Engineering
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KAIST reveals for the first time the mechanism by which alcohol triggers liver inflammation
<(From left)Dr. Keungmo Yang, Professor Won-Il Jeong, Ph.D candidate Kyurae Kim>
Excessive alcohol consumption causes alcoholic liver disease, and about 20% of these cases progress to alcohol-associated steatohepatitis (ASH), which can lead to liver cirrhosis and liver failure. Early diagnosis and treatment are therefore extremely important. A KAIST research team has identified a new molecular mechanism in which alcohol-damaged liver cells increase reactive oxygen species (ROS), leading to cell death and inflammatory responses. In addition, they discovered that Kupffer cells, immune cells residing in the liver, act as a “dual-function regulator” that can either promote or suppress inflammation through interactions with liver cells.
KAIST (President Kwang-Hyung Lee) announced on the 17th that a research team led by Professor Won-Il Jeong from the Graduate School of Medical Science and Engineering, in collaboration with Professor Won Kim’s team at Seoul National University Boramae Medical Center, has uncovered the molecular pathway of liver damage and inflammation caused by alcohol consumption. This finding offers new clues for the diagnosis and treatment of alcohol-associated liver disease (ALD).
Professor Won-Il Jeong’s research team found that during chronic alcohol intake, expression of the vesicular glutamate transporter VGLUT3 increases, leading to glutamate accumulation in hepatocytes. Subsequent binge drinking causes rapid changes in intracellular calcium levels, which then triggers glutamate* secretion. The secreted glutamate stimulates the glutamate receptor mGluR5 on liver-resident macrophages (Kupffer cells), which induces ROS production and activates a pathological pathway resulting in hepatocyte death and inflammation.
*Glutamate: A type of amino acid involved in intercellular signaling, protein synthesis, and energy metabolism in various tissues including the brain and liver. In excess, it can cause overexcitation and death of nerve cells.
<Figure1. Glutamate accumulation in perivenous hepatocytes through vesicular glutamate transporter 3 after 2-week EtOH intake and its release by binge drinking>
A particularly groundbreaking aspect of this study is that damaged hepatocytes and Kupffer cells can form a "pseudosynapse"—a structure similar to a synapse which is previously thought to occur only in the brain—enabling them to exchange signals. This is the first time such a phenomenon has been identified in the liver.
This pseudosynapse forms when hepatocytes expand (ballooning) due to alcohol, becoming physically attached to Kupffer cells. Simply put, the damaged hepatocytes don’t just die—they send distress signals to nearby immune cells, prompting a response.
This discovery proposes a new paradigm: even in peripheral organs, direct structural contact between cells can allow signal transmission. It also shows that damaged hepatocytes can actively stimulate macrophages and induce regeneration through cell death, revealing the liver’s “autonomous recovery function.”
The team also confirmed in animal models that genetic or pharmacological inhibition of VGLUT3, mGluR5, or the ROS-producing enzyme NOX2 reduces alcohol-induced liver damage. They also confirmed that the same mechanism observed in animal models was present in human patients with ALD by analyzing blood and liver tissue samples.
<Figure2. Binge drinking rapidly alters the intracellular calcium levels to release glutamates and activate mGluR5 of Kupffer cells>
Professor Won-Il Jeong of KAIST said, “These findings may serve as new molecular targets for early diagnosis and treatment of ASH in the future.”
This study was jointly led by Dr. Keungmo Yang (now at Yeouido St. Mary’s Hospital) and Kyurae Kim, a doctoral candidate at KAIST, who served as co–first authors. It was conducted in collaboration with Professor Won Kim’s team at Seoul National University Boramae Medical Center and was published in the journal Nature Communications on July 1.
※ Article Title: Binge drinking triggers VGLUT3-mediated glutamate secretion and subsequent hepatic inflammation by activating mGluR5/NOX2 in Kupffer cells ※ DOI: https://doi.org/10.1038/s41467-025-60820-3
This study was supported by the Ministry of Science and ICT through the National Research Foundation of Korea's Global Leader Program, Mid-Career Researcher Program, and the Bio & Medical Technology Development Program.
2025.07.17 View 13 -
KAIST Successfully Implements 3D Brain-Mimicking Platform with 6x Higher Precision
<(From left) Dr. Dongjo Yoon, Professor Je-Kyun Park from the Department of Bio and Brain Engineering, (upper right) Professor Yoonkey Nam, Dr. Soo Jee Kim>
Existing three-dimensional (3D) neuronal culture technology has limitations in brain research due to the difficulty of precisely replicating the brain's complex multilayered structure and the lack of a platform that can simultaneously analyze both structure and function. A KAIST research team has successfully developed an integrated platform that can implement brain-like layered neuronal structures using 3D printing technology and precisely measure neuronal activity within them.
KAIST (President Kwang Hyung Lee) announced on the 16th of July that a joint research team led by Professors Je-Kyun Park and Yoonkey Nam from the Department of Bio and Brain Engineering has developed an integrated platform capable of fabricating high-resolution 3D multilayer neuronal networks using low-viscosity natural hydrogels with mechanical properties similar to brain tissue, and simultaneously analyzing their structural and functional connectivity.
Conventional bioprinting technology uses high-viscosity bioinks for structural stability, but this limits neuronal proliferation and neurite growth. Conversely, neural cell-friendly low-viscosity hydrogels are difficult to precisely pattern, leading to a fundamental trade-off between structural stability and biological function. The research team completed a sophisticated and stable brain-mimicking platform by combining three key technologies that enable the precise creation of brain structure with dilute gels, accurate alignment between layers, and simultaneous observation of neuronal activity.
The three core technologies are: ▲ 'Capillary Pinning Effect' technology, which enables the dilute gel (hydrogel) to adhere firmly to a stainless steel mesh (micromesh) to prevent it from flowing, thereby reproducing brain structures with six times greater precision (resolution of 500 μm or less) than conventional methods; ▲ the '3D Printing Aligner,' a cylindrical design that ensures the printed layers are precisely stacked without misalignment, guaranteeing the accurate assembly of multilayer structures and stable integration with microelectrode chips; and ▲ 'Dual-mode Analysis System' technology, which simultaneously measures electrical signals from below and observes cell activity with light (calcium imaging) from above, allowing for the simultaneous verification of the functional operation of interlayer connections through multiple methods.
< Figure 1. Platform integrating brain-structure-mimicking neural network model construction and functional measurement technology>
The research team successfully implemented a three-layered mini-brain structure using 3D printing with a fibrin hydrogel, which has elastic properties similar to those of the brain, and experimentally verified the process of actual neural cells transmitting and receiving signals within it.
Cortical neurons were placed in the upper and lower layers, while the middle layer was left empty but designed to allow neurons to penetrate and connect through it. Electrical signals were measured from the lower layer using a microsensor (electrode chip), and cell activity was observed from the upper layer using light (calcium imaging). The results showed that when electrical stimulation was applied, neural cells in both upper and lower layers responded simultaneously. When a synapse-blocking agent (synaptic blocker) was introduced, the response decreased, proving that the neural cells were genuinely connected and transmitting signals.
Professor Je-Kyun Park of KAIST explained, "This research is a joint development achievement of an integrated platform that can simultaneously reproduce the complex multilayered structure and function of brain tissue. Compared to existing technologies where signal measurement was impossible for more than 14 days, this platform maintains a stable microelectrode chip interface for over 27 days, allowing the real-time analysis of structure-function relationships. It can be utilized in various brain research fields such as neurological disease modeling, brain function research, neurotoxicity assessment, and neuroprotective drug screening in the future."
<Figure 2. Integration process of stacked bioprinting technology and microelectrode chip>
The research, in which Dr. Soo Jee Kim and Dr. Dongjo Yoon from KAIST's Department of Bio and Brain Engineering participated as co-first authors, was published online in the international journal 'Biosensors and Bioelectronics' on June 11, 2025.
※Paper: Hybrid biofabrication of multilayered 3D neuronal networks with structural and functional interlayer connectivity
※DOI: https://doi.org/10.1016/j.bios.2025.117688
2025.07.16 View 74 -
KAIST Develops Robots That React to Danger Like Humans
<(From left) Ph.D candidate See-On Park, Professor Jongwon Lee, and Professor Shinhyun Choi>
In the midst of the co-development of artificial intelligence and robotic advancements, developing technologies that enable robots to efficiently perceive and respond to their surroundings like humans has become a crucial task. In this context, Korean researchers are gaining attention for newly implementing an artificial sensory nervous system that mimics the sensory nervous system of living organisms without the need for separate complex software or circuitry. This breakthrough technology is expected to be applied in fields such as in ultra-small robots and robotic prosthetics, where intelligent and energy-efficient responses to external stimuli are essential.
KAIST (President Kwang Hyung Lee) announced on July15th that a joint research team led by Endowed Chair Professor Shinhyun Choi of the School of Electrical Engineering at KAIST and Professor Jongwon Lee of the Department of Semiconductor Convergence at Chungnam National University (President Jung Kyum Kim) developed a next-generation neuromorphic semiconductor-based artificial sensory nervous system. This system mimics the functions of a living organism's sensory nervous system, and enables a new type of robotic system that can efficiently responds to external stimuli.
In nature, animals — including humans — ignore safe or familiar stimuli and selectively react sensitively to important or dangerous ones. This selective response helps prevent unnecessary energy consumption while maintaining rapid awareness of critical signals. For instance, the sound of an air conditioner or the feel of clothing against the skin soon become familiar and are disregarded. However, if someone calls your name or a sharp object touches your skin, a rapid focus and response occur. These behaviors are regulated by the 'habituation' and 'sensitization' functions in the sensory nervous system. Attempts have been consistently made to apply these sensory nervous system functions of living organisms in order to create robots that efficiently respond to external environments like humans.
However, implementing complex neural characteristics such as habituation and sensitization in robots has faced difficulties in miniaturization and energy efficiency due to the need for separate software or complex circuitry. In particular, there have been attempts to utilize memristors, a neuromorphic semiconductor. A memristor is a next-generation electrical device, which has been widely utilized as an artificial synapse due to its ability to store analog value in the form of device resistance. However, existing memristors had limitations in mimicking the complex characteristics of the nervous system because they only allowed simple monotonic changes in conductivity.
To overcome these limitations, the research team developed a new memristor capable of reproducing complex neural response patterns such as habituation and sensitization within a single device. By introducing additional layer inside the memristor that alter conductivity in opposite directions, the device can more realistically emulate the dynamic synaptic behaviors of a real nervous system — for example, decreasing its response to repeated safe stimuli but quickly regaining sensitivity when a danger signal is detected.
<New memristor mimicking functions of sensory nervous system such as habituation/sensitization>
Using this new memristor, the research team built an artificial sensory nervous system capable of recognizing touch and pain, an applied it to a robotic hand to test its performance. When safe tactile stimuli were repeatedly applied, the robot hand, which initially reacted sensitively to unfamiliar tactile stimuli, gradually showed habituation characteristics by ignoring the stimuli. Later, when stimuli were applied along with an electric shock, it recognized this as a danger signal and showed sensitization characteristics by reacting sensitively again. Through this, it was experimentally proven that robots can efficiently respond to stimuli like humans without separate complex software or processors, verifying the possibility of developing energy-efficient neuro-inspired robots.
<Robot arm with memristor-based artificial sensory nervous system>
See-On Park, researcher at KAIST, stated, "By mimicking the human sensory nervous system with next-generation semiconductors, we have opened up the possibility of implementing a new concept of robots that are smarter and more energy-efficient in responding to external environments." He added, "This technology is expected to be utilized in various fusion fields of next-generation semiconductors and robotics, such as ultra-small robots, military robots, and medical robots like robotic prosthetics".
This research was published online on July 1st in the international journal 'Nature Communications,' with Ph.D candidate See-On Park as the first author.
Paper Title: Experimental demonstration of third-order memristor-based artificial sensory nervous system for neuro-inspired robotics
DOI: https://doi.org/10.1038/s41467-025-60818-x
This research was supported by the Korea National Research Foundation's Next-Generation Intelligent Semiconductor Technology Development Project, the Mid-Career Researcher Program, the PIM Artificial Intelligence Semiconductor Core Technology Development Project, the Excellent New Researcher Program, and the Nano Convergence Technology Division, National Nanofab Center's (NNFC) Nano-Medical Device Project.
2025.07.16 View 97 -
A KAIST Team Engineers a Microbial Platform for Efficient Lutein Production
<(From Left) Ph.D. Candidate Hyunmin Eun, Distinguished Professor Sang Yup Lee, , Dr. Cindy Pricilia Surya Prabowo>
The application of systems metabolic engineering strategies, along with the construction of an electron channeling system, has enabled the first gram-per-liter scale production of lutein from Corynebacterium glutamicum, providing a viable alternative to plant-derived lutein production.
A research group at KAIST has successfully engineered a microbial strain capable of producing lutein at industrially relevant levels. The team, led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, developed a novel C. glutamicum strain using systems metabolic engineering strategies to overcome the limitations of previous microbial lutein production efforts. This research is expected to be beneficial for the efficient production of other industrially important natural products used in food, pharmaceuticals, and cosmetics.
Lutein is a xanthophyll carotenoid found in egg yolk, fruits, and vegetables, known for its role in protecting our eyes from oxidative stress and reducing the risk of macular degeneration and cataracts. Currently, commercial lutein is predominantly extracted from marigold flowers; however, this approach has several drawbacks, including long cultivation times, high labor costs, and inefficient extraction yields, making it economically unfeasible for large-scale production. These challenges have driven the demand for alternative production methods.
To address these issues, KAIST researchers, including Ph.D. Candidate Hyunmin Eun, Dr. Cindy Pricilia Surya Prabowo, and Distinguished Professor Sang Yup Lee, applied systems metabolic engineering strategies to engineer C. glutamicum, a GRAS (Generally Recognized As Safe) microorganism widely used in industrial fermentation. Unlike Escherichia coli, which was previously explored for microbial lutein production, C. glutamicum lacks endotoxins, making it a safer and more viable option for food and pharmaceutical applications.
The team’s work, entitled “Gram-per-litre scale production of lutein by engineered Corynebacterium,” was published in Nature Synthesis on 04 July , 2025.
This research details the high-level production of lutein using glucose as a renewable carbon source via systems metabolic engineering. The team focused on eliminating metabolic bottlenecks that previously limited microbial lutein synthesis. By employing enzyme scaffold-based electron channeling strategies, the researchers improved metabolic flux towards lutein biosynthesis while minimizing unwanted byproducts.
<Lutein production metabolic pathway engineering>
To enhance productivity, bottleneck enzymes within the metabolic pathway were identified and optimized. It was determined that electron-requiring cytochrome P450 enzymes played a major role in limiting lutein biosynthesis. To overcome this limitation, an electron channeling strategy was implemented, where engineered cytochrome P450 enzymes and their reductase partners were spatially organized on synthetic scaffolds, allowing more efficient electron transfer and significantly increasing lutein production.
The engineered C. glutamicum strain was further optimized in fed-batch fermentation, achieving a record-breaking 1.78 g/L of lutein production within 54 hours, with a content of 19.51 mg/gDCW and a productivity of 32.88 mg/L/h—the highest lutein production performance in any host reported to date. This milestone demonstrates the feasibility of replacing plant-based lutein extraction with microbial fermentation technology.
“We can anticipate that this microbial cell factory-based mass production of lutein will be able to replace the current plant extraction-based process,” said Ph.D. Candidate Hyunmin Eun. He emphasized that the integrated metabolic engineering strategies developed in this study could be broadly applied for the efficient production of other valuable natural products used in pharmaceuticals and nutraceuticals.
<Schematic diagram of microbial-based lutein production platform>
“As maintaining good health in an aging society becomes increasingly important, we expect that the technology and strategies developed here will play pivotal roles in producing other medically and nutritionally significant natural products,” added Distinguished Professor Sang Yup Lee.
This work is supported by the Development of Next-generation Biorefinery Platform Technologies for Leading Bio-based Chemicals Industry project 2022M3J5A1056072 and the Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries project 2022M3J5A1056117 from the National Research Foundation supported by the Korean Ministry of Science and ICT.
Source:
Hyunmin Eun (1st), Cindy Pricilia Surya Prabowo (co-1st), and Sang Yup Lee (Corresponding). “Gram-per-litre scale production of lutein by engineered Corynebacterium”. Nature Synthesis (Online published)
For further information:
Sang Yup Lee, Distinguished Professor of Chemical and Biomolecular Engineering, KAIST (leesy@kaist.ac.kr, Tel: +82-42-350-3930)
2025.07.14 View 217 -
KAIST Ushers in Era of Predicting ‘Optimal Alloys’ Using AI, Without High-Temperature Experiments
<Picture1.(From Left) Prof. Seungbum Hong, Ph.D candidate Youngwoo Choi>
Steel alloys used in automobiles and machinery parts are typically manufactured through a melting process at high temperatures. The phenomenon where the components remain unchanged during melting is called “congruent melting.” KAIST researchers have now addressed this process—traditionally only possible through high-temperature experiments—using artificial intelligence (AI). This study draws attention as it proposes a new direction for future alloy development by predicting in advance how well alloy components will mix during melting, a long-standing challenge in the field.
KAIST (President Kwang Hyung Lee) announced on the 14th of July that Professor Seungbum Hong’s research team from the Department of Materials Science and Engineering, in international collaboration with Professor Chris Wolverton’s group at Northwestern University, has developed a high-accuracy machine learning model that predicts whether alloy components will remain stable during melting. This was achieved using formation energy data derived from Density Functional Theory (DFT)* calculations.
*Density Functional Theory (DFT): A computational quantum mechanical method used to investigate the electronic structure of many-body systems, especially atoms, molecules, and solids, based on electron density.
The research team combined formation energy values obtained via DFT with experimental melting reaction data to train a machine learning model on 4,536 binary compounds.
Among the various machine learning algorithms tested, the XGBoost-based classification model demonstrated the highest accuracy in predicting whether alloys would mix well, achieving a prediction accuracy of approximately 82.5%. The team also applied the Shapley value method* to analyze the key features of the model. One major finding was that sharp changes in the slope of the formation energy curve (referred to as “convex hull sharpness”) were the most significant factor. A steep slope indicates a composition with energetically favorable (i.e., stable) formation.
*Shapley value: An explainability method in AI used to determine how much each feature contributed to a prediction.
The most notable significance of this study is that it predicts alloy melting behavior without performing high-temperature experiments. This is especially useful for materials such as high-entropy alloys or ultra-heat-resistant alloys, which are difficult to handle experimentally. The approach could also be extended to the design of complex multi-component alloy systems in the future.
Furthermore, the physical indicators identified by the AI model showed high consistency with actual experimental results on how well alloys mix and remain stable. This suggests that the model could be broadly applied to the development of various metal materials and the prediction of structural stability.
Professor Seungbum Hong of KAIST stated, “This research demonstrates how data-driven predictive materials development is possible by integrating computational methods, experimental data, and machine learning—departing from the traditional experience-based alloy design.” He added, “In the future, by incorporating state-of-the-art AI techniques such as generative models and reinforcement learning, we could enter an era where completely new alloys are designed automatically.”
<Model performance and feature importance analysis for predicting melting congruency. (a) SHAP summary plot showing the impact of individual features on model predictions. (b) Confusion matrix illustrating the model’s classification performance. (c) Receiver operating characteristic (ROC) curve with an AUC (area under the curve) score of 0.87, indicating a strong classification performance.>
Ph.D. candidate Youngwoo Choi, from the Department of Materials Science and Engineering at KAIST, participated as the first author. The study was published in the May issue of APL Machine Learning, a prestigious journal in the field of machine learning published by the American Institute of Physics, and was selected as a “Featured Article.”
※ Paper title: Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy ※ DOI: 10.1063/5.0247514
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.07.14 View 77 -
KAIST Presents a Breakthrough in Overcoming Drug Resistance in Cancer – Hope for Treating Intractable Diseases like Diabetes
<(From the left) Prof. Hyun Uk Kim, Ph.D candiate Hae Deok Jung, Ph.D candidate Jina Lim, Prof.Yoosik Kim from the Department of Chemical and Biomolecular Engineering>
One of the biggest obstacles in cancer treatment is drug resistance in cancer cells. Conventional efforts have focused on identifying new drug targets to eliminate these resistant cells, but such approaches can often lead to even stronger resistance. Now, researchers at KAIST have developed a computational framework to predict key metabolic genes that can re-sensitize resistant cancer cells to treatment. This technique holds promise not only for a variety of cancer therapies but also for treating metabolic diseases such as diabetes.
On the 7th of July, KAIST (President Kwang Hyung Lee) announced that a research team led by Professors Hyun Uk Kim and Yoosik Kim from the Department of Chemical and Biomolecular Engineering had developed a computational framework that predicts metabolic gene targets to re-sensitize drug-resistant breast cancer cells. This was achieved using a metabolic network model capable of simulating human metabolism.
Focusing on metabolic alterations—key characteristics in the formation of drug resistance—the researchers developed a metabolism-based approach to identify gene targets that could enhance drug responsiveness by regulating the metabolism of drug-resistant breast cancer cells.
< Computational framework that can identify metabolic gene targets to revert the metabolic state of the drug-resistant cells to that of the drug-sensitive parental cells>
The team first constructed cell-specific metabolic network models by integrating proteomic data obtained from two different types of drug-resistant MCF7 breast cancer cell lines: one resistant to doxorubicin and the other to paclitaxel. They then performed gene knockout simulations* on all of the metabolic genes and analyzed the results.
*Gene knockout simulation: A computational method to predict changes in a biological network by virtually removing specific genes.
As a result, they discovered that suppressing certain genes could make previously resistant cancer cells responsive to anticancer drugs again. Specifically, they identified GOT1 as a target in doxorubicin-resistant cells, GPI in paclitaxel-resistant cells, and SLC1A5 as a common target for both drugs.
The predictions were experimentally validated by suppressing proteins encoded by these genes, which led to the re-sensitization of the drug-resistant cancer cells.
Furthermore, consistent re-sensitization effects were also observed when the same proteins were inhibited in other types of breast cancer cells that had developed resistance to the same drugs.
Professor Yoosik Kim remarked, “Cellular metabolism plays a crucial role in various intractable diseases including infectious and degenerative conditions. This new technology, which predicts metabolic regulation switches, can serve as a foundational tool not only for treating drug-resistant breast cancer but also for a wide range of diseases that currently lack effective therapies.”
Professor Hyun Uk Kim, who led the study, emphasized, “The significance of this research lies in our ability to accurately predict key metabolic genes that can make resistant cancer cells responsive to treatment again—using only computer simulations and minimal experimental data. This framework can be widely applied to discover new therapeutic targets in various cancers and metabolic diseases.”
The study, in which Ph.D. candidates JinA Lim and Hae Deok Jung from KAIST participated as co-first authors, was published online on June 25 in Proceedings of the National Academy of Sciences (PNAS), a leading multidisciplinary journal that covers top-tier research in life sciences, physics, engineering, and social sciences.
※ Title: Genome-scale knockout simulation and clustering analysis of drug-resistant breast cancer cells reveal drug sensitization targets ※ DOI: https://doi.org/10.1073/pnas.2425384122 ※ Authors: JinA Lim (KAIST, co-first author), Hae Deok Jung (KAIST, co-first author), Han Suk Ryu (Seoul National University Hospital, corresponding author), Yoosik Kim (KAIST, corresponding author), Hyun Uk Kim (KAIST, corresponding author), and five others.
This research was supported by the Ministry of Science and ICT through the National Research Foundation of Korea, and the Electronics and Telecommunications Research Institute (ETRI).
2025.07.08 View 369 -
KAIST Presents Game-Changing Technology for Intractable Brain Disease Treatment Using Micro OLEDs
<(From left)Professor Kyung Cheol Choi, Hyunjoo J. Lee, 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 researcher 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.
Advanced Functional Materials-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).
2025.07.07 View 232 -
KAIST researcher Se Jin Park develops 'SpeechSSM,' opening up possibilities for a 24-hour AI voice assistant.
<(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.
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.
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.
2025.07.04 View 635 -
KAIST Uses AI to Discover Optimal New Material for Removing Radioactive Iodine Contamination
<(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.
2025.07.03 View 831 -
KAIST Develops AI to Easily Find Promising Materials That Capture Only CO₂
< 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.
2025.06.29 View 1008 -
KAIST Invites World-Renowned Scholars, Elevating Global Competitiveness
< Photo 1. (From left) Professor John Rogers, Professor Gregg Rothermel, Dr. Sang H. Choi >
KAIST announced on June 27th that it has appointed three world-renowned scholars, including Professor John A. Rogers of Northwestern University, USA, as Invited Distinguished Professors in key departments such as Materials Science and Engineering.
Professor John A. Rogers (Northwestern University, USA) will be working with the Department of Materials Science and Engineering from July 2025 to June 2028 with Professor Gregg Rothermel (North Carolina State University, USA) working with the School of Computing from August 2025 to July 2026, and Dr. Sang H. Choi (NASA Langley Research Center, USA) with the Department of Aerospace Engineering from May 2025 to April 2028.
Professor John A. Rogers, a person of global authority in the field of bio-integrated electronics, has been leading advanced convergence technologies such as flexible electronics, smart skin, and implantable sensors. His significant impact on academia and industry is evident through over 900 papers published in top-tier academic journals like Science, Nature, and Cell, and he comes in an H-index of 240*. His research group, the Rogers Research Group at Northwestern University, focuses on "Science that brings Solutions to Society," encompassing areas such as bio-integrated microsystems and unconventional nanofabrication techniques. He is the founding Director of the Querrey-Simpson Institute of Bioelectronics at Northwestern University.
* H-index 240: An H-index is a measurement used to assess the research productivity and impact of an individual authors. H-index 240 means that 240 or more papers have been cited at least 240 times each, indicating a significant impact and the presumable status as a world-class scholar.
The Department of Materials Science and Engineering plans to further enhance its research capabilities in next-generation bio-implantable materials and wearable devices and boost its global competitiveness through the invitation of Professor Rogers. In particular, it aims to create strong research synergies by linking with the development of bio-convergence interface materials, a core task of the Leading Research Center (ERC, total research budget of 13.5 billion KRW over 7 years) led by Professor Kun-Jae Lee.
Professor Gregg Rothermel, a world-renowned scholar in software engineering, was ranked second among the top 50 global researchers by Communications of the ACM. For over 30 years, he has conducted practical research to improve software reliability and quality. He has achieved influential research outcomes through collaborations with global companies such as Boeing, Microsoft, and Lockheed Martin. Dr. Rothermel's research at North Carolina State University focuses on software engineering and program analysis, with significant contributions through initiatives like the ESQuaReD Laboratory and the Software-Artifact Infrastructure Repository (SIR).
The School of Computing plans to strengthen its research capabilities in software engineering and conduct collaborative research on software design and testing to enhance the reliability and safety of AI-based software systems through the invitation of Professor Gregg Rothermel. In particular, he is expected to participate in the Big Data Edge-Cloud Service Research Center (ITRC, total research budget of 6.7 billion KRW over 8 years) led by Professor In-Young Ko of the School of Computing, and the Research on Improving Complex Mobility Safety (SafetyOps, Digital Columbus Project, total research budget of 3.5 billion KRW over 8 years), contributing to resolving uncertainties in machine learning-based AI software and advancing technology.
Dr. Sang H. Choi, a global expert in space exploration and energy harvesting, has worked at NASA Langley Research Center for over 40 years, authoring over 200 papers and reports, holding 45 patents, and receiving 71 awards from NASA. In 2022, he was inducted into the 'Inventors Hall of Fame' as part of NASA's Technology Transfer Program. This is a rare honor, recognizing researchers who have contributed to the private sector dissemination of space exploration technology, with only 35 individuals worldwide selected to date. Dr. Choi's extensive work at NASA includes research on advanced electronic and energetic materials, satellite sensors, and various nano-technologies.
Dr. Choi plans to collaborate with Associate Professor Hyun-Jung Kim (former NASA Research Scientist, 2009-2024), who joined the Department of Aerospace Engineering in September of 2024, to lead the development of core technologies for lunar exploration (energy sources, sensing, in-situ resource utilization ISRU).
KAIST President Kwang Hyung Lee stated, "It is very meaningful to be able to invite these world-class scholars. Through these appointments, KAIST will further strengthen its global competitiveness in research in the fields of advanced convergence technology such as bio-convergence electronics, AI software engineering, and space exploration, securing our position as the leader of global innovations."
2025.06.27 View 1593 -
Military Combatants Usher in an Era of Personalized Training with New Materials
< 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.
2025.06.25 View 1235