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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
- 344
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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
- 2241
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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
- 2492
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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
- 3545
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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
- 4500
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Ultra-Light Ring Delivers Realistic Touch of Virtual Objects to Fingertips
A research group led by Professor Sunkook Kim at Sungkyunkwan University, in collaboration with École Polytechnique Fédérale de Lausanne (EPFL), has developed a laser-processed three-axis force sensor and successfully integrated it into an ultra-light wearable haptic device named OriRing. The system enables finger-level, high-fidelity tactile feedback while maintaining the form factor and comfort of a simple ring. Wearable haptic technologies are gaining increasing attention, particularly in combination with physical AI systems, as they offer new ways to convey sensations from virtual environments to the human body or assist physical interaction. However, conventional haptic devices largely rely on vibration or thermal stimulation, which limits their ability to realistically reproduce forces and material properties. Force-feedback systems that transmit forces at the joint level often suffer from bulky structures and heavy components, significantly reducing wearability. These challenges have driven strong demand for lightweight yet realistic haptic solutions. To address these limitations, the research team developed a thin, flexible three-axis force sensor capable of precisely detecting multi-directional forces generated by finger movements. Using laser processing, the team formed micro-pyramid structures of varying heights on a polymer surface, allowing electrical signals to be clearly distinguished depending on the magnitude and direction of applied forces. Designed in a 2 × 2 pixel configuration, a single sensor unit can simultaneously measure both normal and shear forces, enabling true three-axis force sensing within an ultrathin form factor. By integrating this sensor into OriRing, the researchers achieved an exceptional force-to-weight performance. Excluding the actuation module, the ring weighs only about 18 g, yet it can deliver up to 6.5 N of force feedback, equivalent to lifting a mass of approximately 663 g—a remarkable capability for a compact wearable device. User studies confirmed that changes in the size and stiffness of virtual objects were instantly conveyed as tactile feedback in response to finger movements while wearing OriRing. Furthermore, the system demonstrated novel interaction modes in which users could dynamically modify the physical properties of virtual objects in real time using only finger motions. “OriRing achieves accessory-level wearability while delivering superior force-to-weight performance compared to conventional glove-based haptic devices,” said Professor Kim. “This technology has strong potential for applications not only in virtual reality and gaming, but also in rehabilitation, medical devices, and teleoperated robotic systems.” The results were published online on December 18, 2025, in Nature Electronics (Impact Factor 40.9; top 0.2% in JCR rankings). ※ Title: An 18 g Haptic Feedback Ring with a Three-Axis Force-Sensing Skin ※ Journal: Nature Electronics ※ DOI: https://doi.org/10.1038/s41928-025-01515-x ※ PURE: https://pure.skku.edu/en/persons/sunkook-kim/ ▲ A wearable haptic force-feedback ring with a three-axis force-sensing skin
- No. 352
- 2026-01-19
- 3063
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Development of Intrinsically Stable MXene Inks for Cutting-Edge On-Chip Energy Storage via Electrohydrodynamic Jet Print
Professor Doyoung Byun’s research group at Sungkyunkwan University (SKKU), in collaboration with ENJET, Gyeongsang National University, and Khalifa University (Research and Innovation Center for Graphene and 2D Materials, RIC2D), developed oxidation-stable MXene inks for electrohydrodynamic (EHD) jet printing and demonstrated ultra-high-capacitance micro-supercapacitors (MSCs) based on these inks. In the paper “Micro-supercapacitors of exceptionally high capacitance fabricated using intrinsically stable MXene inks via electrohydrodynamic jet printing,” the authors identify a key limitation of conventional MXene inks: their vulnerability to oxidation and poor dispersibility in organic solvents, which makes it difficult to formulate high-viscosity, organic-based inks suitable for high-resolution printing. The study then presents a practical ink-and-process strategy that directly addresses these issues. In modern microelectronic devices, achieving high-performance energy storage in the space-constrained on-chip environment requires a process that can deliver both fine patterning (high resolution) and high volumetric performance. EHD jet printing is attractive because it enables high-resolution patterning at room temperature and is compatible with flexible substrates, but it also demands a functional ink that is both high-viscosity and highly stable. To meet these requirements, the team designed an ADS-MXene(CMC) ink by combining ADOPA (alkylated 3,4-dihydroxy-L-phenylalanine) functionalized MXene (ADS-MXene) with CMC (carboxymethyl cellulose) in a hybrid organic-solvent system. The ink reportedly achieves, simultaneously, an electrical conductivity of approximately ~3400 S·cm⁻¹, a viscosity of ~4 × 10³ cP, strong oxidation resistance, and long-term dispersion stability on the order of three months. They then optimized the EHD jet printing conditions for this ink formulation and fabricated in-plane interdigitated electrodes with a line width/spacing of 80 μm, achieving a high areal cell density of 6 cells·cm⁻². (This process was implemented using an ENJET system.) The resulting high-density (HD) MSCs delivered an areal capacitance of 402.7 mF·cm⁻² and a volumetric capacitance of 2013 F·cm⁻³, which the authors present as among the highest volumetric capacitance values reported for MXene-printed MSCs. Durability testing showed over 95% capacitance retention after 10,000 cycles and a Coulombic efficiency of 96.5%, supporting both high performance and reliability. Importantly, the work goes beyond performance reporting. Using DFT calculations, the authors verify charge transfer between the ADOPA ligand and the MXene surface, providing a mechanistic basis for improved stability. They also demonstrate stable, high-resolution patterning not only on glass/Si substrates but also on flexible substrates such as PET and PI, and they present a reproducible ink process operating window. The study emphasizes that these contributions can support future standardization of MXene inks for EHD printing and help expand the broader “MXetronics” ecosystem. This work was supported by NRF (Ministry of Education) RS-2023–00239590, MOTIE Technology Innovation 20026376 (1415188205), and KIAT RS-2024–00418086. The results were published in Materials Science & Engineering R: Reports, Volume 168 (2026), Article 101148 (IF 26.8, JCR category ≈ 2%). Figure 1. Schematic illustration of printable MXene ink synthesis and EHD jet printing of symmetric interdigitated HD-MSCs Figure 2. MXene ink stability, printing of on-chip HD-MSCs, and performance comparison ※Title: Micro-supercapacitors of exceptionally high capacitance fabricated using intrinsically stable MXene inks via electrohydrodynamic jet printing ※Joournal: Materials Science & Engineering R (168권, 2026, 101148) ※DOI: https://doi.org/10.1016/j.mser.2025.101148 ※PURE: https://pure.skku.edu/en/persons/do-young-byun/
- No. 351
- 2026-01-16
- 3041
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Development of the Next-Generation Transfer Learning Technology Solving High-Dimensional Data Analysis Challenges
Professor Eun Ryung Lee (Department of Statistics), as a first author, has developed a new statistical methodology to overcome the limitations of high-dimensional analysis caused by data scarcity. In collaboration with Professor Seyoung Park (Yonsei University) and Professor Hongyu Zhao (Yale University), Prof. Lee successfully implemented a 'Transfer Learning Algorithm' that maximizes learning performance by selectively utilizing useful information, based on the insight that the contrast between target data and external source data exhibits a 'Low-rank' structure. This achievement paves the way for dramatically improving prediction accuracy in fields such as rare disease research and precision medicine, where analysis has been difficult due to small sample sizes, by effectively integrating external big data. ■ Innovative Algorithm Design Overcoming Limitations of Existing Transfer Learning This study focused on resolving the predictive uncertainty of 'Small Data', which persists even in the big data era, and the side effects of existing transfer learning. In high-dimensional regression problems like genomic analysis, accurate model estimation is difficult because the number of variables reaches tens of thousands while the target samples of interest are very few. To complement this, transfer learning utilizing external data has been attempted, but 'Negative Transfer' problems, where prediction performance degrades due to the indiscriminate use of data irrelevant to the target, have frequently occurred. To solve these problems, the research team proposed a two-step estimation method that effectively controls the structural difference between the target model and the source model within a 'Low-Rank Regression' framework. In particular, the 'Forward Source Detection (FSD)' technique devised by the team sequentially detects only those information sources among numerous external datasets that practically help target analysis. This amplifies common signals between data and blocks unnecessary noise, enabling precise estimation without bias even in high-dimensional environments. ■ Proven Superior Prediction Performance and Theoretical Optimality Theoretical verification proved that the newly developed transfer learning methodology has a much faster statistical convergence rate than using target data alone and achieves optimal efficiency from a Minimax perspective. Its superiority was also confirmed in actual data application. The research team conducted an experiment predicting anticancer drug responses of specific lung cancer mutations (KRAS-mutant NSCLC), which had only 28 samples, using Cancer Cell Line Encyclopedia (CCLE) data. As a result, the proposed algorithm recorded significantly higher prediction accuracy compared to existing pooled analysis methods or simple marginal screening methods by effectively selecting and integrating data from other cancer types with similar genetic characteristics to lung cancer. ■ Applicability to Various Fields The 'Forward Source Detection Transfer Learning (FSD-Trans-NR)' technology of this study is designed to operate stably even in high-dimensional environments where the data dimension is much larger than the sample size, and can be flexibly applied to complex data situations where low-rank structures and sparse structures are combined. These characteristics are expected to be widely utilized for predictive modeling in various fields, such as financial risk analysis and new material development, where data acquisition is difficult and costly, as well as drug response prediction in the biomedical field. This research was supported by the National Research Foundation of Korea (NRF) and the U.S. National Institutes of Health (NIH). This research outcome was published online in October 2025 in the Journal of the American Statistical Association (JASA), the world's most prestigious journal in the field of statistics. ※Title: Transfer Learning Under Large-Scale Low-Rank Regression Models ※Journal: Journal of the American Statistical Association (JASA) ※DOI: https://doi.org/10.1080/01621459.2025.2555057
- No. 350
- 2026-01-06
- 3586
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Professor Yohan Kim at SKKU Develops “Periportal Liver Assembloids” That Fully Recapitulate Human Liver Tissue In Vitro
Professor Yohan Kim of the Department of MetaBioHealth at Sungkyunkwan University (President Yoo Ji-Beom), in collaboration with the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) in Germany, has successfully developed human periportal liver assembloids that precisely reproduce the periportal region of the human liver outside the body. This achievement was recognized for its scientific significance and published on December 17, 2025 in Nature, the world's most prestigious journal in life sciences. The liver-often referred to as the body's "chemical factory"-is responsible for essential functions such as metabolism, detoxification, and bile production. Until now, researchers have relied on organoids-miniature organ-like structures grown from stem cells-to study liver disease in the laboratory. However, conventional liver organoids have been limited in their ability to reproduce the complex interactions among diverse liver cell types, making it difficult to reflect the liver's highly sophisticated architecture and function in the human body. To overcome these limitations, Professor Kim utilized patient-derived liver tissue obtained during surgery. The researchers established a technology that enables the direct expansion of mature human hepatocytes in vitro and successfully generated hepatocyte organoids. These organoids formed functional bile canaliculi, retained long-term metabolic activity, and demonstrated drug detoxification and energy metabolism comparable to the human liver. Taking the approach further, the team combined hepatocyte organoids with bile duct organoids and periportal fibroblasts derived from the same patient. By assembling these components in a highly controlled three-dimensional architecture-much like building with LEGO blocks-they created periportal liver assembloids. The term assembloid refers to a next-generation engineered tissue constructed by assembling multiple cell types or organoids into a unified functional structure. The resulting assembloids faithfully reproduced the periportal region of the human liver, where hepatocytes, bile ducts, and blood vessels converge and active molecular exchange occurs. Transcriptomic analysis revealed that the assembloids carried out complex liver-specific functions such as gluconeogenesis and urea metabolism. Notably, the team also demonstrated hepatic zonation, in which cells adopt different functional identities depending on their spatial location-one of the most defining features of the native liver. This breakthrough has profound implications for reducing animal experimentation and accelerating patient-specific therapies. By artificially increasing fibroblast populations within the assembloids, the team generated a liver fibrosis model, in which pathological features such as collagen deposition and cell death observed in cirrhotic patients could be faithfully reproduced and studied in vitro. Professor Kim stated, "This is the world's first demonstration that multiple patient-derived liver cell types can be assembled into a single functional tissue that recapitulates the structural and pathological complexity of the human liver in the laboratory. Going forward, this platform will enable the development of new treatments for liver fibrosis, biliary diseases, and liver cancer, and will allow patient-specific drug testing as a precision medicine tool." This study was supported by the Max Planck Society, the German Federal Ministry of Education and Research (BMBF), and the European Research Council (ERC). ※ Title: Human assembloids recapitulate periportal liver tissue in vitro ※ Journal: Nature (Published on 17th of December, 2025) ※ DOI: https://www.nature.com/articles/s41586-025-09884-1 ※ Pure: https://pure.skku.edu/en/persons/yohan-kim/ ▲Schematic of human hepatocyte-derived organoid generation and periportal liver assembloid
- No. 349
- 2025-12-30
- 4662
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Development of a Biomechanobiology-Based Cardiac Regeneration Bioplatform for Myocardial Infarction Treatment and Tendon
Professor Geun Hyung Kim’s research team (first author: Dr. Wonjin Kim) in the School of Medicine has successfully developed a biomimetic gradient tissue construct that continuously recapitulates the tendon–tendon-to-bone interface (TBI)–bone architecture using 3D bioprinting technology, in order to address the clinical challenge of regenerating the tendon-to-bone interface in rotator cuff tears. The team fabricated two types of tissue-specific bioinks based on decellularized extracellular matrices derived from bone and tendon tissues. To precisely reproduce the native microenvironment, hydroxyapatite was incorporated into the bone region, while physical and biochemical cues that induce cellular alignment were applied to the tendon region. In addition, by employing a core–shell nozzle–based gradient bioprinting process, the researchers successfully established a construct exhibiting seamless biological and mechanical continuity from tendon to TBI to bone within a single printing step. The resulting 3D gradient construct provided precise tissue-specific signals that guided human adipose-derived stem cells (hASCs) toward tendon, fibrocartilage, and bone lineages, and notably enhanced fibrocartilage formation within the TBI zone. In vitro assessments further demonstrated that the gradient architecture significantly increased TBI-related gene expression, cytoskeletal organization, and mechanical strength compared with conventional single-tissue models. Collaborative in vivo studies with Professor Sang Cheol Lee’s team in the Department of Rehabilitation Medicine at Yonsei University confirmed that implantation of the gradient construct in a rabbit rotator cuff tear model resulted in robust and continuous regeneration of tendon, TBI, and bone tissues. Professor Kim emphasized, “The tissue-specific bioinks and gradient bioprinting platform introduced in this study represent an important technological breakthrough that overcomes the long-standing challenges associated with the complex structural and mechanical discontinuities of the tendon–TBI–bone interface, offering a promising therapeutic strategy for difficult-to-treat tissue defects such as rotator cuff tears.” The research team further extended this gradient tissue regeneration concept to develop a next-generation cardiac and skeletal muscle regenerative bioplatform that actively harnesses mechanotransduction. Myocardial infarction (MI), in particular, is a representative disease that causes irreversible damage to cardiac tissue, and existing cell-based therapies or bioprinting-based approaches have faced inherent limitations, including restricted cellular responsiveness and insufficient paracrine effects. To overcome these challenges, the team fabricated a bioprinted cellular patch composed of cardiomyocytes, cardiac fibroblasts, and endothelial progenitor cells embedded in a collagen matrix incorporating aligned gold nanowires (AuNWs). Through systematic optimization of AuNW concentration, bioprinting process parameters, and the mixing ratios of the three cell types, stable fabrication of a 3D cardiac patch containing aligned AuNWs was achieved. In vitro analyses demonstrated enhanced cellular alignment, activation of integrin-mediated signaling, increased focal adhesion kinase (FAK) formation, and robust secretion of diverse paracrine factors. These synergistic effects effectively promoted the formation of vascularized cardiac tissue during the culture of the bioprinted cardiac patch. Furthermore, implantation of the 3D cardiac patch into an animal model of myocardial infarction resulted in significantly enhanced angiogenesis and myocardial regeneration, with clear evidence of functional cardiac recovery driven by paracrine mechanisms. In parallel, the research team designed a magnetorheological bioink and established a magnetic field–based mechanobiology bioprinting platform that enables real-time magnetic stimulation during the printing process. In addition, they developed a fabrication process for highly aligned, mechanically reinforced, cell-laden collagen filaments using a catalyst-free collagen peptide bonding technology. These technologies are regarded as next-generation regenerative strategies capable of simultaneously addressing the long-standing challenges of low mechanical stability and insufficient tissue maturation in cardiac and skeletal muscle regeneration. This research was supported by the Ministry of Science and ICT, the National Research Foundation of Korea, and the Korea Disease Control and Prevention Agency. In recognition of its scientific excellence, the outcomes of this work were published in leading international journals, including *Bioactive Materials (IF = 20; development of a tendon–TBI–bone gradient regeneration platform), **Chemical Engineering Journal (IF = 13; bioprinted cardiac regeneration patch), ***Bioactive Materials (development of magnetic field–based bioprinting technology), and ****Advanced Science (IF = 14.3; fabrication of high-strength aligned collagen filament technology). Title: 3D bioprinted multi-layered cell constructs with gradient core-shell interface for tendon-to-bone tissue regeneration. Bioactive Materials 43 (2025) 471–490 Journal: Bioactive Materials DOI: https://doi.org/10.1016/j.bioactmat.2024.10.002 Pure: https://pure.skku.edu/en/persons/geunhyung-kim/ **Bioprinting of cardiac patches with gold-nanowires and tri-culture system for the treatment of myocardial infarction, Chemical Engineering Journal 526 (2025) 171562 ***In situ magnetic-field-assisted bioprinting process using magnetorheological bioink to obtain engineered muscle constructs, Bioactive Materials 45 (2025) 417–433 ****Catalyst-Free Collagen Filament Crosslinking for Engineering Anisotropic and Mechanically Robust Tissue Scaffolds, Adv. Sci. (2025) e14319 Figure. Schematic of the Gradient 3D Bioprinting Process for Tendon–TBI–Bone Composite Tissue Fabrication and the Corresponding In vitro/In vivo Outcomes
- No. 348
- 2025-12-22
- 9714
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Sungkyunkwan University Introduces Economics into Biofoundry Operations, Accelerating Innovation in Biomanufacturing
On the 3rd of December, SKKU's Biofoundry Research Center, led by Professor Han Min Woo of the Department of Food Science and Biotechnology, has announced that they have established a new biofoundry system that maximizes the efficiency of laboratory automation by incorporating economic principles, thereby accelerating the development of technologies for producing high-value biobased materials. This study has been widely recognized for going beyond simple robot-assisted automation by introducing an evaluation model that quantitatively analyzes cost and time efficiency, thereby opening a new paradigm in the field of biomanufacturing. The research team integrated the Experiment Price Index (EPI), which enables an at-a-glance assessment of experimental economics, with the concept of Robot-Assisted Modules (RAMs) that can be assembled like Lego blocks. While conventional biofoundries have primarily focused on executing experiments using robotic automation, the team mathematically calculated the cost and time of each process to design optimal automation pathways that eliminate unnecessary steps and maximize performance. Using this system, the research team established automated workflows for five core bioprocesses, including gene assembly and microbial genome editing, and applied them to the production of compounds with high industrial value. As a result, the team successfully achieved the rapid development of microbial strains capable of producing cannabigerolic acid (CBGA), a key component of medical cannabinoids, as well as the functional amino acid L-tryptophan. Notably, the system demonstrated markedly superior speed and accuracy compared with manual experimentation, thereby validating its potential for commercialization. In addition, the research team presented a techno-economic analysis of biofoundry operations under large-scale project scenarios. The analysis provides concrete data showing that, with an annual utilization rate of 50–75%, the initial capital investment can be recovered within approximately five years, offering practical evidence to support investment decision-making by both industry and government. This achievement is expected to serve as a foundation for future advancements toward a fully autonomous “self-driving laboratory,” in which artificial intelligence (AI) independently designs and executes experiments based on accumulated operational data. This achievement is expected to serve as a foundation for future advancements toward a fully autonomous “self-driving laboratory,” in which artificial intelligence (AI) independently designs and executes experiments based on accumulated operational data. This achievement is expected to serve as a foundation for future advancements toward a fully autonomous “self-driving laboratory,” in which artificial intelligence (AI) independently designs and executes experiments based on accumulated operational data. Meanwhile, the research findings were published in the December 1 online edition of Trends in Biotechnology, a leading international journal in the field of biotechnology. This work was supported by the National Research Foundation of Korea, the Ministry of Science and ICT, and other supporting organizations, including Daesang Corporation. ※ Title: Techno-economic assessment-guided biofoundry for microbial strain development ※ Journal: Trends in Biotechnology (Published by Cell Press; Impact Factor 14.9; top 1.4%) ※ DOI: https://doi.org/10.1016/j.tibtech.2025.11.002 ※ Authors: Han Min Woo, Professor (Corresponding Author, SKKU), Yu Been Heo, Ph.D. (First Author, SKKU) ※ Pure: https://pure.skku.edu/en/persons/han-min-woo/ ※ Video Overview: https://youtu.be/5Yle6oRfBl0 ▲ Automated workflow optimization development by the Experiment Price Index and Techno-economic assessment of biofoundry
- No. 347
- 2025-12-16
- 6595
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Detecting Unknown Defects in Semiconductor Manufacturing Processes Using Artificial Intelligence
Wafer Bin Map (WBM) is a map that visualizes and shows the defective quality/defect status and defective type information of individual semiconductor chips obtained through electrical test results in the semiconductor manufacturing process. As modern semiconductor manufacturing processes become increasingly miniaturized at the nanoscale, accurately detecting defect patterns appearing in the WBM and rapidly identifying their causes are critical challenges for improving semiconductor yield and quality control. While deep learning technology has enabled attempts at automating defect classification, existing supervised learning-based methodologies have limitations in that they only function for predefined defect types. This leads to problems where new types of “unknown defect patterns” arising from product diversification or process miniaturization are either undetected or misclassified under existing definitions. Furthermore, training the model to recognize new patterns incurs significant inefficiency due to the substantial costs of data labeling and model retraining time. To address this, this study developed an integrated defect detection framework based on active learning that maintains high classification performance for known defect patterns while effectively identifying unknown defect patterns and continuously learning. The developed system consists primarily of two stages: unknown defect detection and classification/learning. First, an anomaly detector based on One-Class Support Vector Machine (SVM) preliminarily determines whether the input WBM is a previously learned known defect pattern or a new type of unknown pattern. If identified as an existing pattern, the classification model precisely classifies the specific defect type. (See Figure 1) Conversely, data classified as unknown patterns are clustered into groups with similar characteristics using the DBSCAN algorithm. This clustered data enables efficient labeling with minimal intervention from process engineers. Through active learning techniques, the classifier updates new pattern information in real-time. This process allows the model to adapt to the constantly changing process environment, maintaining and improving its performance autonomously. (See Figure 2) Experimental results using the WM-811K dataset demonstrated that the developed model maintained high classification accuracy for known defects while effectively filtering out unknown patterns. Furthermore, ‘Eye Defect Patterns,’ which is not present in WM-811K but present in the actual mass production line in Samsung Electronics, was successfully detected and learned these unknown patterns, proving the model's applicability and utility in real industrial settings. This research is significant in that it presents a methodology demonstrating how artificial intelligence models can practically contribute to building intelligent defect management systems for semiconductor processes. The research findings were published in Expert Systems with Applications, which is famous in industrial engineering discipline. Figure 1. Multi-step detection process for unknown defect patterns Figure 2. Process of updating the classifier to an unknown pattern ※ Title: A framework for detecting unknown defect patterns on wafer bin maps using active learning ※ Journal: Expert Systems with Applications ※ DOI: https://www.sciencedirect.com/science/article/pii/S0957417424022450 ※ Pure: https://pure.skku.edu/en/persons/donghee-lee/
- No. 346
- 2025-12-12
- 2630
