AI
-
KAIST provides a comprehensive resource on microbial cell factories for sustainable chemical production
In silico analysis of five industrial microorganisms identifies optimal strains and metabolic engineering strategies for producing 235 valuable chemicals
Climate change and the depletion of fossil fuels have raised the global need for sustainable chemical production. In response to these environmental challenges, microbial cell factories are gaining attention as eco-friendly platforms for producing chemicals using renewable resources, while metabolic engineering technologies to enhance these cell factories are becoming crucial tools for maximizing production efficiency. However, difficulties in selecting suitable microbial strains and optimizing complex metabolic pathways continue to pose significant obstacles to practical industrial applications.
KAIST (President Kwang-Hyung Lee) announced on 27th of March that Distinguished Professor Sang Yup Lee’s research team in the Department of Chemical and Biomolecular Engineering comprehensively evaluated the production capabilities of various industrial microbial cell factories using in silico simulations and, based on these findings, identified the most suitable microbial strains for producing specific chemicals as well as optimal metabolic engineering strategies.
Previously, researchers attempted to determine the best strains and efficient metabolic engineering strategies among numerous microbial candidates through extensive biological experiments and meticulous verification processes. However, this approach required substantial time and costs. Recently, the introduction of genome-scale metabolic models (GEMs), which reconstruct the metabolic networks within an organism based on its entire genome information, has enabled systematic analysis of metabolic fluxes via computer simulations. This development offers a new way to overcome limitations of conventional experimental approaches, revolutionizing both strain selection and metabolic pathway design.
Accordingly, Professor Lee’s team at the Department of Chemical and Biomolecular Engineering, KAIST, evaluated the production capabilities of five representative industrial microorganisms—Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, Corynebacterium glutamicum, and Pseudomonas putida—for 235 bio-based chemicals. Using GEMs, the researchers calculated both the maximum theoretical yields and the maximum achievable yields under industrial conditions for each chemical, thereby establishing criteria to identify the most suitable strains for each target compound.
< Figure 1. Outline of the strategy for improving microbial cell factories using a genome-scale metabolic model (GEM) >
The team specifically proposed strategies such as introducing heterologous enzyme reactions derived from other organisms and exchanging cofactors used by microbes to expand metabolic pathways. These strategies were shown to increase yields beyond the innate metabolic capacities of the microorganisms, resulting in higher production of industrially important chemicals such as mevalonic acid, propanol, fatty acids, and isoprenoids.
Moreover, by applying a computational approach to analyze metabolic fluxes in silico, the researchers suggested strategies for improving microbial strains to maximize the production of various chemicals. They quantitatively identified the relationships between specific enzyme reactions and target chemical production, as well as the relationships between enzymes and metabolites, determining which enzyme reactions should be up- or down-regulated. Through this, the team presented strategies not only to achieve high theoretical yields but also to maximize actual production capacities.
< Figure 2. Comparison of production routes and maximum yields of useful chemicals using representative industrial microorganisms >
Dr. Gi Bae Kim, the first author of this paper from the KAIST BioProcess Engineering Research Center, explained, “By introducing metabolic pathways derived from other organisms and exchanging cofactors, it is possible to design new microbial cell factories that surpass existing limitations. The strategies presented in this study will play a pivotal role in making microbial-based production processes more economical and efficient.” In addition, Distinguished Professor Sang Yup Lee noted, “This research serves as a key resource in the field of systems metabolic engineering, reducing difficulties in strain selection and pathway design, and enabling more efficient development of microbial cell factories. We expect it to greatly contribute to the future development of technologies for producing various eco-friendly chemicals, such as biofuels, bioplastics, and functional food materials.”
This research was conducted with the support from the Development of platform technologies of microbial cell factories for the next-generation biorefineries project and Development of advanced synthetic biology source technologies for leading the biomanufacturing industry project (Project Leader: Distinguished Professor Sang Yup Lee, KAIST) from National Research Foundation supported by the Korean Ministry of Science and ICT.
2025.03.27 View 500 -
KAIST Research Team Develops an AI Framework Capable of Overcoming the Strength-Ductility Dilemma in Additive-manufactured Titanium Alloys
<(From Left) Ph.D. Student Jaejung Park and Professor Seungchul Lee of KAIST Department of Mechanical Engineering and , Professor Hyoung Seop Kim of POSTECH, and M.S.–Ph.D. Integrated Program Student Jeong Ah Lee of POSTECH. >
The KAIST research team led by Professor Seungchul Lee from Department of Mechanical Engineering, in collaboration with Professor Hyoung Seop Kim’s team at POSTECH, successfully overcame the strength–ductility dilemma of Ti 6Al 4V alloy using artificial intelligence, enabling the production of high strength, high ductility metal products. The AI developed by the team accurately predicts mechanical properties based on various 3D printing process parameters while also providing uncertainty information, and it uses both to recommend process parameters that hold high promise for 3D printing.
Among various 3D printing technologies, laser powder bed fusion is an innovative method for manufacturing Ti-6Al-4V alloy, renowned for its high strength and bio-compatibility. However, this alloy made via 3D printing has traditionally faced challenges in simultaneously achieving high strength and high ductility. Although there have been attempts to address this issue by adjusting both the printing process parameters and heat treatment conditions, the vast number of possible combinations made it difficult to explore them all through experiments and simulations alone.
The active learning framework developed by the team quickly explores a wide range of 3D printing process parameters and heat treatment conditions to recommend those expected to improve both strength and ductility of the alloy. These recommendations are based on the AI model’s predictions of ultimate tensile strength and total elongation along with associated uncertainty information for each set of process parameters and heat treatment conditions. The recommended conditions are then validated by performing 3D printing and tensile tests to obtain the true mechanical property values. These new data are incorporated into further AI model training, and through iterative exploration, the optimal process parameters and heat treatment conditions for producing high-performance alloys were determined in only five iterations. With these optimized conditions, the 3D printed Ti-6Al-4V alloy achieved an ultimate tensile strength of 1190 MPa and a total elongation of 16.5%, successfully overcoming the strength–ductility dilemma.
Professor Seungchul Lee commented, “In this study, by optimizing the 3D printing process parameters and heat treatment conditions, we were able to develop a high-strength, high-ductility Ti-6Al-4V alloy with minimal experimentation trials. Compared to previous studies, we produced an alloy with a similar ultimate tensile strength but higher total elongation, as well as that with a similar elongation but greater ultimate tensile strength.” He added, “Furthermore, if our approach is applied not only to mechanical properties but also to other properties such as thermal conductivity and thermal expansion, we anticipate that it will enable efficient exploration of 3D printing process parameters and heat treatment conditions.”
This study was published in Nature Communications on January 22 (https://doi.org/10.1038/s41467-025-56267-1), and the research was supported by the National Research Foundation of Korea’s Nano & Material Technology Development Program and the Leading Research Center Program.
2025.02.21 View 1715 -
A Way for Smartwatches to Detect Depression Risks Devised by KAIST and U of Michigan Researchers
- A international joint research team of KAIST and the University of Michigan developed a digital biomarker for predicting symptoms of depression based on data collected by smartwatches
- It has the potential to be used as a medical technology to replace the economically burdensome fMRI measurement test
- It is expected to expand the scope of digital health data analysis
The CORONA virus pandemic also brought about a pandemic of mental illness. Approximately one billion people worldwide suffer from various psychiatric conditions. Korea is one of more serious cases, with approximately 1.8 million patients exhibiting depression and anxiety disorders, and the total number of patients with clinical mental diseases has increased by 37% in five years to approximately 4.65 million. A joint research team from Korea and the US has developed a technology that uses biometric data collected through wearable devices to predict tomorrow's mood and, further, to predict the possibility of developing symptoms of depression.
< Figure 1. Schematic diagram of the research results. Based on the biometric data collected by a smartwatch, a mathematical algorithm that solves the inverse problem to estimate the brain's circadian phase and sleep stages has been developed. This algorithm can estimate the degrees of circadian disruption, and these estimates can be used as the digital biomarkers to predict depression risks. >
KAIST (President Kwang Hyung Lee) announced on the 15th of January that the research team under Professor Dae Wook Kim from the Department of Brain and Cognitive Sciences and the team under Professor Daniel B. Forger from the Department of Mathematics at the University of Michigan in the United States have developed a technology to predict symptoms of depression such as sleep disorders, depression, loss of appetite, overeating, and decreased concentration in shift workers from the activity and heart rate data collected from smartwatches.
According to WHO, a promising new treatment direction for mental illness focuses on the sleep and circadian timekeeping system located in the hypothalamus of the brain, which directly affect impulsivity, emotional responses, decision-making, and overall mood.
However, in order to measure endogenous circadian rhythms and sleep states, blood or saliva must be drawn every 30 minutes throughout the night to measure changes in the concentration of the melatonin hormone in our bodies and polysomnography (PSG) must be performed. As such treatments requires hospitalization and most psychiatric patients only visit for outpatient treatment, there has been no significant progress in developing treatment methods that take these two factors into account. In addition, the cost of the PSG test, which is approximately $1000, leaves mental health treatment considering sleep and circadian rhythms out of reach for the socially disadvantaged.
The solution to overcome these problems is to employ wearable devices for the easier collection of biometric data such as heart rate, body temperature, and activity level in real time without spatial constraints. However, current wearable devices have the limitation of providing only indirect information on biomarkers required by medical staff, such as the phase of the circadian clock.
The joint research team developed a filtering technology that accurately estimates the phase of the circadian clock, which changes daily, such as heart rate and activity time series data collected from a smartwatch. This is an implementation of a digital twin that precisely describes the circadian rhythm in the brain, and it can be used to estimate circadian rhythm disruption.
< Figure 2. The suprachiasmatic nucleus located in the hypothalamus of the brain is the central biological clock that regulates the 24-hour physiological rhythm and plays a key role in maintaining the body’s circadian rhythm. If the phase of this biological clock is disrupted, it affects various parts of the brain, which can cause psychiatric conditions such as depression. >
The possibility of using the digital twin of this circadian clock to predict the symptoms of depression was verified through collaboration with the research team of Professor Srijan Sen of the Michigan Neuroscience Institute and Professor Amy Bohnert of the Department of Psychiatry of the University of Michigan.
The collaborative research team conducted a large-scale prospective cohort study involving approximately 800 shift workers and showed that the circadian rhythm disruption digital biomarker estimated through the technology can predict tomorrow's mood as well as six symptoms, including sleep problems, appetite changes, decreased concentration, and suicidal thoughts, which are representative symptoms of depression.
< Figure 3. The circadian rhythm of hormones such as melatonin regulates various physiological functions and behaviors such as heart rate and activity level. These physiological and behavioral signals can be measured in daily life through wearable devices. In order to estimate the body’s circadian rhythm inversely based on the measured biometric signals, a mathematical algorithm is needed. This algorithm plays a key role in accurately identifying the characteristics of circadian rhythms by extracting hidden physiological patterns from biosignals. >
Professor Dae Wook Kim said, "It is very meaningful to be able to conduct research that provides a clue for ways to apply wearable biometric data using mathematics that have not previously been utilized for actual disease management." He added, "We expect that this research will be able to present continuous and non-invasive mental health monitoring technology. This is expected to present a new paradigm for mental health care. By resolving some of the major problems socially disadvantaged people may face in current treatment practices, they may be able to take more active steps when experiencing symptoms of depression, such as seeking counsel before things get out of hand."
< Figure 4. A mathematical algorithm was devised to circumvent the problems of estimating the phase of the brain's biological clock and sleep stages inversely from the biodata collected by a smartwatch. This algorithm can estimate the degree of daily circadian rhythm disruption, and this estimate can be used as a digital biomarker to predict depression symptoms. >
The results of this study, in which Professor Dae Wook Kim of the Department of Brain and Cognitive Sciences at KAIST participated as the joint first author and corresponding author, were published in the online version of the international academic journal npj Digital Medicine on December 5, 2024. (Paper title: The real-world association between digital markers of circadian disruption and mental health risks) DOI: 10.1038/s41746-024-01348-6
This study was conducted with the support of the KAIST's Research Support Program for New Faculty Members, the US National Science Foundation, the US National Institutes of Health, and the US Army Research Institute MURI Program.
2025.01.20 View 3339 -
KAIST Research Team Develops Stretchable Microelectrodes Array for Organoid Signal Monitoring
< Photo 1. (From top left) Professor Hyunjoo J. Lee, Dr. Mi-Young Son, Dr. Mi-Ok Lee(In the front row from left) Doctoral student Kiup Kim, Doctoral student Youngsun Lee >
On January 14th, the KAIST research team led by Professor Hyunjoo J. Lee from the School of Electrical Engineering in collaboration with Dr. Mi-Young Son and Dr. Mi-Ok Lee at Korea Research Institute of Bioscience and Biotechnology (KRIBB) announced the development of a highly stretchable microelectrode array (sMEA) designed for non-invasive electrophysiological signal measurement of organoids.
Organoids* are highly promising models for human biology and are expected to replace many animal experiments. Their potential applications include disease modeling, drug screening, and personalized medicine as they closely mimic the structure and function of humans.
*Organoids: three-dimensional in vitro tissue models derived from human stem cells
Despite these advantages, existing organoid research has primarily focused on genetic analysis, with limited studies on organoid functionality. For effective drug evaluation and precise biological research, technology that preserves the three-dimensional structure of organoids while enabling real-time monitoring of their functions is needed. However, it’s challenging to provide non-invasive ways to evaluate the functionalities without incurring damage to the tissues. This challenge is particularly significant for electrophysiological signal measurement in cardiac and brain organoids since the sensor needs to be in direct contact with organoids of varying size and irregular shape. Achieving tight contact between electrodes and the external surface of the organoids without damaging the organoids has been a persistent challenge.
< Figure 1. Schematic image of highly stretchable MEA (sMEA) with protruding microelectrodes. >
The KAIST research team developed a highly stretchable microelectrode array with a unique serpentine structure that contacts the surface of organoids in a highly conformal fashion. They successfully demonstrated real-time measurement and analysis of electrophysiological signals from two types of electrogenic organoids (heart and brain). By employing a micro-electromechanical system (MEMS)-based process, the team fabricated the serpentine-structured microelectrode array and used an electrochemical deposition process to develop PEDOT:PSS-based protruding microelectrodes. These innovations demonstrated exceptional stretchability and close surface adherence to various organoid sizes. The protruding microelectrodes improved contact between organoids and the electrodes, ensuring stable and reliable electrophysiological signal measurements with high signal-to-noise ratios (SNR).
< Figure 2. Conceptual illustration, optical image, and fluorescence images of an organoid captured by the sMEA with protruding microelectrodes.>
Using this technology, the team successfully monitored and analyzed electrophysiological signals from cardiac spheroids of various sizes, revealing three-dimensional signal propagation patterns and identifying changes in signal characteristics according to size. They also measured electrophysiological signals in midbrain organoids, demonstrating the versatility of the technology. Additionally, they monitored signal modulations induced by various drugs, showcasing the potential of this technology for drug screening applications.
< Figure 3. SNR improvement effect by protruding PEDOT:PSS microelectrodes. >
Prof. Hyunjoo Jenny Lee stated, “By integrating MEMS technology and electrochemical deposition techniques, we successfully developed a stretchable microelectrode array adaptable to organoids of diverse sizes and shapes. The high practicality is a major advantage of this system since the fabrication is based on semiconductor fabrication with high volume production, reliability, and accuracy. This technology that enables in situ, real-time analysis of states and functionalities of organoids will be a game changer in high-through drug screening.”
This study led by Ph.D. candidate Kiup Kim from KAIST and Ph.D. candidate Youngsun Lee from KRIBB, with significant contributions from Dr. Kwang Bo Jung, was published online on December 15, 2024 in Advanced Materials (IF: 27.4).
< Figure 4. Drug screening using cardiac spheroids and midbrain organoids.>
This research was supported by a grant from 3D-TissueChip Based Drug Discovery Platform Technology Development Program (No. 20009209) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), by the Commercialization Promotion Agency for R&D Outcomes (COMPA) funded by the Ministry of Science and ICT (MSIT) (RS-2024-00415902), by the K-Brain Project of the National Research Foundation (NRF) funded by the Korean government (MSIT) (RS-2023-00262568), by BK21 FOUR (Connected AI Education & Research Program for Industry and Society Innovation, KAIST EE, No. 4120200113769), and by Korea Research Institute of Bioscience and Biotechnology (KRIBB) Research Initiative Program (KGM4722432).
2025.01.14 View 1836 -
“Cross-Generation Collaborative Labs” for Semiconductor, Chemistry, and Computer Science Opened
< Photo of Professor Hoi-Jun Yoo (center) of the School of Electrical Engineering at the signboard unveiling ceremony >
KAIST held a ceremony to mark the opening of three additional ‘Cross-Generation Collaborative Labs’ on the morning of January 7th, 2025.
The “Next-Generation AI Semiconductor System Lab” by Professor Hoi-Jun Yoo of the School of Electrical Engineering, the “Molecular Spectroscopy and Chemical Dynamics Lab” by Professor Sang Kyu Kim of the Department of Chemistry, and the “Advanced Data Computing Lab” by Professor Sue Bok Moon of the School of Computer Science are the three new labs given the honored titled of the “Cross-Generation Collaborative Lab”.
The Cross-Generation Collaborative Lab is KAIST’s unique system that was set up to facilitate the collaboration between retiring professors and junior professors to continue the achievements and know-how the elders have accumulated over their academic career. Since its introduction in 2018, nine labs have been named to be the Cross-Generation Labs, and this year’s new addition brings the total up to twelve.
The ‘Next-Generation AI Semiconductor System Lab’ led by Professor Hoi-Jun Yoo will be operated by Professor Joo-Young Kim of the same school.
Professor Hoi-Jun Yoo is a world-renowned scholar with outstanding research achievements in the field of on-device AI semiconductor design. Professor Joo-Young Kim is an up-and-coming researcher studying large language models and design of AI semiconductors for server computers, and is currently researching technologies to design PIM (Processing-in-Memory), a core technology in the field of AI semiconductors.
Their research goal is to systematically collaborate and transfer next-generation AI semiconductor design technology, including brain-mimicking AI algorithms such as deep neural networks and generative AI, to integrate core technologies, and to maximize the usability of R&D outputs, thereby further solidifying the position of Korean AI semiconductor companies in the global market.
Professor Hoi-Jun Yoo said, “I believe that, we will be able to present a development direction of for the next-generation AI semiconductors industries at home and abroad through collaborative research and play a key role in transferring and expanding global leadership.”
< Professor Sang Kyu Kim of the Department of Chemistry (middle), at the signboard unveiling ceremony for his laboratory >
The “Molecular Spectroscopy and Chemical Dynamics Laboratory”, where Professor Sang Kyu Kim of the Department of Chemistry is in charge, will be operated by Professor Tae Kyu Kim of the same department, and another professor in the field of spectroscopy and dynamics will join in the future.
Professor Sang Kyu Kim has secured technologies for developing unique experimental equipment based on ultrashort lasers and supersonic molecular beams, and is a world leader who has been creatively pioneering new fields of experimental physical chemistry.
The research goal is to describe chemical reactions and verify from a quantum mechanical perspective and introduce new theories and technologies to pursue a complete understanding of the principles of chemical reactions. In addition, the accompanying basic scientific knowledge will be applied to the design of new materials.
Professor Sang Kyu Kim said, “I am very happy to be able to pass on the research infrastructure to the next generation through this system, and I will continue to nurture it to grow into a world-class research lab through trans-generational collaborative research.”
< Photo of Professor Sue Bok Moon (center) at the signboard unveiling ceremony by the School of Computing >
Lastly, the “Advanced Data Computing Lab” led by Professor Sue Bok Moon is joined by Professor Mee Young Cha of the same school and Professor Wonjae Lee of the Graduate School of Culture Technology.
Professor Sue Bok Moon showed the infinite possibilities of large-scale data-based social network research through Cyworld, YouTube, and Twitter, and had a great influence on related fields beyond the field of computer science.
Professor Mee Young Cha is a data scientist who analyzes difficult social issues such as misinformation, poverty, and disaster detection using big data-based AI. She is the first Korean to be recognized for her achievements as the director of the Max Planck Institute in Germany, a world-class basic science research institute. Therefore, there is high expectation for synergy effects from overseas collaborative research and technology transfer and sharing among the participating professors of the collaborative research lab. Professor Wonjae Lee is researching dynamic interaction analysis between science and technology using structural topic models.
They plan to conduct research aimed at improving the analysis and understanding of negative influences occurring online, and in particular, developing a hateful precursor detection model using emotions and morality to preemptively block hateful expressions.
Professor Sue Bok Moon said, “Through this collaborative research lab, we will play a key role in conducting in-depth collaborative research on unexpected negative influences in the AI era so that we can have a high level of competitiveness worldwide.”
The ceremonies for the unveiling of the new Cross-Generation Collaborative Lab signboard were held in front of each lab from 10:00 AM on the 7th, in the attendance of President Kwang Hyung Lee, Senior Vice President for Research Sang Yup Lee, and other key officials of KAIST and the new staff members to join the laboratories.
2025.01.07 View 2106 -
KAIST’s RAIBO2 becomes the World’s First Robo-dog to Successfully Complete a Full-course Marathon
KAIST's quadrupedal walking robot "RAIBO", which can run seamlessly on sandy beaches, has now evolved into "RAIBO2"and achieved the groundbreaking milestone by becomeing the world's first quadrupedal robot to successfully complete a full-course marathon in an official event.
< Photo 1. A group photo of RAIBO2 and the team after completing the full-course marathon >
KAIST (President Kwang Hyung Lee) announced on the 17th of November that Professor Je Min Hwangbo's research team of the Department of Mechanical Engineering participated in the 22nd Sangju Dried-Persimmon Marathon and completed the full-course race (42.195 km) with a time of 4 hours 19 minutes and 52 seconds.
< Photo 2. RAIBO2 after completing the full-course marathon with its official record presented on the photo wall >
The Sangju Dried Persimmon Marathon is known for its challenging course featuring two 50 m elevation climbs, each at the 14 km and 28 km marks, making it defficult for amateur runners. This made it an especially demanding challenge for the walking robot, as unexpected losses in efficiency could occur.
< Photo 3. RAIBO2 with the completion medal around its neck >
To prepare RAIBO2, Professor Hwangbo's team developed a walking controller using reinforcement learning algorithms within their proprietary simulation environment "RaiSim". This simulator allowed the team to simlate diverse terrains such as slopes, stairs, and icy roads to ensure stable walking performance.
In particular, RAIBO2's high torque transparency joint mechanism enable the robot to efficiently harvest energy on the downhill slopes to regain some of the energy used in climbing up steep hill.
In addition, the stability of the robot was greatly improved through the collaboration with RAION ROBOTICS Inc., a company founded by the researchers from Professor Hwangbo’s lab.
< Figure 1. Conceptual diagram of power flow employed by the quadrupedal robot >
< Figure 2. The process of leg posture change of RAIBO2 walking at the most efficient walking speed of 3 m/s. By reducing the ground contact speed of the feet, the collision energy loss was reduced, and by minimizing the slipperiness of the foot upon contact, the body's kinetic energy was maintained towards the direction of the movement. >
Due to the nature of walking, pedal robots must employ highly complex systems that can withstand periodic vibrations from the frequent impacts that occur upon contact with the ground surface. Immediately after development, high efficiency was already recorded in short-distance experiments in the laboratory at the beginning of the year, but the manufacturing technology of RAION ROBOTICS significantly bolstered RAIBO's performance in running safely for a prolonged time of more than 4 hours among random pack of people in an actual marathon.
Compared to previous studies on improving walking efficiency, where external parts or software could not be changed and only limited improvements were made in some areas, Professor Hwangbo’s research team cited the fact that they were able to comprehensively solve problems by developing all steps and parts in-house, including mechanism design, electrical design, software, and artificial intelligence, as a key factor in improving efficiency.
Following the development of RAIBO1, the research team developed RAIBO2 and optimized all aspects of the robot. In particular, the team integrated the motor driver circuitry directly into the robot to minimize actuator losses and increase the control bandwidth, greatly improving walking efficiency and stability.
< Photo 4. RAIBO2 running the full-course marathon along human participants >
Choongin Lee, a Ph.D. Student that co-first author of the studies on RAIBO, said, “Through the marathon project, we demonstrated that RAIBO2 has the walking performance to stably execute services such as delivery and patrol in urban environments with many people and random objects,” and “In follow-up research, we will add autonomous navigation functions to RAIBO and strive to achieve the world’s best walking performance in mountainous and disaster environments.”
< Photo 5. RAIBO2 and co-first authors of the related research at the Ph.D. program of the Department of Mechanical Engineering at KAIST. (From left) Choongin Lee, Donghoon Youm, and Jeongsoo Park >
This research was conducted with the support of Samsung Electronics Future Technology Promotion Center and RAION ROBOTICS Inc.
2024.11.17 View 5157 -
KAIST’s Beach-Roaming Quadrupedal Robot “RAIBO” to Run a Marathon!
“RAIBO”, KAIST’s four-legged robot featuring remarkable agility even on challenging terrains like sandy beaches, is now set to be the first in the world to complete a full marathon.
< Photo 1. A group photo of the research team of Professor Je Min Hwangbo (second from the right in the front row) of the Department of Mechanical Engineering who participated in the marathon event at 2024 Geumsan Insam Festival last September >
On the 17th of November, KAIST (represented by President Kwang Hyung Lee) announced that Professor Je Min Hwangbo’s team from the Department of Mechanical Engineering has developed an upgraded version, “RAIBO2,” which will take on the full 42.195-kilometer course at the "Sangju Dried-Persimmon Marathon".
This is over double the previous maximum distance achieved by quadruped robots, which was limited to around 20 kilometers. The KAIST team has successfully developed a robot that can walk continuously for 43 kilometers on a single charge, completing the course in 4 hours and 40 minutes by following a GPS-guided path on the university’s main athletic field. Through this marathon, the team aims to demonstrate RAIBO2’s walking performance in an actual urban environment.
Previously, most measurements of walking robots’ travel distances were confined to controlled laboratory conditions or theoretical data. This marathon challenge is thus significant in that the robot will run alongside the general public in a real urban setting, marking the first attempt to validate the practical potential of four-legged robots in real environments.
Quadruped robots have shown advantages in challenging terrains, such as ice, sand, and mountainous areas, where they can walk stably. However, limited travel distance and running time have long been obstacles to wider applications.
< Figure 1. Conceptual diagram of power flow employed by the quadruped robot >
Professor Hwangbo’s team designed every component of the robot, from its actuators to its mechanical structure, to overcome these limitations. Notably, they implemented an efficient walking control system based on reinforcement learning using their proprietary dynamic simulator “RaiSim”.
The team also collected and analyzed walking data from outdoor environments, creating a model to address walking losses. This model was then used to iteratively improve walking efficiency over one full year.
< Figure 2. The leg posture change process of RAIBO2 walking at the most efficient walking speed of 3 m/s. By reducing the ground contact speed of the feet, the collision energy loss was reduced, and by minimizing the slipperiness of the foot upon contact, the body's kinetic energy was maintained towards the direction of the movement. >
This is the team’s second attempt. Their first was during the marathon event at “Geumsan Insam Festival” in September when the robot’s battery ran out at the 37-kilometer mark, falling short of completion. The battery drained 10 kilometers earlier than expected due to frequent speed changes as the robot adjusted to the pacing of other runners on the course.
Following the initial attempt, the team focused on technical improvements for a successful finish. They enhanced control efficiency by implementing joint stiffness control directly onto the motor actuator and increased battery capacity by 33% by refining the internal structure. These improvements enabled the robot to cover a maximum distance of 67 kilometers on straight paths.
< Figure 3. Data from completing 43 km on a single charge at the main sports field on campus. Left) GPS data of the driving course used during autonomous running. Middle) Covered distance by the hour. Right) Energy consumption by module >
Choongin Lee, a Ph.D. student of RAI Lab who is one of the co-first author of this study, explained, “Our comprehensive analysis of walking losses in terms of mechanics, electrical systems, and walking methods was crucial to improving walking efficiency. This research marks an important milestone in extending the operating range of quadruped robots to urban environments.”
< Photo 2. A Photo from Practice Run>
This research was supported by the Samsung Electronics Future Technology Development Center and RAION ROBOTICS Co., Ltd.
< Photo 3. A Photo from Practice Run >
2024.11.15 View 2758 -
KAIST Develops a Fire-risk Free Self-Powered Hydrogen Production System
KAIST researchers have developed a new hydrogen production system that overcomes the current limitations of green hydrogen production. By using a water-splitting system with an aqueous electrolyte, this system is expected to block fire risks and enable stable hydrogen production.
KAIST (represented by President Kwang Hyung Lee) announced on the 22nd of October that a research team led by Professor Jeung Ku Kang from the Department of Materials Science and Engineering developed a self-powered hydrogen production system based on a high-performance zinc-air battery*.
*Zinc-air battery: A primary battery that absorbs oxygen from the air and uses it as an oxidant. Its advantage is long life, but its low electromotive force is a disadvantage.
Hydrogen (H₂) is a key raw material for synthesizing high-value-added substances, and it is gaining attention as a clean fuel with an energy density (142 MJ/kg) more than three times higher than traditional fossil fuels (gasoline, diesel, etc.). However, most current hydrogen production methods impose environmental burden as they emit carbon dioxide (CO₂).
While green hydrogen can be produced by splitting water using renewable energy sources such as solar cells and wind power, these sources are subject to irregular power generation due to weather and temperature fluctuations, leading to low water-splitting efficiency.
To overcome this, air batteries that can emit sufficient voltage (greater than 1.23V) for water splitting have been gaining attention. However, achieving sufficient capacity requires expensive precious metal catalysts and the performance of the catalyst materials becomes significantly degraded during prolonged charge and discharge cycles. Thus, it is essential to develop catalysts that are effective for the water-splitting reactions (oxygen and hydrogen evolution) and materials that can stabilize the repeated charge and discharge reactions (oxygen reduction and evolution) in zinc-air battery electrodes.
In response, Professor Kang's research team proposed a method to synthesize a non-precious metal catalyst material (G-SHELL) that is effective for three different catalytic reactions (oxygen evolution, hydrogen evolution, and oxygen reduction) by growing nano-sized, metal-organic frameworks on graphene oxide.
The team incorporated the developed catalyst material into the air cathode of a zinc-air battery, confirming that it achieved approximately five times higher energy density (797Wh/kg), high power characteristics (275.8mW/cm²), and long-term stability even under repeated charge and discharge conditions compared to conventional batteries.
Additionally, the zinc-air battery, which operates using an aqueous electrolyte, is safe from fire risks. It is expected that this system can be applied as a next-generation energy storage device when linked with water electrolysis systems, offering an environmentally friendly method for hydrogen production.
< Figure 1. Illustrations of a trifunctional graphene-sandwiched heterojunction-embedded layered lattice (G-SHELL) structure. Schematic representation of a) synthesis procedures of G-SHELL from a zeolitic imidazole framework, b) hollow core-layered shell structure with trifunctional sites for oxygen reduction evolution (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER), and c) heterojunctions, eterojunction-induced internal electric fields, and the corresponding band structure. >
Professor Kang explained, "By developing a catalyst material with high activity and durability for three different electrochemical catalytic reactions at low temperatures using simple methods, the self-powered hydrogen production system we implemented based on zinc-air batteries presents a new breakthrough to overcome the current limitations of green hydrogen production."
<Figure 2. Electrochemical performance of a ZAB-driven water-splitting cell with G-SHELL. Diagram of a self-driven water-splitting cell integrated by combining a ZAB with an alkaline water electrolyzer.>
PhD candidate Dong Won Kim and Jihoon Kim, a master's student in the Department of Materials Science and Engineering at KAIST, were co-first authors of this research, which was published in the international journal Advanced Science on September 17th in the multidisciplinary field of materials science. (Paper Title: “Trifunctional Graphene-Sandwiched Heterojunction-Embedded Layered Lattice Electrocatalyst for High Performance in Zn-Air Battery-Driven Water Splitting”)
This research was supported by the Nano and Material Technology Development Program of the Ministry of Science and ICT and the National Research Foundation of Korea’s Future Technology Research Laboratory.
2024.10.22 View 3347 -
KAIST Develops Thread-like, Flexible Thermoelectric Materials Applicable in Extreme Environments
A team of Korean researchers developed a thermoelectric material that can be used in wearable devices, such as smart clothing, and while maintaining stable thermal energy performance even in extreme environments. It has dramatically resolved the dilemma of striking the balance between achieving good performance and the mechanical flexibility of thermoelectric materials, which has been a long-standing challenge in the field of thermoelectric materials, and has also proven the possibility of commercialization.
KAIST (President Kwang-Hyung Lee) announced on the 21st that a joint research team of Professor Yeon Sik Jung of the Department of Materials Science and Engineering and Professor Inkyu Park of the Department of Mechanical Engineering, in collaboration with the research teams of Professor Min-Wook Oh of Hanbat National University (President Yong Jun Oh) and Dr. Jun-Ho Jeong of the Korea Institute of Machinery and Materials (President Seoghyun Ryu), have successfully developed ‘bismuth telluride (Bi2Te3) thermoelectric fibers,’ an innovative energy harvesting solution for next-generation flexible electronic devices.
Thermoelectric materials are materials that generate voltage when there is a temperature difference and convert thermal energy into electrical energy. Currently, about 70% of energy being lost as wasted heat, so due attention is being given to research on these as sustainable energy materials that can recover and harvesting energy from this waste heat.
Most of the heat sources around us are curved, such as the human body, vehicle exhaust pipes, and cooling fins. Inorganic thermoelectric materials based on ceramic materials boast high thermoelectric performance, but they are fragile and difficult to produce in curved shapes. On the other hand, flexible thermoelectric materials using existing polymer binders can be applied to surfaces of various shapes, but their performance was limited due to the low electrical conductivity and high thermal resistance of the polymer.
Existing flexible thermoelectric materials contain polymer additives, but the inorganic thermoelectric material developed by the research team is not flexible, so they overcame these limitations by twisting nano ribbons instead of additives to produce a thread-shaped thermoelectric material. Inspired by the flexibility of inorganic nano ribbons, the research team used a nanomold-based electron beam deposition technique to continuously deposit nano ribbons and then twisted them into a thread shape to create bismuth telluride (Bi2Te3) inorganic thermoelectric fibers.
These inorganic thermoelectric fibers have higher bending strength than existing thermoelectric materials, and showed almost no change in electrical properties even after repeated bending and tensile tests of more than 1,000 times. The thermoelectric device created by the research team generates electricity using temperature differences, and if clothes are made with fiber-type thermoelectric devices, electricity can be generated from body temperature to operate other electronic devices.
< Figure 1. Schematic diagram and actual image of the all-inorganic flexible thermoelectric yarn made without polymer additives >
In fact, the possibility of commercialization was proven through a demonstration of collecting energy by embedding thermoelectric fibers in life jackets or clothing. In addition, it opened up the possibility of building a high-efficiency energy harvesting system that recycles waste heat by utilizing the temperature difference between the hot fluid inside a pipe and the cold air outside in industrial settings.
Professor Yeon Sik Jung said, "The inorganic flexible thermoelectric material developed in this study can be used in wearable devices such as smart clothing, and it can maintain stable performance even in extreme environments, so it has a high possibility of being commercialized through additional research in the future." Professor Inkyu Park also emphasized, "This technology will become the core of next-generation energy harvesting technology, and it is expected to play an important role in various fields from waste heat utilization in industrial sites to personal wearable self-power generation devices."
This study, in which Hanhwi Jang, a Ph.D. student at KAIST's Department of Materials Science and Engineering, Professor Junseong Ahn of Korea University, Sejong Campus, and Dr. Yongrok Jeong of Korea Atomic Energy Research Institute contributed equally as joint first authors, was published in the online edition of the international academic journal Advanced Materials on September 17, and was selected as the back-cover paper in recognition of its excellence. (Paper title: Flexible All-Inorganic Thermoelectric Yarns)
Meanwhile, this study was conducted through the Mid-career Researcher Support Program and the Future Materials Discovery Program of the National Research Foundation of Korea, and the support from the Global Bio-Integrated Materials Center, the Ministry of Trade, Industry and Energy, and the Korea Institute of Industrial Technology Evaluation and Planning (KEIT) upon the support by the Ministry of Science and ICT.
2024.10.21 View 2655 -
KAIST Develops Technology for the Precise Diagnosis of Electric Vehicle Batteries Using Small Currents
Accurately diagnosing the state of electric vehicle (EV) batteries is essential for their efficient management and safe use. KAIST researchers have developed a new technology that can diagnose and monitor the state of batteries with high precision using only small amounts of current, which is expected to maximize the batteries’ long-term stability and efficiency.
KAIST (represented by President Kwang Hyung Lee) announced on the 17th of October that a research team led by Professors Kyeongha Kwon and Sang-Gug Lee from the School of Electrical Engineering had developed electrochemical impedance spectroscopy (EIS) technology that can be used to improve the stability and performance of high-capacity batteries in electric vehicles.
EIS is a powerful tool that measures the impedance* magnitude and changes in a battery, allowing the evaluation of battery efficiency and loss. It is considered an important tool for assessing the state of charge (SOC) and state of health (SOH) of batteries. Additionally, it can be used to identify thermal characteristics, chemical/physical changes, predict battery life, and determine the causes of failures. *Battery Impedance: A measure of the resistance to current flow within the battery that is used to assess battery performance and condition.
However, traditional EIS equipment is expensive and complex, making it difficult to install, operate, and maintain. Moreover, due to sensitivity and precision limitations, applying current disturbances of several amperes (A) to a battery can cause significant electrical stress, increasing the risk of battery failure or fire and making it difficult to use in practice.
< Figure 1. Flow chart for diagnosis and prevention of unexpected combustion via the use of the electrochemical impedance spectroscopy (EIS) for the batteries for electric vehicles. >
To address this, the KAIST research team developed and validated a low-current EIS system for diagnosing the condition and health of high-capacity EV batteries. This EIS system can precisely measure battery impedance with low current disturbances (10mA), minimizing thermal effects and safety issues during the measurement process.
In addition, the system minimizes bulky and costly components, making it easy to integrate into vehicles. The system was proven effective in identifying the electrochemical properties of batteries under various operating conditions, including different temperatures and SOC levels.
Professor Kyeongha Kwon (the corresponding author) explained, “This system can be easily integrated into the battery management system (BMS) of electric vehicles and has demonstrated high measurement accuracy while significantly reducing the cost and complexity compared to traditional high-current EIS methods. It can contribute to battery diagnosis and performance improvements not only for electric vehicles but also for energy storage systems (ESS).”
This research, in which Young-Nam Lee, a doctoral student in the School of Electrical Engineering at KAIST participated as the first author, was published in the prestigious international journal IEEE Transactions on Industrial Electronics (top 2% in the field; IF 7.5) on September 5th. (Paper Title: Small-Perturbation Electrochemical Impedance Spectroscopy System With High Accuracy for High-Capacity Batteries in Electric Vehicles, Link: https://ieeexplore.ieee.org/document/10666864)
< Figure 2. Impedance measurement results of large-capacity batteries for electric vehicles. ZEW (commercial EW; MP10, Wonatech) versus ZMEAS (proposed system) >
This research was supported by the Basic Research Program of the National Research Foundation of Korea, the Next-Generation Intelligent Semiconductor Technology Development Program of the Korea Evaluation Institute of Industrial Technology, and the AI Semiconductor Graduate Program of the Institute of Information & Communications Technology Planning & Evaluation.
2024.10.17 View 3625 -
KAIST Changes the Paradigm of Drug Discovery with World's First Atomic Editing
In pioneering drug development, the new technology that enables the easy and rapid editing of key atoms responsible for drug efficacy has been regarded as a fundamental and "dream" technology, revolutionizing the process of discovering potential drug candidates. KAIST researchers have become the first in the world to successfully develop single-atom editing technology that maximizes drug efficacy.
On October 8th, KAIST (represented by President Kwang-Hyung Lee) announced that Professor Yoonsu Park’s research team from the Department of Chemistry successfully developed technology that enables the easy editing and correction of oxygen atoms in furan compounds into nitrogen atoms, directly converting them into pyrrole frameworks, which are widely used in pharmaceuticals.
< Image. Conceptual image illustrating the main idea of the research >
This research was published in the prestigious scientific journal Science on October 3rd under the title "Photocatalytic Furan-to-Pyrrole Conversion."
Many drugs have complex chemical structures, but their efficacy is often determined by a single critical atom. Atoms like oxygen and nitrogen play a central role in enhancing the pharmacological effects of these drugs, particularly against viruses.
This phenomenon, where the introduction of specific atoms into a drug molecule dramatically affects its efficacy, is known as the "Single Atom Effect." In leading-edge drug development, discovering atoms that maximize drug efficacy is key.
However, evaluating the Single Atom Effect has traditionally required multi-step, costly synthesis processes, as it has been difficult to selectively edit single atoms within stable ring structures containing oxygen or nitrogen.
Professor Park’s team overcame this challenge by introducing a photocatalyst that uses light energy. They developed a photocatalyst that acts as a “molecular scissor,” freely cutting and attaching five-membered rings, enabling single-atom editing at room temperature and atmospheric pressure—a world first.
The team discovered a new reaction mechanism in which the excited molecular scissor removes oxygen from furan via single-electron oxidation and then sequentially adds a nitrogen atom.
Donghyeon Kim and Jaehyun You, the study's first authors and candidates in KAIST’s integrated master's and doctoral program in the Department of Chemistry, explained that this technique offers high versatility by utilizing light energy to replace harsh conditions. They further noted that the technology enables selective editing, even when applied to complex natural products or pharmaceuticals. Professor Yoonsu Park, who led the research, remarked, "This breakthrough, which allows for the selective editing of five-membered organic ring structures, will open new doors for building libraries of drug candidates, a key challenge in pharmaceuticals. I hope this foundational technology will be used to revolutionize the drug development process."
The significance of this research was highlighted in the Perspective section of Science, a feature where a peer scientist of prominence outside of the project group provides commentary on an impactful research.
This research was supported by the National Research Foundation of Korea’s Creative Research Program, the Cross-Generation Collaborative Lab Project at KAIST, and the POSCO Science Fellowship of the POSCO TJ Park Foundation.
2024.10.11 View 3437 -
Unraveling Mitochondrial DNA Mutations in Human Cells
Throughout our lifetime, cells accumulate DNA mutations, which contribute to genetic diversity, or “mosaicism”, among cells. These genomic mutations are pivotal for the aging process and the onset of various diseases, including cancer. Mitochondria, essential cellular organelles involved in energy metabolism and apoptosis, possess their own DNA, which are susceptible to mutations. However, studies on mtDNA mutations and mosaicism have been limited due to a variety of technical challenges.
Genomic scientists from KAIST have revealed the genetic mosaicism characterized by variations in mitochondrial DNA (mtDNA) across normal human cells. This study provides fundamental insights into understanding human aging and disease onset mechanisms.
The study, “Mitochondrial DNA mosaicism in normal human somatic cells,” was published in Nature Genetics on July 22. It was led by graduate student Jisong An under the supervision of Professor Young Seok Ju from the Graduate School of Medical Science and Engineering.
Researchers from Seoul National University College of Medicine, Yonsei University College of Medicine, Korea University College of Medicine, Washington University School of Medicine National Cancer Center, Seoul National University Hospital, Gangnam Severance Hospital and KAIST faculty startup company Inocras Inc. also participated in this study.
< Figure 1. a. Flow of experiment. b. Schematic diagram illustrating the origin and dynamics of mtDNA alterations across a lifetime. >
The study involved a bioinformatic analysis of whole-genome sequences from 2,096 single cells obtained from normal human colorectal epithelial tissue, fibroblasts, and blood collected from 31 individuals. The study highlights an average of three significant mtDNA differences between cells, with approximately 6% of these variations confirmed to be inherited as heteroplasmy from the mother.
Moreover, mutations significantly increased during tumorigenesis, with some mutations contributing to instability in mitochondrial RNA. Based on these findings, the study illustrates a computational model that comprehensively elucidates the evolution of mitochondria from embryonic development to aging and tumorigenesis.
This study systematically reveals the mechanisms behind mitochondrial DNA mosaicism in normal human cells, establishing a crucial foundation for understanding the impact of mtDNA on aging and disease onset.
Professor Ju remarked, “By systematically utilizing whole-genome big data, we can illuminate previously unknown phenomena in life sciences.” He emphasized the significance of the study, adding, “For the first time, we have established a method to systematically understand mitochondrial DNA changes occurring during human embryonic development, aging, and cancer development.”
This work was supported by the National Research Foundation of Korea and the Suh Kyungbae Foundation.
2024.07.24 View 3577