Kaist

KAIST Presents Game-Changing Technology for Intrac..

  < (From left)Professor Kyung Cheol Choi, Professor Hyunjoo J. Lee, Dr. Somin Lee from the School of Electrical Engineering >   Optogenetics is a technique that controls neural activity by stimulating neurons expressing light-sensitive proteins with specific wavelengths of light. It has opened new possibilities for identifying causes of brain disorders and developing treatments for intractable neurological diseases. Because this technology requires precise stimulation inside the human brain with minimal damage to soft brain tissue, it must be integrated into a neural probe—a medical device implanted in the brain. KAIST researchers have now proposed a new paradigm for neural probes by integrating micro OLEDs into thin, flexible, implantable medical devices.   KAIST (President Kwang Hyung Lee) announced on the 6th of July that professor Kyung Cheol Choi and professor Hyunjoo J. Lee from the School of Electrical Engineering have jointly succeeded in developing an optogenetic neural probe integrated with flexible micro OLEDs.   Optical fibers have been used for decades in optogenetic research to deliver light to deep brain regions from external light sources. Recently, research has focused on flexible optical fibers and ultra-miniaturized neural probes that integrate light sources for single-neuron stimulation.   The research team focused on micro OLEDs due to their high spatial resolution and flexibility, which allow for precise light delivery to small areas of neurons. This enables detailed brain circuit analysis while minimizing side effects and avoiding restrictions on animal movement. Moreover, micro OLEDs offer precise control of light wavelengths and support multi-site stimulation, making them suitable for studying complex brain functions.   However, the device's electrical properties degrade easily in the presence of moisture or water, which limited their use as implantable bioelectronics. Furthermore, optimizing the high-resolution integration process on thin, flexible probes remained a challenge.   To address this, the team enhanced the operational reliability of OLEDs in moist, oxygen-rich environments and minimized tissue damage during implantation. They patterned an ultrathin, flexible encapsulation layer＊ composed of aluminum oxide and parylene-C (Al₂O₃/parylene-C) at widths of 260–600 micrometers (μm) to maintain biocompatibility. ＊Encapsulation layer: A barrier that completely blocks oxygen and water molecules from the external environment, ensuring the longevity and reliability of the device.   When integrating the high-resolution micro OLEDs, the researchers also used parylene-C, the same biocompatible material as the encapsulation layer, to maintain flexibility and safety. To eliminate electrical interference between adjacent OLED pixels and spatially separate them, they introduced a pixel define layer (PDL), enabling the independent operation of eight micro OLEDs.   Furthermore, they precisely controlled the residual stress and thickness in the multilayer film structure of the device, ensuring its flexibility even in biological environments. This optimization allowed for probe insertion without bending or external shuttles or needles, minimizing mechanical stress during implantation.       < s-Conceptual diagram of a flexible neural probe for integrated optogenetics (Micro-OLED) >   As a result, the team developed a flexible neural probe with integrated micro OLEDs capable of emitting more than one milliwatt per square millimeter (mW/mm²) at 470 nanometers (nm), the optimal wavelength for activating channelrhodopsin-2. This is a significantly high light output for optogenetics and biomedical stimulation applications.   The ultrathin flexible encapsulation layer exhibited a low water vapor transmission rate of 2.66×10⁻⁵ g/m²/day, allowing the device to maintain functionality for over 10 years. The parylene-C-based barrier also demonstrated excellent performance in biological environments, successfully enabling the independent operation of the integrated OLEDs without electrical interference or bending issues.   Dr. Somin Lee, the lead author from Professor Choi’s lab, stated, “We focused on fine-tuning the integration process of highly flexible, high-resolution micro OLEDs onto thin flexible probes, enhancing their biocompatibility and application potential. This is the first reported development of such flexible OLEDs in a probe format and presents a new paradigm for using flexible OLEDs as implantable medical devices for monitoring and therapy.”   This study, with Dr. Somin Lee as the first author, was published online on March 26 in Advanced Functional Materials (IF 18.5), a leading international journal in the field of nanotechnology, and was selected as the cover article for the upcoming July issue.   ※ Title: Advanced Micro-OLED Integration on Thin and Flexible Polymer Neural Probes for Targeted Optogenetic Stimulation ※ DOI: https://doi.org/10.1002/adfm.202420758   The research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea through the Electronic Medicine Technology Development Program (Project title: Development of Core Source Technologies and In Vivo Validation for Brain Cognition and Emotion-Enhancing Light-Stimulating Electronic Medicine).

Development of Core NPU Technology to Improve Chat..

  Latest generative AI models such as OpenAI's ChatGPT-4 and Google's Gemini 2.5 require not only high memory bandwidth but also large memory capacity. This is why generative AI cloud operating companies like Microsoft and Google purchase hundreds of thousands of NVIDIA GPUs. As a solution to address the core challenges of building such high-performance AI infrastructure, Korean researchers have succeeded in developing an NPU (Neural Processing Unit)＊ core technology that improves the inference performance of generative AI models by an average of over 60％ while consuming approximately 44％ less power compared to the latest GPUs.   ＊NPU (Neural Processing Unit): An AI-specific semiconductor chip designed to rapidly process artificial neural networks.     On the 4th, Professor Jongse Park's research team from KAIST School of Computing, in collaboration with HyperAccel Inc. (a startup founded by Professor Joo-Young Kim from the School of Electrical Engineering), announced that they have developed a high-performance, low-power NPU (Neural Processing Unit) core technology specialized for generative AI clouds like ChatGPT.       The technology proposed by the research team has been accepted by the '2025 International Symposium on Computer Architecture (ISCA 2025)', a top-tier international conference in the field of computer architecture.   The key objective of this research is to improve the performance of large-scale generative AI services by lightweighting the inference process, while minimizing accuracy loss and solving memory bottleneck issues. This research is highly recognized for its integrated design of AI semiconductors and AI system software, which are key components of AI infrastructure.   While existing GPU-based AI infrastructure requires multiple GPU devices to meet high bandwidth and capacity demands, this technology enables the configuration of the same level of AI infrastructure using fewer NPU devices through KV cache quantization＊. KV cache accounts for most of the memory usage, thereby its quantization significantly reduces the cost of building generative AI clouds.   ＊KV Cache (Key-Value Cache) Quantization: Refers to reducing the data size in a type of temporary storage space used to improve performance when operating generative AI models (e.g., converting a 16-bit number to a 4-bit number reduces data size by 1/4).   The research team designed it to be integrated with memory interfaces without changing the operational logic of existing NPU architectures. This hardware architecture not only implements the proposed quantization algorithm but also adopts page-level memory management techniques＊ for efficient utilization of limited memory bandwidth and capacity, and introduces new encoding technique optimized for quantized KV cache.   ＊Page-level memory management technique: Virtualizes memory addresses, as the CPU does, to allow consistent access within the NPU.   Furthermore, when building an NPU-based AI cloud with superior cost and power efficiency compared to the latest GPUs, the high-performance, low-power nature of NPUs is expected to significantly reduce operating costs.   Professor Jongse Park stated, "This research, through joint work with HyperAccel Inc., found a solution in generative AI inference lightweighting algorithms and succeeded in developing a core NPU technology that can solve the 'memory problem.' Through this technology, we implemented an NPU with over 60％ improved performance compared to the latest GPUs by combining quantization techniques that reduce memory requirements while maintaining inference accuracy, and hardware designs optimized for this".       He further emphasized, "This technology has demonstrated the possibility of implementing high-performance, low-power infrastructure specialized for generative AI, and is expected to play a key role not only in AI cloud data centers but also in the AI transformation (AX) environment represented by dynamic, executable AI such as 'Agentic AI'."       This research was presented by Ph.D. student Minsu Kim and Dr. Seongmin Hong from HyperAccel Inc. as co-first authors at the '2025 International Symposium on Computer Architecture (ISCA)' held in Tokyo, Japan, from June 21 to June 25. ISCA, a globally renowned academic conference, received 570 paper submissions this year, with only 127 papers accepted (an acceptance rate of 22.7％).     ※Paper Title: Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization   ※DOI: https://doi.org/10.1145/3695053.3731019   Meanwhile, this research was supported by the National Research Foundation of Korea's Excellent Young Researcher Program, the Institute for Information & Communications Technology Planning & Evaluation (IITP), and the AI Semiconductor Graduate School Support Project.

KAIST researcher Se Jin Park develops ＇SpeechSSM,＇..

  < (From Left)Prof. Yong Man Ro and Ph.D. candidate Sejin Park >   Se Jin Park, a researcher from Professor Yong Man Ro’s team at KAIST, has announced 'SpeechSSM', a spoken language model capable of generating long-duration speech that sounds natural and remains consistent. An efficient processing technique based on linear sequence modeling overcomes the limitations of existing spoken language models, enabling high-quality speech generation without time constraints. It is expected to be widely used in podcasts, audiobooks, and voice assistants due to its ability to generate natural, long-duration speech like humans.   Recently, Spoken Language Models (SLMs) have been spotlighted as next-generation technology that surpasses the limitations of text-based language models by learning human speech without text to understand and generate linguistic and non-linguistic information. However, existing models showed significant limitations in generating long-duration content required for podcasts, audiobooks, and voice assistants. Now, KAIST researcher has succeeded in overcoming these limitations by developing 'SpeechSSM,' which enables consistent and natural speech generation without time constraints.   KAIST(President Kwang Hyung Lee) announced on the 3rd of July that Ph.D. candidate Sejin Park from Professor Yong Man Ro's research team in the School of Electrical Engineering has developed 'SpeechSSM,' a spoken. a spoken language model capable of generating long-duration speech.   This research is set to be presented as an oral paper at ICML (International Conference on Machine Learning) 2025, one of the top machine learning conferences, selected among approximately 1％ of all submitted papers. This not only proves outstanding research ability but also serves as an opportunity to once again demonstrate KAIST's world-leading AI research capabilities.   A major advantage of Spoken Language Models (SLMs) is their ability to directly process speech without intermediate text conversion, leveraging the unique acoustic characteristics of human speakers, allowing for the rapid generation of high-quality speech even in large-scale models.       However, existing models faced difficulties in maintaining semantic and speaker consistency for long-duration speech due to increased 'speech token resolution' and memory consumption when capturing very detailed information by breaking down speech into fine fragments.   To solve this problem, Se Jin Park developed 'SpeechSSM,' a spoken language model using a Hybrid State-Space Model, designed to efficiently process and generate long speech sequences.   This model employs a 'hybrid structure' that alternately places 'attention layers' focusing on recent information and 'recurrent layers' that remember the overall narrative flow (long-term context). This allows the story to flow smoothly without losing coherence even when generating speech for a long time. Furthermore, memory usage and computational load do not increase sharply with input length, enabling stable and efficient learning and the generation of long-duration speech.   SpeechSSM effectively processes unbounded speech sequences by dividing speech data into short, fixed units (windows), processing each unit independently, and then combining them to create long speech.   Additionally, in the speech generation phase, it uses a 'Non-Autoregressive' audio synthesis model (SoundStorm), which rapidly generates multiple parts at once instead of slowly creating one character or one word at a time, enabling the fast generation of high-quality speech.   While existing models typically evaluated short speech models of about 10 seconds, Se Jin Park created new evaluation tasks for speech generation based on their self-built benchmark dataset, 'LibriSpeech-Long,' capable of generating up to 16 minutes of speech.   Compared to PPL (Perplexity), an existing speech model evaluation metric that only indicates grammatical correctness, she proposed new evaluation metrics such as 'SC-L (semantic coherence over time)' to assess content coherence over time, and 'N-MOS-T (naturalness mean opinion score over time)' to evaluate naturalness over time, enabling more effective and precise evaluation.       < external_image >   Through these new evaluations, it was confirmed that speech generated by the SpeechSSM spoken language model consistently featured specific individuals mentioned in the initial prompt, and new characters and events unfolded naturally and contextually consistently, despite long-duration generation. This contrasts sharply with existing models, which tended to easily lose their topic and exhibit repetition during long-duration generation.       < external_image >   PhD candidate Sejin Park explained, "Existing spoken language models had limitations in long-duration generation, so our goal was to develop a spoken language model capable of generating long-duration speech for actual human use." She added, "This research achievement is expected to greatly contribute to various types of voice content creation and voice AI fields like voice assistants, by maintaining consistent content in long contexts and responding more efficiently and quickly in real time than existing methods."   This research, with Se Jin Park as the first author, was conducted in collaboration with Google DeepMind and is scheduled to be presented as an oral presentation at ICML (International Conference on Machine Learning) 2025 on July 16th.   Paper Title: Long-Form Speech Generation with Spoken Language Models DOI: 10.48550/arXiv.2412.18603   Ph.D. candidate Se Jin Park has demonstrated outstanding research capabilities as a member of Professor Yong Man Ro's MLLM (multimodal large language model) research team, through her work integrating vision, speech, and language. Her achievements include a spotlight paper presentation at 2024 CVPR (Computer Vision and Pattern Recognition) and an Outstanding Paper Award at 2024 ACL (Association for Computational Linguistics).   For more information, you can refer to the publication and accompanying demo: SpeechSSM Publications.

KAIST Uses AI to Discover Optimal New Material for..

  < (From the Right) Professor Ho Jin Ryu, Department of Nuclear and Quantum Engineering, Dr. Sujeong Lee, a graduate of the KAIST Department of Materials Science and Engineering, and Dr. Juhwan Noh of KRICT’s Digital Chemistry Research Center >     Managing radioactive waste is one of the core challenges in the use of nuclear energy. In particular, radioactive iodine poses serious environmental and health risks due to its long half-life (15.7 million years in the case of I-129), high mobility, and toxicity to living organisms. A Korean research team has successfully used artificial intelligence to discover a new material that can remove iodine for nuclear environmental remediation. The team plans to push forward with commercialization through various industry-academia collaborations, from iodine-adsorbing powders to contaminated water treatment filters.   KAIST (President Kwang Hyung Lee) announced on the 2of July that Professor Ho Jin Ryu's research team from the Department of Nuclear and Quantum Engineering, in collaboration with Dr. Juhwan Noh of the Digital Chemistry Research Center at the Korea Research Institute of Chemical Technology (KRICT, President Young Kook Lee), which operates under the National Research Council of Science & Technology (NST, Chairman Youngsik Kim), developed a technique using AI to discover new materials that effectively remove radioactive iodine contaminants.   Recent studies show that radioactive iodine primarily exists in aqueous environments in the form of iodate (IO₃⁻). However, existing silver-based adsorbents have weak chemical adsorption strength for iodate, making them inefficient. Therefore, it is imperative to develop new adsorbent materials that can effectively remove iodate.   Professor Ho Jin Ryu’s team used a machine learning-based experimental strategy to identify optimal iodate adsorbents among compounds called Layered Double Hydroxides (LDHs), which contain various metal elements.   The multi-metal LDH developed in this study – Cu₃(CrFeAl), based on copper, chromium, iron, and aluminum—showed exceptional adsorption performance, removing over 90％ of iodate. This achievement was made possible by efficiently exploring a vast compositional space using AI-driven active learning, which would be difficult to search through conventional trial-and-error experiments.       <Picture2. Concept of Developed AI-Based Technology for Exploring New Materials for Radioactive Contamination Removal>   The research team focused on the fact that LDHs, like high-entropy materials, can incorporate a wide range of metal compositions and possess structures favorable for anion adsorption. However, due to the overwhelming number of possible metal combinations in multi-metal LDHs, identifying the optimal composition through traditional experimental methods has been nearly impossible.   To overcome this, the team employed AI (machine learning). Starting with experimental data from 24 binary and 96 ternary LDH compositions, they expanded their search to include quaternary and quinary candidates. As a result, they were able to discover the optimal material for iodate removal by testing only 16％ of the total candidate materials.   Professor Ho Jin Ryu stated, “This study shows the potential of using artificial intelligence to efficiently identify radioactive decontamination materials from a vast pool of new material candidates, which is expected to accelerate research for developing new materials for nuclear environmental cleanup.”       The research team has filed a domestic patent application for the developed powder technology and is currently proceeding with an international patent application. They plan to enhance the material’s performance under various conditions and pursue commercialization through industry-academia cooperation in the development of filters for treating contaminated water.   Dr. Sujeong Lee, a graduate of the KAIST Department of Materials Science and Engineering, and Dr. Juhwan Noh of KRICT’s Digital Chemistry Research Center, participated as the co-first authors of the study. The results were published online on May 26 in the internationally renowned environmental publication Journal of Hazardous Materials.   ※ Paper title: Discovery of multi-metal-layered double hydroxides for decontamination of iodate by machine learning-assisted experiments ※ DOI: https://doi.org/10.1016/j.jhazmat.2025.138735   This research was supported by the Nuclear Energy Research Infrastructure Program and the Nano-Materials Technology Development Program funded by the Ministry of Science and ICT and the National Research Foundation of Korea.

KAIST Develops AI to Easily Find Promising Materia..

  < Photo 1. (From left) Professor Jihan Kim, Ph.D. candidate Yunsung Lim and Dr. Hyunsoo Park of the Department of Chemical and Biomolecular Engineering >   In order to help prevent the climate crisis, actively reducing already-emitted CO₂ is essential. Accordingly, direct air capture (DAC) — a technology that directly extracts only CO₂ from the air — is gaining attention. However, effectively capturing pure CO₂ is not easy due to water vapor (H₂O) present in the air. KAIST researchers have successfully used AI-driven machine learning techniques to identify the most promising CO₂-capturing materials among metal-organic frameworks (MOFs), a key class of materials studied for this technology.   KAIST (President Kwang Hyung Lee) announced on the 29th of June that a research team led by Professor Jihan Kim from the Department of Chemical and Biomolecular Engineering, in collaboration with a team at Imperial College London, has developed a machine-learning-based simulation method that can quickly and accurately screen MOFs best suited for atmospheric CO₂ capture.       < Figure 1. Concept diagram of Direct Air Capture (DAC) technology and carbon capture using Metal-Organic Frameworks (MOFs). MOFs are promising porous materials capable of capturing carbon dioxide from the atmosphere, drawing attention as a core material for DAC technology. >   To overcome the difficulty of discovering high-performance materials due to the complexity of structures and the limitations of predicting intermolecular interactions, the research team developed a machine learning force field (MLFF) capable of precisely predicting the interactions between CO₂, water (H₂O), and MOFs. This new method enables calculations of MOF adsorption properties with quantum-mechanics-level accuracy at vastly faster speeds than before.   Using this system, the team screened over 8,000 experimentally synthesized MOF structures, identifying more than 100 promising candidates for CO₂ capture. Notably, this included new candidates that had not been uncovered by traditional force-field-based simulations. The team also analyzed the relationships between MOF chemical structure and adsorption performance, proposing seven key chemical features that will help in designing new materials for DAC.       < Figure 2. Concept diagram of adsorption simulation using Machine Learning Force Field (MLFF). The developed MLFF is applicable to various MOF structures and allows for precise calculation of adsorption properties by predicting interaction energies during repetitive Widom insertion simulations. It is characterized by simultaneously achieving high accuracy and low computational cost compared to conventional classical force fields. >   This research is recognized as a significant advance in the DAC field, greatly enhancing materials design and simulation by precisely predicting MOF-CO₂ and MOF-H₂O interactions.   The results of this research, with Ph.D. candidate Yunsung Lim and Dr. Hyunsoo Park of KAIST as co-first authors, were published in the international academic journal Matter on June 12.   ※Paper Title: Accelerating CO₂ direct air capture screening for metal–organic frameworks with a transferable machine learning force field   ※DOI: 10.1016/j.matt.2025.102203   This research was supported by the Saudi Aramco-KAIST CO₂ Management Center and the Ministry of Science and ICT's Global C.L.E.A.N. Project.

Military Combatants Usher in an Era of Personalize..

  < Photo 1. (From left) Professor Steve Park of Materials Science and Engineering, Kyusoon Pak, Ph.D. Candidate (Army Major) >   Traditional military training often relies on standardized methods, which has limited the provision of optimized training tailored to individual combatants' characteristics or specific combat situations. To address this, our research team developed an e-textile platform, securing core technology that can reflect the unique traits of individual combatants and various combat scenarios. This technology has proven robust enough for battlefield use and is economical enough for widespread distribution to a large number of troops.   On June 25th, Professor Steve Park's research team at KAIST's Department of Materials Science and Engineering announced the development of a flexible, wearable electronic textile (E-textile) platform using an innovative technology that 'draws' electronic circuits directly onto fabric.   The wearable e-textile platform developed by the research team combines 3D printing technology with new materials engineering design to directly print flexible and highly durable sensors and electrodes onto textile substrates. This enables the collection of precise movement and human body data from individual combatants, which can then be used to propose customized training models.   Existing e-textile fabrication methods were often complex or limited in their ability to provide personalized customization. To overcome these challenges, the research team adopted an additive manufacturing technology called 'Direct Ink Writing (DIW)' 3D printing.       < Figure 1. Schematic diagram of e-textile manufactured with Direct Ink Writing (DIW) printing technology on various textiles, including combat uniforms >   This technology involves directly dispensing and printing special ink, which functions as sensors and electrodes, onto textile substrates in desired patterns. This allows for flexible implementation of various designs without the complex process of mask fabrication. This is expected to be an effective technology that can be easily supplied to hundreds of thousands of military personnel.   The core of this technology lies in the development of high-performance functional inks based on advanced materials engineering design. The research team combined styrene-butadiene-styrene (SBS) polymer, which provides flexibility, with multi-walled carbon nanotubes (MWCNT) for electrical conductivity. They developed a tensile/bending sensor ink that can stretch up to 102％ and maintain stable performance even after 10,000 repetitive tests. This means that accurate data can be consistently obtained even during the strenuous movements of combatants.       < Figure 2. Measurement of human movement and breathing patterns using e-textile >   Furthermore, new material technology was applied to implement 'interconnect electrodes' that electrically connect the upper and lower layers of the fabric. The team developed an electrode ink combining silver (Ag) flakes with rigid polystyrene (PS) polymer, precisely controlling the impregnation level (how much the ink penetrates the fabric) to effectively connect both sides or multiple layers of the fabric. This secures the technology for producing multi-layered wearable electronic systems integrating sensors and electrodes.       < Figure 3. Experimental results of recognizing unknown objects after machine learning six objects using a smart glove >   The research team proved the platform's performance through actual human movement monitoring experiments. They printed the developed e-textile on major joint areas of clothing (shoulders, elbows, knees) and measured movements and posture changes during various exercises such as running, jumping jacks, and push-ups in real-time.   Additionally, they demonstrated the potential for applications such as monitoring breathing patterns using a smart mask and recognizing objects through machine learning and perceiving complex tactile information by printing multiple sensors and electrodes on gloves. These results show that the developed e-textile platform is effective in precisely understanding the movement dynamics of combatants.   This research is an important example demonstrating how cutting-edge new material technology can contribute to the advancement of the defense sector. Major Kyusoon Pak of the Army, who participated in this research, considered required objectives such as military applicability and economic feasibility for practical distribution from the research design stage.       < Figure 4. Experimental results showing that a multi-layered e-textile glove connected with interconnect electrodes can measure tensile/bending signals and pressure signals at a single point >   Major Pak stated, "Our military is currently facing both a crisis and an opportunity due to the decrease in military personnel resources caused by the demographic cliff and the advancement of science and technology. Also, respect for life in the battlefield is emerging as a significant issue. This research aims to secure original technology that can provide customized training according to military branch/duty and type of combat, thereby enhancing the combat power and ensuring the survivability of our soldiers."   He added, "I hope this research will be evaluated as a case that achieved both scientific contribution and military applicability."   This research, where Kyusoon Pak, Ph.D. Candidate (Army Major) from KAIST's Department of Materials Science and Engineering, participated as the first author and Professor Steve Park supervised, was published on May 27, 2025, in ＇npj Flexible Electronics (top 1.8％ in JCR field)', an international academic journal in the electrical, electronic, and materials engineering fields.     ＊ Paper Title: Fabrication of Multifunctional Wearable Interconnect E-textile Platform Using Direct Ink Writing (DIW) 3D Printing   ＊ DOI: https://doi.org/10.1038/s41528-025-00414-7   This research was supported by the Ministry of Trade, Industry and Energy and the National Research Foundation of Korea.

KAIST＇s Li-Fi - Achieves 100 Times Faster Speed an..

- KAIST-KRISS Develop 'On-Device Encryption Optical Transmitter' Based on Eco-Friendly Quantum Dots   - New Li-Fi Platform Technology Achieves High Performance with 17.4％ Device Efficiency and 29,000 nit Brightness, Simultaneously Improving Transmission Speed and Security   - Presents New Methodology for High-Speed and Encrypted Communication Through Single-Device-Based Dual-Channel Optical Modulation       < Photo 1. (Front row from left) Seungmin Shin, First Author; Professor Himchan Cho; (Back row from left) Hyungdoh Lee, Seungwoo Lee, Wonbeom Lee; (Top left) Dr. Kyung-geun Lim >   Li-Fi (Light Fidelity) is a wireless communication technology that utilizes the visible light spectrum (400-800 THz), similar to LED light, offering speeds up to 100 times faster than existing Wi-Fi (up to 224 Gbps). While it has fewer limitations in available frequency allocation and less radio interference, it is relatively vulnerable to security breaches as anyone can access it. Korean researchers have now proposed a new Li-Fi platform that overcomes the limitations of conventional optical communication devices and can simultaneously enhance both transmission speed and security.   KAIST (President Kwang Hyung Lee) announced on the 24th that Professor Himchan Cho's research team from the Department of Materials Science and Engineering, in collaboration with Dr. Kyung-geun Lim of the Korea Research Institute of Standards and Science (KRISS, President Ho-Seong Lee) under the National Research Council of Science & Technology (NST, Chairman Young-Sik Kim), has developed 'on-device encryption optical communication device' technology for the utilization of 'Li-Fi,' which is attracting attention as a next-generation ultra-high-speed data communication.   Professor Cho's team created high-efficiency light-emitting triode devices using eco-friendly quantum dots (low-toxicity and sustainable materials). The device developed by the research team is a mechanism that generates light using an electric field. Specifically, the electric field is concentrated in 'tiny holes (pinholes) in the permeable electrode' and transmitted beyond the electrode. This device utilizes this principle to simultaneously process two input data streams.   Using this principle, the research team developed a technology called 'on-device encryption optical transmitter.' The core of this technology is that the device itself converts information into light and simultaneously encrypts it. This means that enhanced security data transmission is possible without the need for complex, separate equipment.   External Quantum Efficiency (EQE) is an indicator of how efficiently electricity is converted into light, with a general commercialization standard of about 20％. The newly developed device recorded an EQE of 17.4％, and its luminance was 29,000 nit, significantly exceeding the maximum brightness of a smartphone OLED screen, which is 2,000 nit, demonstrating a brightness more than 10 times higher.       < Figure 1. Schematic diagram of the device structure developed by the research team and encrypted communication >   Furthermore, to more accurately understand how this device converts information into light, the research team used a method called 'transient electroluminescence analysis.' They analyzed the light-emitting characteristics generated by the device when voltage was instantaneously applied for very short durations (hundreds of nanoseconds = billionths of a second). Through this analysis, they investigated the movement of charges within the device at hundreds of nanoseconds, elucidating the operating mechanism of dual-channel optical modulation implemented within a single device.   Professor Himchan Cho of KAIST stated, "This research overcomes the limitations of existing optical communication devices and proposes a new communication platform that can both increase transmission speed and enhance security."       < Photo 2. Professor Himchan Cho, Department of Materials Science and Engineering >   He added, "This technology, which strengthens security without additional equipment and simultaneously enables encryption and transmission, can be widely applied in various fields where security is crucial in the future."   This research, with Seungmin Shin, a Ph.D. candidate at KAIST's Department of Materials Science and Engineering, participating as the first author, and Professor Himchan Cho and Dr. Kyung-geun Lim of KRISS as co-corresponding authors, was published in the international journal 'Advanced Materials' on May 30th and was selected as an inside front cover paper.     ※ Paper Title: High-Efficiency Quantum Dot Permeable electrode Light-Emitting Triodes for Visible-Light Communications and On-Device Data Encryption   ※ DOI: https://doi.org/10.1002/adma.202503189   This research was supported by the National Research Foundation of Korea, the National Research Council of Science & Technology (NST), and the Korea Institute for Advancement of Technology.

Distinguished Professor Sang Yup Lee Wins 2025 Glo..

  < Distinguished Professor Sang Yup Lee (Senior Vice President for Research) from the Department of Chemical & Biomolecular Engineering >   KAIST announced on the 20th that Professor Sang Yup Lee, who serves as the Vice President for Research and a Distinguished Professor at our university, has been awarded the '2025 Gregory N. Stephanopoulos Award for Metabolic Engineering' by the International Metabolic Engineering Society (IMES). Professor Lee delivered his award lecture at the 16th Metabolic Engineering Conference (ME16), held in Copenhagen, Denmark, from June 15th to 19th.   This award was established through contributions from the American Institute of Chemical Engineers (AIChE) Foundation, as well as fellow colleagues and acquaintances, to honor the achievements of Dr. Gregory Stephanopoulos, widely recognized as one of the pioneers of metabolic engineering. Presented biennially, the award recognizes scientists who have successfully commercialized fundamental research in metabolic engineering or have made outstanding contributions to the quantitative analysis, design, and modeling of metabolic pathways.   Professor Sang Yup Lee boasts an impressive record of over 770 journal papers and more than 860 patents. His groundbreaking research in metabolic engineering and biochemical engineering is highly acclaimed globally.   Throughout his 31 years as a professor at KAIST, Professor Lee has developed various metabolic engineering-based technologies and strategies. These advancements have been transferred to industries, facilitating the production of bulk chemicals, polymers, natural products, pharmaceuticals, and health functional foods. He has also founded companies and actively engages in advisory roles with various enterprises.   The International Metabolic Engineering Society (IMES) defines metabolic engineering as the manipulation of metabolic pathways in microorganisms or cells to produce useful substances (such as pharmaceuticals, biofuels, and chemical products). It utilizes tools like systems biology, synthetic biology, and computational modeling with the aim of enhancing the economic viability and sustainability of bio-based processes.   Furthermore, Professor Lee previously received the Merck Metabolic Engineering Award, a prominent international award in the field, in 2008. In 2018, he was honored with the Eni Award, often referred to as the Nobel Prize in energy, presented by the President of Italy.   Professor Sang Yup Lee remarked, "Metabolic engineering is a discipline that leads the current and future of biotechnology. It is a tremendous honor to receive this meaningful award at a time when the transition to a bio-based economy is accelerating. Together with my students and fellow researchers, we have generated numerous patents and transferred technologies to industry, and also established startups in the fields of biofuels, wound healing, and cosmetics. I will continue to pursue research that encompasses both fundamental research and technological commercialization."   The 'International Metabolic Engineering Society (IMES)' is a specialized society under the American Institute of Chemical Engineers. Its mission is to enable the production of various bio-based products, including pharmaceuticals, food additives, chemicals, and fuels, through metabolic engineering. The society hosts the Metabolic Engineering Conference biennially, offering researchers opportunities for knowledge exchange and collaboration.

KAIST Researchers Unveil an AI that Generates ˝Une..

  < Photo 1. Professor Jaesik Choi, KAIST Kim Jaechul Graduate School of AI >   Recently, text-based image generation models can automatically create high-resolution, high-quality images solely from natural language descriptions. However, when a typical example like the Stable Diffusion model is given the text "creative," its ability to generate truly creative images remains limited. KAIST researchers have developed a technology that can enhance the creativity of text-based image generation models such as Stable Diffusion without additional training, allowing AI to draw creative chair designs that are far from ordinary.   Professor Jaesik Choi's research team at KAIST Kim Jaechul Graduate School of AI, in collaboration with NAVER AI Lab, developed this technology to enhance the creative generation of AI generative models without the need for additional training.       < Photo 2. Gayoung Lee, Researcher at NAVER AI Lab; Dahee Kwon, Ph.D. Candidate at KAIST Kim Jaechul Graduate School of AI; Jiyeon Han, Ph.D. Candidate at KAIST Kim Jaechul Graduate School of AI; Junho Kim, Researcher at NAVER AI Lab >   Professor Choi's research team developed a technology to enhance creative generation by amplifying the internal feature maps of text-based image generation models. They also discovered that shallow blocks within the model play a crucial role in creative generation. They confirmed that amplifying values in the high-frequency region after converting feature maps to the frequency domain can lead to noise or fragmented color patterns. Accordingly, the research team demonstrated that amplifying the low-frequency region of shallow blocks can effectively enhance creative generation.   Considering originality and usefulness as two key elements defining creativity, the research team proposed an algorithm that automatically selects the optimal amplification value for each block within the generative model.   Through the developed algorithm, appropriate amplification of the internal feature maps of a pre-trained Stable Diffusion model was able to enhance creative generation without additional classification data or training.       < Figure 1. Overview of the methodology researched by the development team. After converting the internal feature map of a pre-trained generative model into the frequency domain through Fast Fourier Transform, the low-frequency region of the feature map is amplified, then re-transformed into the feature space via Inverse Fast Fourier Transform to generate an image. >   The research team quantitatively proved, using various metrics, that their developed algorithm can generate images that are more novel than those from existing models, without significantly compromising utility.   In particular, they confirmed an increase in image diversity by mitigating the mode collapse problem that occurs in the SDXL-Turbo model, which was developed to significantly improve the image generation speed of the Stable Diffusion XL (SDXL) model. Furthermore, user studies showed that human evaluation also confirmed a significant improvement in novelty relative to utility compared to existing methods.   Jiyeon Han and Dahee Kwon, Ph.D. candidates at KAIST and co-first authors of the paper, stated, "This is the first methodology to enhance the creative generation of generative models without new training or fine-tuning. We have shown that the latent creativity within trained AI generative models can be enhanced through feature map manipulation."   They added, "This research makes it easy to generate creative images using only text from existing trained models. It is expected to provide new inspiration in various fields, such as creative product design, and contribute to the practical and useful application of AI models in the creative ecosystem."       < Figure 2. Application examples of the methodology researched by the development team. Various Stable Diffusion models generate novel images compared to existing generations while maintaining the meaning of the generated object. >   This research, co-authored by Jiyeon Han and Dahee Kwon, Ph.D. candidates at KAIST Kim Jaechul Graduate School of AI, was presented on June 16 at the International Conference on Computer Vision and Pattern Recognition (CVPR), an international academic conference.   ＊ Paper Title: Enhancing Creative Generation on Stable Diffusion-based Models   ＊ DOI: https://doi.org/10.48550/arXiv.2503.23538   This research was supported by the KAIST-NAVER Ultra-creative AI Research Center, the Innovation Growth Engine Project Explainable AI, the AI Research Hub Project, and research on flexible evolving AI technology development in line with increasingly strengthened ethical policies, all funded by the Ministry of Science and ICT through the Institute for Information & Communications Technology Promotion. It also received support from the KAIST AI Graduate School Program and was carried out at the KAIST Future Defense AI Specialized Research Center with support from the Defense Acquisition Program Administration and the Agency for Defense Development.

KAIST Develops Glare-Free, Heat-Blocking ＇Smart Wi..

• Professor Hong Chul Moon of the Department of Chemical and Biomolecular Engineering develops RECM, a next-generation smart window technology, expecting cooling energy savings and effective indoor thermal management.   • When using the developed RECM, a significantly superior temperature reduction effect is observed compared to conventional windows.   • With a 'pedestrian-friendly smart window' design that eliminates glare by suppressing external reflections, it is expected to be adapted in architectural structures, transportation, and more.       < (From left) First author Hoy Jung Jo, Professor Hong Chul Moon >   In the building sector, which accounts for approximately 40％ of global energy consumption, heat ingress through windows has been identified as a primary cause of wasted heating and cooling energy. Our research team has successfully developed a 'pedestrian-friendly smart window' technology capable of not only reducing heating and cooling energy in urban buildings but also resolving the persistent issue of 'light pollution' in urban living.   On the 17th of June, Professor Hong Chul Moon's research team at KAIST's Department of Chemical and Biomolecular Engineering announced the development of a 'smart window technology' that allows users to control the light and heat entering through windows according to their intent, and effectively neutralize glare from external sources.   Recently, 'active smart window' technology, which enables free adjustment of light and heat based on user operation, has garnered significant attention. Unlike conventional windows that passively react to changes in temperature or light, this is a next-generation window system that can be controlled in real-time via electrical signals.   The next-generation smart window technology developed by the research team, RECM (Reversible Electrodeposition and Electrochromic Mirror), is a smart window system based on a single-structured ＊electrochromic device that can actively control the transmittance of visible light and near-infrared (heat).   ＊Electrochromic device: A device whose optical properties change in response to an electrical signal.   In particular, by effectively suppressing the glare phenomenon caused by external reflected light—a problem previously identified in traditional metal ＊deposition smart windows—through the combined application of electrochromic materials, a 'pedestrian-friendly smart window' suitable for building facades has been realized.   ＊Deposition: A process involving the electrochemical reaction to coat metal ions, such as Ag＋, onto an electrode surface in solid form.   The RECM system developed in this study operates in three modes depending on voltage control.   Mode I (Transparent Mode) is advantageous for allowing sunlight to enter the indoor space during winter, as it transmits both light and heat like ordinary glass.   In Mode II (Colored Mode), ＊Prussian Blue (PB) and ＊＊DHV＋• chemical species are formed through a redox (oxidation-reduction) reaction, causing the window to turn a deep blue color. In this state, light is absorbed, and only a portion of the heat is transmitted, allowing for privacy while enabling appropriate indoor temperature control.   ＊Prussian Blue: An electrochromic material that transitions between colorless and blue upon electrical stimulation.   ＊＊DHV＋•: A radical state colored molecule generated upon electrical stimulation.   Mode III (Colored and Deposition Mode) involves the reduction and deposition of silver (Ag＋) ions on the electrode surface, reflecting both light and heat. Concurrently, the colored material absorbs the reflected light, effectively blocking glare for external pedestrians.   The research team validated the practical indoor temperature reduction effect of the RECM technology through experiments utilizing a miniature model house. When a conventional glass window was installed, the indoor temperature rose to 58.7°C within 45 minutes. Conversely, when RECM was operated in Mode III, the temperature reached 31.5°C, demonstrating a temperature reduction effect of approximately 27.2°C.   Furthermore, since each state transition is achievable solely by electrical signals, it is regarded as an active smart technology capable of instantaneous response according to season, time, and intended use.       < Figure 1. Operation mechanism of the RECM smart window. The RECM system can switch among three states—transparent, colored, and colored & deposition—via electrical stimulation. At -1.6 V, DHV•＋ and Prussian Blue (PB) are formed, blocking visible light to provide privacy protection and heat blocking. At -2.0 V, silver (Ag) is deposited on the electrode surface, reflecting light and heat, while DHV•＋ and Prussian Blue absorb reflected light, effectively suppressing external glare. Through this mechanism, it functions as an active smart window that simultaneously controls light, heat, and glare. >   Professor Hong Chul Moon of KAIST, the corresponding author of this study, stated, "This research goes beyond existing smart window technologies limited to visible light control, presenting a truly smart window platform that comprehensively considers not only active indoor thermal control but also the visual safety of pedestrians." He added, "Various applications are anticipated, from urban buildings to vehicles and trains."       < Figure 2. Analysis of glare suppression effect of conventional reflective smart windows and RECM. This figure presents the results comparing the glare phenomenon occurring during silver (Ag) deposition between conventional reflective smart windows and RECM Mode III. Conventional reflective devices resulted in strong reflected light on the desk surface due to their high reflectivity. In contrast, RECM Mode III, where the colored material absorbed reflected light, showed a 33％ reduction in reflected light intensity, and no reflected light was observed from outside. This highlights the RECM system's distinctiveness and practicality as a 'pedestrian-friendly smart window' optimized for dense urban environments, extending beyond just heat blocking. >   The findings of this research were published on June 13, 2025, in Volume 10, Issue 6 of 'ACS Energy Letters'. The listed authors for this publication are Hoy Jung Jo, Yeon Jae Jang, Hyeon-Don Kim, Kwang-Seop Kim, and Hong Chul Moon.   ※ Paper Title: Glare-Free, Energy-Efficient Smart Windows: A Pedestrian-Friendly System with Dynamically Tunable Light and Heat Regulation   ※ DOI: 10.1021/acsenergylett.5c00637       < Figure 3. Temperature reduction performance verification in a miniature model house. The actual heat blocking effect was evaluated by applying RECM devices to a model building. Under identical conditions, the indoor temperature with ordinary glass rose to 58.7°C, whereas with RECM in Mode III, it reached 31.5°C, demonstrating a maximum temperature reduction effect of 27.2°C. The indoor temperature difference was also visually confirmed through thermal images, which proves the potential for indoor temperature control in urban buildings. >   This research was supported by the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT and the internal research program of the Korea Institute of Machinery and Materials. 

High-Resolution Spectrometer that Fits into Smartp..

- Professor Mooseok Jang's research team at the Department of Bio and Brain Engineering develops an ultra-compact, high-resolution spectrometer using 'double-layer disordered metasurfaces' that generate unique random patterns depending on light's color.   - Unlike conventional dispersion-based spectrometers that were difficult to apply to portable devices, this new concept spectrometer technology achieves 1nm-level high resolution in a device smaller than 1cm, comparable in size to a fingernail.   - It can be utilized as a built-in spectrometer in smartphones and wearable devices in the future, and can be expanded to advanced optical technologies such as hyperspectral imaging and ultrafast imaging.       < Photo 1. (From left) Professor Mooseok Jang, Dong-gu Lee (Ph.D. candidate), Gookho Song (Ph.D. candidate) >   Color, as the way light's wavelength is perceived by the human eye, goes beyond a simple aesthetic element, containing important scientific information like a substance's composition or state. Spectrometers are optical devices that analyze material properties by decomposing light into its constituent wavelengths, and they are widely used in various scientific and industrial fields, including material analysis, chemical component detection, and life science research. Existing high-resolution spectrometers were large and complex, making them difficult for widespread daily use. However, thanks to the ultra-compact, high-resolution spectrometer developed by KAIST researchers, it is now expected that light's color information can be utilized even within smartphones or wearable devices.   KAIST (President Kwang Hyung Lee) announced on the 13th that Professor Mooseok Jang's research team at the Department of Bio and Brain Engineering has successfully developed a reconstruction-based spectrometer technology using double-layer disordered metasurfaces＊.   ＊Double-layer disordered metasurface: An innovative optical device that complexly scatters light through two layers of disordered nanostructures, creating unique and predictable speckle patterns for each wavelength.   Existing high-resolution spectrometers have a large form factor, on the order of tens of centimeters, and require complex calibration processes to maintain accuracy. This fundamentally stems from the operating principle of traditional dispersive elements, such as gratings and prisms, which separate light wavelengths along the propagation direction, much like a rainbow separates colors. Consequently, despite the potential for light's color information to be widely useful in daily life, spectroscopic technology has been limited to laboratory or industrial manufacturing environments.       < Figure 1. Through a simple structure consisting of a double layer of disordered metasurfaces and an image sensor, it was shown that speckles of predictable spectral channels with high spectral resolution can be generated in a compact form factor. The high similarity between the measured and calculated speckles was used to solve the inverse problem and verify the ability to reconstruct the spectrum. >   The research team devised a method that departs from the conventional spectroscopic paradigm of using diffraction gratings or prisms, which establish a one-to-one correspondence between light's color information and its propagation direction, by utilizing designed disordered structures as optical components. In this process, they employed metasurfaces, which can freely control the light propagation process using structures tens to hundreds of nanometers in size, to accurately implement 'complex random patterns (speckle＊)'.   ＊Speckle: An irregular pattern of light intensity created by the interference of multiple wavefronts of light.   Specifically, they developed a method that involves implementing a double-layer disordered metasurface to generate wavelength-specific speckle patterns and then reconstructing precise color information (wavelength) of the light from the random patterns measured by a camera.   As a result, they successfully developed a new concept spectrometer technology that can accurately measure light across a broad range of visible to infrared (440-1,300nm) with a high resolution of 1 nanometer (nm) in a device smaller than a fingernail (less than 1cm) using only a single image capture.       < Figure 2. A disordered metasurface is a metasurface with irregularly arranged structures ranging from tens to hundreds of nanometers in size. In a double-layer structure, a propagation space is placed between the two metasurfaces to control the output speckle with high degrees of freedom, thereby achieving a spectral resolution of 1 nm even in a form factor smaller than 1 cm. >   Dong-gu Lee, a lead author of this study, stated, "This technology is implemented in a way that is directly integrated with commercial image sensors, and we expect that it will enable easy acquisition and utilization of light's wavelength information in daily life when built into mobile devices in the future."   Professor Mooseok Jang said, "This technology overcomes the limitations of existing RGB three-color based machine vision fields, which only distinguish and recognize three color components (red, green, blue), and has diverse applications. We anticipate various applied research for this technology, which expands the horizon of laboratory-level technology to daily-level machine vision technology for applications such as food component analysis, crop health diagnosis, skin health measurement, environmental pollution detection, and bio/medical diagnostics." He added, "Furthermore, it can be extended to various advanced optical technologies such as hyperspectral imaging, which records wavelength and spatial information simultaneously with high resolution, 3D optical trapping technology, which precisely controls light of multiple wavelengths into desired forms, and ultrafast imaging technology, which captures phenomena occurring in very short periods."   This research was collaboratively led by Dong-gu Lee (Ph.D. candidate) and Gookho Song (Ph.D. candidate) from the KAIST Department of Bio and Brain Engineering as co-first authors, with Professor Mooseok Jang as the corresponding author. The findings were published online in the international journal Science Advances on May 28, 2025.   ＊ Paper Title: Reconstructive spectrometer using double-layer disordered metasurfaces ＊ DOI: 10.1126/sciadv.adv2376   This research was supported by the Samsung Research Funding and Incubation Center of Samsung Electronics grant, the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), and the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT).

KAIST Successfully Develops High-Performance Water..

  < Photo 1. (Front row, from left) Jeesoo Park (Ph.D. Candidate), Professor Hee-Tak Kim (Back row, from left) Kyunghwa Seok (Ph.D. Candidate), Dr. Gisu Doo, Euntaek Oh (Ph.D. Candidate) >   Hydrogen is gaining attention as a clean energy source that emits no carbon. Among various methods, water electrolysis, which splits water into hydrogen and oxygen using electricity, is recognized as an eco-friendly hydrogen production method. Specifically, proton exchange membrane water electrolysis (PEMWE) is considered a next-generation hydrogen production technology due to its ability to produce high-purity hydrogen at high pressure. However, existing PEMWE technology has faced limitations in commercialization due to its heavy reliance on expensive precious metal catalysts and coating materials. Korean researchers have now proposed a new solution to address these technical and economic bottlenecks.   KAIST (President Kwang Hyung Lee) announced on June 11th that a research team led by Professor Hee-Tak Kim of the Department of Chemical and Biomolecular Engineering, in a joint study with Dr. Gisu Doo of the Korea Institute of Energy Research (KIER, President Chang-keun Lee), has developed a next-generation water electrolysis technology that achieves high performance without the need for expensive platinum (Pt) coating.   The research team focused on the primary reason why 'iridium oxide (IrOx),' a highly active catalyst for water electrolysis electrodes, fails to perform optimally. They found that this is due to inefficient electron transfer and, for the first time in the world, demonstrated that performance can be maximized simply by controlling the catalyst particle size.   In this study, it was revealed that the reason iridium oxide catalysts do not exhibit excellent performance without platinum coating is due to 'electron transport resistance' that occurs at the interface between the catalyst, the ion conductor (hereinafter referred to as ionomer), and the Ti (titanium) substrate—core components inherently used together in water electrolysis electrodes.   Specifically, they identified that the 'pinch-off' phenomenon, where the electron pathway is blocked between the catalyst, ionomer, and titanium substrate, is the critical cause of reduced conductivity. The ionomer has properties close to an electron insulator, thereby hindering electron flow when it surrounds catalyst particles. Furthermore, when the ionomer comes into contact with the titanium substrate, an electron barrier forms on the surface oxide layer of the titanium substrate, significantly increasing resistance.       < Figure 1. Infographic related to electron transport resistance at the catalyst layer/diffusion layer interface >   To address this, the research team fabricated and compared catalysts of various particle sizes. Through single-cell evaluation and multiphysics simulations, they demonstrated, for the first time globally, that when iridium oxide catalyst particles with a size of 20 nanometers (nm) or larger are used, the ionomer mixed region decreases, ensuring an electron pathway and restoring conductivity.   Moreover, they successfully optimized the interfacial structure through precise design, simultaneously ensuring both reactivity and electron transport. This achievement demonstrated that the previously unavoidable trade-off between catalyst activity and conductivity can be overcome through meticulous interfacial design.   This breakthrough is expected to be a significant milestone not only for the development of high-performance catalyst materials but also for the future commercialization of proton exchange membrane water electrolysis systems that can achieve high efficiency while drastically reducing the amount of precious metals used.   Professor Hee-Tak Kim stated, "This research presents a new interface design strategy that can resolve the interfacial conductivity problem, which was a bottleneck in high-performance water electrolysis technology." He added, "By securing high performance even without expensive materials like platinum, it will be a stepping stone closer to realizing a hydrogen economy."   This research, with Jeesoo Park, a Ph.D. student from the Department of Chemical and Biomolecular Engineering at KAIST, as the first author, was published on June 7th in 'Energy & Environmental Science' (IF: 32.4, 2025), a leading international journal in the energy and environmental fields, and was recognized for its innovativeness and impact. (Paper title: On the interface electron transport problem of highly active IrOx catalysts, DOI: 10.1039/D4EE05816J).   This research was supported by the New and Renewable Energy Core Technology Development Project of the Ministry of Trade, Industry and Energy.