Department of Energy Systems Research

Leading super-convergence renewable energy research and education to achieve carbon-neutrality.

Department of Energy Systems Research

Leading super-convergence renewable energy research and education to achieve carbon-neutrality.

4th BK21 Project News

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#Ajou University #2026 #Times Higher Education #세계대학평가

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2026 THE World University Rankings: Top 400 for the First Time, 8th Among Domestic Comprehensive Universities

Ajou University has entered the top 400 for the first time in the 2026 rankings published by Times Higher Education (THE), the British university-ranking organization. Among comprehensive universities in South Korea, Ajou achieved 8th place, up one notch from 9th last year. This year’s global university evaluation covered 2,191 institutions across 115 countries and assessed based on using 17 indicators from five categories: Teaching, Research Environment, Research Quality, International Outlook, and Industry. The THE ranking is known to place more emphasis on research and teaching conditions rather than just traditional reputation. Ajou University previously entered the top 500 in both 2024 and 2025. With this year’s leap into the top 400, the university has demonstrated its consistent advancement. In the Research Quality category Ajou’s score rose by 4.5 points to 58.4, and recorded a substantial 11.6 point increase to 59.4 in the International Outlook category — both contributing to the rise in ranking.

#AJOU #metal nanoparticles #Electrical Engineering #Department of Intelligent Semiconductor Engineering

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Prof. Sungjun Park's team proposes a strategy for large-scale production of high-purity metal nanoparticles using light

A research team from Ajou University has proposed a method to mass-produce high-purity metal nanoparticles through a light-assisted nanomaterial synthesis technique. This breakthrough is expected to be widely applicable in next-generation artificial sensory systems and neuromorphic devices that mimic the human brain, where precise sensing capabilities are essential. Led by Professor Sungjun Park from the Department of Electrical and Computer Engineering and the Department of Intelligent Semiconductor Engineering, the team introduced a novel strategy for producing high-purity metal nanoparticles without chemical reactions, using a light-based laser ablation in liquid (LAL) technique. This approach allows for the synthesis of nanoparticles free of organic residues on a large scale. The research findings were published in July in a review article titled “Scalable metal-based nanoparticle synthesis via laser ablation in liquids for transformative sensory and synaptic devices” in the prestigious journal International Journal of Extreme Manufacturing (Impact Factor 21.3, top 0.7% in JCR in manufacturing and process engineering). The study was led by Dr. Junkyu Choi from Ajou University’s Institute of Information & Electronics Research, with Sukhyeon Baek, a Ph.D. student in the Department of Intelligent Semiconductor Engineering, as a co-author. Junghoon Lee, a Ph.D. candidate in the same department and currently working at Samsung Electronics DS Division's Materials Technology Team, also participated as a co-corresponding author, along with Professor Sungjun Park. Artificial sensory systems are technologies that mimic the human five senses, converting external stimuli into electrical signals. These technologies are rapidly gaining attention as essential components in various fields, including: the metaverse, extended reality (XR), wearable medical devices, human-machine interfaces. There is increasing demand for applying lighter and more flexible materials, instead of bulky hardware, to real-life applications. As a result, research into next-generation artificial sensory systems is accelerating. In such devices, metal nanoparticles play a crucial role in maximizing sensor performance, thanks to their tunable electrical, optical, and chemical properties. In sensory applications, they enhance sensitivity and response speed to external stimuli while maintaining high selectivity and stability in complex signal environments. In neuromorphic devices, metal nanoparticles enable precise control of synaptic responses and plasticity, serving as key components in implementing biomimetic learning functions. These properties greatly contribute to improving the precision and efficiency of next-generation artificial sensory systems. However, conventional nanoparticle synthesis methods rely on physical techniques requiring high-temperature vacuum equipment, or wet-chemical reactions using surfactants and reducing agents. These approaches involve complex processes and often leave organic residues on the particles, which can degrade electrical properties and reduce sensor reliability. Moreover, they face significant limitations in large-area fabrication and mass production, posing challenges for commercialization. To address these issues, the Ajou University team focused on "Laser Ablation in Liquids (LAL)", a non-contact, physical-based synthesis method that enables the production of high-purity metal nanoparticles without chemical reactions. Unlike traditional physical deposition methods that yield only a few hundred milligrams per hour, the proposed LAL technology can achieve production rates of over 8 grams per hour, significantly boosting its potential for real-world industrial applications. Laser ablation in liquids process (left) and actual formation of nanoparticles (right) The review paper also holds significant value in that it expands the application potential of Laser Ablation in Liquids (LAL)-based metal nanoparticles beyond their conventional use in catalysis and electrochemistry, extending it to the broader field of the electronics industry. In particular, the study demonstrates that applying high-purity nanoparticles synthesized via LAL to next-generation artificial sensory systems—such as electronic skin (e-skin), neuromorphic devices, and wearable electronics, where precise detection of external stimuli is critical—enables both long-term stability and high sensitivity.This work clearly highlights the potential for expansion into various smart electronic applications, including: human–machine interaction, AI-based robotics, medical monitoring technologies. Through this, the research offers a new direction for the electronics industry as a whole. Professor Sungjun Park stated, “This achievement goes beyond introducing a new technique—it represents a turning point for ‘light-based nanomanufacturing technologies’ that could drive the commercialization of next-generation smart sensors and neuromorphic systems. It marks a major milestone for the materials and sensor industries, both domestically and internationally.” He further emphasized, “To realize commercial applications, follow-up studies on long-term storage stability and synthesis optimization of LAL-based metal nanoparticles are essential. A strategic roadmap is needed to bridge foundational nanotechnology and real-world industrial deployment.” This research was supported by the National Research Foundation of Korea (NRF) through the Nano·Materials Technology Development Program, including the Global Young Connect Project and the Nano Future Materials Core Technology Development Project.

#Ajou University #Department of Physics #Department of Energy Systems Engineering #Cryptography and AI/Machine Learning

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Prof. Hyungwoo Lee’s Team develops new Novel Core Technology

A joint research team led by Professor Hyungwoo Lee from Ajou University has successfully developed a new foundational technology for a random number generation (RNG) device, offering a novel approach with high potential to advance cryptographic security and artificial intelligence (AI) computing technologies. Professor Lee (Department of Physics and Graduate School of Energy Systems), in collaboration with researchers from several institutions, has proposed a new type of two-level quantum system (TLQS) that minimizes interference from external variables by utilizing discrete fluctuations in tunneling current. This advancement introduces a more stable and secure method for generating physical random numbers. The study, titled "Highly stable two-level current fluctuation in complex oxide heterostructures," was published in Nature Communications in July. Key contributors included Do-Yeob Kim (graduate student, Ajou University) and Professor Jung-Woo Lee (Hongik University), who served as co-first authors. Professors Hyungwoo Lee (Ajou University), Ki-Tae Um (Gachon University, Semiconductor Engineering), and Sunwoo Lee (Inha University, Computer Engineering) led the research as corresponding authors. International collaborators included Professor Tula R. Paudel’s team at the South Dakota School of Mines & Technology and Professor Yongsoo Yang’s group at KAIST. The researchers engineered a highly stable two-level current fluctuation phenomenon using a complex oxide heterostructure composed of SrRuO₃/LaAlO₃/Nb-doped SrTiO₃ (SRO/LAO/Nb:STO). Based on this structure, they developed a physical entropy source suitable for high-quality RNG. Random numbers—sequences generated unpredictably within a defined range—are critical in a wide range of applications, including encryption, cybersecurity, simulations, and gaming. In the context of AI and machine learning, random data is essential for efficient model training, especially in large-scale datasets. Unlike pseudo-random number generators (PRNGs) based on deterministic software algorithms, physical random number generators (PRNGs) leverage inherent randomness in nature, offering true unpredictability and resistance to hacking. This makes them highly valuable for secure computing and is especially crucial for neuromorphic systems, which aim to mimic the human brain’s structure through hardware-based neural networks. Conventional TLQS systems—typically based on Random Telegraph Noise (RTN) caused by charge trapping at defect sites—have struggled with instability due to environmental dependencies. To overcome this, the team intentionally introduced a dual-defect system comprising oxygen vacancies (V_O) and antisite Ti defects (Ti_Al). Their interaction induces discrete fluctuations in tunneling current that are minimally affected by external conditions. The TLQS device demonstrated stable two-level current fluctuations lasting over 169 seconds at room temperature, with operational stability maintained for over a year. Furthermore, the team validated the RNG functionality of their TLQS by converting analog current signals into binary random sequences (0s and 1s) and conducting rigorous randomness evaluations. Results confirmed the TLQS’s ability to generate high-quality random data. To test its practical applicability, the team integrated the TLQS-based random number data into a Very Deep Super-Resolution (VDSR) neural network—a model designed to restore blurry images with high clarity. The TLQS-based RNG data outperformed traditional software-based RNGs (e.g., Numpy Random Generator in Python) in both accuracy and training speed. Randomness is crucial in AI training, as it helps shuffle datasets and initialize neural network parameters, promoting diverse and robust learning. If the randomness is predictable or biased, it can lead to skewed AI behavior—underscoring the value of true, hardware-based RNG. Professor Lee emphasized the practical potential of the device, stating, “The TLQS device we developed is compatible with CMOS semiconductor technology, which is widely used in computers and smartphones. Considering its scalability, this represents a highly practical and foundational technology for RNG.” He added, “This research proposes RNG design at the level of fundamental materials science, with strong implications for future applications in hardware-based cryptographic security and AI computation.” This study was supported by the National Research Foundation of Korea (NRF) through the G-LAMP project, the Mid-Career Research Program, and the Basic Research Laboratory (BRL) program. The image below shows the results of applying different types of random number data to image super-resolution training using a Very Deep Super-Resolution (VDSR) neural network. The leftmost image shows the result without using any random number data, the center image uses pseudo-random numbers generated by Python’s NumPy library, and the rightmost image uses random numbers generated by the newly developed TLQS system. Image above – A conceptual diagram of the two-level quantum system (TLQS) developed by the joint research team, along with a schematic of the implemented material structure. Compared to conventional methods, the TLQS maintains a more stable signal while achieving comparable accuracy and training speed.