College+of+Engineering
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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 233 -
KAIST Develops Eco-Friendly, Nylon-Like Plastic Using Microorganisms
Poly(ester amide) amide is a next-generation material that combines the advantages of PET (polyester) and nylon (polyamide), two widely used plastics. However, it could only be produced from fossil fuels, which posed environmental concerns. Using microorganisms, KAIST researchers have successfully developed a new bio-based plastic to replace conventional plastic.
KAIST (represented by President Kwang Hyung Lee) announced on the 20th of March that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering has developed microbial strains through systems metabolic engineering to produce various eco-friendly, bio-based poly(ester amide)s. The team collaborated with researchers from the Korea Research Institute of Chemical Technology (KRICT, President Young-Kook Lee) to analyze and confirm the properties of the resulting plastic.
Professor Sang Yup Lee’s research team designed new metabolic pathways that do not naturally exist in microorganisms, and developed a platform microbial strain capable of producing nine different types of poly(ester amide)s, including poly(3-hydroxybutyrate-ran-3-aminopropionate) and poly(3-hydroxybutyrate-ran-4-aminobutyrate).
Using glucose derived from abundant biomass sources such as waste wood and weeds, the team successfully produced poly(ester amide)s in an eco-friendly manner. The researchers also confirmed the potential for industrial-scale production by demonstrating high production efficiency (54.57 g/L) using fed-batch fermentation of the engineered strain.
In collaboration with researchers Haemin Jeong and Jihoon Shin from KRICT, the KAIST team analyzed the properties of the bio-based plastic and found that it exhibited characteristics similar to high-density polyethylene (HDPE). This means the new plastic is not only eco-friendly but also strong and durable enough to replace conventional plastics.
The engineered strains and strategies developed in this study are expected to be useful not only for producing various poly(ester amide)s but also for constructing metabolic pathways for the biosynthesis of other types of polymers.
Professor Sang Yup Lee stated, “This study is the first to demonstrate the possibility of producing poly(ester amide)s (plastics) through a renewable bio-based chemical process rather than relying on the petroleum-based chemical industry. We plan to further enhance the production yield and efficiency through continued research.”
The study was published online on March 17 in the international journal Nature Chemical Biology.
·Title: Biosynthesis of poly(ester amide)s in engineered Escherichia coli
·DOI: 10.1038/s41589-025-01842-2
·Authors: A total of seven authors including Tong Un Chae (KAIST, first author), So Young Choi (KAIST, second author), Da-Hee Ahn (KAIST, third author), Woo Dae Jang (KAIST, fourth author), Haemin Jeong (KRICT, fifth author), Jihoon Shin (KRICT, sixth author), and Sang Yup Lee (KAIST, corresponding author).
This research was supported by the Ministry of Science and ICT (MSIT) under the Eco-Friendly Chemical Technology Development Project as part of the "Next-Generation Biorefinery Technology Development to Lead the Bio-Chemical Industry" initiative (project led by Distinguished Professor Sang Yup Lee at KAIST).
2025.03.24 View 710 -
No More Touch Issues on Rainy Days! KAIST Develops Human-Like Tactile Sensor
Recent advancements in robotics have enabled machines to handle delicate objects like eggs with precision, thanks to highly integrated pressure sensors that provide detailed tactile feedback. However, even the most advanced robots struggle to accurately detect pressure in complex environments involving water, bending, or electromagnetic interference. A research team at KAIST has successfully developed a pressure sensor that operates stably without external interference, even on wet surfaces like a smartphone screen covered in water, achieving human-level tactile sensitivity.
KAIST (represented by President Kwang Hyung Lee) announced on the 10th of March that a research team led by Professor Jun-Bo Yoon from the School of Electrical Engineering has developed a high-resolution pressure sensor that remains unaffected by external interference such as "ghost touches" caused by moisture on touchscreens.
Capacitive pressure sensors, widely used in touch systems due to their simple structure and durability, are essential components of human-machine interface (HMI) technologies in smartphones, wearable devices, and robots. However, they are prone to malfunctions caused by water droplets, electromagnetic interference, and curves.
To address these issues, the research team investigated the root causes of interference in capacitive pressure sensors. They identified that the "fringe field" generated at the sensor’s edges is particularly susceptible to external disturbances.
The researchers concluded that, to fundamentally resolve this issue, suppressing the fringe field was necessary. Through theoretical analysis, they determined that reducing the electrode spacing to the nanometer scale could effectively minimize the fringe field to below a few percent.
Utilizing proprietary micro/nanofabrication techniques, the team developed a nanogap pressure sensor with an electrode spacing of 900 nanometers (nm). This newly developed sensor reliably detected pressure regardless of the material exerting force and remained unaffected by bending or electromagnetic interference.
Furthermore, the team successfully implemented an artificial tactile system utilizing the developed sensor’s characteristics. Human skin contains specialized pressure receptors called Merkel’s disks. To artificially mimic them, the exclusive detection of pressure was necessary, but hadn’t been achieved by conventional sensors.
Professor Yoon’s research team overcame these challenges, developing a sensor achieving a density comparable to Merkel’s discs and enabling wireless, high-precision pressure sensing.
To explore potential applications, the researcher also developed a force touch pad system, demonstrating its ability to capture pressure magnitude and distribution with high resolution and without interference.
Professor Yoon stated, “Our nanogap pressure sensor operates reliably even in rainy conditions or sweaty environments, eliminating common touch malfunctions. We believe this innovation will significantly enhance everyday user experiences.”
He added, “This technology has the potential to revolutionize various fields, including precision tactile sensors for robotics, medical wearable devices, and next-generation augmented reality (AR) and virtual reality (VR) interfaces.”
The study was led by Jae-Soon Yang (Ph.D.), Myung-Kun Chung (Ph.D. candidate), and Jae-Young Yoo (Assistant Professor at Sungkyunkwan University, a KAIST Ph.D. graduate). The research findings were published in Nature Communications on February 27, 2025. (Paper title: “Interference-Free Nanogap Pressure Sensor Array with High Spatial Resolution for Wireless Human-Machine Interface Applications”, DOI: 10.1038/s41467-025-57232-8)
This study was supported by the National Research Foundation of Korea’s Mid-Career Researcher Program and Leading Research Center Support Program.
2025.03.14 View 950 -
KAIST Develops World-Leading Ammonia Catalyst for Hydrogen Economy
Hydrogen production using renewable energy is a key technology for eco-friendly energy and chemical production. However, storing and transporting hydrogen remains a challenge. To address this, researchers worldwide are investigating methods to store hydrogen in the form of ammonia (NH₃), which is carbon-free and easier to liquify. A research team at KAIST has successfully developed a high-performance catalyst that enables ammonia synthesis at very low temperatures and pressures without energy loss.
KAIST (represented by President Kwang Hyung Lee) announced on the 11th of March that a research team led by Professor Minkee Choi from the Department of Chemical and Biomolecular Engineering has developed an innovative catalytic system that significantly enhances ammonia production while drastically reducing energy consumption and CO₂ emissions.
< (From left) Baek Ye-jun, Ph.D. candidate in the Department of Biochemical Engineering, Professor Choi Min-ki >
Currently, ammonia is produced using the Haber-Bosch process, a technology over a century old that relies on iron (Fe)-based catalysts. This method requires extreme conditions—temperatures above 500°C and pressures exceeding 100 atmospheres—resulting in enormous energy consumption and contributing significantly to global CO₂ emissions. Additionally, ammonia is primarily produced in large-scale industrial plants, leading to high distribution costs.
As an alternative, there is growing interest in an eco-friendly process that synthesizes ammonia using green hydrogen—produced via water electrolysis—under mild conditions (300°C, 10 atmospheres). However, developing catalysts that can achieve high ammonia productivity at such low temperatures and pressures is essential, as current technologies struggle to maintain efficiency under these conditions.
The research team developed a novel catalyst by incorporating ruthenium (Ru) nanoparticles and highly basic barium oxide (BaO) particles onto a conductive carbon surface, allowing it to function like a chemical capacitor*.
*Capacitor: A device that stores electrical energy by separating positive and negative charges.
During ammonia synthesis, hydrogen molecules (H₂) first dissociate into hydrogen atoms (H) on the ruthenium catalyst. These hydrogen atoms are further split into protons (H⁺) and electrons (e⁻). The study revealed that the acidic protons are stored in the strongly basic BaO, while the remaining electrons are separated and stored in ruthenium and carbon.
This unique chemical capacitor effect significantly enhances the ruthenium catalyst's electron density, accelerating nitrogen (N₂) dissociation—the rate-limiting step of ammonia synthesis—thereby dramatically increasing catalytic activity.
Furthermore, the team discovered that optimizing the nanostructure of the carbon material further boosts the electron density of ruthenium, maximizing catalytic performance. As a result, the new catalyst demonstrated over seven times higher ammonia synthesis performance compared to state-of-the-art catalysts under mild conditions (300°C, 10 atm).
< Schematic diagram showing the mechanism of ruthenium catalyst activity enhancement by barium oxide cocatalyst >
Professor Minkee Choi stated, “This research has garnered significant attention for demonstrating that catalytic activity can be greatly enhanced by controlling electron transfer within a thermal catalytic reaction system, not just in electrochemical processes.”
He further explained, “Our findings confirm that high-performance catalysts can enable efficient ammonia synthesis under low-temperature and low-pressure conditions. This could shift ammonia production from centralized, large-scale industrial plants to decentralized, small-scale production, making the hydrogen economy more sustainable and flexible.”
The study was led by Professor Minkee Choi as corresponding author and Yaejun Baik, a Ph.D. candidate, as first author. The research findings were published in Nature Catalysis on February 24.
(Paper title: “Electron and proton storage on separate Ru and BaO domains mediated by conductive low-work-function carbon to accelerate ammonia synthesis,” https://doi.org/10.1038/s41929-025-01302-z)
This research was supported by the Korea Institute of Energy Research and the National Research Foundation of Korea.
2025.03.11 View 661 -
AI-Driven Wearable Blood Pressure Sensor for Continuous Health Monitoring – Published in Nature Reviews Cardiology
A KAIST research team led by Professor Keon Jae Lee has proposed an innovative theoretical framework and research strategies for AI-based wearable blood pressure sensors, paving the way for continuous and non-invasive cardiovascular monitoring.
Hypertension is a leading chronic disease affecting over a billion people worldwide and is a major risk factor for severe cardiovascular conditions such as myocardial infarction, stroke, and heart failure. Traditional blood pressure measurement relies on intermittent, cuff-based methods, which fail to capture real-time fluctuations and present challenges in continuous patient monitoring.
Wearable blood pressure sensors offer a non-invasive solution for continuous blood pressure monitoring, enabling real-time tracking and personalized cardiovascular health management. However, current technologies lack the accuracy and reliability required for medical applications, limiting their practical use. To address these challenges, advancements in high-sensitivity sensor technology and AI signal processing algorithms are essential.
Building on their previous study in Advanced Materials (doi.org/10.1002/adma.202301627), which validated the clinical feasibility of flexible piezoelectric blood pressure sensors, Professor Lee’s team conducted an in-depth review of the latest advancements in cuffless wearable sensors, focusing on key technical and clinical challenges. Their review highlights clinical aspects of clinical implementation, real-time data transmission, signal quality degradation, and AI algorithm accuracy.
Professor Keon Jae Lee said, “This paper systematically demonstrates the feasibility of medical-grade wearable blood pressure sensors, overcoming what was previously considered an insurmountable challenge. We propose theoretical strategies to address technical barriers, opening new possibilities for future innovations in this field. With continued advancements, we expect these sensors to gain trust and be commercialized soon, significantly improving quality of life.”
This review entitled “Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation” was published in the February 18 issue of Nature Reviews Cardiology (Impact Factor: 41.7). (doi.org/10.1038/s41569-025-01127-0)
< Figure 1. Overview of wearable blood pressure sensor technologies for cardiovascular health care >
[Reference] Min S. et al., (2025) “Wearable blood pressure sensors for
cardiovascular monitoring and machine learning algorithms for blood pressure estimation.” Nature Reviews Cardiology
(doi.org/10.1038/s41569-025-01127-0)
[Main Author] Seongwook Min (Korea Advanced Institute of Science and Technology), Jaehun An (Korea Advanced Institute of Science and Technology), Jae Hee Lee (Northwestern University),
* Contact email : Professor Keon Jae Lee (keonlee@kaist.ac.kr)
2025.03.04 View 1292 -
KAIST perfectly reproduces Joseon-era Irworobongdo without pigments
Typically, chemical pigments that absorb specific wavelengths of light within the visible spectrum are required to produce colors. However, KAIST researchers have successfully reproduced the Joseon-era Irworobongdo [일월오봉도] painting using ultra-precise color graphics without any chemical pigments, allowing for the permanent and eco-friendly preservation of color graphics without fading or discoloration.
< (From left) Chaerim Son, a graduate of the Department of Biochemical Engineering (lead author), Seong Kyeong Nam, a graduate of the PhD program, Jiwoo Lee, a PhD student, and Professor Shin-Hyun Kim >
KAIST (represented by President Kwang Hyung Lee) announced on the 26th of February that a research team led by Professor Shinhyun Kim from the Department of Biological and Chemical Engineering had developed a technology that enables high-resolution color graphics without using any chemical pigments by employing hemisphere-shaped microstructures.
Morpho butterflies that are brilliant blue in color or Panther chameleons that change skin color exhibit coloration without chemical pigments, as ordered nanostructures within a material reflect visible light through optical interference. Since structural colors arise from physical structures rather than chemical substances, a single material can produce a wide range of colors.
However, the artificial implementation of structural coloration is highly challenging due to the complexity of creating ordered nanostructures. Additionally, it is difficult to produce a variety of colors and to pattern them precisely into complex designs.
< Figure 1. Principle of structural color expression using micro-hemispheres (left) and method of forming micro-hemisphere patterns based on photolithography (right) >
Professor Kim’s team overcame these challenges by using smooth-surfaced hemispherical microstructures instead of ordered nanostructures, enabling the high-precision patterning of diverse structural colors.
When light enters the inverted hemispherical microstructures, the portion of light entering from the sides undergoes total internal reflection along the curved surface, creating retroreflection. When the hemisphere diameter is approximately 10 micrometers (about one-tenth the thickness of a human hair), light traveling along different reflection paths interferes within the visible spectrum, producing structural coloration.
< Figure 2. “Irworobongdo”, the Painting of the Sun, Moon, and the Five Peaks, reproduced in fingernail size without pigment using approximately 200,000 micro-hemispheres >
The structural color can be tuned by adjusting the size of the hemispheres. By arranging hemispheres of varying sizes, much like mixing paints on a palette, an infinite range of colors can be generated.
To precisely pattern microscale hemispheres of different sizes, the research team employed photolithography* using positive photoresists** commonly used in semiconductor processing. They first patterned photoresists into micropillar structures, then induced reflow*** by heating the material, forming hemispherical microstructures.
*Photolithography: A technique used in semiconductor fabrication to pattern microscale structures.
**Positive photoresist: A photosensitive polymer that dissolves more easily in a developer solution after exposure to ultraviolet light.
***Reflow: A process in which a polymer material softens and reshapes into a curved structure when heated.
This method enables the formation of hemisphere-shaped microstructures with the desired sizes and colors in a single-step fabrication process. It also allows for the reproduction of arbitrary color graphics using a single material without any pigments.
The ultra-precise color graphics created with this technique can exhibit color variations depending on the angle of incident light or the viewing perspective. The pattern appears colored from one direction while remaining transparent from the opposite side, exhibiting a Janus effect. These structural color graphics achieve resolution comparable to cutting-edge LED displays, allowing complex color images to be captured within a fingernail-sized area and projected onto large screens.
< Figure 3. “Irworobongdo” that displays different shades depending on the angle of light and viewing direction >
Professor Shinhyun Kim, who led the research, stated, “Our newly developed pigment-free color graphics technology can serve as an innovative method for artistic expression, merging art with advanced materials. Additionally, it holds broad application potential in optical devices and sensors, anti-counterfeiting materials, aesthetic photocard printing, and many other fields.”
This research, with KAIST researcher Chaerim Son as the first author, was published in the prestigious materials science journal Advanced Materials on February 5.
(Paper title: “Retroreflective Multichrome Microdome Arrays Created by Single-Step Reflow”, DOI: 10.1002/adma.202413143 )
< Figure 4. Famous paintings reproduced without pigment: “Impression, Sunrise” (left), “Girl with a Pearl Earring” (right) >
The study was supported by the National Research Foundation of Korea through the Pioneer Converging Technology R&D Program and the Mid-Career Researcher Program.
2025.02.26 View 931 -
KAIST develops a new, bone-like material that strengthens with use in collaboration with GIT
Materials used in apartment buildings, vehicles, and other structures deteriorate over time under repeated loads, leading to failure and breakage. A joint research team from Korea and the United States has successfully developed a bioinspired material that becomes stronger with use, taking inspiration from the way bones synthesize minerals from bodily fluids under stress, increasing bone density.
< (From left) Professor Sung Hoon Kang of the Department of Materials Science and Engineering, Johns Hopkins University Ph.D. candidates Bohan Sun and Grant Kitchen, Professor Yuhang Hu and Ph.D. candidate Dongjung He of Georgia Institute of Technology >
KAIST (represented by President Kwang Hyung Lee) announced on the 20th of February that a research team led by Professor Sung Hoon Kang from the Department of Materials Science and Engineering, in collaboration with Johns Hopkins University and the Georgia Institute of Technology, had developed a new material that strengthens with repeated use, similar to how bones become stronger with exercise.
Professor Kang’s team sought to address the issue of conventional materials degrading with repeated use. Inspired by the biological process where stress triggers cells to form minerals that strengthen bones, the team developed a material that synthesizes minerals under stress without relying on cellular activity. This innovation is expected to enable applications in a variety of fields.
To replace the function of cells, the research team created a porous piezoelectric substrate that converts mechanical force into electricity and actually generates more charge under greater force. They then synthesized a composite material by infusing it with an electrolyte containing mineral components similar to those in blood.
< Figure 1. Schematic diagram of the biomimetic concept based on bone and pitcher plants, the reversible strengthening mechanism, the process of fabricating porous composites, the mechanical property changes with increasing stiffness and energy dissipation after cyclic loading, and the reprogrammable self-folding mechanism and applications >
After subjecting the material to periodic forces and measuring changes in its properties, they observed that its stiffness increased proportionally with the frequency and magnitude of stress and that its energy dissipation capability improved.
The reason for such properties was found to be due to minerals forming inside the porous material under repeated stress, as observed through micro-CT imaging of its internal structure. When subjected to large forces, these minerals fractured and dissipated energy, only to reform under further cyclic stress.
Unlike conventional materials that weaken with repeated use, this new material simultaneously enhances stiffness and impact absorption over time.
< Figure 2. Comparison of the changes in properties of the newly developed new material (LIPPS) with other materials under cyclic loading. (A) Graph showing the relative change rate of energy dissipation after cyclic loading and the relative change rate of elastic modulus upon unloading. LIPPS is in a new area that existing materials have not reached, and shows the characteristics of simultaneous increases in elastic modulus and energy dissipation. (B) Graph comparing the performance of LIPPS with current state-of-the-art mechanically adaptive materials. (Left) The maximum property change rate compared to the baseline after cyclic loading, LIPPS shows much higher changes in elastic modulus, dissipated energy density and ratio, toughness (impact resistance), and stored energy density than the existing adaptive materials. (Right) The absolute value range of the reported properties before and after cyclic loading shows that LIPPS has higher elastic modulus and toughness than the existing adaptive materials. >
Moreover, because its properties improve in proportion to the magnitude and frequency of applied stress, it can self-adjust to achieve mechanical property distributions suitable for different structural applications. It also possesses self-healing capabilities.
Professor Kang stated, "This newly developed material, which strengthens and absorbs impact better with repeated use compared to conventional materials, holds great potential for applications in artificial joints, as well as in aircraft, ships, automobiles, and structural engineering."
This study, with Professor Sung Hoon Kang as the corresponding author, was published in Science Advances (Vol. 11, Issue 6, February).
(Paper title: “A material dynamically enhancing both load-bearing and energy-dissipation capability under cyclic loading”) DOI: 10.1126/sciadv.adt3979
This research was conducted as a joint effort with Johns Hopkins University's Extreme Materials Institute and the Georgia Institute of Technology, supported by the National Research Foundation of Korea’s Brain Pool Plus program.
2025.02.22 View 1034 -
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 1592 -
Ultralight advanced material developed by KAIST and U of Toronto
< (From left) Professor Seunghwa Ryu of KAIST Department of Mechanical Engineering, Professor Tobin Filleter of the University of Toronto, Dr. Jinwook Yeo of KAIST, and Dr. Peter Serles of the University of Toronto >
Recently, in advanced industries such as automobiles, aerospace, and mobility, there has been increasing demand for materials that achieve weight reduction while maintaining excellent mechanical properties. An international joint research team has developed an ultralight, high-strength material utilizing nanostructures, presenting the potential for various industrial applications through customized design in the future.
KAIST (represented by President Kwang Hyung Lee) announced on the 18th of February that a research team led by Professor Seunghwa Ryu from the Department of Mechanical Engineering, in collaboration with Professor Tobin Filleter from the University of Toronto, has developed a nano-lattice structure that maximizes lightweight properties while maintaining high stiffness and strength.
In this study, the research team optimized the beam shape of the lattice structure to maintain its lightweight characteristics while maximizing stiffness and strength.
Particularly, using a multi-objective Bayesian optimization algorithm*, the team conducted an optimal design process that simultaneously considers tensile and shear stiffness improvement and weight reduction. They demonstrated that the optimal lattice structure could be predicted and designed with significantly less data (about 400 data points) compared to conventional methods.
*Multi-objective Bayesian optimization algorithm: A method that finds the optimal solution while considering multiple objectives simultaneously. It efficiently collects data and predicts results even under conditions of uncertainty.
< Figure 1. Multi-objective Bayesian optimization for generative design of carbon nanolattices with high compressive stiffness and strength at low density. The upper is the illustration of process workflow. The lower part shows top four MBO CFCC geometries with their 2D Bézier curves. (The optimized structure is predicted and designed with much less data (approximately 400) than the conventional method >
Furthermore, to maximize the effect where mechanical properties improve as size decreases at the nanoscale, the research team utilized pyrolytic carbon* material to implement an ultralight, high-strength, high-stiffness nano-lattice structure.
*Pyrolytic carbon: A carbon material obtained by decomposing organic substances at high temperatures. It has excellent heat resistance and strength, making it widely used in industries such as semiconductor equipment coatings and artificial joint coatings, where it must withstand high temperatures without deformation.
For this, the team applied two-photon polymerization (2PP) technology* to precisely fabricate complex nano-lattice structures, and mechanical performance evaluations confirmed that the developed structure simultaneously possesses strength comparable to steel and the lightness of Styrofoam.
*Two-photon polymerization (2PP) technology: An advanced optical manufacturing technique based on the principle that polymerization occurs only when two photons of a specific wavelength are absorbed simultaneously.
Additionally, the research team demonstrated that multi-focus two-photon polymerization (multi-focus 2PP) technology enables the fabrication of millimeter-scale structures while maintaining nanoscale precision.
Professor Seunghwa Ryu explained, "This technology innovatively solves the stress concentration issue, which has been a limitation of conventional design methods, through three-dimensional nano-lattice structures, achieving both ultralight weight and high strength in material development."
< Figure 2. FESEM image of the fabricated nano-lattice structure and (bottom right) the macroscopic nanolattice resting on a bubble >
He further emphasized, "By integrating data-driven optimal design with precision 3D printing technology, this development not only meets the demand for lightweight materials in the aerospace and automotive industries but also opens possibilities for various industrial applications through customized design."
This study was led by Dr. Peter Serles of the Department of Mechanical & Industrial Engineering at University of Toronto and Dr. Jinwook Yeo from KAIST as co-first authors, with Professor Seunghwa Ryu and Professor Tobin Filleter as corresponding authors.
The research was published on January 23, 2025 in the international journal Advanced Materials (Paper title: “Ultrahigh Specific Strength by Bayesian Optimization of Lightweight Carbon Nanolattices”).
DOI: https://doi.org/10.1002/adma.202410651
This research was supported by the Multiphase Materials Innovation Manufacturing Research Center (an ERC program) funded by the Ministry of Science and ICT, the M3DT (Medical Device Digital Development Tool) project funded by the Ministry of Food and Drug Safety, and the KAIST International Collaboration Program.
2025.02.18 View 1691 -
KAIST Develops Wearable Carbon Dioxide Sensor to Enable Real-time Apnea Diagnosis
- Professor Seunghyup Yoo’s research team of the School of Electrical Engineering developed an ultralow-power carbon dioxide (CO2) sensor using a flexible and thin organic photodiode, and succeeded in real-time breathing monitoring by attaching it to a commercial mask
- Wearable devices with features such as low power, high stability, and flexibility can be utilized for early diagnosis of various diseases such as chronic obstructive pulmonary disease and sleep apnea
< Photo 1. From the left, School of Electrical Engineering, Ph.D. candidate DongHo Choi, Professor Seunghyup Yoo, and Department of Materials Science and Engineering, Bachelor’s candidate MinJae Kim >
Carbon dioxide (CO2) is a major respiratory metabolite, and continuous monitoring of CO2 concentration in exhaled breath is not only an important indicator for early detection and diagnosis of respiratory and circulatory system diseases, but can also be widely used for monitoring personal exercise status. KAIST researchers succeeded in accurately measuring CO2 concentration by attaching it to the inside of a mask.
KAIST (President Kwang-Hyung Lee) announced on February 10th that Professor Seunghyup Yoo's research team in the Department of Electrical and Electronic Engineering developed a low-power, high-speed wearable CO2 sensor capable of stable breathing monitoring in real time.
Existing non-invasive CO2 sensors had limitations in that they were large in size and consumed high power. In particular, optochemical CO2 sensors using fluorescent molecules have the advantage of being miniaturized and lightweight, but due to the photodegradation phenomenon of dye molecules, they are difficult to use stably for a long time, which limits their use as wearable healthcare sensors.
Optochemical CO2 sensors utilize the fact that the intensity of fluorescence emitted from fluorescent molecules decreases depending on the concentration of CO2, and it is important to effectively detect changes in fluorescence light.
To this end, the research team developed a low-power CO2 sensor consisting of an LED and an organic photodiode surrounding it. Based on high light collection efficiency, the sensor, which minimizes the amount of excitation light irradiated on fluorescent molecules, achieved a device power consumption of 171 μW, which is tens of times lower than existing sensors that consume several mW.
< Figure 1. Structure and operating principle of the developed optochemical carbon dioxide (CO2) sensor. Light emitted from the LED is converted into fluorescence through the fluorescent film, reflected from the light scattering layer, and incident on the organic photodiode. CO2 reacts with a small amount of water inside the fluorescent film to form carbonic acid (H2CO3), which increases the concentration of hydrogen ions (H+), and the fluorescence intensity due to 470 nm excitation light decreases. The circular organic photodiode with high light collection efficiency effectively detects changes in fluorescence intensity, lowers the power required light up the LED, and reduces light-induced deterioration. >
The research team also elucidated the photodegradation path of fluorescent molecules used in CO2 sensors, revealed the cause of the increase in error over time in photochemical sensors, and suggested an optical design method to suppress the occurrence of errors.
Based on this, the research team developed a sensor that effectively reduces errors caused by photodegradation, which was a chronic problem of existing photochemical sensors, and can be used continuously for up to 9 hours while existing technologies based on the same material can be used for less than 20 minutes, and can be used multiple times when replacing the CO2 detection fluorescent film.
< Figure 2. Wearable smart mask and real-time breathing monitoring. The fabricated sensor module consists of four elements (①: gas-permeable light-scattering layer, ②: color filter and organic photodiode, ③: light-emitting diode, ④: CO2-detecting fluorescent film). The thin and light sensor (D1: 400 nm, D2: 470 nm) is attached to the inside of the mask to monitor the wearer's breathing in real time. >
The developed sensor accurately measured CO2 concentration by being attached to the inside of a mask based on the advantages of being light (0.12 g), thin (0.7 mm), and flexible. In addition, it showed fast speed and high resolution that can monitor respiratory rate by distinguishing between inhalation and exhalation in real time.
< Photo 2. The developed sensor attached to the inside of the mask >
Professor Seunghyup Yoo said, "The developed sensor has excellent characteristics such as low power, high stability, and flexibility, so it can be widely applied to wearable devices, and can be used for the early diagnosis of various diseases such as hypercapnia, chronic obstructive pulmonary disease, and sleep apnea." He added, "In particular, it is expected to be used to improve side effects caused by rebreathing in environments where dust is generated or where masks are worn for long periods of time, such as during seasonal changes."
This study, in which KAIST's Department of Materials Science and Engineering's undergraduate student Minjae Kim and School of Electrical Engineering's doctoral student Dongho Choi participated as joint first authors, was published in the online version of Cell's sister journal, Device, on the 22nd of last month. (Paper title: Ultralow-power carbon dioxide sensor for real-time breath monitoring) DOI: https://doi.org/10.1016/j.device.2024.100681
< Photo 3. From the left, Professor Seunghyup Yoo of the School of Electrical Engineering, MinJae Kim, an undergraduate student in the Department of Materials Science and Engineering, and Dongho Choi, a doctoral student in the School of Electrical Engineering >
This study was supported by the Ministry of Trade, Industry and Energy's Materials and Components Technology Development Project, the National Research Foundation of Korea's Original Technology Development Project, and the KAIST Undergraduate Research Participation Project. This work was supported by the (URP) program.
2025.02.13 View 2364 -
KAIST Discovers Molecular Switch that Reverses Cancerous Transformation at the Critical Moment of Transition
< (From left) PhD student Seoyoon D. Jeong, (bottom) Professor Kwang-Hyun Cho, (top) Dr. Dongkwan Shin, Dr. Jeong-Ryeol Gong >
Professor Kwang-Hyun Cho’s research team has recently been highlighted for their work on developing an original technology for cancer reversal treatment that does not kill cancer cells but only changes their characteristics to reverse them to a state similar to normal cells. This time, they have succeeded in revealing for the first time that a molecular switch that can induce cancer reversal at the moment when normal cells change into cancer cells is hidden in the genetic network.
KAIST (President Kwang-Hyung Lee) announced on the 5th of February that Professor Kwang-Hyun Cho's research team of the Department of Bio and Brain Engineering has succeeded in developing a fundamental technology to capture the critical transition phenomenon at the moment when normal cells change into cancer cells and analyze it to discover a molecular switch that can revert cancer cells back into normal cells.
A critical transition is a phenomenon in which a sudden change in state occurs at a specific point in time, like water changing into steam at 100℃. This critical transition phenomenon also occurs in the process in which normal cells change into cancer cells at a specific point in time due to the accumulation of genetic and epigenetic changes.
The research team discovered that normal cells can enter an unstable critical transition state where normal cells and cancer cells coexist just before they change into cancer cells during tumorigenesis, the production or development of tumors, and analyzed this critical transition state using a systems biology method to develop a cancer reversal molecular switch identification technology that can reverse the cancerization process. They then applied this to colon cancer cells and confirmed through molecular cell experiments that cancer cells can recover the characteristics of normal cells.
This is an original technology that automatically infers a computer model of the genetic network that controls the critical transition of cancer development from single-cell RNA sequencing data, and systematically finds molecular switches for cancer reversion by simulation analysis. It is expected that this technology will be applied to the development of reversion therapies for other cancers in the future.
Professor Kwang-Hyun Cho said, "We have discovered a molecular switch that can revert the fate of cancer cells back to a normal state by capturing the moment of critical transition right before normal cells are changed into an irreversible cancerous state."
< Figure 1. Overall conceptual framework of the technology that automatically constructs a molecular regulatory network from single-cell RNA sequencing data of colon cancer cells to discover molecular switches for cancer reversion through computer simulation analysis. Professor Kwang-Hyun Cho's research team established a fundamental technology for automatic construction of a computer model of a core gene network by analyzing the entire process of tumorigenesis of colon cells turning into cancer cells, and developed an original technology for discovering the molecular switches that can induce cancer cell reversal through attractor landscape analysis. >
He continued, "In particular, this study has revealed in detail, at the genetic network level, what changes occur within cells behind the process of cancer development, which has been considered a mystery until now." He emphasized, "This is the first study to reveal that an important clue that can revert the fate of tumorigenesis is hidden at this very critical moment of change."
< Figure 2. Identification of tumor transition state using single-cell RNA sequencing data from colorectal cancer. Using single-cell RNA sequencing data from colorectal cancer patient-derived organoids for normal and cancerous tissues, a critical transition was identified in which normal and cancerous cells coexist and instability increases (a-d). The critical transition was confirmed to show intermediate levels of major phenotypic features related to cancer or normal tissues that are indicative of the states between the normal and cancerous cells (e). >
The results of this study, conducted by KAIST Dr. Dongkwan Shin (currently at the National Cancer Center), Dr. Jeong-Ryeol Gong, and doctoral student Seoyoon D. Jeong jointly with a research team at Seoul National University that provided the organoids (in vitro cultured tissues) from colon cancer patient, were published as an online paper in the international journal ‘Advanced Science’ published by Wiley on January 22nd.
(Paper title: Attractor landscape analysis reveals a reversion switch in the transition of colorectal tumorigenesis) (DOI: https://doi.org/10.1002/advs.202412503)
< Figure 3. Reconstruction of a dynamic network model for the transition state of colorectal cancer.
A new technology was established to build a gene network computer model that can simulate the dynamic changes between genes by integrating single-cell RNA sequencing data and existing experimental results on gene-to-gene interactions in the critical transition of cancer. (a). Using this technology, a gene network computer model for the critical transition of colorectal cancer was constructed, and the distribution of attractors representing normal and cancer cell phenotypes was investigated through attractor landscape analysis (b-e). >
This study was conducted with the support of the National Research Foundation of Korea under the Ministry of Science and ICT through the Mid-Career Researcher Program and Basic Research Laboratory Program and the Disease-Centered Translational Research Project of the Korea Health Industry Development Institute (KHIDI) of the Ministry of Health and Welfare.
< Figure 4. Quantification of attractor landscapes and discovery of transcription factors for cancer reversibility through perturbation simulation analysis. A methodology for implementing discontinuous attractor landscapes continuously from a computer model of gene networks and quantifying them as cancer scores was introduced (a), and attractor landscapes for the critical transition of colorectal cancer were secured (b-d). By tracking the change patterns of normal and cancer cell attractors through perturbation simulation analysis for each gene, the optimal combination of transcription factors for cancer reversion was discovered (e-h). This was confirmed in various parameter combinations as well (i). >
< Figure 5. Identification and experimental validation of the optimal target gene for cancer reversion. Among the common target genes of the discovered transcription factor combinations, we identified cancer reversing molecular switches that are predicted to suppress cancer cell proliferation and restore the characteristics of normal colon cells (a-d). When inhibitors for the molecular switches were treated to organoids derived from colon cancer patients, it was confirmed that cancer cell proliferation was suppressed and the expression of key genes related to cancer development was inhibited (e-h), and a group of genes related to normal colon epithelium was activated and transformed into a state similar to normal colon cells (i-j). >
< Figure 6. Schematic diagram of the research results. Professor Kwang-Hyun Cho's research team developed an original technology to systematically discover key molecular switches that can induce reversion of colon cancer cells through a systems biology approach using an attractor landscape analysis of a genetic network model for the critical transition at the moment of transformation from normal cells to cancer cells, and verified the reversing effect of actual colon cancer through cellular experiments. >
2025.02.05 View 14977 -
KAIST Develops AI-Driven Performance Prediction Model to Advance Space Electric Propulsion Technology
< (From left) PhD candidate Youngho Kim, Professor Wonho Choe, and PhD candidate Jaehong Park from the Department of Nuclear and Quantum Engineering >
Hall thrusters, a key space technology for missions like SpaceX's Starlink constellation and NASA's Psyche asteroid mission, are high-efficiency electric propulsion devices using plasma technology*. The KAIST research team announced that the AI-designed Hall thruster developed for CubeSats will be installed on the KAIST-Hall Effect Rocket Orbiter (K-HERO) CubeSat to demonstrate its in-orbit performance during the fourth launch of the Korean Launch Vehicle called Nuri rocket (KSLV-2) scheduled for November this year.
*Plasma is one of the four states of matter, where gases are heated to high energies, causing them to separate into charged ions and electrons. Plasma is used not only in space electric propulsion but also in semiconductor manufacturing, display processes, and sterilization devices.
On February 3rd, the research team from the KAIST Department of Nuclear and Quantum Engineering’s Electric Propulsion Laboratory, led by Professor Wonho Choe, announced the development of an AI-based technique to accurately predict the performance of Hall thrusters, the engines of satellites and space probes.
Hall thrusters provide high fuel efficiency, requiring minimal propellant to achieve significant acceleration of spacecrafts or satellites while producing substantial thrust relative to power consumption. Due to these advantages, Hall thrusters are widely used in various space missions, including the formation flight of satellite constellations, deorbiting maneuvers for space debris mitigation, and deep space missions such as asteroid exploration.
As the space industry continues to grow during the NewSpace era, the demand for Hall thrusters suited to diverse missions is increasing. To rapidly develop highly efficient, mission-optimized Hall thrusters, it is essential to predict thruster performance accurately from the design phase.
However, conventional methods have limitations, as they struggle to handle the complex plasma phenomena within Hall thrusters or are only applicable under specific conditions, leading to lower prediction accuracy.
The research team developed an AI-based performance prediction technique with high accuracy, significantly reducing the time and cost associated with the iterative design, fabrication, and testing of thrusters. Since 2003, Professor Wonho Choe’s team has been leading research on electric propulsion development in Korea. The team applied a neural network ensemble model to predict thruster performance using 18,000 Hall thruster training data points generated from their in-house numerical simulation tool.
The in-house numerical simulation tool, developed to model plasma physics and thrust performance, played a crucial role in providing high-quality training data. The simulation’s accuracy was validated through comparisons with experimental data from ten KAIST in-house Hall thrusters, with an average prediction error of less than 10%.
< Figure 1. This research has been selected as the cover article for the March 2025 issue (Volume 7, Issue 3) of the AI interdisciplinary journal, Advanced Intelligent Systems. >
The trained neural network ensemble model acts as a digital twin, accurately predicting the Hall thruster performance within seconds based on thruster design variables.
Notably, it offers detailed analyses of performance parameters such as thrust and discharge current, accounting for Hall thruster design variables like propellant flow rate and magnetic field—factors that are challenging to evaluate using traditional scaling laws.
This AI model demonstrated an average prediction error of less than 5% for the in-house 700 W and 1 kW KAIST Hall thrusters and less than 9% for a 5 kW high-power Hall thruster developed by the University of Michigan and the U.S. Air Force Research Laboratory. This confirms the broad applicability of the AI prediction method across different power levels of Hall thrusters.
Professor Wonho Choe stated, “The AI-based prediction technique developed by our team is highly accurate and is already being utilized in the analysis of thrust performance and the development of highly efficient, low-power Hall thrusters for satellites and spacecraft. This AI approach can also be applied beyond Hall thrusters to various industries, including semiconductor manufacturing, surface processing, and coating, through ion beam sources.”
< Figure 2. The AI-based prediction technique developed by the research team accurately predicts thrust performance based on design variables, making it highly valuable for the development of high-efficiency Hall thrusters. The neural network ensemble processes design variables, such as channel geometry and magnetic field information, and outputs key performance metrics like thrust and prediction accuracy, enabling efficient thruster design and performance analysis. >
Additionally, Professor Choe mentioned, “The CubeSat Hall thruster, developed using the AI technique in collaboration with our lab startup—Cosmo Bee, an electric propulsion company—will be tested in orbit this November aboard the K-HERO 3U (30 x 10 x 10 cm) CubeSat, scheduled for launch on the fourth flight of the KSLV-2 Nuri rocket.”
This research was published online in Advanced Intelligent Systems on December 25, 2024 with PhD candidate Jaehong Park as the first author and was selected as the journal’s cover article, highlighting its innovation.
< Figure 3. Image of the 150 W low-power Hall thruster for small and micro satellites, developed in collaboration with Cosmo Bee and the KAIST team. The thruster will be tested in orbit on the K-HERO CubeSat during the KSLV-2 Nuri rocket’s fourth launch in Q4 2025. >
This research was supported by the National Research Foundation of Korea’s Space Pioneer Program (200mN High Thrust Electric Propulsion System Development).
(Paper Title: Predicting Performance of Hall Effect Ion Source Using Machine Learning, DOI: https://doi.org/10.1002/aisy.202400555 )
< Figure 4. Graphs of the predicted thrust and discharge current of KAIST’s 700 W Hall thruster using the AI model (HallNN). The left image shows the Hall thruster operating in KAIST Electric Propulsion Laboratory’s vacuum chamber, while the center and right graphs present the prediction results for thrust and discharge current based on anode mass flow rate. The red lines represent AI predictions, and the blue dots represent experimental results, with a prediction error of less than 5%. >
2025.02.03 View 2534