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SKKU Research Team Develops Experimentally Validated AI Model to Predict the Virulence of Tomato Yellow Leaf Curl Virus
A CBBL research team led by Professor Balachandran Manavalan from the Department of Integrative Biotechnology at Sungkyunkwan University has developed DeepTYLCV, an accurate and interpretable artificial intelligence model for predicting the virulence of Tomato Yellow Leaf Curl Virus (TYLCV). The study was conducted with co-first authors Dr. Nattanong Bupi, Hariharan Sangaraju, and Duong Thanh Tran, was published in the leading plant science journal Plant Communications (Impact Factor: 11.6; JCR: 6/273; Top 2.2% in the Plant Sciences category). TYLCV is one of the most destructive viral pathogens affecting tomato production worldwide. Severe TYLCV strains can cause leaf curling, yellowing, stunted growth, and major yield losses. In recent years, highly virulent strains have continued to spread across regions and, in some cases, overcome genetic resistance in tomato cultivars. These challenges highlight the urgent need for accurate, early, scalable, and sequence-based disease surveillance. Prof. Manavalan’s team has been working extensively at the interface of biology and artificial intelligence, developing AI-based solutions for peptide therapeutics, prediction of RNA/DNA modifications, protein function analysis, toxicity prediction, plant science, and biomedical applications. In 2023, the team developed IML-TYLCV, the first genome-based TYLCV severity prediction tool, which was published in the high-impact journal Research (IF: 10.9). However, IML-TYLCV was mainly trained on Korean isolates, limiting its applicability to globally diverse TYLCV strains. This challenge motivated the development of DeepTYLCV, a more robust AI framework for predicting TYLCV virulence across global viral isolates. Unlike conventional field diagnosis or image-based AI models, which depend on visible symptoms and can be influenced by environmental conditions, DeepTYLCV uses viral genome-derived sequence information. This enables the model to identify mild and severe strains before symptom-based confirmation and provides a scalable strategy for monitoring emerging viral variants. DeepTYLCV integrates protein language model embeddings with a hybrid architecture that combines a Transformer encoder and a multi-scale convolutional neural network, enabling the model to capture both global sequence patterns and local virulence-associated motifs. By combining deep sequence representations with optimized conventional feature descriptors, DeepTYLCV achieved superior predictive performance compared with the previous IML-TYLCV model. A key strength of this study is its experimental validation. The research team performed blind predictions for 15 TYLCV isolates, including both international reference isolates and Korean field isolates. These predictions were validated using tomato plant infection assays, symptom severity scoring, and viral accumulation analysis. Remarkably, DeepTYLCV achieved 100% concordance between predicted and experimentally observed virulence classes, demonstrating its practical value for identifying emerging severe TYLCV variants. This work provides a powerful example of how AI, viral genomics, and experimental plant pathology can be integrated to support precision agriculture and plant disease management. DeepTYLCV may serve as a valuable tool for early viral surveillance, resistance breeding programs, and rapid assessment of newly emerging TYLCV strains. This research was supported by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT, Republic of Korea (Grant No. RS-2024-00344752). Additional support was provided by the BK21 FOUR Project of the Department of Integrative Biotechnology, Sungkyunkwan University (SKKU), Republic of Korea. Published in plant communications SKKU RESEARCH STORY DeepTYLCV: An interpretable and experimentally validated AI model for predicting virulence of different tomato yellow leaf curl virus strains Access Publication (DOI) BM BALACHANDRAN MANAVALAN PURE Profile→ Figure 1. Overview of the DeepTYLCV framework. The framework involves six key stages: (A) Collection and pre-processing of global TYLCV genomes into open reading frames. (B) Projection and stacking of PLM/NLP embeddings to capture sequence context. (C) A hybrid Transformer encoder and Multi-scale CNN module for learning global and local virulence patterns. (D) Selection of optimal conventional descriptor features. (E) A multi-layer perceptron (MLP) classifier for severity prediction. (F) Deployment of a user-friendly web server. Figure 2. Experimental validation of DeepTYLCV predictions through symptom development and viral quantification in tomato plants. Plants were agro-inoculated with 15 TYLCV infectious clones. (A) Prediction probabilities from the current method, DeepTYLCV, and the previous method, IML-TYLCV. (B) Viral DNA accumulation at 21 days. (C) Symptom severity was monitored for 21 days. (D) Visual symptoms of infected plants at 21 days. (E) PCR detection confirming viral infection.
- No. 375
- 2026-05-15
- 604
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Professor Junghee Bae’s Research Team Identifies Performance Differences in Social Enterprises by Legal Form
The research team led by Professor Junghee Bae from the Department of Social Welfare at Sungkyunkwan University analyzed the full population dataset of certified social enterprises in South Korea to compare the social and economic performance of Work Integration Social Enterprises (WISEs) across different legal forms—nonprofit, for-profit, and cooperative organizations. Work Integration Social Enterprises are organizations that pursue both social and economic goals by providing employment opportunities to vulnerable populations who are marginalized in the labor market, while generating revenue through business activities. These enterprises play a crucial role in promoting social integration and economic self-sufficiency among disadvantaged groups. In South Korea, under the Social Enterprise Promotion Act enacted in 2007, organizations must adopt legally recognized forms—such as corporations, social welfare foundations, nonprofit organizations, or cooperatives—in order to receive official certification as social enterprises. The findings reveal that even among social enterprises with the same objective of creating jobs for vulnerable populations, performance varies significantly depending on legal form. In particular, nonprofit social enterprises were found to employ a larger number of vulnerable individuals and to have a higher proportion of such employees in their workforce, indicating stronger social performance compared to for-profit and cooperative counterparts. Moreover, nonprofit social enterprises demonstrated relatively higher net income by leveraging diverse revenue sources, including government subsidies, private donations, and public-sector market sales. In contrast, for-profit and cooperative social enterprises showed relatively higher working hours and wage levels for vulnerable employees. However, these organizations tended to rely more heavily on private market revenues, which was associated with comparatively lower overall financial performance. This study highlights that the choice of legal form plays a critical role in shaping both social value creation and financial sustainability in social enterprises, providing empirical support for institutional theory, which emphasizes the influence of institutional environments on organizational performance. In particular, amid the recent rapid growth of corporation-type social enterprises in South Korea, the findings suggest the need for balanced ecosystem development and stronger policy support for nonprofit social enterprises, especially in light of their effectiveness in achieving the core mission of employing vulnerable populations. The study was published in the leading international journal in the nonprofit field, Nonprofit and Voluntary Sector Quarterly, Volume 55, Issue 2. Published in nonprofit and voluntary sector quarterly SKKU RESEARCH STORY Work integration social enterprises with different legal forms: Performance comparison between nonprofit, for-profit, and cooperative organizations Access Publication (DOI) JB Junghee Bae Profile →
- No. 374
- 2026-05-15
- 150
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From Common Natural Sweetener to High-Performance Energy Material
Professor Kyungwho Choi’s team (co-first authors: Thien Trung Luu and Bui Minh Quang) of the School of Mechanical Engineering at Sungkyunkwan University, in collaboration with Professor Jinsoo Kim’s team in the Department of Chemical Engineering at Kyung Hee University, proposed a strategy that simultaneously overcomes the limitations of conventional hydrogel-based triboelectric nanogenerators (TENGs) — namely low output performance, poor mechanical strength, and insufficient transparency — by utilizing biomimetic stevia. By incorporating stevia into polyvinyl alcohol (PVA), the abundant hydroxyl groups (-OH) simultaneously reinforced the hydrogen bond-based crosslinking structure and crystalline domains, dramatically improving both mechanical strength and ionic conductivity. As a result, the stevia-PVA hydrogel TENG (S-TENG) demonstrated approximately 2–5 times greater mechanical strength and 3–8 times higher electrical output compared to conventional TENGs based on 2D materials, biomaterials, and transparent materials, while maintaining over 70% visible light transmittance. The tensile strength exceeded 25 MPa (in the hydrated state) with an elongation at break surpassing 510%. Furthermore, the research team demonstrated that the S-TENG maintained stable output (~800 V) through 16,000 contact-separation cycles, and confirmed no degradation in electrical output after 30 days of storage at room temperature. The stevia hydrogel can also be recycled via a water-assisted dissolution and re-gelation process, retaining a high output voltage of approximately 600 V after recycling, thus demonstrating its potential as an eco-friendly material. In addition, the research team attached the S-TENG to various body parts — including the wrist, elbow, knee, finger, and throat — and utilized it as a self-powered sensor for detecting diverse human body motions. The rise time in response to finger bending was as fast as 13 ms, and among eleven machine learning models evaluated for motion classification, the XGBoost algorithm achieved the highest classification accuracy of 95.29%. Professor Kyungwho Choi, the corresponding author, stated: "It is highly significant that we successfully developed a hydrogel electrode derived from biomass-based stevia that simultaneously improves transparency, mechanical performance, and electrical output while also securing recyclability. We plan to continue research on applying this technology to a wide range of fields, including IoT-based wearable devices, rehabilitation monitoring, and intelligent human-machine interfaces." This research was supported by the 4th BK21 Future HRD Education and Research Center for Human-Centered Convergence Mechanical Solution and by the Korea government (MSIT). The results were published in Advanced Materials(IF 26.8, within the top 3% of JCR) in April 2026. In addition, this paper was selected for the inside front cover of Advanced Materials. ▲Schematic diagram of the structure and motion recognition system of a stevia-enhanced PVA hydrogel-based wearable sensor ▲ Selected as the Inside Front Cover article in the journal Advanced Materials Published in Advanced Materials High-Performance Transparent, Deformable, and Recoverable Biomimetic Stevia–PVA Hydrogel Triboelectric Nanogenerator with Machine Learning-Assisted Motion Recognitions Access Publication (DOI) KC Kyungwho Choi PURE Profile →
- No. 373
- 2026-05-11
- 665
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Does Personalized Virtual Try-On Turn Imagination into Reality?
When shopping for clothing online, how confident can we be without actually trying the product on? In digital shopping environments, consumers often experience uncertainty—such as “Will this really suit me?”—due to the inability to physically interact with products. To address this limitation, virtual try-on technology has emerged. More recently, beyond basic virtual fitting, personalized virtual try-on technologies that reflect individual body shapes and styles have been rapidly advancing. Professor Seeun Kim of Sungkyunkwan University, in collaboration with a research team from Oklahoma State University, conducted an empirical study examining the impact of personalized virtual try-on on consumer decision-making. The research focused particularly on how easily consumers can imagine products and how this process contributes to psychological confidence in purchase decisions. The findings revealed that personalized virtual try-on significantly enhances product imagination. By viewing virtual images that closely resemble their own bodies, consumers are able to vividly imagine themselves wearing the product. This imagination extends beyond a simple cognitive process and directly influences decision-making. The more vividly consumers can imagine a product, the more comfortable and confident they feel about their choices. This suggests that virtual try-on reduces uncertainty—such as “Will this product suit me?”—and facilitates more stable decision-making. Interestingly, these effects were not uniform across all consumers. The study identified spatial processing perception—an individual cognitive trait—as a key moderating factor. The effects of virtual try-on were stronger among consumers with lower spatial processing ability. This can be explained by the fact that individuals who have difficulty mentally visualizing products rely more heavily on the visual information provided by virtual try-on. In contrast, consumers with higher spatial processing ability can already imagine products effectively, making the additional benefits of virtual try-on relatively limited. In other words, personalized virtual try-on is not merely a “better technology,” but rather a technology that is more beneficial for certain consumers. This study is meaningful in that it uncovers the underlying mechanism through which virtual try-on goes beyond visual experience to influence consumers’ psychological decision-making processes. By identifying how product imagination translates into decision comfort, the research provides important implications for designing consumer experiences in online shopping environments. Looking ahead, fashion and e-commerce companies should move beyond simply adopting new technologies and instead develop personalized strategies that align with consumers’ cognitive characteristics to deliver more effective shopping experiences. The study has been published in the internationally recognized SSCI journal Journal of Research in Interactive Marketing. Published in Journal of Research in Interactive Marketing SKKU RESEARCH STORY Unveiling product imagination and decision comfort through personalized virtual try-on: the moderating role of spatial processing perception Available Access Publication (DOI) SK Seeun Kim SKKU Professor →
- No. 372
- 2026-05-07
- 763
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Revolutionizing Clinical Trials with Machine Learning
Professor Yeonhee Park of the Department of Statistics at Sungkyunkwan University has developed a novel statistical framework — MARGO (Machine Learning-Assisted Adaptive Randomization for Group Sequential Trials Based on Overlap Weights) — that makes machine learning practically applicable in clinical trial design. This work provides the first rigorous solution to the fundamental statistical challenges that arise when integrating ML/AI-driven decision-making into the scientifically demanding environment of clinical trials. The Promise and the Barrier: Why ML/AI Alone Is Not Enough Machine learning and artificial intelligence have garnered widespread attention as transformative tools for personalized treatment assignment in clinical trials. In particular, adaptive randomization — which dynamically adjusts treatment allocation based on accumulating trial data — is a promising approach for improving patient outcomes by steering more participants toward more effective treatments. However, applying this approach in practice can introduce a critical statistical problem. When patient characteristics (e.g., biomarkers) are used to guide treatment assignment, systematic imbalances can emerge between treatment groups. This covariate imbalance leads to biased treatment effect estimates and an inflated type I error rate, risking false conclusions. The problem is further compounded in group sequential designs, which include planned interim analyses for early stopping decisions. Machine Learning Meets Causal Inference: A Two-in-One Solution To address this fundamental challenge, MARGO integrates machine learning-based predictive models with overlap weights (OW), a propensity score–based approach widely used in causal inference to adjust for covariate imbalance. MARGO uses patient covariate information to predict the probability of treatment success via machine learning, then uses these predictions to preferentially assign patients to the more effective treatment. Simultaneously, OW corrects covariate imbalance across treatment groups, effectively controlling the bias and type I error inflation induced by adaptive randomization. The framework was evaluated using four machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Multi-Layer Perceptron (MLP). Rigorously Validated Performance Through extensive simulation studies, MARGO demonstrated superior performance over conventional fixed randomization and existing adaptive randomization methods across three key dimensions. First, MARGO allocated a greater proportion of patients to the more effective treatment. Second, it maintained the overall type I error rate below the target threshold of 0.05 — even in scenarios where conventional methods inflated the error rate to as high as 0.08–0.18. Third, it preserved high statistical power under alternative scenarios while reducing the number of treatment failures. Together, these results demonstrate that MARGO can simultaneously improve the ethical standards and scientific integrity of clinical trials. Beyond "Using AI" — Toward "Trusting AI in Clinical Trials" The most important contribution of this research goes beyond simply applying machine learning to clinical trials — it rigorously resolves the fundamental statistical problems that emerge in that process. MARGO is designed to accommodate a wide range of AI models and holds broad potential for extension to precision medicine and data-driven decision-making across diverse fields. This study was published in Statistics in Medicine in 2025. Published in Statistics in Medicine (2025) SKKU RESEARCH STORY MARGO: Machine Learning-Assisted Adaptive Randomization for Group Sequential Trials Based on Overlap Weights Access Publication (DOI) YP Yeonhee Park SKKU Professor → Figure 1. Design Framework for Adaptive Randomization with Interim Analyses Figure 2. Simulation results: Controlled Type I Error Rates Under the Nominal Level
- No. 371
- 2026-05-07
- 425
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SKKU Revolutionizes Battery Manufacturing with Density Dry Electrode Technology; Targets Foundry Commercialization
A research team led by Professor Young-Jun Kim at the Sungkyunkwan Advanced Institute of Nano Technology (SAINT) of SKKU has announced a breakthrough in "Dry Electrode" technology-a next-generation manufacturing process for batteries. The team has secured original technology for electrodes with world-class energy density by developing materials optimized for solvent-free electrode processing, a feat expected to shift the paradigm of battery production. Dry electrode technology eliminates the use of toxic liquid solvents in the production of lithium-ion and all-solid-state batteries. By directly compacting solid raw materials into electrode films, the process removes the need for energy-intensive drying stages, making it both eco-friendly and highly cost-efficient. This field is currently a focal point for global industry leaders, including Tesla, as companies race to secure dominance in the future battery market. To overcome the chronic challenges of dry processing-specifically the difficulty of uniform mixing and large-scale production-Professor Kim’s team developed a "One-body" material that integrates active materials (for energy storage) and conductive agents (for electron conduction) into a single architecture. This innovation enables the mass production of high-quality, high-loading electrodes. The technical reliability and performance of this method were rigorously validated through collaborative simulations with Professor Yong-Min Lee’s team at Yonsei University. "Dry electrode technology is more than just an eco-friendly process; it is the ultimate solution to dramatically enhancing battery performance, quality, and safety," said Professor Young-Jun Kim. "The specialized materials and production techniques we've developed will serve as a critical stepping stone to significantly reducing manufacturing costs while ensuring global competitiveness in battery performance.“ Beyond academic achievement, the team is aggressively pursuing commercialization. Through Corenergy Solution, a laboratory-backed startup, the team plans to launch a "Battery Electrode Foundry" business specializing in dry electrode manufacturing. In collaboration with other SKKU faculty members with extensive industry experience at South Korean battery giants such as Samsung SDI and LG Energy Solution, the venture aims to advance dry electrode design and cell manufacturing technologies to strengthen the domestic battery ecosystem. This research was supported by the Nano-Material Technology Development Program through the National Research Foundation of Korea (NRF). The team's findings on dry cathode technology were published in Joule (IF 35.4), a premier global journal in the energy field, while their research on anodes was featured in the online edition of Carbon Energy (IF 24.2), gaining worldwide academic recognition. SKKU RESEARCH STORY A continuous carbon nanotube sheath enables ultrahigh energy density and fast charging in dry-processed thick electrodes Joule (DOI) Dry-Processed Graphite Electrodes Enabling Ultra-High Areal Capacity and Stable Fast-Charging Performance Carbon Energy (DOI) YK Young Jun Kim PURE Profile → ▲ Overview of Dry Electrode Manufacturing Process and Breakthrough Material Technologies for Battery Innovation
- No. 370
- 2026-04-28
- 909
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Prof. Eun Ryung Lee Receives the 2025 Korea Statistical Researcher of the Year for Her Annals of Statistics Paper
Professor Eun Ryung Lee of the Department of Statistics at Sungkyunkwan University received the 2025 2nd Korea Statistical Researcher of the Year in recognition of her paper “Efficient Functional Lasso Kernel Smoothing for High-Dimensional Additive Regression”, which was published in Annals of Statistics in August 2024. The award ceremony was held on August 28, 2025, at the 14th National Statistics Development Forum in Seoul. This award, presented by Statistics Korea, honors outstanding researchers who have made significant contributions to the development of statistics. The paper addresses a fundamental challenge in modern data analysis: how to identify truly important variables and accurately estimate their nonlinear effects when the number of variables is much larger than the sample size. In ultra-high-dimensional settings, it is difficult to achieve variable selection, flexible modeling, computational feasibility, and statistical inference at the same time. This study provides a new solution to that problem. Professor Lee and her collaborators developed a new kernel-based methodology that combines functional Lasso with smooth backfitting. The proposed method can automatically select important variables while flexibly estimating their effects through nonlinear functions, and it is supported by both computationally efficient algorithms and rigorous theoretical analysis. In addition, the study introduces a debiased inference procedure, making it possible not only to improve prediction accuracy but also to construct confidence intervals and conduct hypothesis testing. The proposed method was further applied to large-scale gene expression and anticancer drug response data from cancer cell lines, where it showed strong empirical performance and successfully identified biologically meaningful genes associated with drug response. The study is expected to have broad impact in bioinformatics, precision medicine, finance, environmental science, and other fields where high-dimensional data are increasingly common. This award recognizes both the originality and the practical importance of Professor Lee’s contribution to modern statistical methodology. Published in Annals of Statistics SKKU RESEARCH STORY Efficient functional Lasso kernel smoothing for high-dimensional additive regression Annals of Publication (DOI) EL Eun Ryung Lee Profile→ ▲ A three-stage graphical abstract — "input → method → output" summarizing the core idea of fLasso-SBF Left (Input) A grid of scatter plots for many candidate covariates, with the total number allowed to be much larger than the sample size. Most covariates (grey) carry essentially no information about the response and behave like pure noise, while only the three highlighted ones (red, green, blue) are truly active with genuine nonlinear effects. This depicts the high-dimensional sparse additive regression setting that motivates the paper. Middle (Method) The proposed fLasso-SBF method minimizes an objective that combines kernel-based smooth backfitting with a functional Lasso penalty. Its solution is obtained by iteratively applying a simple "soft-threshold + projection" update, which adds only a single thresholding step to the standard smooth backfitting algorithm and keeps both the implementation and the theoretical analysis clean. Right (Output) Overlay of the estimated component functions produced by fLasso-SBF. Only the three genuinely active components are recovered as smooth curves, while the estimates for the remaining inactive covariates are automatically shrunk to zero. Variable selection and nonparametric function estimation are thus carried out simultaneously in a single procedure, and the accompanying debiased version further enables pointwise confidence intervals and hypothesis testing.
- No. 369
- 2026-04-28
- 536
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Photoactivatable Oligoelectrolytes Engendering Pyroptotic Vesicles
A research team led by Professor Jin Yong Lee from the Department of Chemistry (Co-first author: HyoungChul Ham), in collaboration with research teams from Korea University and the National University of Singapore, has developed a next-generation phototherapeutic agent, ‘NDI-COE’. This agent induces pyroptosis (inflammatory cell death) in hypoxic tumor tissues by directly oxidizing intracellular water. Designed to overcome the oxygen-dependency limits of conventional photodynamic therapy by intercalating into the cell membrane to oxidize water, this research was published in the Journal of the American Chemical Society (IF: 15.7) on January 28, 2026 Notably, Professor Lee's team elucidated the superior photochemical mechanism of NDI-COE at the molecular level based on Density Functional Theory (DFT) calculations. Non-covalent interaction (NCI) analysis revealed that NDI-COE forms double hydrogen bonds with water molecules, stably trapping them with a much stronger binding energy (-5.21 kcal/mol) compared to the control group. Furthermore, electronic structure analysis demonstrated that the spin-orbit coupling (SOC) efficiency for the transition to the highly reactive triplet state (T1) upon light irradiation was approximately 7.5 times higher (2.87 cm-1) than the control, proving its outstanding photochemical reactivity. Moreover, redox potential calculations verified that the excited-state oxidation potential of NDI-COE (-0.39 V) provides the thermodynamic conditions to promote spontaneous electron transfer to oxygen molecules, thereby generating reactive oxygen species highly efficiently. The properties of this novel material, quantitatively characterized through these DFT calculations, are expected to serve as a crucial theoretical foundation for designing innovative and precise next-generation phototherapeutics capable of overcoming hypoxic tumor environments. ▲(A) Non-Covalent Interaction (NCI) analysis of NDI-COE. Blue indicates hydrogen bond, green indicates van der Waals (vdW) interaction, and red indicates steric repulsion, respectively. (B) Bond length and binding site between NDI-COE and water. (C) Calculated Raman spectral comparison of NDI-COE in the presence of water. (D) NCI analysis of Ben-COE. (E) Bond length and binding site between Ben-COE and water. (F) Calculated Raman spectral comparison of Ben-COE in the presence of water. Energy level diagram and photophysical processes of (G) Ben-COE and (H) NDI-COE. In 3G, S0 : neutral molecule in the singlet ground state D0 : ground state doublet of the anion formed after accepting one electron. AEA: adiabatic electron affinity. (I) Ground state and excited state redox potentials (V vs NHE) were calculated by DFT using the polarizable continuum model (PCM) with water as a solvent. (J) Ground state and excited state redox potentials (Eₒₓ, Eᵣₑd, Eₒₓ*, Eᵣₑd*; V vs NHE) and Frontier orbital energies (HOMO, LUMO; eV) of NDI-COE and Ben-COE, calculated by DFT using PCM with water as a solvent. Published in journal of the american chemical society SKKU RESEARCH STORY Photoactivatable Oligoelectrolytes Engendering Pyroptotic Vesicles Access Publication (DOI) JL Jinyong Lee PURE Profile →
- No. 368
- 2026-04-24
- 903
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SKKU Publishes Report on Technological Advances and Research Trends in Perovskite Solar Cells
Sungkyunkwan University announced the publication of a comprehensive report analyzing technological advancements and global research trends in perovskite solar cells (PSCs), which are emerging as a next-generation solar energy technology. The report was jointly prepared by the research group of Professor Nam-Gyu Park from the Department of Chemical Engineering at SKKU and the university’s Performance Analysis Team, in collaboration with global academic data company Clarivate. It covers the fundamental concepts and technological evolution of perovskite solar cells, as well as the global research landscape and key trends in the field. The report highlights a major turning point in 2012, when researchers at SKKU successfully developed the world’s first solid-state perovskite solar cell. Since then, the technology has achieved rapid efficiency improvements over a short period and has emerged as a promising next-generation solar technology. It is now considered a key solution with the potential to complement or even replace conventional silicon-based solar cells. Based on data from Web of Science, the report also analyzes research performance by country, institution, and researcher. The findings show that the field of perovskite solar cells has grown rapidly since 2012, with China, the United States, and South Korea leading global research efforts. Among them, Sungkyunkwan University demonstrates world-class research competitiveness, ranking highly in key indicators such as publication output, citation impact, and the proportion of top 1% highly cited papers, establishing itself as a major research hub in the field. The report further emphasizes that perovskite solar cells are a critical technology from both carbon neutrality and ESG perspectives, owing to their high efficiency, low-cost fabrication processes, and lightweight and flexible properties. In particular, advancements in tandem solar cell structures that surpass the efficiency limits of conventional solar cells are expected to further accelerate their commercialization potential. Professor Nam-Gyu Park stated, “As future energy systems are expected to be largely electrified, the importance of electrification technologies continues to grow. Solar energy, which produces no carbon emissions, is anticipated to play a central role in the future energy landscape. In this context, low-cost, high-efficiency perovskite solar cells are expected to serve as a key technology in the next-generation energy society. Sungkyunkwan University has played a pivotal role from the early conceptual development to technological advancements in this field, and will continue to lead global research efforts.” Meanwhile, Clarivate and Sungkyunkwan University plan to co-host a global webinar in June to highlight research trends in perovskite solar cells. Published with Clarivate SKKU RESEARCH STORY Perovskite Solar Cells: A Roadmap for Advancing Future Energy Innovation Report Download NP Nam-Gyu Park Profile → → ▲ Diverse device architecture and efficiency progress in PSCs
- No. 367
- 2026-04-20
- 1109
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SKKU Researchers Develop Breakthrough “Gas Battery” Technology that Generates Electricity from Greenhouse Gases
Professor Ji-Soo Jang from the Department of Nanoengineering, in collaboration with Professor Taekwang Yoon of Ajou University and Professor Hansel Kim of Chungbuk National University, has developed a novel energy device that generates electricity during the process of capturing greenhouse gases. The research team introduced a new concept device termed the Gas Capture and Electricity Generator (GCEG), which produces electrical power as greenhouse gases are adsorbed from the atmosphere. This innovation goes beyond conventional approaches that merely capture greenhouse gases, transforming them into a usable energy resource. Amid growing global efforts to address climate change, carbon capture, utilization, and storage (CCUS) technologies have gained attention. However, existing CCUS systems typically require substantial energy input for gas collection and processing. To overcome this limitation, the research team proposed a fundamentally new mechanism that directly converts the physicochemical energy generated during gas adsorption on electrode surfaces into electrical energy. The developed GCEG device consists of an asymmetric structure combining carbon-based electrodes with hydrogel materials. When greenhouse gases such as nitrogen oxides (NOx) or carbon dioxide (CO₂) are adsorbed, charge redistribution and ion migration occur within the device, enabling continuous direct current (DC) power generation without any external power source. In essence, atmospheric pollutants act as the “fuel” for electricity generation, simultaneously purifying the environment while supplying energy. This technology is expected to be widely applicable in self-powered smart environmental sensors, battery-free IoT systems, and industrial facilities where large volumes of emissions are generated. In such settings, it could enable simultaneous energy harvesting and carbon reduction. In particular, its integration into distributed energy systems is anticipated to accelerate the realization of carbon neutrality. Professor Ji-Soo Jang stated, “This research demonstrates that greenhouse gases are not merely pollutants to be managed, but can serve as a new energy resource. We aim to further develop this technology into an environmental platform that not only achieves carbon neutrality but also generates energy.” The research findings were published in Energy & Environmental Science (Impact Factor: 31.0), one of the world’s leading journals in materials science, and were selected as a Front Cover article in recognition of their excellence and originality. Published in energy & environmental science SKKU RESEARCH STORY Electrical power generation from asymmetric greenhouse gas capture Access Publication (DOI) JJ Ji-Soo Jang Profile → ▲ Cover image of the paper on the greenhouse gas adsorption-based power generation device, “Gas Capture and Electricity Generator (GCEG)” ▲ Schematic illustration explaining the working principle of the greenhouse gas adsorption-based power generation device, “Gas Capture and Electricity Generator (GCEG)”
- No. 366
- 2026-04-16
- 1653
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Roll-to-roll manufacturing marks a major step toward commercialization of flat optics
A collaborative research group led by Professor Gyoujin Cho and Professor Inki Kim of Department of Biophysics at Sungkyunkwan University, in partnership with Professor Junsuk Rho of the Department of Mechanical Engineering at POSTECH, has developed a fully automated roll-to-roll manufacturing platform capable of producing large-area visible metalenses at a rate of 300 units per second, marking a major breakthrough in translating metasurface technology from the laboratory to real-world industrial deployment. A new study published in Nature, entitled “300-unit-per-second roll-to-roll manufacturing of visible metalenses,” addresses long-standing technical barriers that have hindered the commercialization of metalenses. Metalenses are flat, ultrathin optical components composed of two-dimensional arrays of subwavelength nanostructures that precisely control light, offering a compact and lightweight alternative to conventional bulky lenses. By engineering the phase, amplitude and polarization of light at the nanoscale, they enable advanced optical functions in an exceptionally thin form factor, making them a promising candidate for the next revolution in optical technology. However, despite their enormous potential, the industrial adoption of metalenses has been constrained by a major manufacturing challenge: how to produce high-performance devices over large areas, at high speed and low cost, while maintaining the uniformity and yield required for practical industrial applications. To overcome this limitation, the research team first produced 12-inch silicon master stamp by replicating a single pattern originally fabricated by electron beam lithography through deep-ultraviolet ArF photolithography. The team then employed a custom-built roll-to-roll nanoimprint lithography system that operates in a fully automated manner at the 12-inch scale and achieves feature resolution down to 80 nm with a remarkable imprinting speed of one 12-inch mould every 1.5 seconds equivalent to a lens throughput of 300 metalenses per second, each with a diameter of 1 cm. This throughput is approximately two orders of magnitude higher than that of conventional nanoimprint lithography systems. To enable scalable and low-cost manufacturing, a flexible polymer replica mould was developed by directly replicating a master stamp onto a polyethylene terephthalate (PET) backplane foil using an ultraviolet (UV)-curable resin. This approach eliminates the need for the conventional electroformed nickel shim - a major bottleneck in roll-to-roll manufacturing, thereby substantially reduces production cost and manufacturing time by allowing many flexible moulds to be rapidly produced from a single master wafer. The replicated metalenses are subsequently coated with a high-index titanium dioxide (TiO₂) layer via atomic layer deposition (ALD), a well-established and scalable industrial process, to further enhance optical performance. For the first time, the team demonstrated the mass production of 200-metre-long metalens arrays with high yield and consistent functionality across a large area, validating the effectiveness of the roll-to-roll nanoimprint lithography approach for the scalable manufacture of high-quality metasurfaces. This study represents a significant advance in the scalable and sustainable production of metalenses, paving the way for the commercialization of metaphotonic devices. According to Professor Kim: “Metasurfaces have long been seen as a powerful platform for next-generation optics, but scalable manufacturing has remained a major bottleneck. By demonstrating roll-to-roll production of visible metalenses at 300 units per second, we show that mass production is possible. The throughput can be scaled even further by increasing the size of the imprint rollers and optimizing the mould design. Moreover, if our roll-to-roll technology is also applied to mould manufacturing, the cost of each metalens could become almost negligible. We believe this work offers a compelling route for translating metasurface optics into everyday technologies.” The team expects that the approach could support broader industrial efforts to integrate metasurface optics into next-generation imaging, display, sensing and consumer electronic devices. Published in Nature (2026) SKKU RESEARCH STORY 300-unit-per-second roll-to-roll manufacturing of visible metalenses Access Publication (DOI) IK Inki Kim SKKU Professor → GC Gyoujin Cho SKKU Professor → ▲Figure 1. Mass production process of metalens based on roll-to-roll nanoimprinting (a) Fabrication of polymer molds and roll-to-roll operation utilizing them (b) Photograph of the roll-to-roll nanoimprinting facility independently designed by the research team ▲ Figure 2. Fabricated large-area metalens array (a) Photograph of a metalens array film fabricated via a 200m-long continuous process (b) Photograph of a fabricated 12-inch wafer-sized metalens array (c) Image of metalens nanostructures captured via SEM
- No. 365
- 2026-04-16
- 2864
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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
- 1635
