Atom
-
KAIST Enables On-Site Disease Diagnosis in Just 3 Minutes... Nanozyme Reaction Selectivity Improved 38-Fold
<(From Left) Professor Jinwoo Lee, Ph.D candidate Seonhye Park and Ph.D candidate Daeeun Choi from Chemical & Biomolecular Engineering>
To enable early diagnosis of acute illnesses and effective management of chronic conditions, point-of-care testing (POCT) technology—diagnostics conducted near the patient—is drawing global attention. The key to POCT lies in enzymes that recognize and react precisely with specific substances. However, traditional natural enzymes are expensive and unstable, and nanozymes (enzyme-mimicking catalysts) have suffered from low reaction selectivity. Now, a Korean research team has developed a high-sensitivity sensor platform that achieves 38 times higher selectivity than existing nanozymes and allows disease diagnostics visible to the naked eye within just 3 minutes.
On the 28th, KAIST (President Kwang Hyung Lee) announced that Professor Jinwoo Lee’s research team from the Department of Chemical & Biomolecular Engineering, in collaboration with teams led by Professor Jeong Woo Han at Seoul National University and Professor Moon Il Kim at Gachon University, has developed a new single-atom catalyst that selectively performs only peroxidase-like reactions while maintaining high reaction efficiency.
Using bodily fluids such as blood, urine, or saliva, this diagnostic platform enables test results to be read within minutes even outside hospital settings—greatly improving medical accessibility and ensuring timely treatment. The key lies in the visual detection of biomarkers (disease indicators) through color changes triggered by enzyme reactions. However, natural enzymes are expensive and easily degraded in diagnostic environments, limiting their storage and distribution.
To address this, inorganic nanozyme materials have been developed as substitutes. Yet, they typically lack selectivity—when hydrogen peroxide is used as a substrate, the same catalyst triggers both peroxidase-like reactions (which cause color change) and catalase-like reactions (which remove the substrate), reducing diagnostic signal accuracy.
To control catalyst selectivity at the atomic level, the researchers used an innovative structural design: attaching chlorine (Cl) ligands in a three-dimensional configuration to the central ruthenium (Ru) atom to fine-tune its chemical properties. This enabled them to isolate only the desired diagnostic signal.
<Figure1. The catalyst in this study (ruthenium single-atom catalyst) exhibits peroxidase-like activity with selectivity akin to natural enzymes through three-dimensional directional ligand coordination. Due to the absence of competing catalase activity, selective peroxidase-like reactions proceed under biomimetic conditions. In contrast, conventional single-atom catalysts with active sites arranged on planar surfaces exhibit dual functionality depending on pH. Under neutral conditions, their catalase activity leads to hydrogen peroxide depletion, hindering accurate detection. The catalyst in this study eliminates such interference, enabling direct detection of biomarkers through coupled reactions with oxidases without the need for cumbersome steps like buffer replacement. The ability to simultaneously detect multiple target substances under biomimetic conditions demonstrates the practicality of ruthenium single-atom catalysts for on-site diagnostics>
Experimental results showed that the new catalyst achieved over 38-fold improvement in selectivity compared to existing nanozymes, with significantly increased sensitivity and speed in detecting hydrogen peroxide. Even in near-physiological conditions (pH 6.0), the catalyst maintained its performance, proving its applicability in real-world diagnostics.
By incorporating the catalyst and oxidase into a paper-based sensor, the team created a system that could simultaneously detect four key biomarkers related to health: glucose, lactate, cholesterol, and choline—all with a simple color change.
This platform is broadly applicable across various disease diagnostics and can deliver results within 3 minutes without complex instruments or pH adjustments. The findings show that diagnostic performance can be dramatically improved without changing the platform itself, but rather by engineering the catalyst structure.
<Figure 2.(a) Schematic diagram of the paper sensor (Zone 1: glucose oxidase immobilized; Zone 2: lactate oxidase immobilized; Zone 3: choline oxidase immobilized; Zone 4: cholesterol oxidase immobilized; Zone 5: no oxidase enzyme). (b) Single biomarker (single disease indicator) detection using the ruthenium single‑atom catalyst–based paper sensor.(c) Multiple biomarker (multiple disease indicator) detection using the ruthenium single‑atom catalyst–based paper sensor>
Professor Jinwoo Lee of KAIST commented, “This study is significant in that it simultaneously achieves enzyme-level selectivity and reactivity by structurally designing single-atom catalysts.” He added that “the structure–function-based catalyst design strategy can be extended to the development of various metal-based catalysts and other reaction domains where selectivity is critical.”
Seonhye Park and Daeeun Choi, both Ph.D. candidates at KAIST, are co-first authors. The research was published on July 6, 2025, in the prestigious journal Advanced Materials
-Title: Breaking the Selectivity Barrier of Single-Atom Nanozymes Through Out-of-Plane Ligand Coordinatio
- Authors: Seonhye Park (KAIST, co–first author), Daeeun Choi (KAIST, co–first author), Kyu In Shim (SNU, co–first author), Phuong Thy Nguyen (Gachon Univ., co–first author), Seongbeen Kim (KAIST), Seung Yeop Yi (KAIST), Moon Il Kim (Gachon Univ., corresponding author), Jeong Woo Han (SNU, corresponding author), Jinwoo Lee (KAIST, corresponding author
-DOI: https://doi.org/10.1002/adma.202506480
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea (NRF).
2025.07.29 View 287 -
KAIST Designs a New Atomic Catalyst for Air Pollution Reduction
<(From Left)Professor Jong Hun Kim from Inha University, Dr. Gyuho Han and Professor Jeong Young Park from KAIST>
Platinum diselenide (PtSe2) is a two-dimensional multilayer material in which each layer is composed of platinum (Pt) and selenium (Se). It is known that its excellent crystallinity and precise control of interlayer interactions allow modulation of various physical and chemical properties. Due to these characteristics, it has been actively researched in multiple fields, including semiconductors, photodetectors, and electrochemical devices. Now, a research team has proposed a new design concept in which atomically dispersed platinum on the surface of platinum diselenide can function as a catalyst for gas reactions. Through this, they have proven its potential as a next-generation gas-phase catalyst technology for high-efficiency carbon dioxide conversion and carbon monoxide reduction.
KAIST (President Kwang Hyung Lee) announced on July 22 that a joint research team led by Endowed Chair Professor Jeong Young Park from the Department of Chemistry, along with Professor Hyun You Kim's team from Chungnam National University and Professor Yeonwoong (Eric) Jung's team from the University of Central Florida (UCF), has achieved excellent carbon monoxide oxidation performance by utilizing platinum atoms exposed on the surface of platinum diselenide, a type of two-dimensional transition metal dichalcogenide (TMD).
To maximize catalytic performance, the research team designed the catalyst by dispersing platinum atoms uniformly across the surface, departing from the conventional use of bulk platinum. This strategy allows more efficient catalytic reactions using a smaller amount of platinum. It also enhances electronic interactions between platinum and selenium by tuning the surface electronic structure. As a result, the platinum diselenide film with a thickness of a few nanometers showed superior carbon monoxide oxidation performance across the entire temperature range compared to a conventional platinum thin film under identical conditions.
In particular, carbon monoxide and oxygen were evenly adsorbed on the surface in similar proportions, increasing the likelihood that they would encounter each other and react, which significantly enhanced the catalytic activity. This improvement is primarily attributed to the increased exposure of surface platinum atoms resulting from selenium vacancies (Se-vacancies), which provide adsorption sites for gas molecules.
The research team confirmed in real-time that these platinum atoms served as active adsorption sites during the actual reaction process, using ambient-pressure X-ray photoelectron spectroscopy (AP-XPS) conducted at the Pohang Accelerator Laboratory. This high-precision analysis was enabled by advanced instrumentation capable of observing surfaces at the nanometer scale under ambient pressure conditions. At the same time, computer simulations based on density functional theory (DFT) demonstrated that platinum diselenide exhibits distinct electronic behavior compared to conventional platinum.
*Density Functional Theory (DFT): A quantum mechanical method for calculating the total energy of a system based on electron density.
Professor Jeong Young Park stated, “This research presents a new design strategy that utilizes platinum diselenide, a two-dimensional layered material distinct from conventional platinum catalysts, to enable catalytic functions optimized for gas-phase reactions.” He added, “The electronic interaction between platinum and selenium created favorable conditions for the balanced adsorption of carbon monoxide and oxygen. By designing the catalyst to exhibit higher reactivity across the entire temperature range than conventional platinum, we improved its practical applicability. This enabled a high-efficiency catalytic reaction mechanism through atomic-level design, a two-dimensional material platform, and precise adsorption control.”
This research was co-authored by Dr. Gyuho Han from the Department of Chemistry at KAIST, Dr. Hyuk Choi from the Department of Materials Science and Engineering at Chungnam National University, and Professor Jong Hun Kim from Inha University. The study was published on July 3 in the world-renowned journal Nature Communications.
Paper Title: Enhanced catalytic activity on atomically dispersed PtSe2 two-dimensional layers
DOI: 10.1038/s41467-025-61320-0
This research was supported by the Mid-Career Researcher Program of the Ministry of Science and ICT, the Core Research Institute Program of the Ministry of Education, the National Strategic Technology Materials Development Project, the U.S. National Science Foundation (NSF) CAREER Program, research funding from Inha University, and the Postdoctoral Researcher Program (P3) at UCF. Accelerator-based analysis was conducted in cooperation with the Pohang Accelerator Laboratory and the Korea Basic Science Institute (KBSI).
2025.07.22 View 358 -
Why Do Plants Attack Themselves? The Secret of Genetic Conflict Revealed
<Professor Ji-Joon Song of the KAIST Department of Biological Sciences>
Plants, with their unique immune systems, sometimes launch 'autoimmune responses' by mistakenly identifying their own protein structures as pathogens. In particular, 'hybrid necrosis,' a phenomenon where descendant plants fail to grow healthily and perish after cross-breeding different varieties, has long been a difficult challenge for botanists and agricultural researchers. In response, an international research team has successfully elucidated the mechanism inducing plant autoimmune responses and proposed a novel strategy for cultivar improvement that can predict and avoid these reactions.
Professor Ji-Joon Song's research team at KAIST, in collaboration with teams from the National University of Singapore (NUS) and the University of Oxford, announced on the 21st of July that they have elucidated the structure and function of the 'DM3' protein complex, which triggers plant autoimmune responses, using cryo-electron microscopy (Cryo-EM) technology.
This research is drawing attention because it identifies defects in protein structure as the cause of hybrid necrosis, which occurs due to an abnormal reaction of immune receptors during cross-breeding between plant hybrids.
This protein (DM3) is originally an enzyme involved in the plant's immune response, but problems arise when the structure of the DM3 protein is damaged in a specific protein combination called 'DANGEROUS MIX (DM)'.
Notably, one variant of DM3, the 'DM3Col-0' variant, forms a stable complex with six proteins and is recognized as normal, thus not triggering an immune response. In contrast, another 'DM3Hh-0' variant has improper binding between its six proteins, causing the plant to recognize it as an 'abnormal state' and trigger an immune alarm, leading to autoimmunity.
The research team visualized this structure using atomic-resolution cryo-electron microscopy (Cryo-EM) and revealed that the immune-inducing ability is not due to the enzymatic function of the DM3 protein, but rather to 'differences in protein binding affinity.'
<Figure 1. Mechanism of Plant Autoimmunity Triggered by the Collapse of the DM3 Protein Complex>
This demonstrates that plants can initiate an immune response by recognizing not only 'external pathogens' but also 'internal protein structures' when they undergo abnormal changes, treating them as if they were pathogens.
The study shows how sensitively the plant immune system changes and triggers autoimmune responses when genes are mixed and protein structures change during the cross-breeding of different plant varieties. It significantly advanced the understanding of genetic incompatibility that can occur during natural cross-breeding and cultivar improvement processes.
Dr. Gijeong Kim, the co-first author, stated, "Through international research collaboration, we presented a new perspective on understanding the plant immune system by leveraging the autoimmune phenomenon, completing a high-quality study that encompasses structural biochemistry, genetics, and cell biological experiments."
Professor Ji-Joon Song of the KAIST Department of Biological Sciences, who led the research, said, "The fact that the immune system can detect not only external pathogens but also structural abnormalities in its own proteins will set a new standard for plant biotechnology and crop breeding strategies. Cryo-electron microscopy-based structural analysis will be an important tool for understanding the essence of gene interactions."
This research, with Professor Ji-Joon Song and Professor Eunyoung Chae of the University of Oxford as co-corresponding authors, Dr. Gijeong Kim (currently a postdoctoral researcher at the University of Zurich) and Dr. Wei-Lin Wan of the National University of Singapore as co-first authors, and Ph.D candidate Nayun Kim, as the second author, was published on July 17th in Molecular Cell, a sister journal of the international academic journal Cell.
This research was supported by the KAIST Grand Challenge 30 project.
Article Title: Structural determinants of DANGEROUS MIX 3, an alpha/beta hydrolase that triggers NLR-mediated genetic incompatibility in plants DOI: https://doi.org/10.1016/j.molcel.2025.06.021
2025.07.21 View 390 -
KAIST Ushers in Era of Predicting ‘Optimal Alloys’ Using AI, Without High-Temperature Experiments
<Picture1.(From Left) Prof. Seungbum Hong, Ph.D candidate Youngwoo Choi>
Steel alloys used in automobiles and machinery parts are typically manufactured through a melting process at high temperatures. The phenomenon where the components remain unchanged during melting is called “congruent melting.” KAIST researchers have now addressed this process—traditionally only possible through high-temperature experiments—using artificial intelligence (AI). This study draws attention as it proposes a new direction for future alloy development by predicting in advance how well alloy components will mix during melting, a long-standing challenge in the field.
KAIST (President Kwang Hyung Lee) announced on the 14th of July that Professor Seungbum Hong’s research team from the Department of Materials Science and Engineering, in international collaboration with Professor Chris Wolverton’s group at Northwestern University, has developed a high-accuracy machine learning model that predicts whether alloy components will remain stable during melting. This was achieved using formation energy data derived from Density Functional Theory (DFT)* calculations.
*Density Functional Theory (DFT): A computational quantum mechanical method used to investigate the electronic structure of many-body systems, especially atoms, molecules, and solids, based on electron density.
The research team combined formation energy values obtained via DFT with experimental melting reaction data to train a machine learning model on 4,536 binary compounds.
Among the various machine learning algorithms tested, the XGBoost-based classification model demonstrated the highest accuracy in predicting whether alloys would mix well, achieving a prediction accuracy of approximately 82.5%. The team also applied the Shapley value method* to analyze the key features of the model. One major finding was that sharp changes in the slope of the formation energy curve (referred to as “convex hull sharpness”) were the most significant factor. A steep slope indicates a composition with energetically favorable (i.e., stable) formation.
*Shapley value: An explainability method in AI used to determine how much each feature contributed to a prediction.
The most notable significance of this study is that it predicts alloy melting behavior without performing high-temperature experiments. This is especially useful for materials such as high-entropy alloys or ultra-heat-resistant alloys, which are difficult to handle experimentally. The approach could also be extended to the design of complex multi-component alloy systems in the future.
Furthermore, the physical indicators identified by the AI model showed high consistency with actual experimental results on how well alloys mix and remain stable. This suggests that the model could be broadly applied to the development of various metal materials and the prediction of structural stability.
Professor Seungbum Hong of KAIST stated, “This research demonstrates how data-driven predictive materials development is possible by integrating computational methods, experimental data, and machine learning—departing from the traditional experience-based alloy design.” He added, “In the future, by incorporating state-of-the-art AI techniques such as generative models and reinforcement learning, we could enter an era where completely new alloys are designed automatically.”
<Model performance and feature importance analysis for predicting melting congruency. (a) SHAP summary plot showing the impact of individual features on model predictions. (b) Confusion matrix illustrating the model’s classification performance. (c) Receiver operating characteristic (ROC) curve with an AUC (area under the curve) score of 0.87, indicating a strong classification performance.>
Ph.D. candidate Youngwoo Choi, from the Department of Materials Science and Engineering at KAIST, participated as the first author. The study was published in the May issue of APL Machine Learning, a prestigious journal in the field of machine learning published by the American Institute of Physics, and was selected as a “Featured Article.”
※ Paper title: Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy ※ DOI: 10.1063/5.0247514
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.07.14 View 373 -
KAIST Turns an Unprecedented Idea into Reality: Quantum Computing with Magnets
What started as an idea under KAIST’s Global Singularity Research Project—"Can we build a quantum computer using magnets?"—has now become a scientific reality. A KAIST-led international research team has successfully demonstrated a core quantum computing technology using magnetic materials (ferromagnets) for the first time in the world.
KAIST (represented by President Kwang-Hyung Lee) announced on the 6th of May that a team led by Professor Kab-Jin Kim from the Department of Physics, in collaboration with the Argonne National Laboratory and the University of Illinois Urbana-Champaign (UIUC), has developed a “photon-magnon hybrid chip” and successfully implemented real-time, multi-pulse interference using magnetic materials—marking a global first.
< Photo 1. Dr. Moojune Song (left) and Professor Kab-Jin Kim (right) of KAIST Department of Physics >
In simple terms, the researchers developed a special chip that synchronizes light and internal magnetic vibrations (magnons), enabling the transmission of phase information between distant magnets. They succeeded in observing and controlling interference between multiple signals in real time. This marks the first experimental evidence that magnets can serve as key components in quantum computing, serving as a pivotal step toward magnet-based quantum platforms.
The N and S poles of a magnet stem from the spin of electrons inside atoms. When many atoms align, their collective spin vibrations create a quantum particle known as a “magnon.”
Magnons are especially promising because of their nonreciprocal nature—they can carry information in only one direction, which makes them suitable for quantum noise isolation in compact quantum chips. They can also couple with both light and microwaves, enabling the potential for long-distance quantum communication over tens of kilometers.
Moreover, using special materials like antiferromagnets could allow quantum computers to operate at terahertz (THz) frequencies, far surpassing today’s hardware limitations, and possibly enabling room-temperature quantum computing without the need for bulky cryogenic equipment.
To build such a system, however, one must be able to transmit, measure, and control the phase information of magnons—the starting point and propagation of their waveforms—in real time. This had not been achieved until now.
< Figure 1. Superconducting Circuit-Based Magnon-Photon Hybrid System. (a) Schematic diagram of the device. A NbN superconducting resonator circuit fabricated on a silicon substrate is coupled with spherical YIG magnets (250 μm diameter), and magnons are generated and measured in real-time via a vertical antenna. (b) Photograph of the actual device. The distance between the two YIG spheres is 12 mm, a distance at which they cannot influence each other without the superconducting circuit. >
Professor Kim’s team used two tiny magnetic spheres made of Yttrium Iron Garnet (YIG) placed 12 mm apart with a superconducting resonator in between—similar to those used in quantum processors by Google and IBM. They input pulses into one magnet and successfully observed lossless transmission of magnon vibrations to the second magnet via the superconducting circuit.
They confirmed that from single nanosecond pulses to four microwave pulses, the magnon vibrations maintained their phase information and demonstrated predictable constructive or destructive interference in real time—known as coherent interference.
By adjusting the pulse frequencies and their intervals, the researchers could also freely control the interference patterns of magnons, effectively showing for the first time that electrical signals can be used to manipulate magnonic quantum states.
This work demonstrated that quantum gate operations using multiple pulses—a fundamental technique in quantum information processing—can be implemented using a hybrid system of magnetic materials and superconducting circuits. This opens the door for the practical use of magnet-based quantum devices.
< Figure 2. Experimental Data. (a) Measurement results of magnon-magnon band anticrossing via continuous wave measurement, showing the formation of a strong coupling hybrid system. (b) Magnon pulse exchange oscillation phenomenon between YIG spheres upon single pulse application. It can be seen that magnon information is coherently transmitted at regular time intervals through the superconducting circuit. (c,d) Magnon interference phenomenon upon dual pulse application. The magnon information state can be arbitrarily controlled by adjusting the time interval and carrier frequency between pulses. >
Professor Kab-Jin Kim stated, “This project began with a bold, even unconventional idea proposed to the Global Singularity Research Program: ‘What if we could build a quantum computer with magnets?’ The journey has been fascinating, and this study not only opens a new field of quantum spintronics, but also marks a turning point in developing high-efficiency quantum information processing devices.”
The research was co-led by postdoctoral researcher Moojune Song (KAIST), Dr. Yi Li and Dr. Valentine Novosad from Argonne National Lab, and Prof. Axel Hoffmann’s team at UIUC. The results were published in Nature Communications on April 17 and npj Spintronics on April 1, 2025.
Paper 1: Single-shot magnon interference in a magnon-superconducting-resonator hybrid circuit, Nat. Commun. 16, 3649 (2025)
DOI: https://doi.org/10.1038/s41467-025-58482-2
Paper 2: Single-shot electrical detection of short-wavelength magnon pulse transmission in a magnonic ultra-thin-film waveguide, npj Spintronics 3, 12 (2025)
DOI: https://doi.org/10.1038/s44306-025-00072-5
The research was supported by KAIST’s Global Singularity Research Initiative, the National Research Foundation of Korea (including the Mid-Career Researcher, Leading Research Center, and Quantum Information Science Human Resource Development programs), and the U.S. Department of Energy.
2025.06.12 View 4035 -
KAIST Develops Virtual Staining Technology for 3D Histopathology
Moving beyond traditional methods of observing thinly sliced and stained cancer tissues, a collaborative international research team led by KAIST has successfully developed a groundbreaking technology. This innovation uses advanced optical techniques combined with an artificial intelligence-based deep learning algorithm to create realistic, virtually stained 3D images of cancer tissue without the need for serial sectioning nor staining. This breakthrough is anticipated to pave the way for next-generation non-invasive pathological diagnosis.
< Photo 1. (From left) Juyeon Park (Ph.D. Candidate, Department of Physics), Professor YongKeun Park (Department of Physics) (Top left) Professor Su-Jin Shin (Gangnam Severance Hospital), Professor Tae Hyun Hwang (Vanderbilt University School of Medicine) >
KAIST (President Kwang Hyung Lee) announced on the 26th that a research team led by Professor YongKeun Park of the Department of Physics, in collaboration with Professor Su-Jin Shin's team at Yonsei University Gangnam Severance Hospital, Professor Tae Hyun Hwang's team at Mayo Clinic, and Tomocube's AI research team, has developed an innovative technology capable of vividly displaying the 3D structure of cancer tissues without separate staining.
For over 200 years, conventional pathology has relied on observing cancer tissues under a microscope, a method that only shows specific cross-sections of the 3D cancer tissue. This has limited the ability to understand the three-dimensional connections and spatial arrangements between cells.
To overcome this, the research team utilized holotomography (HT), an advanced optical technology, to measure the 3D refractive index information of tissues. They then integrated an AI-based deep learning algorithm to successfully generate virtual H&E* images.* H&E (Hematoxylin & Eosin): The most widely used staining method for observing pathological tissues. Hematoxylin stains cell nuclei blue, and eosin stains cytoplasm pink.
The research team quantitatively demonstrated that the images generated by this technology are highly similar to actual stained tissue images. Furthermore, the technology exhibited consistent performance across various organs and tissues, proving its versatility and reliability as a next-generation pathological analysis tool.
< Figure 1. Comparison of conventional 3D tissue pathology procedure and the 3D virtual H&E staining technology proposed in this study. The traditional method requires preparing and staining dozens of tissue slides, while the proposed technology can reduce the number of slides by up to 10 times and quickly generate H&E images without the staining process. >
Moreover, by validating the feasibility of this technology through joint research with hospitals and research institutions in Korea and the United States, utilizing Tomocube's holotomography equipment, the team demonstrated its potential for full-scale adoption in real-world pathological research settings.
Professor YongKeun Park stated, "This research marks a major advancement by transitioning pathological analysis from conventional 2D methods to comprehensive 3D imaging. It will greatly enhance biomedical research and clinical diagnostics, particularly in understanding cancer tumor boundaries and the intricate spatial arrangements of cells within tumor microenvironments."
< Figure 2. Results of AI-based 3D virtual H&E staining and quantitative analysis of pathological tissue. The virtually stained images enabled 3D reconstruction of key pathological features such as cell nuclei and glandular lumens. Based on this, various quantitative indicators, including cell nuclear distribution, volume, and surface area, could be extracted. >
This research, with Juyeon Park, a student of the Integrated Master’s and Ph.D. Program at KAIST, as the first author, was published online in the prestigious journal Nature Communications on May 22.
(Paper title: Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining.
[https://doi.org/10.1038/s41467-025-59820-0]
This study was supported by the Leader Researcher Program of the National Research Foundation of Korea, the Global Industry Technology Cooperation Center Project of the Korea Institute for Advancement of Technology, and the Korea Health Industry Development Institute.
2025.05.26 View 4406 -
KAIST Develops Novel Catalyst With 100-Fold Platinum Efficiency
Propylene, a key building block used in producing plastics, textiles, automotive components, and electronics, is essential to the petrochemical industry. A KAIST research team has developed a novel catalyst that dramatically enhances the efficiency of propylene production while significantly reducing costs.
< Photo. Professor Minkee Choi (left), and Ph.D. Candidate Susung Lee (right) of the Department of Chemical and Biomolecular Engineering >
KAIST (represented by President Kwang-Hyung Lee) announced on the 12th of May that a research group led by Professor Minkee Choi from the Department of Chemical and Biomolecular Engineering has successfully developed a new catalyst using inexpensive metals—gallium (Ga) and alumina (Al₂O₃)—with only a trace amount of platinum (100 ppm, or 0.01%). Remarkably, this new catalyst outperforms conventional industrial catalysts that use high concentrations of platinum (10,000 ppm).
Propylene is commonly produced through the propane dehydrogenation (PDH) process, which removes hydrogen from propane. Platinum has long been used as a catalyst in PDH due to its high efficiency in breaking carbon-hydrogen bonds and facilitating hydrogen removal. However, platinum is costly and suffers from performance degradation over repeated use.
To address this, the KAIST team engineered a catalyst that incorporates only a minimal amount of platinum, relying on gallium and alumina as the primary components.
< Figure 1. Schematic diagram showing the catalytic cooperation between gallium (Ga) and platinum (Pt) >
The core mechanism of the catalyst involves a cooperative function between the metals: gallium activates the C–H bond in propane to produce propylene, while platinum bonds the residual hydrogen atoms on the surface to form hydrogen gas (H₂), which is then released. This division of roles allows for high catalytic efficiency despite the drastic reduction in platinum content.
The researchers identified an optimal platinum-to-gallium ratio that delivered peak performance and provided a scientific rationale and quantitative metrics to predict this ideal composition.
Additionally, the team addressed a major limitation of traditional platinum catalysts: sintering—the agglomeration of platinum particles during repeated use, which causes performance loss. By adding a small amount of cerium (Ce), the researchers successfully suppressed this aggregation. As a result, the new catalyst maintained stable performance even after more than 20 reaction-regeneration cycles.
< Figure 2. Performance comparison of KAIST's newly developed catalyst (100 ppm platinum) and existing commercial platinum catalyst (10,000 ppm platinum) >
Professor Choi stated, “This research demonstrates the possibility of reducing platinum usage to 1/100th of current levels without compromising, and even enhancing, performance. It presents significant economic and environmental advantages, including lower catalyst costs, extended replacement intervals, and reduced catalyst waste.”
He added, “We are planning to evaluate this technology for large-scale process demonstration and commercialization. If adopted in industry, it could greatly improve the economic viability and efficiency of propylene production.”
The study was led by Professor Minkee Choi as corresponding author, with Ph.D. candidate Susung Lee as the first author. The findings were published in the Journal of the American Chemical Society (JACS), a leading journal in chemistry and chemical engineering, on February 13.※ Paper title: Ideal Bifunctional Catalysis for Propane Dehydrogenation over Pt-Promoted Gallia-Alumina and Minimized Use of Precious Elements※ DOI: https://pubs.acs.org/doi/10.1021/jacs.4c13787
The research was supported by the National Research Foundation of Korea and Hanwha Solutions Corporation.
2025.05.12 View 2522 -
KAIST Captures Hot Holes: A Breakthrough in Light-to-Electricity Energy Conversion
When light interacts with metallic nanostructures, it instantaneously generates plasmonic hot carriers, which serve as key intermediates for converting optical energy into high-value energy sources such as electricity and chemical energy. Among these, hot holes play a crucial role in enhancing photoelectrochemical reactions. However, they thermally dissipate within picoseconds (trillionths of a second), making practical applications challenging. Now, a Korean research team has successfully developed a method for sustaining hot holes longer and amplifying their flow, accelerating the commercialization of next-generation, high-efficiency, light-to-energy conversion technologies.
KAIST (represented by President Kwang Hyung Lee) announced on the 12th of March that a research team led by Distinguished Professor Jeong Young Park from the Department of Chemistry, in collaboration with Professor Moonsang Lee from the Department of Materials Science and Engineering at Inha University, has successfully amplified the flow of hot holes and mapped local current distribution in real time, thereby elucidating the mechanism of photocurrent enhancement.
The team designed a nanodiode structure by placing a metallic nanomesh on a specialized semiconductor substrate (p-type gallium nitride) to facilitate hot hole extraction at the surface. As a result, in gallium nitride substrates aligned with the hot hole extraction direction, the flow of hot holes was amplified by approximately two times compared to substrates aligned in other directions.
To fabricate the Au nanomesh, a polystyrene nano-bead monolayer assembly was first placed on a gallium nitride (p-GaN) substrate, and then the polystyrene nano-beads were etched to form a nanomesh template (Figure 1A). Then, a 20 nm thick gold nano-film was deposited, and the etched polystyrene nano-beads were removed to realize the gold nano-mesh structure on the GaN substrate (Figure 1B). The fabricated Au nanomesh exhibited strong light absorption in the visible range due to the plasmonic resonance effect (Figure 1C). >
Furthermore, using a photoconductive atomic force microscopy (pc-AFM)-based photocurrent mapping system, the researchers analyzed the flow of hot holes in real time at the nanometer scale (one hundred-thousandth the thickness of a human hair). They observed that hot hole activation was strongest at "hot spots," where light was locally concentrated on the gold nanomesh. However, by modifying the growth direction of the gallium nitride substrate, hot hole activation extended beyond the hot spots to other areas as well.
Through this research, the team discovered an efficient method for converting light into electrical and chemical energy. This breakthrough is expected to significantly advance next-generation solar cells, photocatalysts, and hydrogen production technologies.
Professor Jeong Young Park stated, "For the first time, we have successfully controlled the flow of hot holes using a nanodiode technique. This innovation holds great potential for various optoelectronic devices and photocatalytic applications. For example, it could lead to groundbreaking advancements in solar energy conversion technologies, such as solar cells and hydrogen production. Additionally, the real-time analysis technology we developed can be applied to the development of ultra-miniaturized optoelectronic devices, including optical sensors and nanoscale semiconductor components."
The study was led by Hyunhwa Lee (PhD., KAIST Department of Chemistry) and Yujin Park (Postdoc Researcher, University of Texas at Austin Department of Chemical Engineering) as co-first authors and Professors Moonsang Lee (Inha University, Department of Materials Science and Engineering) and Jeong Young Park (KAIST, Department of Chemistry) serving as corresponding authors. The research findings were published online in Science Advances on March 7.
(Paper Title: “Reconfiguring hot-hole flux via polarity modulation of p-GaN in plasmonic Schottky architectures”, DOI: https://www.science.org/doi/10.1126/sciadv.adu0086)
This research was supported by the National Research Foundation of Korea (NRF).
2025.03.17 View 5019 -
KAIST Develops World-Leading Ammonia Catalyst for Hydrogen Economy
Hydrogen production using renewable energy is a key technology for eco-friendly energy and chemical production. However, storing and transporting hydrogen remains a challenge. To address this, researchers worldwide are investigating methods to store hydrogen in the form of ammonia (NH₃), which is carbon-free and easier to liquify. A research team at KAIST has successfully developed a high-performance catalyst that enables ammonia synthesis at very low temperatures and pressures without energy loss.
KAIST (represented by President Kwang Hyung Lee) announced on the 11th of March that a research team led by Professor Minkee Choi from the Department of Chemical and Biomolecular Engineering has developed an innovative catalytic system that significantly enhances ammonia production while drastically reducing energy consumption and CO₂ emissions.
< (From left) Baek Ye-jun, Ph.D. candidate in the Department of Biochemical Engineering, Professor Choi Min-ki >
Currently, ammonia is produced using the Haber-Bosch process, a technology over a century old that relies on iron (Fe)-based catalysts. This method requires extreme conditions—temperatures above 500°C and pressures exceeding 100 atmospheres—resulting in enormous energy consumption and contributing significantly to global CO₂ emissions. Additionally, ammonia is primarily produced in large-scale industrial plants, leading to high distribution costs.
As an alternative, there is growing interest in an eco-friendly process that synthesizes ammonia using green hydrogen—produced via water electrolysis—under mild conditions (300°C, 10 atmospheres). However, developing catalysts that can achieve high ammonia productivity at such low temperatures and pressures is essential, as current technologies struggle to maintain efficiency under these conditions.
The research team developed a novel catalyst by incorporating ruthenium (Ru) nanoparticles and highly basic barium oxide (BaO) particles onto a conductive carbon surface, allowing it to function like a chemical capacitor*.
*Capacitor: A device that stores electrical energy by separating positive and negative charges.
During ammonia synthesis, hydrogen molecules (H₂) first dissociate into hydrogen atoms (H) on the ruthenium catalyst. These hydrogen atoms are further split into protons (H⁺) and electrons (e⁻). The study revealed that the acidic protons are stored in the strongly basic BaO, while the remaining electrons are separated and stored in ruthenium and carbon.
This unique chemical capacitor effect significantly enhances the ruthenium catalyst's electron density, accelerating nitrogen (N₂) dissociation—the rate-limiting step of ammonia synthesis—thereby dramatically increasing catalytic activity.
Furthermore, the team discovered that optimizing the nanostructure of the carbon material further boosts the electron density of ruthenium, maximizing catalytic performance. As a result, the new catalyst demonstrated over seven times higher ammonia synthesis performance compared to state-of-the-art catalysts under mild conditions (300°C, 10 atm).
< Schematic diagram showing the mechanism of ruthenium catalyst activity enhancement by barium oxide cocatalyst >
Professor Minkee Choi stated, “This research has garnered significant attention for demonstrating that catalytic activity can be greatly enhanced by controlling electron transfer within a thermal catalytic reaction system, not just in electrochemical processes.”
He further explained, “Our findings confirm that high-performance catalysts can enable efficient ammonia synthesis under low-temperature and low-pressure conditions. This could shift ammonia production from centralized, large-scale industrial plants to decentralized, small-scale production, making the hydrogen economy more sustainable and flexible.”
The study was led by Professor Minkee Choi as corresponding author and Yaejun Baik, a Ph.D. candidate, as first author. The research findings were published in Nature Catalysis on February 24.
(Paper title: “Electron and proton storage on separate Ru and BaO domains mediated by conductive low-work-function carbon to accelerate ammonia synthesis,” https://doi.org/10.1038/s41929-025-01302-z)
This research was supported by the Korea Institute of Energy Research and the National Research Foundation of Korea.
2025.03.11 View 4150 -
KAIST Scientifically Identifies a Method to Prevent Dental Erosion from Carbonated Drinks
A Korean research team, which had previously visualized and scientifically proven the harmful effects of carbonated drinks like cola on dental health using nanotechnology, has now identified a mechanism for an effective method to prevent tooth damage caused by these beverages.
KAIST (represented by President Kwang Hyung Lee) announced on the 5th of December that a team led by Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with Seoul National University's School of Dentistry (Departments of Pediatric Dentistry and Oral Microbiology) and Professor Hye Ryung Byon’s research team from the Department of Chemistry, has revealed through nanotechnology that silver diamine fluoride (SDF)* forms a fluoride-containing protective layer on the tooth surface, effectively inhibiting cola-induced erosion.
*SDF (Silver Diamine Fluoride): A dental agent primarily used for the treatment and prevention of tooth decay. SDF strengthens carious lesions, suppresses bacterial growth, and halts the progression of cavities.
The team analyzed the surface morphology and mechanical properties of tooth enamel on a nanoscale using atomic force microscopy (AFM). They also examined the chemical properties of the nano-film formed by SDF treatment using X-ray photoelectron spectroscopy (XPS)* and Fourier-transform infrared spectroscopy (FTIR)*.
*XPS (X-ray Photoelectron Spectroscopy): A powerful surface analysis technique used to investigate the chemical composition and electronic structure of materials.
*FTIR (Fourier-Transform Infrared Spectroscopy): An analytical method that identifies the molecular structure and composition of materials by analyzing how they absorb or transmit infrared light.
The findings showed significant differences in surface roughness and elastic modulus between teeth exposed to cola with and without SDF treatment. Teeth treated with SDF exhibited minimal changes in surface roughness due to erosion (from 64 nm to 70 nm) and maintained a high elastic modulus (from 215 GPa to 205 GPa).
This was attributed to the formation of a fluoroapatite* layer by SDF, which acted as a protective shield.
*Fluoroapatite: A phosphate mineral with the chemical formula Ca₅(PO₄)₃F (calcium fluoro-phosphate). It can occur naturally or be synthesized biologically/artificially and plays a crucial role in strengthening teeth and bones.
< Figure 1. Schematic of the workflow. Surface morphology and mechanical properties of untreated and treated silver diamine fluoride (SDF) treated enamel exposed to cola were analyzed over time using atomic force microscopy (AFM). >
Professor Young J. Kim from Seoul National University's Department of Pediatric Dentistry noted, "This technology could be applied to prevent dental erosion and strengthen teeth for both children and adults. It is a cost-effective and accessible dental treatment."
< Figure 2. Changes in surface roughness and elastic modulus according to time of exposure to cola for SDF untreated and treated teeth. After 1 hour, the surface roughness of the SDF untreated teeth rapidly became rougher from 83 nm to 287 nm and the elastic modulus weakened from 125 GPa to 13 GPa, whereas the surface roughness of the SDF treated teeth changed only slightly from 64 nm to 70 nm and the elastic modulus barely changed from 215 GPa to 205 GPa, maintaining a similar state to the initial state. >
Professor Seungbum Hong emphasized, "Dental health significantly impacts quality of life. This research offers an effective non-invasive method to prevent early dental erosion, moving beyond traditional surgical treatments. By simply applying SDF, dental erosion can be prevented and enamel strengthened, potentially reducing pain and costs associated with treatment."
This study, led by the first author Aditi Saha, a PhD student in KAIST’s Department of Materials Science and Engineering, was published in the international journal Biomaterials Research on November 7 under the title "Nanoscale Study on Noninvasive Prevention of Dental Erosion of Enamel by Silver Diamine Fluoride". The research was supported by the National Research Foundation of Korea.
2024.12.11 View 6573 -
KAIST Proposes AI Training Method that will Drastically Shorten Time for Complex Quantum Mechanical Calculations
- Professor Yong-Hoon Kim's team from the School of Electrical Engineering succeeded for the first time in accelerating quantum mechanical electronic structure calculations using a convolutional neural network (CNN) model
- Presenting an AI learning principle of quantum mechanical 3D chemical bonding information, the work is expected to accelerate the computer-assisted designing of next-generation materials and devices
The close relationship between AI and high-performance scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for their AI-related research contributions in their respective fields of study. KAIST researchers succeeded in dramatically reducing the computation time for highly sophisticated quantum mechanical computer simulations by predicting atomic-level chemical bonding information distributed in 3D space using a novel AI approach.
KAIST (President Kwang-Hyung Lee) announced on the 30th of October that Professor Yong-Hoon Kim's team from the School of Electrical Engineering developed a 3D computer vision artificial neural network-based computation methodology that bypasses the complex algorithms required for atomic-level quantum mechanical calculations traditionally performed using supercomputers to derive the properties of materials.
< Figure 1. Various methodologies are utilized in the simulation of materials and materials, such as quantum mechanical calculations at the nanometer (nm) level, classical mechanical force fields at the scale of tens to hundreds of nanometers, continuum dynamics calculations at the macroscopic scale, and calculations that mix simulations at different scales. These simulations are already playing a key role in a wide range of basic research and application development fields in combination with informatics techniques. Recently, there have been active efforts to introduce machine learning techniques to radically accelerate simulations, but research on introducing machine learning techniques to quantum mechanical electronic structure calculations, which form the basis of high-scale simulations, is still insufficient. >
The quantum mechanical density functional theory (DFT) calculations using supercomputers have become an essential and standard tool in a wide range of research and development fields, including advanced materials and drug design, as they allow fast and accurate prediction of material properties.
*Density functional theory (DFT): A representative theory of ab initio (first principles) calculations that calculate quantum mechanical properties from the atomic level.
However, practical DFT calculations require generating 3D electron density and solving quantum mechanical equations through a complex, iterative self-consistent field (SCF)* process that must be repeated tens to hundreds of times. This restricts its application to systems with only a few hundred to a few thousand atoms.
*Self-consistent field (SCF): A scientific computing method widely used to solve complex many-body problems that must be described by a number of interconnected simultaneous differential equations.
Professor Yong-Hoon Kim’s research team questioned whether recent advancements in AI techniques could be used to bypass the SCF process. As a result, they developed the DeepSCF model, which accelerates calculations by learning chemical bonding information distributed in a 3D space using neural network algorithms from the field of computer vision.
< Figure 2. The deepSCF methodology developed in this study provides a way to rapidly accelerate DFT calculations by avoiding the self-consistent field process (orange box) that had to be performed repeatedly in traditional quantum mechanical electronic structure calculations through artificial neural network techniques (green box). The self-consistent field process is a process of predicting the 3D electron density, constructing the corresponding potential, and then solving the quantum mechanical Cohn-Sham equations, repeating tens to hundreds of times. The core idea of the deepSCF methodology is that the residual electron density (δρ), which is the difference between the electron density (ρ) and the sum of the electron densities of the constituent atoms (ρ0), corresponds to chemical bonding information, so the self-consistent field process is replaced with a 3D convolutional neural network model. >
The research team focused on the fact that, according to density functional theory, electron density contains all quantum mechanical information of electrons, and that the residual electron density — the difference between the total electron density and the sum of the electron densities of the constituent atoms — contains chemical bonding information. They used this as the target for machine learning.
They then adopted a dataset of organic molecules with various chemical bonding characteristics, and applied random rotations and deformations to the atomic structures of these molecules to further enhance the model’s accuracy and generalization capabilities. Ultimately, the research team demonstrated the validity and efficiency of the DeepSCF methodology on large, complex systems.
< Figure 3. An example of applying the deepSCF methodology to a carbon nanotube-based DNA sequence analysis device model (top left). In addition to classical mechanical interatomic forces (bottom right), the residual electron density (top right) and quantum mechanical electronic structure properties such as the electronic density of states (DOS) (bottom left) containing information on chemical bonding are rapidly predicted with an accuracy corresponding to the standard DFT calculation results that perform the SCF process. >
Professor Yong-Hoon Kim, who supervised the research, explained that his team had found a way to map quantum mechanical chemical bonding information in a 3D space onto artificial neural networks. He noted, “Since quantum mechanical electron structure calculations underpin materials simulations across all scales, this research establishes a foundational principle for accelerating material calculations using artificial intelligence.”
Ryong-Gyu Lee, a PhD candidate in the School of Electrical Engineering, served as the first author of this research, which was published online on October 24 in Npj Computational Materials, a prestigious journal in the field of material computation. (Paper title: “Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints”)
This research was conducted with support from the KAIST High-Risk Research Program for Graduate Students and the National Research Foundation of Korea’s Mid-career Researcher Support Program.
2024.10.30 View 7467 -
KAIST Develops Thread-like, Flexible Thermoelectric Materials Applicable in Extreme Environments
A team of Korean researchers developed a thermoelectric material that can be used in wearable devices, such as smart clothing, and while maintaining stable thermal energy performance even in extreme environments. It has dramatically resolved the dilemma of striking the balance between achieving good performance and the mechanical flexibility of thermoelectric materials, which has been a long-standing challenge in the field of thermoelectric materials, and has also proven the possibility of commercialization.
KAIST (President Kwang-Hyung Lee) announced on the 21st that a joint research team of Professor Yeon Sik Jung of the Department of Materials Science and Engineering and Professor Inkyu Park of the Department of Mechanical Engineering, in collaboration with the research teams of Professor Min-Wook Oh of Hanbat National University (President Yong Jun Oh) and Dr. Jun-Ho Jeong of the Korea Institute of Machinery and Materials (President Seoghyun Ryu), have successfully developed ‘bismuth telluride (Bi2Te3) thermoelectric fibers,’ an innovative energy harvesting solution for next-generation flexible electronic devices.
Thermoelectric materials are materials that generate voltage when there is a temperature difference and convert thermal energy into electrical energy. Currently, about 70% of energy being lost as wasted heat, so due attention is being given to research on these as sustainable energy materials that can recover and harvesting energy from this waste heat.
Most of the heat sources around us are curved, such as the human body, vehicle exhaust pipes, and cooling fins. Inorganic thermoelectric materials based on ceramic materials boast high thermoelectric performance, but they are fragile and difficult to produce in curved shapes. On the other hand, flexible thermoelectric materials using existing polymer binders can be applied to surfaces of various shapes, but their performance was limited due to the low electrical conductivity and high thermal resistance of the polymer.
Existing flexible thermoelectric materials contain polymer additives, but the inorganic thermoelectric material developed by the research team is not flexible, so they overcame these limitations by twisting nano ribbons instead of additives to produce a thread-shaped thermoelectric material. Inspired by the flexibility of inorganic nano ribbons, the research team used a nanomold-based electron beam deposition technique to continuously deposit nano ribbons and then twisted them into a thread shape to create bismuth telluride (Bi2Te3) inorganic thermoelectric fibers.
These inorganic thermoelectric fibers have higher bending strength than existing thermoelectric materials, and showed almost no change in electrical properties even after repeated bending and tensile tests of more than 1,000 times. The thermoelectric device created by the research team generates electricity using temperature differences, and if clothes are made with fiber-type thermoelectric devices, electricity can be generated from body temperature to operate other electronic devices.
< Figure 1. Schematic diagram and actual image of the all-inorganic flexible thermoelectric yarn made without polymer additives >
In fact, the possibility of commercialization was proven through a demonstration of collecting energy by embedding thermoelectric fibers in life jackets or clothing. In addition, it opened up the possibility of building a high-efficiency energy harvesting system that recycles waste heat by utilizing the temperature difference between the hot fluid inside a pipe and the cold air outside in industrial settings.
Professor Yeon Sik Jung said, "The inorganic flexible thermoelectric material developed in this study can be used in wearable devices such as smart clothing, and it can maintain stable performance even in extreme environments, so it has a high possibility of being commercialized through additional research in the future." Professor Inkyu Park also emphasized, "This technology will become the core of next-generation energy harvesting technology, and it is expected to play an important role in various fields from waste heat utilization in industrial sites to personal wearable self-power generation devices."
This study, in which Hanhwi Jang, a Ph.D. student at KAIST's Department of Materials Science and Engineering, Professor Junseong Ahn of Korea University, Sejong Campus, and Dr. Yongrok Jeong of Korea Atomic Energy Research Institute contributed equally as joint first authors, was published in the online edition of the international academic journal Advanced Materials on September 17, and was selected as the back-cover paper in recognition of its excellence. (Paper title: Flexible All-Inorganic Thermoelectric Yarns)
Meanwhile, this study was conducted through the Mid-career Researcher Support Program and the Future Materials Discovery Program of the National Research Foundation of Korea, and the support from the Global Bio-Integrated Materials Center, the Ministry of Trade, Industry and Energy, and the Korea Institute of Industrial Technology Evaluation and Planning (KEIT) upon the support by the Ministry of Science and ICT.
2024.10.21 View 5863