-
Breaking Fuel Cell Barriers: New Platinum Catalyst Brings High-Efficiency Hydrogen Vehicles Closer to Commercialization
A research team led by Professor Sang Uck Lee of the School of Chemical Engineering at Sungkyunkwan University, with Ph.D. candidate Jun Ho Seok as a co-first author and Dr. Sung Chan Cho, in collaboration with Professor Kwangyeol Lee’s team at Korea University and Dr. Sung Jong Yoo’s team at the Korea Institute of Science and Technology (KIST), has developed a next-generation platinum-based catalyst that improves both activity and durability in hydrogen fuel cells. The study was published online on January 6, 2026, in Advanced Materials. Hydrogen fuel cells generate electricity through the electrochemical reaction of hydrogen and oxygen and are considered a promising clean energy technology. However, their broader commercialization has been hindered by the sluggish oxygen reduction reaction (ORR) at the cathode and by catalyst degradation during long-term operation. Conventional platinum-based intermetallic catalysts are known for their structural stability, but their atomic composition and arrangement are difficult to tune precisely. This has limited efforts to optimize their electronic structure and has made it challenging to achieve both high catalytic activity and long-term durability under demanding operating conditions, such as those required for hydrogen-powered vehicles. To address these challenges, the research team developed a new catalyst design strategy that enables more precise control over atomic composition and electronic structure while maintaining the structural stability of platinum-based intermetallic catalysts. Using this method, they designed a ternary intermetallic nanocatalyst made of platinum (Pt), cobalt (Co), and manganese (Mn). By utilizing oxygen vacancies formed at the interface between the catalyst and the oxide support, the team was able to guide atomic ordering within the catalyst and successfully develop a ternary Pt-based intermetallic structure that had previously been hard to achieve. A key aspect of the study was the use of a new theoretical approach to uncover the interfacial synthesis mechanism at the precursor stage, which is difficult to observe directly in experiments. The team showed that oxygen vacancies formed early at the interface play a decisive role in driving the ordering of manganese atoms, providing a theoretical explanation for how the ternary intermetallic structure forms. This goes beyond conventional catalyst performance analysis by offering an atomic-level framework for understanding and designing the synthesis process itself. The newly developed catalyst delivered both high ORR activity and outstanding durability through its optimized electronic structure. In electrochemical tests, it exhibited mass activity more than ten times higher than that of commercial Pt/C catalysts and retained more than 96% of its initial performance after 150,000 cycles of accelerated durability testing. In membrane electrode assembly (MEA) tests, the catalyst exceeded the 2025 performance targets set by the U.S. Department of Energy (DOE). It also maintained higher power output than conventional catalysts under high-load operating conditions, highlighting its potential for use in hydrogen electric vehicles and stationary fuel cell systems. ※Title: Tailoring Interfacial Oxygen Vacancy-Mediated Ordering in Ternary Pt3(Co,Mn)1 Intermetallic Nanoparticles for Enhanced Oxygen Reduction Reaction ※Journal: Advanced Materials ※DOI: https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202521036 ※PURE: https://pure.skku.edu/en/persons/sang-uck-lee/ (Left) Schematic illustration of the formation of a Pt–Co–Mn ternary intermetallic structure, where oxygen vacancies generated at the MnO interface drive atomic ordering within the catalyst. (Top right) The synthesized nanocatalyst exhibits a uniform atomic-scale structure with an even distribution of Mn, Co, and Pt. (Bottom right) Owing to these structural characteristics, the catalyst delivers high ORR activity and outstanding durability, outperforming conventional catalysts under practical fuel cell conditions.
- No. 364
- 2026-04-03
- 1100
-
Breaking Recalcitrant Lignin Bonds with Electricity for Conversion into Value-Added Chemicals: An e-Biorefinery
A research team led by Professor Jaehoon Kim at Sungkyunkwan University and Dr. Dong Ki Lee at the Korea Institute of Science and Technology (KIST) has developed a highly efficient catalytic process that electrochemically converts lignin, a key component of woody biomass, into value-added aromatic compounds and cyclohexene-based compounds. This study demonstrates that the recalcitrant ether bonds in lignin can be selectively cleaved under relatively mild conditions without the use of external hydrogen gas, while simultaneously upgrading lignin into useful chemical precursors. The research results were published in Applied Catalysis B: Environment and Energy (IF 21.1, top 2% in JCR) in February 2026. As interest in carbon neutrality and sustainable chemical industries continues to grow, active efforts are being made to replace fossil resource-based aromatic chemicals with biomass-derived materials. Among them, lignin is regarded as a promising source of a wide range of aromatic compounds because it is the most carbon-rich component in woody biomass. However, its selective conversion is extremely difficult due to its complex polymeric structure and strong C–O and C–C bonds. In particular, 4–O–5 and α–O–4 diaryl ether bonds have previously been targeted for cleavage under high-temperature and high-pressure hydrogen atmospheres, but such approaches have been limited by high energy consumption and low selectivity. In addition, previous electrochemical lignin depolymerization studies have also suffered from low monomer yields and insufficient direct identification of actual lignin-derived products. To overcome these limitations, the research team proposed an electroreductive lignin conversion strategy using a 5 wt% Pd/C catalyst. This process operates by utilizing reactive hydrogen formed on the catalyst surface during water electrolysis to cleave ether bonds in lignin. In other words, it enables simultaneous lignin depolymerization and subsequent hydrogenation using only electrical energy, without any external hydrogen supply, while allowing precise control over the amount of surface-adsorbed hydrogen through current density regulation. The team validated the performance of this approach using both model compounds representing 4–O–5 and α–O–4 bonds and real birch-derived lignin solvolysate. As a result, the 4–O–5 bond model compounds diphenyl ether (DPE) and phenyl tolyl ether (PTE) were completely converted within 90 minutes at 70°C and 50 mA cm⁻², while the α–O–4 bond model compound benzyl phenyl ether (BPE) was also fully converted at the lower temperature of 30°C. High selectivity was also confirmed in terms of product formation. DPE produced cyclohexanol at 99.8% and cyclohexane at 85.2%; PTE produced 4-methyl cyclohexanol at 99.5% and methyl cyclohexane at 95.6%; and BPE yielded cyclohexanol at 99.2%, toluene at 51.8%, and methyl cyclohexane at 46.3%. These results show that, following ether bond cleavage in lignin, the resulting aromatic intermediates can be selectively hydrogenated into useful upgraded products. The research team also identified the optimal conditions for improving reaction efficiency. When isopropanol (IPA) was introduced as a co-solvent, both substrate solubility and hydrogen transfer characteristics were enhanced simultaneously. In particular, at 30 wt% IPA, DPE conversion reached 100% and the Faradaic efficiency reached 70.2%. In addition, the best performance was observed at a current density of 50 mA cm⁻², whereas at higher current densities the competing hydrogen evolution reaction increased, which in turn reduced the efficiency of the target reaction. These results experimentally demonstrate that precise control of co-solvent composition and electrochemical conditions is critical for lignin electrochemical conversion. Important findings were also obtained regarding the catalyst operating mechanism. The research team proposed a bifunctional mechanism in which PdO and metallic Pd in the Pd/C catalyst play different roles. PdO drives the cleavage of C–O bonds in lignin, while the subsequently generated Pd⁰ is responsible for hydrogenating intermediates such as phenol and benzene into cyclohexanol and cyclohexane. In fact, when only Pd foil was used, DPE conversion was limited to 19.3%, and when only PdO was used, it reached only 57.4%; by contrast, Pd/C exhibited the highest activity and selectivity. In addition, Pd/C showed better conversion performance than Pt/C, Ru/C, Ag/C, and Ni/C, together with the highest TOF of 468.0 h⁻¹, and maintained 95.0% DPE conversion even after five cycles, confirming its excellent durability. The team further demonstrated the scalability of this technology by applying it to real birch biomass. Methanol solvolysis first achieved a delignification yield of 81 wt%, but the yield of lignin-derived phenolic monomers at this stage was only 5.0 C%. When the Pd/C-based electrochemical process was subsequently applied, efficiency was limited under strongly acidic conditions due to rapid repolymerization. However, when the system was switched to a milder 0.5 M acetate buffer (pH ≈ 5), the monomer yield increased to 13.6 C% after 1 hour and 19.6 C% after 4 hours. In particular, a high selectivity of 41.6% was obtained for 4-n-propanol syringol, and GC×GC–TOF/MS analysis confirmed the formation of various monomer products, including 4-n-propyl syringol, 4-n-propyl guaiacol, 4-n-propanol guaiacol, and syringylacetone. This study is significant in that it presents a new biorefinery platform capable of selectively breaking recalcitrant lignin bonds and simultaneously converting them into value-added chemicals using electricity alone, unlike conventional high-temperature and high-pressure hydrogenation-based lignin upgrading processes. In particular, the study demonstrates mild processing conditions without external hydrogen, applicability to real woody biomass, and the functional division mechanism of PdO/Pd⁰, suggesting strong potential as a key technology for the future production of sustainable chemical materials and biofuel precursors. ※Title: Highly efficient electro-reductive conversion of lignin into aromatics and cyclohexenes ※Jounral: Applied Catalysis B: Environment and Energy ※DOI: https://doi.org/10.1016/j.apcatb.2025.125851 ※Authors: First author Neha Karanwal; co-authors Seoyeon Kim and Yasora Liyanage; corresponding authors Dong Ki Lee and Jaehoon Kim ※PURE: https://pure.skku.edu/en/persons/jaehoon-kim/ Conceptual illustration of useful product generation from lignin using a renewable electricity-based e-biorefinery Reaction principle showing the selective cleavage of recalcitrant C–O bonds in lignin model compounds and their conversion into useful chemicals through a Pd/C catalyst-based electrochemical process
- No. 363
- 2026-03-27
- 1252
-
AI Technology for Recognizing Actions Using Only a Few Example Videos
A research team led by Jae-Pil Heo, Professor in the Department of Software at Sungkyunkwan University, has developed an Artificial Intelligence (AI) technology that can accurately recognize new actions from only a small number of example videos. Typically, AI requires massive amounts of training data to understand complex human actions. However, in real-world scenarios, it is often difficult to secure sufficient video data for specific actions. To address this limitation, the research team focused on few-shot action recognition, which enables AI to learn and distinguish the characteristics of new actions from only a few examples. The research team’s core idea is to compare videos by efficiently summarizing only their key movements, rather than relying on conventional complex computations that compare entire videos frame by frame in temporal order. To achieve this, the team extracts and organizes key movement patterns from each video based on several criteria, enabling the AI to compare actions more effectively and identify similarities and differences more accurately. A key strength of this technology is its robustness to variations in action speed and duration. Even when the same action is performed at different speeds or over different durations due to individual habits or filming conditions, the algorithm can reliably capture the essence of the action and recognize it effectively despite such temporal variations. This achievement has been internationally recognized for its academic significance and technical excellence. The paper was selected for an Oral Presentation at CVPR 2025, one of the most prestigious conferences in computer vision and artificial intelligence. This technology is expected to play an important role in a wide range of applications that require advanced video understanding, including sports motion analysis, intelligent security systems for detecting dangerous situations, and autonomous behavior learning for robots. ※Title: Temporal Alignment-Free Video Matching for Few-shot Action Recognition ※Conference: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2025 ※Presentation Type: Oral Presentation ※DOI: 10.1109/CVPR52734.2025.00509 ※Author: SuBeen Lee, WonJun Moon, Hyun Seok Seong, Jae-Pil Heo ※PURE: https://pure.skku.edu/en/persons/jae-pil-heo/ ▲Conceptual Illustration of a Pattern-based Video Summarization and Comparison Method for Action Recognition
- No. 362
- 2026-03-26
- 995
-
Development of Hafnium Oxide-Based Next-Generation Memory Devices for AI Hardware
A research team led by Professor Taesung Kim from the School of Mechanical Engineering at Sungkyunkwan University has developed hafnium oxide-based ferroelectric transistor arrays and successfully demonstrated their application in next-generation artificial intelligence (AI) hardware. With the rapid expansion of artificial intelligence (AI) and Internet of Things (IoT) technologies, there is a growing demand for new computing architectures capable of processing large volumes of data at high speed while minimizing power consumption. However, most conventional computing systems adopt the von Neumann architecture, in which memory and processing units are physically separated. This structural separation results in data transfer bottlenecks, leading to latency and high energy consumption. As an alternative to overcome these limitations, in-memory computing—where computations are performed directly within memory devices—has emerged as a key enabling technology for next-generation AI hardware. The research team implemented a novel ferroelectric transistor structure using hafnium–zirconium oxide (HfZrO₂), a material highly compatible with conventional semiconductor fabrication processes. This material can maintain electric polarization even at nanometer-scale thickness and can be directly integrated into existing CMOS processes. Using atomic layer deposition (ALD), the team precisely and repeatedly stacked HfO₂ and ZrO₂ at the atomic level to synthesize ultrathin HfZrO₂ films. Subsequently, rapid thermal annealing (RTA) below 400°C was employed to reliably induce ferroelectric properties. The core of this study lies in a design strategy known as “lattice engineering.” Inside a material, atoms are arranged in a regular lattice structure, and the physical properties—particularly ferroelectricity—are determined by this atomic arrangement. Instead of modifying the chemical composition or introducing additional elements, the researchers controlled the atomic arrangement by utilizing microscopic stress generated during thermal processing. By placing the ferroelectric thin film between metal electrodes with different thermal expansion coefficients, tensile stress was generated during annealing. This stress reorganized the atomic structure within HfZrO₂ and selectively stabilized the orthorhombic phase, which is responsible for ferroelectric behavior. This approach demonstrates a new method of precisely controlling material properties through externally engineered mechanical stress without altering the material’s chemical composition. As a result, devices incorporating tungsten (W) electrodes achieved a wide memory window of approximately 11 V and an on/off current ratio exceeding 10⁶. The devices also maintained stable operation over more than 10¹² switching cycles under 80 ns pulse conditions. Furthermore, uniform switching voltage distributions were observed across arrays consisting of 350 devices, experimentally confirming scalability for large-area integration. Each device exhibited up to 22 distinct conductance states, with a conductance ratio (Gmax/Gmin) of approximately 160, providing a sufficient dynamic range for analog weight representation. Long-term potentiation (LTP) and long-term depression (LTD) characteristics were also stably demonstrated, successfully emulating synaptic learning behavior. Notably, this study distinguishes itself by numerically demonstrating that device operation modes can be controlled solely through electrode engineering while maintaining the same HfZrO₂ film stack. In symmetric W/HZO/W structures, ferroelectricity was maximized, resulting in nonvolatile memory operation, whereas other electrode combinations exhibited volatile or semi-volatile behavior. This reconfigurable structure enables selective implementation of logic and memory functions within a single process platform. By incorporating experimentally measured device characteristics into a convolutional neural network (CNN) simulation based on VGG-8, the team achieved an image classification accuracy of 97.2% on the CIFAR-10 dataset. This result confirms that high AI inference performance can be maintained even when accounting for practical device nonidealities such as nonlinearity, weight asymmetry, and device-to-device variations. Additionally, a 9×2 ferroelectric transistor array was utilized to directly implement edge detection and image filtering operations in the analog domain, experimentally verifying the feasibility of in-memory multiply–accumulate (MAC) operations. Professor Taesung Kim stated, “This study is significant in that we precisely controlled the ferroelectric phase through stress-driven lattice engineering without altering the chemical composition.” He added, “By simultaneously achieving high endurance and precise analog weight control, we have presented a practical platform expandable to low-power edge AI and neuromorphic semiconductor applications. This work provides a technological foundation for the physical integration of memory and computation and marks an important milestone in the development of next-generation in-memory computing technologies.” This research was published online on January 27 in ACS Nano (Impact Factor 16.1, JCR top 5% in the field of nanoscience). ※ Title: Thermal Expansion-Engineered Ferroelectric Transistor Arrays for Scalable Edge AI Computing ※ Journal: ACS Nano ※ DOI: https://pubs.acs.org/doi/10.1021/acsnano.5c14095 ※ PURE: https://pure.skku.edu/en/persons/taesung-kim/ ※ Authors: -Corresponding author: Prof. Taesung Kim -First author: Geonwook Kim (integrated M.S./Ph.D. program); Hyunho Seok (Postdoctoral Researcher); Sihoon Son (integrated M.S./Ph.D. program); Hyunbin Choi (Ph.D. candidate). ▲High-Performance Ferroelectric Transistor Realized through Stress-Modulated Lattice Engineering of Hafnium Oxide ▲Conceptual Illustration of Hafnium–Zirconium Oxide Ferroelectric Transistor Arrays with Tungsten Electrodes for AI Hardware Implementation
- No. 361
- 2026-03-11
- 1596
-
Discovering the ‘Brain Fingerprints’ of Chronic Pain
A research team led by Woo Choong-Wan, Associate Professor in the Department of Biomedical Engineering at Sungkyunkwan University and Associate Director of the Center for Neuroscience Imaging Research (CNIR) at the Institute for Basic Science (IBS), has successfully decoded the intensity of spontaneous pain using functional magnetic resonance imaging (fMRI) in collaboration with Professor Cho Sungkun’s team at Chungnam National University. Chronic pain is one of the most common reasons for hospital visits worldwide, affecting approximately one in five adults. Despite its prevalence, there has been no objective method to measure pain intensity, unlike blood pressure or body temperature. A hallmark of chronic pain is that pain arises spontaneously without an external trigger. As a result, medical tests often appear normal, making an objective assessment extremely challenging. Consequently, treatment tends to focus on symptom relief rather than addressing underlying causes, potentially leading to long-term issues such as side effects and drug dependence. In recent years, researchers have increasingly focused on the brain, which ultimately constructs the experience of pain. Structural and functional abnormalities in the brain may underlie chronic pain. However, most previous studies have sought neural correlates that are common across patients. Because pain is inherently subjective and varies from person to person, such approaches face fundamental limitations in accuracy and clinical utility. Recognizing the need to account for individual differences, the research team adopted a personalized approach tailored to each patient. The team conducted repeated fMRI scans over several months in patients with fibromyalgia, a disorder characterized by widespread, persistent pain. fMRI detects changes in blood oxygen levels to reveal which brain regions are active. By applying machine learning techniques to this extensive brain imaging dataset, the researchers identified each patient’s unique functional brain connectome for pain—a map representing the complex interactions through which different brain regions communicate. These newly developed biomarkers successfully predicted fluctuations in each patient’s pain intensity over several months based solely on brain imaging data, achieving a high level of accuracy. The most significant finding of the study is that pain-related brain response patterns differ across individuals. A pain connectome identified in one patient could not explain the pain experienced by another. This provides scientific evidence that chronic pain reflects highly individualized brain responses and underscores the necessity of personalized approaches that account for each patient’s unique characteristics. “The fact that pain cannot be seen adds to the suffering of patients with chronic pain. This study empirically demonstrates that precision neuroimaging can be useful for evaluating invisible pain more objectively,” stated Dr. Choong-Wan WOO, who led the study. “Hopefully, our findings will ultimately help facilitate the application of precision medicine approaches to pain assessment and treatment.” Jae-Joong LEE, a postdoctoral researcher at SKKU IBS and the first author of this study, emphasized, “The key finding of this study is that each patient has a unique brain connectivity pattern associated with pain,” adding, “We will continue our research so that neuroscience-based precision diagnostics can be actively implemented in clinical settings.” ※ Title: Personalized Brain Decoding of Spontaneous Pain in Individuals With Chronic Pain ※ Journal: Nature Neuroscience ※ DOI: https://www.nature.com/articles/s41593-026-02221-3 ※ PURE: https://pure.skku.edu/en/persons/choong-wan-woo/ ▲ Figure 1. Pain intensity predicted by fMRI-based chronic pain marker The horizontal axis represents the actual pain intensity reported by each participant, and the vertical axis represents the pain intensity predicted by the brain imaging marker, demonstrating the correspondence between actual and predicted pain. Different colors indicate different temporal units of prediction. ▲ Figure 2. Regional feature importance of fMRI-based chronic pain marker The color of each region represents the importance value, calculated as the decrease in prediction accuracy when that region is excluded from the marker, with the top five regions with the highest importance values additionally marked. The regions with high importance values differ across participants.
- No. 360
- 2026-03-06
- 1333
-
Uncovering the secret of luxury brand fragrances through EEG (brainwave) analysis
A research team led by Professor Jo Woon Chong of the School of Electronic and Electrical Engineering, in collaboration with researchers from Texas Tech University in the United States, has identified, through EEG (electroencephalogram) analysis, the impact of “fragrance” on consumers’ emotions, memory, and deep emotional bonds with luxury brands. Going beyond conventional survey-based approaches, this study has drawn significant attention from both academia and industry by employing neuroscientific methods that measure human brain responses in real time. The research findings are scheduled to be published in the March 2026 issue of the Journal of Retailing and Consumer Services (top 2% in JCR), one of the world’s most prestigious journals in the fields of business and retailing. Professor Chong, who has led research at Sungkyunkwan University on human-centered AI and multimodal signal processing to bridge engineering and consumer experience, oversaw the EEG-based experimental design and data analysis framework in this study. From a neuroscientific perspective, Professor Chong systematically analyzed the effects of olfactory stimuli on consumers’ brain responses and brand perception, scientifically demonstrating that scent is one of the most intuitive yet powerful senses shaping brand experience. He explained the significance of the research by stating, “Olfaction is closely connected to brain regions responsible for emotion and memory,” and added, “This study clearly shows, through EEG data, how the harmony between fragrance and brand image creates meaningful differences in consumers’ emotional responses and memory formation.” The research team designed and conducted experiments that closely simulated real luxury brand environments, carefully comparing conditions in which a fragrance was congruent with the brand image and those in which it was not. Results from EEG analysis and quantitative survey data showed that when fragrance and brand image were well aligned, consumers’ brains exhibited emotional stability, while brand memory, favorability, and the sense of “brand resonance,” feeling a unity between the consumer and the brand, were enhanced overall. In contrast, when a fragrance did not match the brand image, immediate emotional responses such as pleasure were observed; however, these responses were less likely to translate into positive brand evaluations or long-term memory. Nevertheless, the study also found that such incongruent conditions could leave consumers with a strong and unexpected impression, suggesting that scent strategies have the potential to fundamentally alter how brands are perceived. This study is particularly significant as the outcome of a global, interdisciplinary collaboration among experts in engineering, consumer science, and medical science from South Korea and the United States. In addition to Professor Chong’s team, the research involved Professor Hyo Jung Chang and doctoral student, Sanghee Kim from the Department of Hospitality and Retail Management at Texas Tech University, as well as Dr. Bengie Ortiz from the University of Michigan Health, ensuring strong interdisciplinary expertise. Based on these findings, the research team presents scientific evidence for the importance of an “Olfactory Identity Strategy,” in which companies go beyond simply using pleasant scents to deliberately design fragrances that align with a brand’s identity. This approach is expected to serve as a new milestone for designing sensory-based brand experiences not only in the luxury sector but across a wide range of industries. ※ Title: The role of scent congruence with luxury brand image in consumers’ emotions, memory, and brand resonance: A mixed-methods approach using EEG and quantitative analyses ※ Journal: Journal of Retailing and Consumer Services ※ DOI: https://doi.org/10.1016/j.jretconser.2025.104663 ※ PURE: https://pure.skku.edu/en/persons/jowoon-chong/
- No. 359
- 2026-02-27
- 1426
-
Paradigm Shift in Immune Checkpoint Biology
A research team led by Prof. Ki-Young Lee at the College of Medicine, Sungkyunkwan University, has uncovered a previously unrecognized tumor-intrinsic role of the immune checkpoint molecule PD-L1, providing new mechanistic insight into lung cancer progression. PD-L1 (Programmed death-ligand 1) has been widely known for its role in helping tumors evade immune surveillance by suppressing anti-tumor immune responses. However, emerging evidence suggests that PD-L1 may also regulate intracellular signaling pathways within cancer cells. By integrating transcriptomic analyses of patient-derived non-small cell lung cancer (NSCLC) datasets with functional and molecular experiments, Prof. Lee’s research team demonstrated that PD-L1 acts as a critical regulator of autophagy and metastasis-related signaling networks within tumor cells. Using CRISPR-Cas9-mediated gene editing and protein interaction analyses, the researchers identified a novel molecular mechanism in which PD-L1 directly regulates autophagy signaling. Importantly, PD-L1 depletion in lung cancer models resulted in: reduced cell proliferation, decreased migration and colony-forming capacity, and suppressed tumor growth and metastasis in xenograft models. These findings demonstrate that tumor-intrinsic PD-L1 plays a functional role in cancer progression beyond its canonical immune regulatory activity. Prof. Lee’s research group plans to further expand multi-omics-based cancer signaling research and translational precision medicine platforms to strengthen global research competitiveness. This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea (MRC and Mid-career Researcher Programs). The study was published online on February 18, 2026, in the international journal Experimental Hematology & Oncology (Impact Factor 13.5, top 5.6% in JCR). ※Title: Tumor-intrinsic PD-L1 drives lung cancer progression in response to TLR stimulation by promoting autophagy through the TRAF6-BECN1 signaling axis ※Journal: Experimental Hematology & Oncology ※DOI: https://doi.org/10.1186/s40164-026-00761-9 ※PURE: https://pure.skku.edu/en/persons/ki-young-lee-2/
- No. 358
- 2026-02-26
- 1075
-
Stretchable Electronic Circuits You Can Assemble Your Way
A joint research team led by Professors Donghee Son and Jin-Hong Park at Sungkyunkwan University has developed a stretchable, self-healing, and reconfigurable electronic circuit platform that can autonomously recover from damage and be disassembled and reassembled on demand. This work introduces a new paradigm for electronic devices that simultaneously addresses long-term stability and personalized reconfigurability, which are critical for electronic skin (e-skin) and next-generation wearable and implantable electronics. Electronic skin is a core technology for sensing and processing diverse physiological signals through direct skin contact or implantation inside the body. For long-term, comfortable use, e-skin must be thin, soft, and mechanically compliant, closely matching the properties of biological tissues. However, these requirements also make such devices highly vulnerable to mechanical deformation, including stretching, bending, and tearing, which can lead to device failure and loss of function during repeated use or prolonged wear. In medical and healthcare applications, where physiological conditions and functional demands continuously change, reconfigurability—the ability to flexibly modify electrical functions and circuit architectures—is as important as mechanical robustness. To address these challenges, the research team developed stretchable transistors in which all key components—electrodes, semiconductors, and dielectric layers—are based on self-healing polymers. The electrodes and semiconductor layers were formed by incorporating carbon nanotubes and organic semiconductors into self-healing polymer matrices, achieving both high electrical performance and efficient self-recovery. The dielectric layer was also realized as a thin film of a self-healing polymer. These transistors can be assembled via a transfer process without conventional soldering or permanent bonding steps and maintain stable electrical characteristics after more than 100 cycles of 30% tensile strain. Even after severe physical damage, the devices effectively restored their electrical performance through autonomous self-healing. Beyond unit-level devices, the team integrated the self-healing transistors into a 5 × 5 array, demonstrating uniform drain current characteristics and stable operation even under water. Furthermore, biocompatibility assessments and animal studies confirmed that the devices retained their electrical performance after one week of implantation in vivo, highlighting their potential for implantable bioelectronic applications. A key innovation of this work is the concept of modular electronic circuits that can be assembled, disassembled, and reconfigured. By combining self-healing transistors with carbon nanotube-based resistors, the researchers constructed logic circuits that operated reliably under mechanical deformation. Leveraging the self-healing properties, they experimentally demonstrated that an assembled NOR gate could be disassembled and reconfigured into a NAND gate, proving that a single hardware platform can flexibly switch functions without replacing components. In addition, the team integrated carbon nanotube-based resistive tactile sensor modules and light-emitting capacitive display modules with the self-healing transistor array to create a wearable electronic skin system that provides visual feedback in response to touch. When attached to the skin, mechanical stimuli were detected by the tactile sensors, and the corresponding light-emitting pixels were activated through the self-healing transistor array, enabling intuitive and interactive user interfaces. The researchers stated that “the recovered electrical performance after self-healing is nearly indistinguishable from the pristine state.” They emphasized that this technology, which can autonomously heal and be reconfigured according to user needs, is expected to serve as a core platform for next-generation wearable medical devices, robotic skin, and intelligent prosthetic systems. This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea. The results were published online on May 19, 2025, in Nature Electronics. ※Title: Reconfigurable assembly of self-healing stretchable transistors and circuits for integrated systems ※Journal: Nature Electronics) ※DOI: https://doi.org/10.1038/s41928-025-01389-z ※PURE -Professor Donghee Son: https://pure.skku.edu/en/persons/donghee-son/ -Professor Jin-Hong Park: https://pure.skku.edu/en/persons/jin-hong-park/ Figure 1. Reconfigurable stretchable self-healing electronic circuits By positioning stretchable self-healing transistors and carbon nanotube electrodes at predefined locations on a self-healing polymer substrate, the components autonomously assemble through self-healing interactions to form stretchable electronic circuits. Using two self-healing transistors and a load resistor, NAND and NOR logic gates were assembled, and their logic states were reliably maintained under 20% tensile strain. Moreover, the preassembled self-healing electronic circuits can be cut and reassembled, enabling circuit reconfiguration. Specifically, when a transistor in the NOR gate is severed, repositioned, and reassembled, the circuit can be successfully reconfigured into a NAND gate. Figure 2. Wearable and implantable system applications of stretchable self-healing electronic Circuits Stretchable self-healing semiconductors and electrodes can be assembled into diverse functional modules, including tactile sensors, display elements, and transistors. The assembled modules can be integrated onto a self-healing substrate to form a stretchable electronic skin system capable of detecting mechanical deformation and providing localized visual feedback at the corresponding sites. Owing to their intrinsic moisture resistance, the self-healing transistors remain operational under implantation conditions. In vivo animal experiments confirmed that the self-healing transistors functioned reliably when implanted subcutaneously, demonstrating their feasibility for implantable bioelectronic systems.
- No. 357
- 2026-02-12
- 1606
-
Clopidogrel Shown to Be Superior to Aspirin for Long-Term Antiplatelet Therapy After Coronary Stenting
A research team led by Professors Joo-Yong Hahn, Young Bin Song, and Ki Hong Choi of the Division of Cardiology at Samsung Medical Center, Sungkyunkwan University School of Medicine, together with Professor Yong Hwan Park of Samsung Changwon Hospital, has demonstrated that clopidogrel is more effective than aspirin as a long-term antiplatelet therapy in patients at high risk of recurrent cardiovascular events after coronary stent implantation. The findings come from the SMART-CHOICE 3 trial, a large, multicenter randomized clinical study conducted at 26 hospitals across South Korea, and were recently published in The Lancet, one of the world’s most influential medical journals. [Addressing an Unresolved Question in Long-Term Care After PCI] After percutaneous coronary intervention (PCI) with drug-eluting stents, patients typically receive dual antiplatelet therapy (DAPT) for a fixed period to prevent thrombotic complications. While this early treatment strategy is well established, the optimal choice of single antiplatelet therapy for long-term maintenance after completion of DAPT has remained uncertain. Current clinical guidelines have traditionally recommended lifelong aspirin therapy in this setting. However, high-quality randomized evidence directly comparing aspirin with alternative agents, such as clopidogrel, has been limited—particularly in patients at high risk of recurrent ischemic events. [Design and Key Findings of the SMART-CHOICE 3 Trial] The SMART-CHOICE 3 trial enrolled more than 5,500 adult patients who had successfully completed a standard course of DAPT following PCI and who were considered to be at high risk for future ischemic events due to factors such as a prior myocardial infarction, diabetes requiring medication, or complex coronary artery disease. Participants were randomly assigned to receive either clopidogrel (75 mg once daily) or aspirin (100 mg once daily) as long-term monotherapy and were followed for a median of more than two years. The primary outcome was a composite of all-cause death, myocardial infarction, or stroke. The study showed that clopidogrel significantly reduced the risk of this composite outcome compared with aspirin. This benefit was mainly driven by a lower incidence of myocardial infarction, while rates of death and stroke were similar between the two groups. Importantly, the risk of clinically significant bleeding did not differ between patients receiving clopidogrel and those receiving aspirin. [Clinical Significance and Broader Implication] These results provide strong evidence that clopidogrel can offer superior protection against serious cardiovascular events without increasing bleeding risk in patients who require long-term antiplatelet therapy after PCI. Unlike previous studies that included broader or softer clinical endpoints, SMART-CHOICE 3 focused on hard clinical outcomes and specifically targeted patients with a high ischemic risk, strengthening the clinical relevance of its findings. Although the study population consisted entirely of Korean patients and included relatively fewer women and patients at very high bleeding risk, the results represent a major step forward in refining long-term secondary prevention strategies after coronary stenting. In recognition of its clinical importance, the SMART-CHOICE 3 trial was selected as a Late-Breaking Clinical Trial at the 2025 American College of Cardiology (ACC) Annual Scientific Session and was simultaneously published in The Lancet. The investigators conclude that clopidogrel should be considered a preferred option over aspirin for long-term antiplatelet monotherapy in high-risk patients who have completed standard DAPT after PCI, potentially influencing future clinical practice and guideline recommendations. ※Title: Efficacy and safety of clopidogrel versus aspirin monotherapy in patients at high risk of subsequent cardiovascular event after percutaneous coronary intervention (SMART-CHOICE 3): a randomised, open-label, multicentre trial ※Journal: The Lancet ※DOI: https://doi.org/10.1016/S0140-6736(25)00449-0 ※PURE -Professor Joo-Yong Hahn: https://pure.skku.edu/en/persons/joo-yong-hahn/ -Professor Young Bin Song: https://pure.skku.edu/en/persons/young-bin-song/ -Professor Ki Hong Choi: https://pure.skku.edu/en/persons/ki-hong-choi/
- No. 356
- 2026-02-05
- 3209
-
Development of an Intent-Based Closed-Loop Security Control System for Cloud-Based Security Services
Prof. Jaehoon (Paul) Jeong at Sungkyunkwan University and Dr. Patrick Lingga who was an M.S.-Ph.D.-combined student at SKKU developed a Cloud-Based Intelligent Security Service System. They had the data models of the interfaces for this system approved as Internet Standards by the Internet Engineering Task Force (IETF) that is a De facto standards organization for the Internet. ▲(From left) Prof. Jaehoon (Paul) Jeong in the Department of Computer Science and Engineering and Dr. Patrick Lingga as the 1st Author The research group of Prof. Jaehoon (Paul) Jeong published a journal paper entitled “ICSC: Intent-Based Closed-Loop Security Control System for Cloud-Based Security Services”. In this paper, they introduce the implementation of the Security Service System that supports Intent-Based Networking (IBN) intelligently addressing a user’s intent, prove the concept of ICSC, and verify its performance. When they use various security solutions together, the legacy cloud security service systems lacked the unified standardized interfaces, so an individual interface per security solution was designed and implemented to configure security policies in each vendor’s security solutions and manage them. To resolve this inconvenience and inefficiency, a new Working Group called “Interface to Network Security Functions (I2NSF)” was formed in the IETF. I2NSF WG has standardized five YANG Data Models for I2NSF standard interfaces and I2NSF Applicability as Request for Comments (RFCs) that are standard documents. Prof. Jeong and Dr. Lingga contributed to this I2NSF standardization as a document editor and a YANG data model editor, respectively. Prof. Jeong’s research group have implemented and verified the ICSC System on the basis of their standardization results in the IETF I2NSF WG for the last eight years. To provide security services, this ICSC System performs two phases such as (i) Intent Fulfillment and (ii) Intent Assurance. First, in the phase of Intent Fulfillment, the intent of a user’s security service request is configured in an appropriate Network Security Function (NSF) in the ICSC system. In this ICSC system, I2NSF User, which is a software used by a security administrator, composes a high-level security policy and sends it to Security Controller that is a core control and management component in the ICSC system. A Security Policy Translator (SPT) in Security Controller translates the high-level security policy into the corresponding low-level security policy that an NSF can understand. The SPT selects an appropriate NSF to be able to perform the translated low-level security policy and sends the security policy to the NSF. After receiving the security policy, the NSF performs a security service corresponding to the policy. Second, in the phase of Intent Assurance, the ICSC system validates whether NSFs perform the requested security services well according to the user’s security intent or not. The NSFs deliver their monitoring data to I2NSF Analyzer either periodically or on every occurrence of an important event. I2NSF Analyzer analyzes the NSF monitoring data by Artificial Intelligence (AI) and Machine Learning (ML) algorithms. Through this analysis, I2NSF Analyzer can find out either new security attacks or hardware issues of an NSF (e.g., the resource lack of computing power, memory capacity, and network bandwidth). For the new security attacks, I2NSF Analyzer generates Policy Reconfiguration as a low-level security policy to cope with such attacks and then sends it to Security Controller. Security Controller delivers the security policy to an appropriate NSF. Also, for the hardware issues, I2NSF Analyzer generates Feedback Information including an issue and a possible resolution and then sends it to Security Controller. Security Controller sends a request message related to the feedback information to Developer’s Management System (DMS). DMS performs either the scale-up of the existing NSF or the generation of a new NSF according to the NSF hardware request message. This research measured and analyzed the metrics of security attack detection time and security attack response time for the two countermeasures such as Automatic Countermeasure with the ICSC system and Passive Countermeasure with the legacy manual system. Through the measurement of Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR), it can be seen that the ICSC approach outperforms the manual approach in both the security attack detection time and the reaction time. Currently, this research group is developing an Intent Translator that accommodates a security service request in a natural language for the sake of a security administrator in the ICSC system. This Intent Translator can translate a security intent into a high-level security policy with both Large Language Model (LLM) and Knowledge Graph (KG). This research was performed by the Information and Communication (ICT) Standards Development Support Program of the Institute of Information & Communications Technology Planning & Evaluation (IITP) in the Ministry of Science and ICT (MSIT) of the Republic of Korea. The result of this research was published in a top international journal entitled IEEE Communications Magazine whose Impact Factor (IF) is 8.3 and that is ranked within top 5% in Journal Citation Reports (JCR). ▲Logical Structure of I2NSF System for Cloud-Based Security Services ▲Procedure of Security Intent Provisioning through Closed-Loop Security Control in I2NSF Framework ▲Logical Structure of Security Policy Translator for I2NSF Framework ▲Flow Diagram for Executing Multiple Security Services through Service Function Chaining (e.g., Firewall and Web Filter) ▲Performance Comparison between ICSC System’s Automatic Operation and Administrator’s Manual Operation ※Title: ICSC: Intent-Based Closed-Loop Security Control System for Cloud-Based Security Services ※Journal: IEEE Communications Magazine (Volume 63, Issue 4, April 2025) ※DOI: https://doi.org/10.1109/MCOM.001.2400022 ※Reference Document: https://datatracker.ietf.org/wg/i2nsf/documents/ ※PURE: https://pure.skku.edu/en/persons/jae-hoon-jeong/ ※Professor Webpage: http://iotlab.skku.edu/people-jaehoon-jeong.php
- No. 355
- 2026-02-02
- 3132
-
World’s First AI-Based Optical Diagnostic Platform to Distinguish Nasal Secretion from Cerebrospinal Fluid
A research team led by Professor Jinsung Park of the Department of Biomechatronics Engineering(co–first authors: Eugene Park, M.S.; Dr. Hyunjun Park; Dr. Woochang Kim) has developed the world’s first AI-based optical diagnostic platform through a collaborative study with Dr. Minhee Kang of the Biomedical Engineering Research Center at Samsung Medical Center and Professor Gwanghui Ryu’s Otolaryngology team. This platform enables rapid and accurate differentiation—within minutes—between ordinary nasal secretion and cerebrospinal fluid (CSF) leaking from the nose. Cerebrospinal fluid (CSF) is a vital liquid that circulates around the brain and spinal cord, protecting them from external shocks. However, due to head trauma, aging, or transnasal brain surgery, CSF can leak through the nasal cavity—a condition known as CSF rhinorrhea. Because leaked CSF appears as a clear, water-like fluid, it is visually indistinguishable from normal nasal secretion. As a result, many patients mistakenly attribute the symptom to rhinitis or a common cold and delay treatment, allowing bacteria to enter the brain and potentially cause life-threatening complications such as meningitis. To address this challenge, Professor Park’s team focused on Raman spectroscopy, an analytical technique that reads the molecular “fingerprints” of substances through light scattering. The researchers fabricated nanoscale pillar structures composed of gold and silver, dramatically amplifying the weak signals of various biomolecules in liquid samples by tens of thousands of times. By integrating artificial intelligence (AI)–based machine learning, the system was trained to autonomously learn and distinguish the distinct spectral patterns of CSF and nasal secretions. When evaluated using clinical samples from patients at Samsung Medical Center, the platform achieved an exceptionally high diagnostic accuracy of 90.8% in identifying CSF leakage. Notably, the researchers introduced a specialized calibration algorithm to overcome variations in spectral resolution across different Raman instruments. As a result, the platform delivered equally accurate performance not only on high-end hospital equipment but also on compact, portable devices. This advancement suggests the potential for near-instant diagnosis within approximately one minute even in emergency rooms or small outpatient clinics. By presenting the world’s first AI-based optical diagnostic platform capable of distinguishing visually indistinguishable nasal secretion and CSF, this study overcomes a long-standing limitation in the immediate clinical confirmation of CSF leakage. The proposed technology is expected to serve as a reliable monitoring and diagnostic platform for patients suspected of CSF rhinorrhea in real-world medical settings. This research was supported by the National Research Foundation of Korea through the Mid-Career Research Program (No. NRF-2023R1A2C2004964), the Bio & Medical Technology Development R&D Program (RS-2024-00438542), and the Sejong Science Fellowship (RS-2025-00554830, RS-2024-00353529), as well as the SKKU–SMC Future Convergence Research Program and the SKKU–KBSMC Future Clinical Convergence Research Program. In recognition of its scientific excellence, the study was published online on December 3 in the Journal of Materials Science & Technology (Impact Factor: 14.3), one of the world’s leading journals in metallurgy and materials science. ※Title: Ultrasensitive CSF rhinorrhea screening via machine learning-aided SERS on Au@Ag nanopillars ※Journal: Journal of Materials Science & Technology ※DOI: https://www.sciencedirect.com/science/article/pii/S1005030225012083 ※PURE: https://pure.skku.edu/en/persons/jinsung-park-2/ Schematic illustration of the development of the AI-based CSF leakage diagnostic platform Morphology and SERS characteristics of the optical substrate, the core component of the platform Raman spectroscopic SERS detection results of cerebrospinal fluid (CSF) and nasal secretion (NS) samples Comparison, validation, and interpretation of prediction results across various machine-learning pipelines Application of the cross-instrument spectral preprocessing (CISP) algorithm to overcome inter-instrument resolution differences and platform validation using a portable Raman spectrometer
- No. 354
- 2026-01-27
- 4239
-
Develops the Off-grid Filtration Technology Removing Over 99% of Nanoplastics Smaller Than 50 nm
Professor Jeong-Min Baik’s research group of the School of Advanced Materials Science and Engineering has, for the first time in the world, developed a reusable electro-kinetic filtration platform capable of filtering more than 99% of ultrafine nanoplastics particles smaller than 50 nm even under commercial-level high-flow conditions. Plastic pollution, which has surged in recent years through industrialization and the pandemic era, poses a direct threat to human health. In particular, nanoplastics smaller than 100 nm-thousands of times thinner than a human hair-can readily pass through biological membranes in the body and trigger serious diseases such as immune dysregulationand carcinogenicity. However, conventional water purification systems have struggled to effectively remove nanoplastics of such small sizes, highlighting technological limitations; studies have even reported the presence of hundreds of thousands of particles in a single bottle of bottled water. To overcome these limitations, Professor Baik’s research group introduced a strategy that electrokinetically activates a porous metallic filter. By coating the filter surface with magnesium oxide (MgO) and a cationic engineered polymer compound and applying an external potential, the research team implemented a filter that strongly attracts negatively charged nanoplastics within water. The platform achieved over 99% removal of 50 nm nanoplastics even under commercial-level high throughput flux. One noteworthy of this study is that the system can operate without an external battery or power supply. The platform was integrated with a triboelectric generator, which converts mechanical energy directly into electricity, thereby realizing energy self-sufficiency. In addition, by reversing the direction of the electric field, the plastic particles captured on the filter can be detached, enabling filter regeneration. The system maintained the performance even after the filter was reused more than 20 times, demonstrating strong economic feasibility. The system also showed consistent performance across diverse real-world water conditions, including tap water and river water, and demonstrated purification capability that meets World Health Organization (WHO) drinking-water standards. Professor Baik stated, “This study is academically significant in that it mathematically clarifies the combined electro-kinetic filtration mechanism of underwater nanoplastics,” adding, “Going forward, the technology can be extended to various water purification applications, including bacterial removal and selective capture of valuable metal resources.” This research was supported in 2025 by the Future-Pioneering Convergence Science and Technology Development Program and the MSIT Individual Basic Research Program. The findings were published in the December 2025 at Materials Today (IF 22.0), a leading journal in materials science. The research group has completed a domestic patent application for the technology and is accelerating follow-up studies toward commercialization. ※ Title: High-efficiency, reusable electrokinetic filtration platform for high-flux nanoplastic sequestration and self-powered operation ※ Journal: Materials Today (published in 2025. 12.) ※ DOI: https://doi.org/10.1016/j.mattod.2025.12.008 ※ Pure: https://pure.skku.edu/en/persons/jeong-min-baik/ ▲ Schematics and performance of electrokinetic filtration platform
- No. 353
- 2026-01-23
- 5329
