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KAIST Develops High-Efficiency, Eco-Friendly Hydrogen Separation Membrane That Filters Hydrogen Through a Molecular “Network”
For hydrogen to be widely used as a clean energy source in everyday life, technologies that can extract only hydrogen with high purity from mixed gases are essential. KAIST researchers have presented a new strategy for developing high-performance separation membranes that can selectively filter hydrogen for clean hydrogen energy production.
KAIST (President Choongsik Bae) announced on the 13th of August that a research team led by Professor Tae-Hyun Bae of the Department of Chemical and Biomolecular Engineering has successfully introduced hydrogen-selective transport pathways at the angstrom scale inside polymer membranes and clarified their separation performance through the concept of “network completeness.”
*Angstrom (Å): An extremely small unit of length used to measure wavelengths of light or the size of atoms and molecules. One angstrom is one hundred-millionth of a centimeter, or one ten-billionth of a meter, roughly one-millionth the thickness of a human hair.
Hydrogen is drawing attention as an eco-friendly energy source because it does not emit pollutants when used. However, separating hydrogen with high purity from mixed gases generated during production remains a key challenge for commercialization.
Crystalline porous materials such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) are advantageous because their pores can be designed uniformly. However, they are difficult to fabricate over large areas without defects and have limitations in separating small molecules such as hydrogen. Polymer membranes, by contrast, are easier to process and scale up to large areas, but because their pore formation is difficult to control precisely, it has been challenging to raise their separation performance beyond a certain level.
To combine the advantages of both types of materials, the research team designed a modular network structure in which polymer chains are linked by crosslinkers. In this process, the team focused on the limitation that conventional indicators such as the degree of crosslinking (CD) and effective crosslinking degree (ECD), which have been used to describe the extent of crosslinking, cannot determine whether pores useful for separation have actually been formed.
The researchers therefore proposed a new metric called the Bridge Connectivity Degree (BCD), which refers to the proportion of crosslinkers that are connected at both ends to form complete pathways. This made it possible to quantitatively apply the concept of “complete framework connectivity,” which has been emphasized in inorganic porous materials, to polymer networks as well.
The newly developed membrane, ms-oDMB-DB50, achieved a high bridge connectivity degree of 73%, and both its hydrogen permeability and hydrogen/nitrogen selectivity improved significantly compared with the original material, DB50. Analysis showed that the membrane contains numerous ultramicropores smaller than 3 Å, which carbon dioxide cannot access. The research team also proposed a “density-probe method,” using helium molecules, which are smaller than hydrogen, as probes to experimentally verify the existence of these ultramicropores.
The newly developed membrane also operated stably for 100 hours without any loss of performance. Its tensile strength, the force the membrane can withstand without breaking, was about twice that of previously reported high-performance polymer membranes, confirming that it also has the robust durability needed for industrial processes.
Dr. Hongju Lee said, “There have been previous attempts to combine the advantages of these two types of materials, but this study is different in that it defines ‘how completely the network is connected’ as a quantitative value and directly links that value to separation performance,” adding, “We hope this study will serve as a starting point for extending reticular synthesis, a design principle used for inorganic molecular sieves, to polymer membranes.”
Professor Tae-Hyun Bae said, “By stitching polymer chains together with crosslinkers that fit together like Lego blocks, we formed a network inside the membrane that selectively allows only small hydrogen gas molecules to pass through.”
This paper was led by Dr. Hongju Lee, currently a postdoctoral researcher at the Korea Institute of Science and Technology, as first author, with Professor Tae-Hyun Bae as corresponding author. The research was published on July 23 in the international journal Nature Communications.
Paper title: Network completeness enables angstrom-scale transport pathways in polymer membranes,
DOI: https://doi.org/10.1038/s41467-026-73860-
Author information: Hongju Lee, formerly of KAIST and currently at the Korea Institute of Science and Technology, first author; Suhyeon Choi, KAIST, second author; and Tae-Hyun Bae, KAIST, corresponding author
This research was supported by the 2025 Global C.L.E.A.N. Project and the Mid-Career Researcher Program under the Basic Research Program, funded by the Ministry of Science and ICT.
2026.08.13 View 342 -
KAIST Finds Algal Blooms Make Plastic More Prone to Breaking Apart, Revealing a Mechanism of Microplastic Formation
Every summer, algal blooms turn rivers and lakes green. Although they are widely known as a major form of water pollution that makes the water murky, a KAIST research team has now shown for the first time that algal bloom conditions can make discarded plastics, such as plastic bags, more prone to breaking apart, potentially accelerating the formation of microplastics. The study points to a new direction for the era of climate change: water pollution and plastic pollution need to be managed together, rather than as separate problems.
KAIST (President Choongsik Bae) announced on July 29 that a research team led by Professor Jaewook Myung from the Department of Civil and Department of Civil and Environmental Engineering has found, through a microcosm experiment using water collected from Duck Pond, a pond on the KAIST campus, that algal blooms alter the microbial ecosystem on the surface of low-density polyethylene (LDPE) — a common plastic used in plastic bags — and accelerate its early-stage weathering, in which the surface oxidizes and develops microscopic cracks.
The study is significant because it suggests that, in natural environments, plastic pollution and the eutrophication that drives algal blooms can interact and lead to new ecological changes.
Over time, plastic debris discarded in rivers and lakes becomes colonized by a wide variety of microorganisms, creating a small ecosystem of its own on the plastic surface. This ecosystem is known as the “plastisphere.” The plastisphere is known to influence the spread of pathogens and the transport of microplastics, but little has been known about how algal blooms, a serious form of water pollution, affect this microbial ecosystem.
To investigate this question, the research team constructed microcosms — small-scale experimental systems that recreate natural environments in the laboratory — in which algal blooms were artificially induced by controlling light exposure and nutrient concentrations. Over the following six weeks, the researchers closely analyzed the biofilms forming on the plastic surface, the succession of microbial communities, and changes in their functional gene profiles.
The analysis showed that under eutrophic conditions in which excessive nutrients trigger algal blooms that cyanobacteria, a major group of photosynthetic bacteria, proliferated alongside a variety of other bacteria, forming a thicker biofilm on the plastic surface. Microorganisms capable of producing large amounts of extracellular polymeric substances (EPS) also became significantly more abundant. EPS is a sticky, mucilage-like material that binds microorganisms together and helps them adhere to plastic surfaces.
As the microbial ecosystem on the plastic surface changed, the early weathering of the plastic — including surface oxidation and the formation of microscopic cracks — also accelerated. The team found that microorganisms harboring genes encoding enzymes associated with plastic oxidation and early-stage degradation became more abundant under eutrophic conditions.
Analyses using Fourier-transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM) directly confirmed these changes. Oxygen-containing functional groups associated with oxidation, including carbonyl and hydroxyl groups, increased on the plastic surface, while more fine, hairline cracks appeared. These changes indicate that the plastic had become more susceptible to further physical weathering and fragmentation. The results suggest that these changes were driven not by a single microbial species, but by the combined activity of a microbial ecosystem comprising photosynthetic and other bacteria.
Professor Myung said, “As algal blooms become more frequent because of climate change, we expect this work to provide an important scientific basis for integrated environmental management strategies that consider water quality management and plastic waste management together.”
The study was led by first author Youngju Kim, a doctoral student from the Department of Civil and Environmental Engineering, and was published online in the international environmental journal Water Research on May 25, 2026.
Paper title: Eutrophication drives taxonomic and functional trajectories in plastic-associated biofilms
DOI: 10.1016/j.watres.2026.126183
Author information: Youngju Kim, KAIST, first author; Yijin Wang, HKUST; Wenqian Xu, HKUST; Charmaine C.M. Yung, HKUST; and Jaewook Myung, KAIST, corresponding author — five authors in total
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS- 2023–00209472, RS-2024–00437656, and RS-2026–25393325), by the Ministry of Oceans and Fisheries, Korea (20200104), and by the grant for the “KAIST Grand Challenge 30 Program” funded by the Korea Advanced Institute of Science and Technology (N11250072)
2026.07.29 View 850 -
KAIST Develops Low-Temperature Technique for Growing Crystal-Aligned Semiconductor Films
Building next-generation semiconductors and low-power electronic devices requires precisely stacking materials with different functions. In this process, it is essential to preserve each material's intrinsic properties, as well as the interface where the two materials meet, without damage. Layered van der Waals materials, including transition metal dichalcogenides (TMDs), have attracted considerable attention as next-generation semiconductor platforms because their layers interact through weak forces, enabling different materials to be stacked while maintaining atomically clean interfaces.
KAIST (President Choongsik Bae) announced on the 27th of July that a research team led by Professor Joonki Suh from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor Bonggeun Shong's team at Hanyang University and Professor Yimo Han's team at Rice University in the United States, has developed a new semiconductor manufacturing technique based on atomic layer deposition (ALD). ALD is a thin-film deposition process in which semiconductor precursor are supplied sequentially, enabling uniform thin films to be deposited with atomic-level control over their thickness.
The research team focused on van der Waals materials. These two-dimensional semiconductor materials consist of multiple atomic layers held together by weak interlayer forces, allowing them to be peeled apart into sheets as thin as paper. Because different materials can be freely stacked, van der Waals materials are attracting attention as key building blocks for next-generation AI chips and ultra-low-power semiconductor devices.
However, their chemically stable surfaces make it difficult to grow new semiconductor layers in a uniformly aligned orientation. This challenge becomes even greater at lower temperatures, where atoms tend to nucleate and grow in random directions, making it difficult to produce high-performance semiconductor films.
The research team developed a new method that allows tellurium (Te)-containing precursors—molecular building blocks used to fabricate semiconductors—to move freely across the surface, find the most energetically stable positions, and form a thin film.
Tellurium is attracting attention as a key material for next-generation semiconductors and optoelectronic devices, including photodetectors and light-emitting diodes (LEDs), because it combines highly direction-dependent electrical conductivity with excellent light-controlling properties.
Using this approach, the research team succeeded in achieving epitaxial growth of tellurium uniformly in a single direction on van der Waals materials— next-generation two-dimensional semiconductor materials composed of multiple atomically thin layers stacked like sheets of paper— at a low temperature of 150°C using ALD. In epitaxial growth, a new semiconductor film grows in an ordered manner following the atomic arrangement of the underlying crystal, enabling the precise fabrication of high-quality semiconductor films. This process is comparable to stacking bricks neatly in the same direction rather than placing them randomly, which can improve electrical transport and enhance semiconductor performance.
The researchers also confirmed that the technology could be applied to a range of van der Waals materials, including tungsten diselenide (WSe2), molybdenum disulfide (MoSS), rhenium diselenide (ReSe2), and mica. This demonstrates that the method is not limited to a single material but can be broadly used with various next-generation semiconductor materials.
The team further used the resulting semiconductor films to fabricate transistors, key semiconductor devices that control the flow of electrical current, as well as optoelectronic devices that detect or emit light. This demonstrated that the new manufacturing technology is not confined to laboratory-scale material growth but can also be applied to the fabrication of functional semiconductor devices.
"This study is the first to demonstrate that high-quality semiconductor films can be grown on van der Waals materials at low temperature without damaging the underlying materials," said Professor Suh. "We expect this technology to serve as a key manufacturing platform for integrating a wide range of next-generation semiconductors on a single chip," he added.
The study, with Changhwan Kim, a doctoral student, as first author and Professor Suh as corresponding author, was published in the journal Science Advances on July 24.
Paper title: Van der Waals template-encoded soft epitaxy of tellurium enabled by atomic layer deposition
DOI: 10.1126/sciadv.aef1430
Author information: Changhwan Kim (Korea Advanced Institute of Science and Technology / Ulsan National Institute of Science and Technology, first author) and Joonki Suh (Korea Advanced Institute of Science and Technology, corresponding author)
This research was supported by the National Research Foundation of Korea, under the Ministry of Science and ICT.
2026.07.27 View 947 -
KAIST and NVIDIA Launch Human Physical AI NVAITC
A new era of Physical AI is taking shape, enabling wearable robots and humanoids to understand and predict human movement and achieve more precise control. KAIST, which possesses world-class research capabilities in wearable robotics, and NVIDIA will collaborate to develop a Human Motion Foundation Model that enables AI to learn human movement and physical intelligence.
KAIST, led by President Choongsik Bae, announced on July 25 that it will establish a NVIDIA AI Technology Center (NVAITC) with NVIDIA to advance collaborative research in Physical AI.
As the Korean government advances Physical AI as a key national initiative for the country’s future, the collaboration aims to secure core technologies for next-generation Physical AI by combining KAIST’s human-centered robotics technologies and real-world human motion data with NVIDIA AI technologies and global research network.
The collaboration will be carried out through the establishment of the Human Physical AI NVAITC by the KAIST Department of Mechanical Engineering and NVIDIA. The Human Physical AI Research Center at the KAIST Department of Mechanical Engineering will serve as the core research hub for the NVAITC . Building on this foundation, the two organizations plan to progressively expand the scope of their collaboration across the full spectrum of Physical AI, including wearable robots, humanoids, digital twins, and manufacturing.
“Competitiveness in the era of Physical AI will depend not simply on AI itself, but on domain-specific technologies and data grounded in a deep understanding of humans and robots,” said KAIST President Choongsik Bae. “By combining KAIST’s accumulated expertise in human-centered research with NVIDIA’s world-leading AI infrastructure and physical AI technologies, we will realize Physical AI that better understands and supports people and develop KAIST into a global hub leading Physical AI research and industry beyond Korea.”
The Human Physical AI Research Center was established around the laboratories of Professor Kyoungchul Kong, a leading researcher in wearable robotics, and Professor Jung Kim, a leading researcher in biorobotics. Professors Kim and Kong serve as co-directors of the Center.
The Center conducts research to understand how humans move, exert force, and maintain balance in real-world environments and to reproduce these capabilities through AI and robotics. In particular, the large-scale human motion data and gait and movement control technologies accumulated through wearable robotics research are regarded as a critical foundation for developing human-centered Physical AI.
Co-director Professor Kyoungchul Kong is a world-renowned researcher in wearable robotics who has developed robotic technologies for gait assistance and rehabilitation. Through Angel Robotics, a company he founded, he has also led the commercialization of wearable robotics by translating research outcomes into real-world products and services. Through the NVAITC , Professor Kong will lead the development of the Human Motion Foundation Model based on the human motion data and robotic control technologies accumulated by his research team.
On NVIDIA’s side, Charles Cheung, Senior Manager at the NVIDIA AI Technology Center (NVAITC), will participate by providing expert technical consultation and developer support. The NVAITC will also operate research and educational programs using NVIDIA Omniverse and digital twin platforms.
“The KAIST Human Physical AI Research Center has world-class human motion data and research capabilities in wearable robotics,” said Charles Cheung. “This research, which seeks to reproduce human movement through AI, is expected to open new possibilities for Physical AI.”
To ensure the systematic operation of the collaborative research, the two organizations will establish a Steering Committee and review research goals and progress every six months. They also plan to hold an annual international symposium that will bring together researchers from Korea and abroad to share the latest research outcomes and industry trends in Physical AI.
A Student Ambassador Program will also be offered to KAIST students. Through the program, NVIDIA experts will provide lectures and regular office hours and carry out projects with participating students.
The first cohort is expected to consist of five to 10 students. Participants will receive training focused on NVIDIA Omniverse and digital twin technologies and will be awarded certificates upon completion of the program.
The primary objective of the first phase of the collaborative research is to develop a Human Motion Foundation Model.
The Human Motion Foundation Model is a generative AI-based model trained on large-scale human motion data to understand, predict, and generate a wide range of human movements. It is expected to serve as a core enabling technology that will allow wearable robots and humanoids to more accurately identify users’ intentions and movements and respond more naturally.
The technologies developed through the NVAITC are expected to be applied not only to wearable robots that support the rehabilitation and daily lives of people with gait impairments, but also to humanoids that work alongside humans, human movement assessment, and digital healthcare.
Ultimately, the researchers aim to explain from an AI perspective how humans plan movement and control their muscles and joints. Based on this understanding, they seek to create next-generation robotic systems that help people overcome gait impairments and expand human physical capabilities.
The collaboration is also significant because its impact is expected to extend beyond an individual research project and contribute to the broader Physical AI industrial ecosystem in Korea.
Co-directors Professors Jung Kim and Kyoungchul Kong are currently leading in a Deep Tech Scale-up Valley project in the field of Physical AI. By combining Angel Robotics’ experience in technology commercialization, KAIST’s capabilities in robotics, mechanical engineering, and AI, and NVIDIA’s AI technologies and global professional network, the collaboration is expected to support a broad range of activities spanning research and development, talent cultivation, startup support, and technology commercialization.
“This collaboration will provide an important opportunity to take AI research in the Department of Mechanical Engineering to the next level,” said Professor Hyung-Soon Park, Head of the KAIST Department of Mechanical Engineering. “Centered around the Human Physical AI Research Center, we will expand Physical AI research into nationally strategic industries, including robotics and manufacturing.”
2026.07.26 View 2674 -
KAIST Gives Antibodies “Eyes” to Detect Cancer, Targeting Cancer Mutations Inside Cells
Antibodies are like “guided missiles” that find and attack cancer cells, but cancer-causing mutations inside cells have remained a “blind spot” for treatment because antibodies cannot reach them. KAIST researchers have now succeeded in precisely targeting even intracellular cancer mutations using a newly designed antibody created through computational methods. This achievement is expected to open a new path toward next-generation precision therapies for difficult-to-treat cancers, going beyond the limitations of conventional antibody treatments.
KAIST (President Choongsik Bae) announced on the 24th of July that a research team led by Professor Byung-Ha Oh from the Department of Biological Sciences, together with researchers from Therazyne, a KAIST faculty startup specializing in protein design and headed by Professor Oh, has developed an antibody that selectively recognizes only cancer cells carrying KRAS(G12D), a representative cancer-driving mutation. By combining computational antibody design with experimental validation, the team designed a new antibody that would have been difficult to develop through conventional approaches and is now verifying its efficacy in animal disease models.
KRAS(G12D) is a mutated form of the KRAS protein, which regulates cell growth and proliferation. It is one of the most common cancer-driving mutations found in pancreatic, colorectal, and lung cancers. However, because the KRAS protein exists inside cells, it has long been considered an “undruggable target” that is difficult to directly target with conventional antibody therapeutics.
The research team focused on the natural process by which cells break down aged or damaged proteins into small fragments. The KRAS(G12D) protein inside cells is also processed in this way into small protein fragments, known as neoantigens, which serve as clues that allow immune cells to distinguish cancer cells. Some of these fragments are then transported to the cell surface and presented to immune cells. By combining computational protein design with experimental screening, the team developed a TCR-like antibody that precisely recognizes only this cancer-mutation-derived fragment.
TCR, or T cell receptor, acts as a “sensor” that allows T cells, the body’s immune cells, to read protein fragments displayed on the surface of cells and identify cancer cells or virus-infected cells. The TCR-like antibody developed in this study works on a similar principle, effectively giving an antibody the “eyes” of a T cell. It was designed to selectively recognize traces of intracellular cancer mutations that conventional antibodies cannot easily access.
Experimental results confirmed that the antibody developed by the team selectively recognizes only cancer cells carrying the KRAS(G12D) mutation, while showing little to no reaction with normal cells or other proteins. When applied to immunotherapy, it was also shown to effectively eliminate only cancer cells carrying the mutation. This finding suggests the possibility of expanding antibody therapy to intracellular cancer-driving proteins that conventional antibody treatments have been unable to target. It is also expected to serve as a platform technology for developing next-generation precision antibody therapies targeting not only KRAS but also a wide range of cancer mutations.
Professor Byung-Ha Oh said, “The antibody developed in this study can selectively identify only cancer cells carrying the KRAS(G12D) mutation, demonstrating the potential for precision antibody therapeutics that minimize damage to normal cells.” He added, “The computational antibody design technology developed in this research is expected to be widely applicable to the development of next-generation antibody therapeutics targeting KRAS as well as various other cancer mutations.”
Both the first author and corresponding authors of this study are KAIST-affiliated researchers. SangPhil Ahn, a researcher at Therazyne, participated as the first author, while Professor Byung-Ha Oh and Bo-Seong Jeong, Head of Research at Therazyne, jointly led the study as co-corresponding authors. The research was published online on June 3 in Molecular Therapy, a leading international journal in the field of gene and cell therapy.
Paper title: Discovery of TCR-like antibodies to the KRAS G12D neoantigen via in silico-in vitro workflow DOI: https://doi.org/10.1016/j.ymthe.2026.05.032
This research was conducted in collaboration with Therazyne and the New Drug Development Center of the Osong Biomedical Innovation Foundation, and was supported by the Ministry of Science and ICT’s Industry-Academia-Research Linked New Drug Development Program and the National Research Foundation of Korea’s Bio & Medical Technology Development Program.
2026.07.24 View 1215 -
KAIST Develops Ultra-Precise Inspection Technology to Prevent Electric Vehicle Battery Fires
An ultra-precise inspection technology that could help prevent electric vehicle battery fires and improve battery safety has been developed. A KAIST research team has developed a method capable of detecting minute variations in battery electrode thickness that can contribute to thermal runaway with a precision equivalent to approximately one ten-thousandth the diameter of a human hair, all without disassembling or damaging the battery. The technology is expected to improve battery safety and quality by identifying invisible defects during the manufacturing process.
KAIST (President Choongsik Bae) announced on 23rd of July that a research team led by Professor Young-Jin Kim from the Department of Mechanical Engineering has developed a technology that measures the thickness of lithium-ion battery electrodes in a non-contact and non-destructive manner.
The technology combines terahertz waves (electromagnetic waves in the spectral region between light and radio waves) to obtain information from inside battery electrodes with an optical frequency comb, which divides the frequency of light into evenly spaced intervals like the markings on a ruler and serves as a reference for ultra-precise measurements.
The electrodes in lithium-ion batteries, which are widely used in electric vehicles, are essential components through which electric current flows. Even a slight variation in electrode thickness can cause current to become concentrated in certain areas when charging and discharging, generating heat. If the heat continues to accumulate, it may lead to thermal runaway, a phenomenon in which the internal temperature of a battery rises rapidly and can result in a fire or explosion. Maintaining uniform electrode thickness is therefore critically important during battery manufacturing.
Existing inspection technologies, however, have limitations when applied to production environments. X-ray computed tomography can provide detailed images of internal structures, but its relatively long inspection time makes it difficult to use on high-speed production lines. Ultrasonic acoustic microscopy requires direct contact with a liquid medium, while laser displacement sensors can perform rapid measurements but have difficulty precisely analyzing structures inside an electrode.
The research team overcame these limitations by combining optical frequency comb and terahertz technologies. The researchers first directed terahertz waves at a battery electrode and collected signals generated as the waves were repeatedly reflected within the electrode. They then used an optical frequency comb as a reference to analyze the signals with exceptionally high precision and calculate the electrode thickness. This enabled nanometer-scale measurements of the electrode’s internal structure without damaging the battery.
At the core of the technology is Fabry–Pérot interference, a regularly spaced interference pattern produced as terahertz waves repeatedly travel back and forth between the front and rear surfaces of an electrode. Much like measuring length by reading the markings on a ruler, the researchers precisely analyzed the interference pattern using the optical frequency comb as a reference to determine the electrode thickness.
As a result, the team successfully measured both the electrode thickness and its complex refractive index (a material’s optical property indicating how strongly it transmits and absorbs electromagnetic waves) in a single measurement without requiring a separate calibration process.
The researchers validated the technology using battery electrodes measuring between 50 and 150 micrometers in thickness, comparable to the diameter of a human hair. With a measurement time of just 0.2 seconds, the system detected thickness differences as small as 70.1 nanometers in the anode (approximately one fourteen-hundredth the diameter of a human hair) and 465.5 nanometers in the cathode. This measurement speed is considered sufficient for use on rapidly moving battery production lines.
When the measurement time was increased to 25.6 seconds, the precision improved further. The system distinguished differences as small as 7.8 nanometers in the anode (approximately one ten-thousandth the diameter of a human hair) and 25.2 nanometers in the cathode. This represents up to a 100-fold improvement in precision compared with conventional time-domain analysis methods, enabling the detection of thickness variations that are completely invisible to the naked eye.
The technology is not limited to measuring thickness at a single point. It can generate a three-dimensional map of thickness across an entire electrode and track gradual thickness variations in real time during production. The researchers also confirmed that the system could accurately measure an electrode tilted at an angle of approximately 45 degrees, demonstrating its potential for application to fast-moving, real-world battery manufacturing lines.
The study is significant because it presents a new inspection technology capable of identifying invisible microscopic defects during production without disassembling or damaging batteries. In addition to lithium-ion batteries, the technology is expected to serve as a key quality-control tool for manufacturing next-generation all-solid-state batteries, which use solid electrolytes instead of liquid electrolytes. By detecting defects at an early stage, the technology could improve battery safety and quality while enabling more stable manufacturing processes.
“This technology is an integrated metrology platform that can simultaneously measure electrode thickness and material properties without requiring a separate calibration process,” said Professor Kim. “We expect it to become a key technology for the real-time quality control of production lines for next-generation lithium-ion batteries and all-solid-state batteries.”
The study was led by Dr. Guseon Kang from the KAIST Department of Mechanical Engineering, currently with the Korea Institute of Industrial Technology, as the first author, with Professor Young-Jin Kim serving as the corresponding author. The research findings were published in the international journal Nature Communications on June 10.
Paper title: Nanometre-precision terahertz interferometry for battery electrode metrology
DOI: https://doi.org/10.1038/s41467-026-74193-8
This work was financially supported by the National Research Foundation of Korea (NRF) (RS-2024-00401786, RS-2025-00523273, RS-2025-25455397, RS-2026-25540567, and NRF-2022M1A3C2069728) and from the Korean government’s Defense Acquisition Program Administration (DAPA) (KRIT-CT-22-040).
2026.07.23 View 1238 -
A Single Blood Sample May Improve the Prediction of Colorectal Cancer Recurrence and Progression, KAIST Study Finds
A single preoperative blood sample may help improve the prediction of recurrence or metastasis in patients with colorectal cancer. A joint research team from KAIST, Gangnam Severance Hospital, and Asan Medical Center has shown that, as colorectal cancer advances, the network of relationships among circulating amino acids (a kind of metabolic map) undergoes systematic remodeling. Building on this finding, the researchers developed an analytical method that showed higher predictive performance than a CEA-only model and models based solely on individual amino acid levels.
KAIST (President Choongsik Bae) announced on July 22 that a joint research team led by Professor Ji Min Lee from the Graduate School of Medical Science and Engineering and Professor Hyunwoo Kim from the Department of Chemistry, in collaboration with researchers at Gangnam Severance Hospital and Asan Medical Center, has developed a new framework for analyzing networks of circulating amino acids, which reflect the body’s metabolic state. Using this framework, the team showed that the circulating amino acid network undergoes stage-dependent remodeling that reflects systemic metabolic reprogramming. The team then used these network-derived features to develop a new analytical strategy for predicting recurrence or metastasis.
Cancer cells require large amounts of nutrients to grow and proliferate. Amino acids are not only the building blocks of proteins but also essential for energy production and DNA synthesis, making them critical to cancer cell survival and growth. Colorectal cancer, in particular, is characterized by pronounced changes in amino acid metabolism.
These changes are not confined to tumor tissue; they also appear in the bloodstream. As a result, blood amino acids have drawn attention as an important metabolic biomarker reflecting the body's overall metabolic state. Until now, however, research has focused mainly on the concentrations of individual amino acids, leaving the question of how amino acids are interconnected and change together largely unexplored.
The team used fluorine-19 nuclear magnetic resonance (¹⁹F NMR) spectroscopy to simultaneously quantify 18 circulating amino acids in a small serum sample. By analyzing not only the relative abundance of each amino acid but also the relationships among them as a network, the researchers showed that the circulating amino acid network is progressively remodeled as colorectal cancer advances.
The researchers interpreted this remodeling as evidence of systemic metabolic reprogramming (broad changes in metabolism associated with tumor progression).
As colorectal cancer progressed, the proportion of branched-chain amino acids (BCAAs) such as valine and leucine, which play key roles in muscle and energy metabolism, decreased, while the proportion of glycine and serine, which cancer cells need to synthesize DNA and proliferate rapidly, increased. This shift suggests that systemic amino acid utilization changes with advancing disease.
Glycine proved particularly notable. Although glycine is actively used by rapidly proliferating cancer cells, its relative abundance in the blood increased rather than decreased. The team also observed the emergence of a glycine-centered interaction pattern, providing further evidence of systemic metabolic remodeling during colorectal cancer progression.
The team then applied the pairwise amino acid interaction features into machine-learning models designed to identify patients with recurrence or metastasis.
In nested cross-validation, the correlation-based model showed higher predictive performance than a CEA-only model, while the combined model incorporating CEA, individual amino acid levels, and interaction-derived features achieved the highest overall performance. It also outperformed a model based solely on individual amino acid levels.
The findings suggest that examining how amino acids interact and change together, rather than considering their levels alone, provides a more informative picture of cancer progression. The study is the first to show that the interaction network among circulating amino acids could serve as a blood-based metabolic biomarker.
"We hope this will lead to new precision medicine technologies that can predict recurrence risk more accurately using a blood sample alone and help establish personalized treatment strategies," said Professor Ji Min Lee.
The research began with an interdisciplinary idea proposed through KAIST’s Master’s and PhD Venture Research Program. Graduate students in medical science and chemistry jointly conceived an interdisciplinary approach to studying cancer progression through networks of circulating amino acids. The proposal was selected for support and ultimately led to publication in the internationally renowned journal Advanced Science.
"This research embodies the spirit of KAIST by showing how students’ creative ideas and interdisciplinary collaboration can open new possibilities,” said KAIST President Choongsik Bae. He added that KAIST will continue to foster an environment in which students and researchers can freely pursue challenges across disciplinary boundaries and support creative interdisciplinary research that produces innovative technologies contributing to public health and quality of life.
The study's co-first authors are Ji-Yeon Lee, a student in the integrated master’s and doctoral program at the Graduate School of Medical Science and Engineering, and Dr. Jumi Kim, a postdoctoral researcher in the Department of Chemistry. Professors Ji Min Lee and Hyunwoo Kim of KAIST and Professor Eun Jung Park, affiliated with Gangnam Severance Hospital and Asan Medical Center, served as co-corresponding authors. The findings were published online in Advanced Science, which has a Journal Impact Factor of 14.1, on June 9, 2026.
Paper title: Circulating Amino Acid Network Remodeling Reveals Systemic Metabolic Reprogramming Predictive of Colorectal Cancer Recurrence and Metastasis
DOI: 10.1002/advs.76044
This research was supported by the Samsung Research Funding & Incubation Center of Samsung Electronics, the National Research Foundation of Korea, a Faculty Research Grant from the Department of Surgery at Asan Medical Center, and grants from the Asan Institute for Life Sciences, among others.
2026.07.22 View 1004 -
KAIST Makes Buttons Rise with Light
No wires. No actuators. Shine light on the metal surface, and it rises like a button. KAIST researchers have developed a metal structure that changes shape using light, without any light-absorbing coating. This technology could open new possibilities for tactile interfaces with physical pop-up buttons, shape displays, next-generation wearable devices, and soft robots.
KAIST (President Choongsik Bae) announced on the 20th of July that a research team led by Professor Il-Kwon Oh from the Department of Mechanical Engineering has developed a technology that transforms a flat NiTi shape-memory alloy (SMA) sheet into a “photothermally driven meta-morphing structure” that rises from a flat surface into a three-dimensional form when exposed to light, using only a single UV-laser process.
Next-generation wearable devices and soft robots require technologies that are thin and lightweight while also being capable of changing into desired shapes when needed. Such technologies are attracting attention as a foundation for shape displays, adaptive surfaces, wearable interfaces, and soft robots.
The research team designed precise cutting and folding patterns in a flat metal sheet so that it would transform into a predetermined three-dimensional shape. The design principle is based on kirigami, the art of creating three-dimensional structures by cutting paper.
Shape-memory alloys are special metals that return to a pre-programmed shape when heated to a specific temperature, even after being deformed. Because they are lightweight and can generate large forces, they are widely used as key materials for soft robots and wearable actuators. However, conventional photothermal shape-memory alloy actuators have faced a limitation: nickel-titanium alloy (NiTi) surfaces do not absorb near-infrared light efficiently. To compensate for this, separate light-absorbing coatings such as graphene oxide, polymer composites, or titanium nitride (TiN) thin films have typically been applied.
These external coatings can peel off during repeated operation and require additional processing. They can also increase heat capacity, which may slow the response, creating limitations in both manufacturability and actuation performance.
To address this problem, the research team used UV laser micromachining. Through this process, they formed kirigami structures on thin shape-memory alloy (SMA) sheets while simultaneously generating a micro-nano porous titanium oxide (TiOₓ) layer on the surface through laser-induced oxidation. As a result, they were able to significantly increase the absorption of near-infrared light without any separate external coating.
The team also implemented a platform that can precisely control the height of three-dimensional deformation and the resulting force output by adjusting structural parameters such as hinge width and slit width. In other words, the core of this research lies in simultaneously programming both how the structure mechanically deforms and how efficiently it absorbs light within a single metal structure.
Furthermore, the team applied a patterning technique that spatially controls the degree of laser-induced oxidation. This made it possible for different regions to deform sequentially at different speeds, even when exposed uniformly to light of the same intensity. The researchers describe this as “spatiotemporal actuation control.” This means that the order and timing of deformation are encoded directly into the material itself through light-absorption properties, without any separate electrical control. It can be seen as a form of photonic logic.
The research team further expanded the photothermal SMA metastructure into three-dimensional shape displays and haptic interfaces by integrating it with a multi-channel near-infrared (NIR) LED array. Each SMA kirigami unit moves independently in response to selectively applied light. Based on this, the team successfully displayed the letter sequence K→A→I→S→T and implemented tactile navigation signals that indicate direction.
Professor Il-Kwon Oh said, “The laser programming technology developed in this study is a manufacturing-friendly platform that encodes both mechanical deformation and optical properties into a single metallic structure without any separate coating process,” adding, “It can be widely applied to next-generation intelligent morphing interfaces controlled by light, including adaptive surfaces, interactive haptics, and photothermal soft robots.”
Hyunsoo Kim, a master’s student in the Department of Mechanical Engineering, served as the first author, while Professor Il-Kwon Oh was the corresponding author. The results were published in the international journal Advanced Science, and the work was also selected for the Inside Back Cover of Advanced Science, Vol. 13, No. 31, published on June 4, 2026.
Paper title: Monolithic UV-Laser Programming of Photothermally Meta-Morphing SMA Structures: Dual-Encoded Kirigami Mechanics and Photonic Absorbance
DOI: https://doi.org/10.1002/advs.74930
Related Video: https://drive.google.com/drive/folders/16Q9C1D6EMjoM2ypNmtDGU9ruMBdZeUFs
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00345241 and RS-2023-00302525). This research was supported by the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (RS-2025-25441263). This research was supported by the InnoCORE program of the Ministry of Science and ICT (N10250154).
2026.07.20 View 1191 -
KAIST Develops Robot That Judges Its Surroundings and Walks, Runs, and Jumps Like an Animal
An era in which robots decide "how to walk" on their own has arrived. A four-legged robot has been developed that, much like a person or an animal, autonomously chooses the appropriate gait strategy for its surroundings — changing its gait on stairs, leaping over gaps, and keeping its balance on forest trails.
KAIST (President Choongsik Bae) announced on the 16th of July that a research team led by Professor Hae-Won Park from the Department of Mechanical Engineering has developed a core control technology for four-legged robots that lets a single controller select and switch in real time among walking, running, jumping, and other locomotion skills, allowing the robot to move quickly and stably, even in real outdoor environments.
Four-legged robots move on four legs, giving them an advantage over wheeled robots on rough terrain. But in real outdoor settings, obstacles such as stairs, ledges, stepping stones, gaps, and tree branches appear one after another in different forms, meaning the ability to simply walk and run fast is not enough.
Existing four-legged robots have excelled at running quickly across flat ground or clearing simple obstacles, but they have struggled to maintain both speed and stability in real-world environments where obstacles combine in complex ways. Because walking, running, jumping, and other gaits had to be controlled individually, the robots were also limited in how naturally they could switch between them as conditions changed.
To overcome these limitations, the research team developed a new learning-based control technology called APT-RL (Action Pretrained Transformer-based Reinforcement Learning).
APT-RL is a control technology designed to enable a robot to first learn a range of locomotion skills — such as walking, running, and jumping — and then freely combine and transition among them in real-world environments as the situation demands.
Rather than filming the movements of real people or animals, the team generated 15.5 hours of training data covering a variety of gaits using computer simulations alone, in just eight minutes. That data was used to teach the robot basic movement capabilities, drawing on robot dynamics (a mathematical model of how a robot moves) and trajectory optimization (a technique for calculating the efficient path of movement). The approach is far faster and more efficient than earlier methods that relied on motion capture, a technology that records human or animal movement using sensors.
The team then applied reinforcement learning — an artificial intelligence technique in which an agent learns optimal behavior through repeated trial and error — so the robot could autonomously select and switch gaits suited to complex three-dimensional terrain such as stairs, ledges, and gaps. Finally, the team combined a depth camera (which measures the distance to objects in order to obtain three-dimensional information) with LiDAR (Laser Detection and Ranging, a sensor that uses lasers to measure the distance and shape of the surrounding environment in three dimensions), enabling the robot to recognize its surroundings and target speed in real time and choose the most appropriate walking strategy.
The team tested the control technology on its own four-legged robot, 'KAIST HOUND.' The experiments were conducted not only on an indoor obstacle course but also in real outdoor environments, including KAIST’s campus and forest trails.
KAIST HOUND moved stably across urban terrain that included stairs, grass, and slopes, as well as irregular natural terrain such as fallen trees, exposed roots, and paths covered in fallen leaves, switching gaits in real time to match the conditions. In rugged terrain with obstacles, the robot reached a peak instantaneous speed of six meters per second (about 22 kilometers per hour), demonstrating that it can achieve both fast movement and stability in real outdoor environments.
The experiments showed that KAIST HOUND autonomously selected and switched between a trot (alternating diagonal legs) and a bound (a leaping gait using the front and back leg pairs together) depending on the terrain and target speed, and that it could integrate walking, running, jumping, and ledge-clearing into a single controller.
Professor Hae-Won Park said "We expect this to become a foundational technology that expands the potential uses of physical-AI-based walking robots in rugged environments such as disaster sites, defense missions, and industrial facility inspections."
Jun-Gill Kang (affiliated with the Agency for Defense Development (ADD) at the time of the research) and Jaehyun Park, a Ph.D. candidate in KAIST's Department of Mechanical Engineering, are co-first authors of the study. Professor Hae-Won Park and Professor Seungwoo Hong from Korea University are co-corresponding authors. The research was selected as the cover paper for the July issue of Science Robotics, the world's leading academic journal in robotics, and was published on July 15 (U.S. Eastern time).
Paper title: Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
DOI: 10.1126/scirobotics.adz7397
Authors: Jun-Gill Kang (the Agency for Defense Development at the time of the research, co-first author), Jaehyun Park (KAIST, co-first author), Hae-Won Park (KAIST, corresponding author), Seungwoo Hong (Korea University, corresponding author)
This research was supported by funding from the Ministry of Trade, Industry and Resources (MOTIR) and the Korea Planning & Evaluation of Industrial Technology (KEIT) (RS-2024-00427719), as well as by the Agency for Defense Development's Future Challenge Defense Technology R&D program (912768601).
2026.07.16 View 1875 -
KAIST Opens the Era of “Space Sensors” with Optical Functions Reconfigurable by Electrical Signals Alone
Until now, satellites and space payloads have required new optical filters and sensors to be designed whenever their missions changed. A future is now on the horizon in which a single ultra-compact optical chip can perform a variety of roles—including those of a thermal imaging sensor, spectrometer, and infrared camera—using electrical signals alone.
KAIST (President Choongsik Bae) announced on 14th of July that a research team led by Professor Hyun Jung Kim from the Department of Aerospace Engineering, in collaboration with a research team led by Professor Juejun Hu at the Massachusetts Institute of Technology (MIT), has demonstrated the first transmissive mid-infrared amplitude-only spatial light modulator based on a scalable two-dimensional, electrically addressable metasurface architecture.
The key achievement of this research is that a single optical chip can perform a variety of sensor functions using electrical signals alone. Previously, new optical filters and sensors had to be fabricated for each new mission. In the future, the technology is expected to enable the realization of “software-defined sensors,” whose functions can be changed without replacing the hardware.
The device developed by the research team is a transmissive mid-infrared spatial light modulator, or SLM, based on a metasurface. A metasurface is an ultrathin optical structure that uses microscopic patterns much smaller than the width of a human hair to freely control the intensity, direction, and wavelength of light.
A spatial light modulator controls the spatial distribution of light on a pixel-by-pixel basis. In the present device, each pixel switches the intensity of transmitted mid-infrared light between two programmed states. The research team succeeded, for the first time in the world, in electrically and independently controlling each individual pixel.
Conventional spatial light modulators face significant limitations in the mid-infrared. Liquid-crystal-based devices suffer from material absorption and relatively slow response, while digital micromirror devices operate in reflection. Transmissive mid-infrared SLMs have therefore remained largely unexplored. This has limited their application to satellite sensors, ultra-compact spectrometers—which analyze light according to wavelength—and adaptive optical systems, which automatically adjust their optical performance in response to changes in the surrounding environment.
To address these limitations, the researchers used GSST—Ge₂Sb₂Se₄Te, or germanium-antimony-selenium-tellurium—an optical phase-change material (PCM) whose light transmittance changes when it receives an electrical signal.
Once GSST receives an electrical signal, it retains its state and continues to maintain the same optical performance even after the power is turned off. This nonvolatile characteristic eliminates the need for a continuous power supply, making the material suitable for satellites and space payloads, where the available electrical power is limited.
As the number of pixels on an optical chip increases, electrical current can flow into pixels other than the selected pixel, causing unintended pixels to operate as well. This is known as the “sneak-path” problem.
The research team solved this problem by integrating a silicon PIN diode into each pixel. A PIN diode is a semiconductor device that allows electrical current to flow only to the intended pixel. This enabled the researchers to accurately select and control only the desired pixels.
Using this approach, the team independently controlled all the pixels in a 6 × 6 pixel array and successfully produced desired optical patterns. The device also maintained stable performance after more than 16,700 switching cycles, demonstrating approximately 13 times greater endurance than previous technology.
The device was fabricated using silicon photonics, a technology that produces optical devices through standard semiconductor manufacturing processes. This makes it relatively easy to scale the technology to larger optical chips containing hundreds, thousands, or even more pixels.
The current device controls only the amount of transmitted light. In the future, however, more sophisticated metasurface designs are expected to enable the technology to develop into “universal reconfigurable optics,” capable of freely controlling the direction and polarization of light as well.
The greatest significance of this research is that it presents a new concept in which “optics, too, can be changed like software.” In other words, the study provides a foundation for programmable optical hardware that could support different sensing functions through reconfiguration rather than hardware replacement. In the future, this is expected to usher in an era of software-defined sensors, in which a single optical chip can perform different functions depending on the situation, serving as a thermal imaging sensor, spectrometer, infrared camera, or optical communication device.
Once commercialized, the technology is expected to make it possible to implement a wide range of optical systems on a single platform. Potential applications include satellites and space payloads, launch-vehicle health diagnostics, thermal monitoring of space stations, measurement of in-space manufacturing processes, infrared imaging, and optical communications.
This research is an achievement that further advances MIT–NASA collaborative research initiated in 2018, when Professor Hyun Jung Kim was working as a researcher at the National Aeronautics and Space Administration (NASA), and subsequently continued at KAIST.
Building on this foundation, KAIST’s STAR Lab and Professor Juejun Hu’s research team at MIT are currently conducting joint research on active meta-optics, silicon photonics, and space sensor systems, with the goal of applying the technology in actual space environments.
The two teams have established a full-cycle international collaborative research framework encompassing material development, chip design and fabrication, sensor-system integration, space-environment verification, and future flight demonstrations.
Professor Kim’s research team is now developing the technology into an operational space sensor. Under the Ministry of Science and ICT’s Young Researcher Program, the team is developing an ultra-precise system for measuring the surface temperature of launch vehicles.
The research is also being expanded through the “Space Services and Manufacturing Research Center” under the Innovation Research Center Program. The team is conducting research to develop the technology into a common optical platform that can be used for space-station thermal monitoring, anomaly diagnosis, measurement of in-space manufacturing processes, and optical communications.
“This research is not simply about creating one more new optical device,” said Professor Kim. “It presents the foundation for an era of software-defined sensors, in which a single optical chip performs a variety of functions depending on the mission.”
“By combining MIT’s nanophotonics technology—which uses nanostructures to control light—with KAIST’s space sensor technology, we plan to develop this technology into an actual space system,” she added. The research was published online in the international journal Nature Communications on July 7.
Paper title: “Two-Dimensional Pixel-Level Addressable Mid-Infrared Metasurface Spatial Light Modulator”
DOI: 10.1038/s41467-026-75346-5
This work was funded by the Air Force SBIR Program under contract FA2394-23-C-5076, the National Science Foundation under awards 2329088 and 2132929, and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2025-00515651 and RS-2025-02213804).
2026.07.14 View 1109 -
KAIST Develops AI Technology to Detect Early Warning Signs of Cerebrovascular Disease at Home
Cerebrovascular disease can lead to serious aftereffects if treatment is delayed, but it is difficult to detect before symptoms appear. KAIST researchers have developed an AI technology that analyzes real-life daily activity and environmental data from older adults to identify digital behavioral markers of cerebrovascular disease risk based on subtle changes at home.
KAIST (President Choongsik Bae) announced on the 12th of July that a research team led by Professor Lisa Lim from the Department of Civil and Environmental Engineering, in collaboration with Professor Jo Woon Chong from the School of Electronic and Electrical Engineering at Sungkyunkwan University (President Ji-Beom Yoo) and Professor Kyung-Hee Cho from the Department of Neurology at Korea University Anam Hospital (President Dongwon Kim), has developed an AI framework that uses long-term lifelog data collected in the homes of older adults to identify the prodromal phase of cerebrovascular disease and assess imminent diagnostic risk.
The study was based on lifelog data from 1,224 older adults collected by LivOn Care Co., Ltd. in real residential environments. The research team analyzed a total of 13,362 two-week lifelog samples, demonstrating the possibility of detecting early warning signs through subtle changes in daily life, rather than relying only on the conventional approach of treating the disease after it has already occurred.
The research team developed AI technology that identifies cerebrovascular disease risk stages by analyzing daily activity, sleep, circadian rhythm, and indoor environmental information, together with age and chronic disease data. This shows that changes in everyday living patterns, which are difficult to capture through hospital examinations alone, can serve as important clues for detecting early risk signals of cerebrovascular disease.
The team also succeeded in assessing whether a cerebrovascular disease diagnosis was approaching by analyzing changes in lifestyle patterns over time. When lifelog data from within four weeks before diagnosis were classified as the “imminent diagnostic risk period” and data from 12 weeks before diagnosis were classified as the “non-imminent period,” the AI distinguished between the two periods with a high accuracy of 96.53%. This result suggests that even before a hospital visit, small changes in daily life may help identify whether the risk of cerebrovascular disease has increased.
Another key feature of this study is that the AI does not simply determine whether a risk exist, but also applies explainable AI to identify the lifestyle patterns and environmental factors behind its judgment.
The analysis showed that older adults in the prodromal phase of cerebrovascular disease tended to show frequent continuous activity between 10 p.m. and 2 a.m., a time when the body would normally be preparing for sleep. In other words, irregular daily rhythms, such as delayed sleep onset and a reduced distinction between day and night activity, were closely associated with prodromal signals of cerebrovascular disease.
The researchers also found that as the time of diagnosis approached, the frequency of continuous activity during the evening period from 6 p.m. to 10 p.m. noticeably decreased, while inactive time increased. Low indoor humidity, indicating a dry indoor environment, also emerged as an important factor in identifying an imminent diagnostic risk.
The research team expects this technology to be used as a digital healthcare tool that can objectively monitor the health status of older adults who may have difficulty clearly describing their own condition, while providing useful early warning indicators to medical professionals and caregivers.
However, the team explained that this study does not predict the exact onset of cerebrovascular disease or replace clinical diagnosis. Rather, it is a supportive technology intended to aid prevention and early medical consultation, and prospective validation in larger patient groups will be necessary before actual clinical application.
Professor Lisa Lim said, “The key point of this study is not that AI should replace a hospital diagnosis, but that it can first detect risk signals in small lifestyle changes at home and help connect patients to medical care at the right time,” adding, “We expect this technology to contribute to a shift from a healthcare system that treats disease after it occurs to one that supports prevention and early intervention.”
This study, with KAIST Dr. Jeongyeop Baek as the first author, was published on June 2 in npj Digital Medicine, a leading international journal in digital healthcare published by Nature Portfolio, with an impact factor of 15.1 and ranked in the top 0.3% of JCR journals.
※ Paper title: AI home monitoring for behavioral markers of cerebrovascular disease
DOI: https://doi.org/10.1038/s41746-026-02836-7
This work was also supported by the National Research Foundation (NRF) grant funded by the Korea government (Ministry of Science and ICT) (RS-2025-16068234).
2026.07.13 View 1184 -
KAIST Automates the Search for “Dream Semiconductor” 2D Semiconductors
The era of researchers manually searching for two-dimensional semiconductors, which are drawing attention as next-generation AI semiconductors, is coming to an end. KAIST researchers have automated semiconductor screening and device fabrication, analyzed thousands of devices, and revealed the relationship between thickness and performance that had long been difficult to identify. This achievement is expected to shift next-generation semiconductor research toward a data-driven approach and accelerate the commercialization of AI semiconductors and ultra-low-power semiconductors.
KAIST (President Choongsik Bae) announced on the 9th that a research team led by Professor Jimin Kwon of the School of Electrical Engineering and the Department of AI System has developed a technology that automatically identifies two-dimensional semiconductors from optical microscope images alone and connects the process to transistor fabrication, through joint research with UNIST, Hanbat National University, Hanyang University, and Washington University in St. Louis in the United States.
Two-dimensional semiconductors are ultrathin semiconductors only a few atomic layers thick. They are called “dream semiconductors” because they can enable smaller semiconductors that consume less electricity than conventional silicon semiconductors. Today’s silicon semiconductors are approaching physical limits, as continued miniaturization of circuits leads to greater power loss and heat generation. Two-dimensional semiconductors, which are attracting attention as next-generation materials to overcome these limits, are expected to be used in a wide range of future technologies, including AI semiconductors, smartphones, data centers, wearable devices, foldable or stretchable electronics, and ultra-small medical sensors.
However, in two-dimensional semiconductors made through solution processing, the position, size, and thickness of each small semiconductor flake all differ, requiring researchers to find the desired samples one by one under a microscope. They then had to manually design electrodes according to the identified positions, requiring substantial time and effort, and making it practically difficult to analyze thousands or more devices at once.
The research team used molybdenum disulfide (MoS₂), a representative two-dimensional semiconductor material. By using the fact that the RGB red, green, and blue brightness values seen under a microscope change depending on thickness, the team enabled a computer to automatically identify the desired semiconductor and automatically design the electrodes. Verification using atomic force microscopy (AFM) confirmed that even subtle thickness differences of three to eight layers could be accurately distinguished.
Through this approach, the team successfully selected suitable samples automatically from more than 120,000 semiconductor flakes and fabricated and analyzed 1,615 transistors.
The large-scale analysis also produced meaningful results. The team statistically clarified for the first time that as the semiconductor becomes thicker, current flows more easily, but the ability to switch electricity on and off actually decreases. This characteristic had been difficult to confirm previously because only a small number of samples could be analyzed, but the team revealed it through large-scale data.
The greatest significance of this study is that it did not simply automate the fabrication process, but transformed two-dimensional semiconductor research, which had relied on human experience, into data-driven research. Going forward, the technology is expected to enable researchers to fabricate and analyze more semiconductors more quickly, identify high-performance materials, and ultimately expand into research in which AI designs new semiconductors.
This study was conducted with Professor Jimin Kwon, Dr. Haksoon Jung, and Dr. Yongwoo Lee of KAIST as co-corresponding authors, and Sanghyun Lee of UNIST as the first author. The research results were published on April 3 in Advanced Functional Materials, a leading international journal in materials science, and were also selected as an Inside Back Cover article in the field of 2D Materials & Electronics.
※ Paper title: Statistically Resolving Thickness-Dependent Electrical Characteristics in Multilayer-MoS₂ Transistors, DOI: 10.1002/adfm.202532204
※ Author information: Professor Jimin Kwon (KAIST, corresponding author), Dr. Haksoon Jung (KAIST, corresponding author), Dr. Yongwoo Lee (KAIST, corresponding author), Sanghyun Lee (UNIST, first author), and participating researchers from partner institutions: Sumin Hong (UNIST), Minho Park (UNIST), Professor Seongju Kim (Hanbat National University), Professor Sang-Hoon Baek (Hanyang University), Professor Joonki Suh (KAIST), Seonguk Yang (KAIST), Professor Sang-Hoon Bae (Washington University in St. Louis), and Dr. Chang-Soo Lee (TDS)
This research was supported by the Individual Basic Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT), and by the Advanced Strategic Industry Super-Gap Technology Development Program of the Korea Planning & Evaluation Institute of Industrial Technology (KEIT), funded by the Ministry of Trade, Industry and Energy (MOTIE).
2026.07.09 View 1436