusion.” This technique places a special auxiliary material (a vapor source) next to the cell and irradiates it with microwaves to quickly diffuse vapor. When the temperature reaches approximately 800°C, the vapor released from the auxiliary material moves toward the electrolyte and tightly bonds the ceramic particles. Thanks to this technology, a process that previously required 1,500°C can now be completed at just 980°C. In other words, a world-first ceramic electrochemical cell fabrication technology has been created that produces high-performance electricity at a “low temperature” without damaging the electrolyte.
A cell fabricated with this process produced 2 W of power stably from a 1 cm² cell (roughly the size of a fingernail) at 600°C and generated 205 mL of hydrogen per hour at 600°C (about the volume of a small paper cup, among the highest in the industry). It also maintained stability without performance degradation during 500 hours of continuous operation.
In other words, this technology reduces the production temperature (−500°C), lowers the operating temperature (600°C), doubles performance (2 W/cm²), and extends the lifespan (500-hour stability), achieving world-class performance in ceramic cell technology.
The research team also enhanced the reliability of the technology by using digital twins (virtual simulations) to analyze gas-transport phenomena occurring in the microscopic internal structure of the cell − phenomena that are difficult to observe in actual experiments.
<Figure 1. (a) Schematic of the vapor-diffusion-based process; (b) Surface microstructure of the electrolyte; (c) Internal barium composition ratio of the electrolyte according to processing conditions; (d) Comparison of power-generation performance with previous studies>
< Figure 2. (a) Three-dimensional reconstructed image of the protonic ceramic electrochemical cell fuel electrode according to processing conditions (b) Pore structure (c) Gas-transport simulation results >
Professor Kang Taek Lee emphasized, “This study is the world’s first case of using vapor to lower the heat-treatment temperature by more than 500°C while still producing a high-performance, high-stability cell.” He added, “It is expected to become a key manufacturing technology that addresses the power challenges of the AI era and accelerates the hydrogen society.”
Dongyeon Kim (KAIST PhD) and Yejin Kang (KAIST PhD candidate) participated as co–first authors. The research results were published in Advanced Materials (IF: 26.8), one of the world’s leading journals in energy and materials science, and were selected as the Inside Front Cover article on October 29.
(Paper title: “Sub-1000°C Sintering of Protonic Ceramic Electrochemical Cells via Microwave-Driven Vapor Phase Diffusion,” DOI: https://doi.org/10.1002/adma.202506905)
This research was supported by the MSIT’s Mid-career Researcher Program and the H2 Next Round Program.
2025.12.05 View 2279 -
Efficient Quantum Process Tomography for Enabling Scalable Optical Quantum Computing
<(From Left) Ph.D candidate Geunhee Gwak, Professor Young-Sik Ra, Dr. Chan Roh, Ph.D candidate Young-Do Yoon from KAIST, (Top Left) Professor M.S Kim from Imperial College London>
Optical quantum computers are gaining attention as a next-generation computing technology with high speed and scalability. However, accurately characterizing complex optical processes, where multiple optical modes interact to generate quantum entanglement, has been considered an extremely challenging task. KAIST research team has overcome this limitation, developing a highly efficient technique that enables complete characterization of complex multimode quantum operations in experiment. This technology, which can analyze large-scale operations with less data, represents an important step toward scalable quantum computing and quantum communication technologies.
KAIST announced on November 17th that a research team led by Professor Young-Sik Ra from the Department of Physics has developed a Multimode Quantum Process Tomography technique capable of efficiently identifying the characteristics of second-order nonlinear optical quantum processes that are essential for optical quantum computing.
Efficient 'CT Scan' Technology for Quantum Computers
'Tomography' is a technique, similar to a medical CT scan, that reconstructs an invisible internal structure from diverse measurements. Similarly, quantum computing requires a method that reconstructs the internal workings of quantum operations using various measurement data. To outperform conventional computers, a quantum computer must be capable of manipulating a large number of quantum units (qubits or qumodes) at the same time. However, as the number of qubits or quantum optical modes (qumodes) increases, the resources required for tomography grows exponentially, making existing technologies unable to analyze systems with even five or more optical modes.
With the newly developed technique, the research team is now able to clearly determine what actually happens inside an optical quantum computer, as if taking a CT scan.
Introducing a New Mathematical Framework Based on Amplification and Noise Matrices
Inside a quantum computer, multiple optical modes interact in a highly complex and entangled way. The research team has introduced a new mathematical framework that precisely describes multimode second-order nonlinear optical quantum processes.
This method analyzes how input states change under a given operation using two key components: the 'Amplification matrix,' which describes how the mean fields of light are transformed, and the 'Noise matrix,' which captures the noise or loss introduced through environmental interactions.
Together, these components create a 'quantum state map' that enables accurate and simultaneous observation of both the ideal quantum evolution of light (unitary changes) and the unavoidable noise (non-unitary changes) present in real devices. This leads to a much more realistic characterization of how an optical quantum computer actually operates.
Reducing the Required Measurement Data and Expanding Analysis to 16 Modes
To determine how a quantum operation works, the research team input several types of quantum states and observed how the outputs changed. They then applied a statistical method known as Maximum Likelihood Estimation to reconstruct the internal operation that most accurately explains the collected data while satisfying the necessary physical conditions.
Using this approach, the research team dramatically reduced the amount of measurement data required. Whereas existing methods quickly become impractical—requiring enormous datasets even for systems with slightly more than a few modes and typically limiting analysis to about five modes—the new technique overcomes this bottleneck. The team successfully performed the world’s first experimental characterization of a large-scale optical quantum operation involving 16 modes, an unprecedented milestone in the field.
<Figure1.Experimental scheme. (Left) Various coherent states are used as input probes to determine the amplification matrix. (Right) A vacuum input state is used to additionally determine the noise matrix.>
<Figure2.Characterization results. (a) 16-mode second-order nonlinear optical quantum process. (b) Cluster state generation. (c) Mode-dependent loss with nonlinear interaction. (d) Quantum noise channel. Left and right columns show the amplification and noise matrices, respectively>
Professor Young-Sik Ra stated, "This research significantly increases the efficiency of Quantum Process Tomography, a foundational technology essential for quantum computing. The acquired technology will greatly contribute to enhancing the scalability and reliability of various quantum technologies, including quantum computing, quantum communication, and quantum sensing."
The study, in which Geunhee Gwak (Integrated M.S, Ph.D. Candidate, Department of Physics) participated as the first author, and Dr. Chan Roh (Postdoctoral Researcher), Young-Do Yoon (Integrated M.S./Ph.D. Candidate), and Professor Myungshik Kim (Imperial College London) participated as co-authors, was formally published online in the prominent international academic journal 'Nature Photonics' on November 11, 2025.
※ Article Title: Completely characterizing multimode second-order nonlinear optical quantum processes, DOI:10.1038/s41566-025-01787-x
This research was supported by the National Research Foundation of Korea (Quantum Computing Technology Development Project, Mid-career Researcher Support Project, Quantum Simulator Development for Material Innovation Project, Quantum Technology R&D Flagship Project, Basic Research Lab Support Project), the Institute of Information & Communications Technology Planning & Evaluation (Core Source Technology for Quantum Internet Project, University ICT Research Center Support Project), and the US Air Force Research Laboratory.
2025.11.18 View 2396 -
Professor Sang Yup Lee Selected as IETI 'Laureate Distinguished Fellow'
<Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering>
Professor Sang Yup Lee of KAIST Department of Chemical and Biomolecular Engineering has been selected as a 'Laureate Distinguished Fellow,' the highest rank of fellow, by the International Engineering and Technology Institute (IETI).
Professor Lee is a globally renowned biotechnologist who has been leading research on the sustainable production of bio-based chemicals, and he received the 'ENI Award' in 2018. With this selection, he stands shoulder-to-shoulder with the world's top scholars, including recipients of the Nobel, Fields, and Turing Prizes.
IETI is an international academic organization established in Hong Kong in 2015 to promote innovation and international cooperation in the fields of engineering, technology, and science. Each year, the institute selects researchers with significant academic influence worldwide and appoints them into three grades: Laureate Distinguished Fellow, Distinguished Fellow, and Fellow. Professor Lee has been named to the most prestigious grade among these.
<IETI 2025 Fellow Selection Photo>
A total of 70 new fellows were selected in 2025. Among them, 14 individuals were named Laureate Distinguished Fellows, which includes recipients of top honors such as the Nobel, Fields, and Turing Prizes. Besides Professor Lee, this group includes Dudley Herschbach of Harvard University (Nobel Prize in Chemistry), Vint Cerf of Google (Turing Award), and Shigefumi Mori of Kyoto University (Fields Medal).
IETI stated that the selection process involved a rigorous five-step procedure: nomination, qualification review, document screening, expert voting, and final evaluation. It also expressed hope that the newly appointed fellows will demonstrate academic leadership in their respective research fields and contribute to global scientific and technological innovation and the promotion of international cooperation.
2025.11.10 View 2016 -
3D Worlds from Just a Few Phone Photos
<(From Left) Ph.D candidate Jumin Lee, Ph.D candidate Woo Jae Kim, Ph.D candidate Youngju Na, Ph.D candidate Kyu Beom Han, Professor Sung-eui Yoon>
Existing 3D scene reconstructions require a cumbersome process of precisely measuring physical spaces with LiDAR or 3D scanners, or correcting thousands of photos along with camera pose information. The research team at KAIST has overcome these limitations and introduced a technology enabling the reconstruction of 3D —from tabletop objects to outdoor scenes—with just two to three ordinary photographs. The breakthrough suggests a new paradigm in which spaces captured by camera can be immediately transformed into virtual environments.
KAIST announced on November 6 that the research team led by Professor Sung-Eui Yoon from the School of Computing has developed a new technology called SHARE (Shape-Ray Estimation), which can reconstruct high-quality 3D scenes using only ordinary images, without precise camera pose information.
Existing 3D reconstruction technology has been limited by the requirement of precise camera position and orientation information at the time of shooting to reproduce 3D scenes from a small number of images. This has necessitated specialized equipment or complex calibration processes, making real-world applications difficult and slowing widespread adoption.
To solve these problems, the research team developed a technology that constructs accurate 3D models by simultaneously estimating the 3D scene and the camera orientation using just two to three standard photographs. The technology has been recognized for its high efficiency and versatility, enabling rapid and precise reconstruction in real-world environments without additional training or complex calibration processes.
While existing methods calculate 3D structures from known camera poses, SHARE autonomously extracts spatial information from images themselves and infers both camera pose and scene structure. This enables stable 3D reconstruction without shape distortion by aligning multiple images taken from different positions into a single unified space.
<Representative Image of SHARE Technology>
"The SHARE technology is a breakthrough that dramatically lowers the barrier to entry for 3D reconstruction,” said Professor Sung-Eui Yoon. “It will enable the creation of high-quality content in various industries such as construction, media, and gaming using only a smartphone camera. It also has diverse application possibilities, such as building low-cost simulation environments in the fields of robotics and autonomous driving."
<SHARE Technology, Precise Camera Information and 3D Scene Prediction Technology>
Ph.D. Candidate Youngju Na and M.S candidate Taeyeon Kim participated as co-first authors on the research. The results were presented on September 17th at the IEEE International Conference on Image Processing (ICIP 2025), where the paper received the Best Student Paper Award.
The award, given to only one paper among 643 accepted papers this year—a selection rate of 0.16 percent—once again underscores the excellent research capabilities of the KAIST research team.
Paper Title: Pose-free 3D Gaussian Splatting via Shape-Ray Estimation, DOI: https://arxiv.org/abs/2505.22978
Award Information: https://www.linkedin.com/posts/ieeeicip_congratulations-to-the-icip-2025-best-activity-7374146976449335297-6hXz
This achievement was carried out with support from the Ministry of Science and ICT's SW Star Lab Project under the task 'Development of Perception, Action, and Interaction Algorithms for Unspecified Environments for Open World Robot Services.'
2025.11.10 View 2449 -
KAIST Develops Room-Temperature 3D Printing Technology for ‘Electronic Eyes’—Miniaturized Infrared Sensors
<(From Left) Professor Ji Tae Kim of the Department of Mechanical Engineering, Professor Soong Ju Oh of Korea University and Professor Tianshuo Zhao of the University of Hong Kong>
The “electronic eyes” technology that can recognize objects even in darkness has taken a step forward. Infrared sensors, which act as the “seeing” component in devices such as LiDAR for autonomous vehicles, 3D face recognition systems in smartphones, and wearable healthcare devices, are regarded as key components in next-generation electronics. Now, a research team at KAIST and their collaborators have developed the world’s first room-temperature 3D printing technology that can fabricate miniature infrared sensors in any desired shape and size.
KAIST (President Kwang Hyung Lee) announced on the 3rd of November that the research team led by Professor Ji Tae Kim of the Department of Mechanical Engineering, in collaboration with Professor Soong Ju Oh of Korea University and Professor Tianshuo Zhao of the University of Hong Kong, has developed a 3D printing technique capable of fabricating ultra-small infrared sensors—smaller than 10 micrometers (µm)—in customized shapes and sizes at room temperature.
Infrared sensors convert invisible infrared signals into electrical signals and serve as essential components in realizing future electronic technologies such as robotic vision. Accordingly, miniaturization, weight reduction, and flexible form-factor design have become increasingly important.
Conventional semiconductor fabrication processes were well suited for mass production but struggled to adapt flexibly to rapidly changing technological demands. They also required high-temperature processing, which limited material choices and consumed large amounts of energy.
To overcome these challenges, the research team developed an ultra-precise 3D printing process that uses metal, semiconductor, and insulator materials in the form of liquid nanocrystal inks, stacking them layer by layer within a single printing platform.
This method enables direct fabrication of core components of infrared sensors at room temperature, allowing for the realization of customized miniature sensors of various shapes and sizes.
Particularly, the researchers achieved excellent electrical performance without the need for high-temperature annealing by applying a “ligand-exchange” process, where insulating molecules on the surface of nanoparticles are replaced with conductive ones.
As a result, the team successfully fabricated ultra-small infrared sensors measuring less than one-tenth the thickness of a human hair (under 10 µm).
<Figure 1. 3D printing of infrared sensors.a. Room-temperature printing process for the electrodes and photoactive layer that make up the infrared sensor.b. Structure and chemical composition of the printed infrared microsensor. c.Printed infrared sensor micropixel array.>
Professor Ji Tae Kim commented, “The developed 3D printing technology not only advances the miniaturization and lightweight design of infrared sensors but also paves the way for the creation of innovative new form-factor products that were previously unimaginable. Moreover, by reducing the massive energy consumption associated with high-temperature processes, this approach can lower production costs and enable eco-friendly manufacturing—contributing to the sustainable development of the infrared sensor industry.”
The research results were published online in Nature Communications on October 16, 2025, under the title “Ligand-exchange-assisted printing of colloidal nanocrystals to enable all-printed sub-micron optoelectronics” (DOI: https://doi.org/10.1038/s41467-025-64596-4).
This research was supported by the Ministry of Science and ICT of Korea through the Excellent Young Researcher Program (RS−2025−00556379), the National Strategic Technology Material Development Program (RS−2024−00407084), and the International Cooperation Research Program for Original Technology Development (RS−2024−00438059).
2025.11.03 View 4634 -
KAIST Develops AI Technology That Predicts and Assembles Cell Drug Responses Like Lego Blocks
<(From left) Dr. Younghyun Han, (top center) Dr. Chun-Kyung Lee, (bottom center) Prof. Kwang-Hyun Cho,Ph.D. candidate Hyunjin Kim>
Controlling the state of a cell in a desired direction is one of the central challenges in life sciences, including drug development, cancer treatment, and regenerative medicine. However, identifying the right drug or genetic target for that purpose is extremely difficult. To address this, researchers at KAIST have mathematically modeled the interaction between cells and drugs in a modular “Lego block” manner—breaking them down and recombining them—to develop a new AI technology that can predict not only new cell–drug reactions never before tested but also the effects of arbitrary genetic perturbations.
KAIST (President Kwang Hyung Lee) announced on the 16th of October that a research team led by Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering has developed a generative AI-based technology capable of identifying drugs and genetic targets that can guide cells toward a desired state.
“Latent space” is an invisible mathematical map used by image-generating AI to organize the essential features of objects or cells. The research team succeeded in separating the representations of cell states and drug effects within this space and then recombining them to predict the reactions of previously untested cell–drug combinations. They further extended this principle to show that the model can also predict how a cell’s state would change when a specific gene is regulated.
The team validated this approach using real experimental data. As a result, the AI identified molecular targets capable of reverting colorectal cancer cells toward a normal-like state, which the team later confirmed through cell experiments.
This finding demonstrates that the method is not limited to cancer treatment—it serves as a general platform capable of predicting various untrained cell-state transitions and drug responses. In other words, the technology not only determines whether or not a drug works but also reveals how it functions inside the cell, making the achievement particularly meaningful.
<Latent Space Direction Vector–Based Cell Transition Modeling>
The research provides a powerful tool for designing methods to induce desired cell-state changes. It is expected to have broad applications in drug discovery, cancer therapy, and regenerative medicine, such as restoring damaged cells to a healthy state.
Professor Kwang-Hyun Cho stated, “Inspired by image-generation AI, we applied the concept of a ‘direction vector,’ an idea that allows us to transform cells in a desired direction.” He added, “This technology enables quantitative analysis of how specific drugs or genes affect cells and even predicts previously unknown reactions, making it a highly generalizable AI framework.”
The study was conducted with Dr. Younghyun Han, Ph.D. candidate Hyunjin Kim, and Dr. Chun-Kyung Lee of KAIST. The research findings were published online in Cell Systems, a journal by Cell Press, on October 15.
※ Paper title: “Identifying an Optimal Perturbation to Induce a Desired Cell State by Generative Deep Learning” (DOI: 10.1016/j.cels.2025.101405)
The study was supported by the National Research Foundation of Korea (NRF) through the Ministry of Science and ICT’s Mid-Career Researcher Program and the Basic Research Laboratory (BRL) Program.
2025.10.16 View 3177 -
KAIST Develops AI Crowd Prediction Technology to Prevent Disasters like the Itaewon Tragedy
<(From Left) Ph.D candidate Youngeun Nam from KAIST, Professor Jae-Gil Lee from KAIST, Ji-Hye Na from KAIST, (Top right, from left) Professor Soo-Sik Yoon from Korea University, Professor HwanJun Song from KAIST>
To prevent crowd crush incidents like the Itaewon tragedy, it's crucial to go beyond simply counting people and to instead have a technology that can detect the real-
inflow and movement patterns of crowds. A KAIST research team has successfully developed new AI crowd prediction technology that can be used not only for managing large-scale events and mitigating urban traffic congestion but also for responding to infectious disease outbreaks.
On the 17th, KAIST (President Kwang Hyung Lee) announced that a research team led by Professor Jae-Gil Lee from the School of Computing has developed a new AI technology that can more accurately predict crowd density.
The dynamics of crowd gathering cannot be explained by a simple increase or decrease in the number of people. Even with the same number of people, the level of risk changes depending on where they are coming from and which direction they are heading.
Professor Lee's team expressed this movement using the concept of a "time-varying graph." This means that accurate prediction is only possible by simultaneously analyzing two types of information: "node information" (how many people are in a specific area) and "edge information" (the flow of people between areas).
In contrast, most previous studies focused on only one of these factors, either concentrating on "how many people are gathered right now" or "which paths are people moving along." However, the research team emphasized that combining both is necessary to truly capture a dangerous situation.
For example, a sudden increase in density in a specific alleyway, such as Alley A, is difficult to predict with just "current population" data. But by also considering the flow of people continuously moving from a nearby area, Area B, towards Area A (edge information), it's possible to pre-emptively identify the signal that "Area A will soon become dangerous."
To achieve this, the team developed a "bi-modal learning" method. This technology simultaneously considers population counts (node information) and population flow (edge information), while also learning spatial relationships (which areas are connected) and temporal changes (when and how movement occurs).
Specifically, the team introduced a 3D contrastive learning technique. This allows the AI to learn not only 2D spatial (geographical) information but also temporal information, creating a 3D relationship. As a result, the AI can understand not just whether the population is "large or small right now," but "what pattern the crowd is developing into over time." This allows for a much more accurate prediction of the time and place where congestion will occur than previous methods.
<Figure 1. Workflow of the bi-modal learning-based crowd congestion risk prediction developed by the research team.
The research team developed a crowd congestion risk prediction model based on bi-modal learning. The vertex-based time series represents indicator changes in a specific area (e.g., increases or decreases in crowd density), while the edge-based time series captures the flow of population movement between areas over time. Although these two types of data are collected from different sources, they are mapped onto the same network structure and provided together as input to the AI model. During training, the model simultaneously leverages both vertex and edge information based on a shared network, allowing it to capture complex movement patterns that might be overlooked when relying on only a single type of data. For example, a sudden increase in crowd density in a particular area may be difficult to predict using vertex information alone, but by additionally considering the steady inflow of people from adjacent areas (edge information), the prediction becomes more accurate. In this way, the model can precisely identify future changes based on past and present information, ultimately predicting high-risk crowd congestion areas in advance.>
The research team built and publicly released six real-world datasets for their study, which were compiled from sources such as Seoul, Busan, and Daegu subway data, New York City transit data, and COVID-19 confirmed case data from South Korea and New York.
The proposed technology achieved up to a 76.1% improvement in prediction accuracy over recent state-of-the-art methods, demonstrating strong perf
Professor Jae-Gil Lee stated, "It is important to develop technologies that can have a significant social impact," adding, "I hope this technology will greatly contribute to protecting public safety in daily life, such as in crowd management for large events, easing urban traffic congestion, and curbing the spread of infectious diseases."
Youngeun Nam, a Ph.D candidate in the KAIST School of Computing, was the first author of the study, and Jihye Na, another Ph.D candidate, was a co-author. The research findings were presented at the Knowledge Discovery and Data Mining (KDD) 2025 conference, a top international conference in the field of data mining, this past August.
※ Paper Title: Bi-Modal Learning for Networked Time Series ※ DOI: https://doi.org/10.1145/3711896.3736856
This technology is the result of research projects including the "Mid-Career Researcher Project" (RS-2023-NR077002, Core Technology Research for Crowd Management Systems Based on AI and Mobility Big Data) and the "Human-Centered AI Core Technology Development Project" (RS-2022-II220157, Robust, Fair, and Scalable Data-Centric Continuous Learning).
2025.09.17 View 2302