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KAIST Identifies Cause of Artifacts in Battery Nanoscale Analysis, Paving the Way for More Reliable Measurements
A signal that appears to show ions moving inside a battery may, in fact, be an illusion caused by an uneven surface. A KAIST research team has identified the origin of this type of artifacts, which can lead researchers to misinterpret what is happening inside a battery, and has developed a method to reduce it. The findings are expected to enable more accurate analysis of ion movement and improve the reliability of next-generation battery-material development, including that of solid-state and sodium-ion batteries.
KAIST (President Choongsik Bae) announced on September 7 that a research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the research groups of Professor Jong Min Yuk from the same department and Professor Nam-Soon Choi from the Department of Chemical and Biomolecular Engineering, has identified the cause of a measurement artifact in nanoscale battery analysis that can be mistaken for actual ion transport. The team also proposed a method for effectively reducing this artifact.
During charging and discharging, lithium or sodium ions move back and forth within a battery. The speed and ease with which these ions move affect the battery’s performance and lifespan. Developing better batteries therefore requires researchers to precisely determine where ions can move freely and where their movement is hindered.
One technique used for this type of analysis is Electrochemical Strain Microscopy (ESM), which is based on Atomic Force Microscopy (AFM). ESM scans the surface of a battery material with an extremely fine tip and measures nanoscale changes in the material associated with ion movement, allowing researchers to indirectly track ion transport.
The problem is that when the surface of a battery material is rough, similar signals can appear even in the absence of actual ion movement. If these signals are interpreted as evidence of ion transport, researchers may incorrectly identify where ions are moving within the material.
To investigate the origin of theseartifactss, the team created fine trenches on the surface of an ionically inactive single-crystal silicon sample. This provided an experimental environment in which no ions were moving, while the sample surface remained uneven.
The results quantitatively demonstrated that variations in surface height alone can alter the degree of contact between the microscope tip and the sample, producing signals similar to those generated by actual ion movement.
The same phenomenon was also observed in actual battery materials. When the team analyzed a graphite anode and the sodium solid electrolyte Na₂Zn₂TeO₆, the ESM signals likewise varied according to surface topography. This confirmed that the issue is not limited to a particular material but is a phenomenon that researchers must account for when conducting nanoscale analyses of a wide range of battery materials.
As a solution, the team proposed making the surfaces of battery materials as smooth and flat as possible. To achieve this, the researchers used a cooling cross-section polisher (CCP), which employs an argon (Ar) ion beam to precisely polish sample cross sections. Because argon is chemically inert under most conditions, this technique allows the surface to be processed precisely without significantly altering the properties of the sample.
This treatment substantially reduced surface roughness and, in turn, decreased measurement artifacts caused by uneven surfaces. The mechanism is comparable to a car moving up and down while traveling over a bumpy road: as the scanning tip passes over height variations on the surface of a battery material, the degree of contact between the tip and the sample changes. These changes can generate signals resembling those produced by actual ion movement.
In particular, the team examined signals detected at grain boundaries—the interfaces at which the small crystals that make up a battery material meet, much like the seams between adjacent tiles.
Before the surface was smoothed, strong ESM signals appeared at these grain boundaries. After the surface was polished, however, the enhanced signals disappeared. This finding indicates that some signals previously interpreted as evidence of “pathways that facilitate ion transport” may actually have resulted from variations in surface height rather than genuine ion movement.
This study is significant because it experimentally demonstrates how this type of measurement artifact arises in nanoscale battery analysis and shows that it can be reduced using the practical approach of smoothing battery-material surfaces.
The findings are expected to provide a more accurate understanding of where ions move freely and where their movement is hindered within a battery. Such insights could provide an important foundation for designing battery materials that facilitate ion transport, thereby enabling faster charging and longer battery life.
The team expects this analytical approach to be applicable not only to widely used lithium-ion batteries but also to next-generation battery systems. These include solid-state batteries, which use solid rather than liquid electrolytes, and sodium-ion batteries, which use sodium ions in place of lithium ions. The approach could help researchers more accurately understand how these batteries operate and support the design of new materials.
Furthermore, the accumulation of reliable nanoscale analysis data could provide high-quality training datasets for artificial intelligence (AI) and machine-learning research aimed at designing new battery materials and predicting their performance.
“This research clearly demonstrates how variations in surface height affect the results of nanoscale battery-material analysis,” said Professor Hong. “We expect our findings to enable more accurate tracking of ion movement within batteries and contribute to understanding the operating mechanisms of next-generation battery materials and designing improved materials.”
Dongyan Chen, a PhD student in the Department of Materials Science and Engineering, served as the first author of the study, which was published in Small Methods, an international journal specializing in materials science and nanotechnology.
Paper title: Quantitative Analysis of Topographic Crosstalk in DART-ESM Arising from Feedback-Loop-Delay-Induced Contact Stiffness Variations in Battery Materials
DOI: https://doi.org/10.1002/smtd.70763
This work was supported by National Research Foundation of Korea (NRF) grants funded by the Korean government’s Ministry of Science and ICT (MSIT) (Nos. RS-2026-25468150 and RS-2023-00247245).
2026.09.07 View 238 -
KAIST Develops AI Technology That Fixes SQL Errors Without Starting Over
“Find the best-selling product from last year.” When an AI system attempts to answer a question like this by querying a company database, even a single reference to a nonexistent item can cause the query to fail. Until now, correcting such an error often required regenerating the entire SQL query from scratch. A KAIST research team has developed a technology that instead identifies and fixes only the erroneous part. The technology is expected to make AI-powered data retrieval faster and more accurate, accelerating the adoption of AI work assistants in enterprise environments.
KAIST (President Choongsik Bae) announced on September 4 that a research team led by Professor Min-Soo Kim from the School of Computing has developed SafeQL, a technology that detects and corrects errors that arise when natural-language questions are translated into Structured Query Language (SQL).
Text-to-SQL technology enables AI systems to convert everyday questions, such as “Which product saw the largest increase in sales last year?” or “Which items are running low in stock?”, into SQL queries. This allows users to retrieve sales, customer, and inventory data using natural language without having to understand complex database commands.
However, AI systems can make mistakes when generating SQL. For example, they may refer to a table or column that does not exist or join tables incorrectly. These errors can prevent the query from running and leave the AI unable to retrieve the requested data.
Conventional correction methods send the database error back to a large language model (LLM) and ask it to regenerate the entire query. This is similar to rewriting an entire report to correct a single word. In the process, parts that were already correct may be altered, new errors may be introduced, and repeated calls to the LLM increase both cost and processing time.
SafeQL takes a different approach. Instead of discarding and regenerating the entire query after an execution failure, it interprets feedback from the database management system to precisely locate the faulty component, such as a relation, attribute, function, or value. It then incrementally repairs that component while preserving the valid structure and logic of the original query.
To achieve this, the research team developed a “safe query space” approach. Among the candidate corrections that can be executed on the database, SafeQL searches for the one closest to the query originally generated by the AI. The system prioritizes the most promising candidates and filters out unsuitable ones in advance, reducing the time required for correction.
The team implemented SafeQL as a PostgreSQL extension and integrated it with the database system’s parser, binder, and type analyzer. This enables SafeQL to precisely locate errors even in complex SQL queries. The system calls the LLM again when search-based refinement cannot resolve the error within a predefined threshold, thereby reducing unnecessary AI use.
The research team evaluated SafeQL using BIRD and Spider, two widely used benchmarks for assessing the database querying capabilities of AI systems.
On the BIRD benchmark, SafeQL resolved execution errors in up to 87.4% of initially erroneous SQL queries and improved execution accuracy by up to 5.8 percentage points over the unrefined baseline. Compared with regenerating the entire query, SafeQL reduced token use by a factor of up to 15.1 and refinement latency by a factor of up to 29.6.
SafeQL is expected to be particularly useful for enterprises that handle large volumes of data requests. If an error occurs while an AI system is searching internal sales, customer, or inventory data, SafeQL can repair only the affected part instead of regenerating the entire query. This can reduce the cost and time required to operate enterprise AI systems and support reliable work automation powered by AI agents and corporate data.
Professor Min-Soo Kim said, “For AI to perform real-world tasks in enterprise environments, it must be able to accurately retrieve the data it needs.”
He added, “When AI makes an error during a database search, SafeQL fixes only the affected part instead of starting over from scratch. By reducing errors, costs, and processing time, we expect the technology to accelerate reliable AI-powered work automation.”
Geonho Lee, a PhD student in the KAIST School of Computing, participated in the study as first author, with Professor Min-Soo Kim serving as corresponding author. The findings will be presented at the International Conference on Very Large Data Bases (VLDB), a leading international database conference, to be held in Boston, USA, from September 1 to 5.
Paper title: SafeQL: Search-based Refinement for Safe and Efficient LLM-based Text-to-SQL
DOI: 10.14778/3819518.3819545
Authors: Geonho Lee (KAIST, first author) and Min-Soo Kim (KAIST, corresponding author)
This research was supported by the National Research Foundation of Korea (NRF) and the SW Star Lab program of the Institute of Information & Communications Technology Planning & Evaluation (IITP), both funded by the Ministry of Science and ICT (MSIT).
2026.09.04 View 397 -
KAIST Tames a Semiconductor Greenhouse Gas 6,000 Times More Potent Than CO₂ with the ‘Power of Disorder’
Among the gases used in semiconductor manufacturing, tetrafluoromethane (CF₄) is a greenhouse gas over 6,000 times more potent than carbon dioxide. A KAIST research team has developed a technology that removes this gas with high efficiency while extending the usable lifetime of the catalyst that helps break it down by harnessing the ‘power of disorder,’ in which mixing multiple metal atoms together actually stabilizes the catalyst’s structure.
KAIST (President Choongsik Bae) announced on September 3 that a research team led by Professor Minkee Choi from the Department of Chemical and Biomolecular Engineering, working in collaboration with researchers from Samsung Electronics, has developed a new catalyst capable of removing CF₄, a greenhouse gas used in processes such as the fabrication of fine semiconductor circuits with high efficiency over long periods of use.
CF₄ is used in processes such as dry etching, in which unwanted portions of a semiconductor wafer are selectively removed to create fine circuit patterns. The problem lies in the CF₄ left over after use. Because its carbon and fluorine atoms are bound together extremely tightly, the gas does not easily decompose, and once released into the atmosphere, it can persist for roughly 50,000 years. Its impact on global warming is also more than 6,000 times greater than that of carbon dioxide.
To prevent CF₄ from being released as is, semiconductor manufacturing sites currently decompose it at high temperatures using steam and a catalyst. A catalyst speeds up chemical reactions, much like those used to reduce pollutants in car exhaust.
However, conventional catalysts have suffered from declining performance the longer they are used. This is because hydrogen fluoride (HF), generated as CF₄ decomposes, combines with moisture to create a highly corrosive environment, causing the catalyst’s fine particles to aggregate or its structure to change. When small catalyst particles clump together into larger masses, the surface area in contact with the CF₄ to be treated shrinks, and performance declines accordingly.
The research team solved this problem, paradoxically, by harnessing the ‘power of disorder.’
Mixing multiple atom types creates a complex, disordered structure that resists phase changes and remains stable. This process is called entropy stabilization. In simple terms, it is a principle in which evenly mixing multiple kinds of atoms makes it difficult for a catalyst to clump together or change into another structure.
Using this principle, the research team evenly incorporated multiple metals — aluminum (Al), zinc (Zn), gallium (Ga), nickel (Ni), and cobalt (Co) — into a single aluminate crystal structure. Aluminate is a material in which several metals are bonded around a basic framework of aluminum and oxygen. Through this approach, the team developed an ‘entropy-stabilized aluminate (ESA) catalyst’ that resists aggregation and structural deformation even under the harsh conditions of high temperature, moisture, and fluorine occurring together.
The performance gap was clear. The new catalyst’s intrinsic activity for decomposing CF₄ was approximately 2.3 times higher than that of a conventional alumina catalyst. Notably, in an accelerated test conducted at about 800°C for 150 hours, the CF₄ conversion of the conventional alumina catalyst dropped from 93% to 48%. The new catalyst, by contrast, maintained a high level, declining only from 98% to 92%. This demonstrated that the catalyst can remove CF₄ with high efficiency while sustaining its performance over extended periods.
The researchers also revealed the decomposition mechanism of CF₄. To do this, they used oxygen isotopes, which allow the movement of oxygen atoms to be tracked. In simple terms, this involves attaching a ‘tag’ to oxygen atoms so that where the oxygen comes from and where it moves to during the reaction can be traced.
The results confirmed that the catalyst first uses the oxygen within its own structure to decompose CF₄, and that the reaction continues as surrounding steam replenishes the oxygen that has been depleted. In effect, the catalyst functions as a kind of ‘oxygen refill system,’ in which steam restores the oxygen the catalyst draws upon. Through this, the research team provided the world’s first experimental confirmation of a CF₄ decomposition process that had previously only been proposed in theory.
The significance of this research goes beyond developing a single catalyst that decomposes CF₄ effectively; it presents a new catalyst design strategy capable of achieving both high decomposition performance and a long service life at the same time. The approach is expected to be applicable to the future development of catalysts for treating a range of semiconductor process gases by varying the types and combinations of metals used.
Professor Choi said, “By applying the principle that disorder in nature can actually make a structure more stable to catalyst design, we achieved both high CF₄ decomposition performance and long-term stability at the same time.” He added, “This work is meaningful in that it presents a new materials design strategy that can be extended to catalysts for treating a range of semiconductor process gases by varying the types and combinations of metals used.”
The study was led by Dr. Seunghyuck Chi, a postdoctoral researcher in KAIST’s Department of Chemical and Biomolecular Engineering, who served as first author, with researchers from Samsung Electronics participating as co-authors. The findings were published in June in the international chemistry journal Angewandte Chemie International Edition.
Paper title: Entropy-Stabilized Aluminate Catalysts that Break the Activity–Stability Tradeoff in CF₄ Hydrolysis,
DOI: 10.1002/anie.6752036
This research was supported by the National Research Foundation of Korea (RS‐2024‐00333937 and RS‐2024‐00405261).
2026.09.03 View 423 -
KAIST Skin-Conformable Micro-LED Mask Boosts Skin Rejuvenation, Brightening, and Synergistic Benefits with Polynucleotide (PN) Injections
Home beauty devices that let users care for their skin conveniently at home have grown popular recently, but conventional LED masks are limited not only by their rigid structures but also by their point-emitting LEDs, which must be positioned away from the skin to spread light over a broader area. This inherently prevents close skin contact and increases optical loss.
KAIST (President Chung-Sik Bae) announced on September 2 that a joint research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering confirmed skin-brightening and elasticity-improving effects using a face-conforming LED mask. The mask combines a flexible surface-emitting micro-LED layer, consisting of a dense micro-LED array and a light-diffusing layer for uniform illumination, with a three-dimensional elastic scaffold that conforms to facial contours.
In a 2024 study published in Advanced Materials, Professor Lee clinically demonstrated that a flexible surface-emitting micro-LED mask produced up to 340% greater improvement in deep skin elasticity than conventional LED masks.
In the present study, the 3D elastic scaffold adapted to different facial contours, increasing skin-contact area from 46.9% to 78.1% and reducing the light-source-to-skin distance to 1.8 mm, while achieving 93.83% light uniformity across eight facial measurement sites.
The researchers evaluated the mask in a split-face clinical study involving 33 participants. All of the participants received PN injections across the entire face, while the micro-LED mask was applied to only one side for eight weeks. The micro-LED-treated side showed greater improvement across all five skin-brightening indices, including skin brightness, tone uniformity, skin exfoliation, melasma count, and melasma area. Consistent with the clinical findings, human-derived skin tissue treated with micro-LEDs also showed reduced expression of the melanogenesis-related markers MITF and TYR, supporting a direct contribution of the LED treatment to the brightening effect. PN injections are primarily known for skin rejuvenation, with limited evidence of a direct skin-brightening effect when used alone.
The synergy between PN injection and the micro-LED mask was also evident in skin regeneration and post-procedure recovery. Deep skin elasticity improved by 12.8% on the LED+PN side, compared with 3.1% on the PN-only side, representing approximately 4.1 times the improvement observed with PN alone. Skin-barrier recovery was faster, while post-procedure redness was reduced to a greater extent, consistent with the known anti-inflammatory and tissue-repair effects of red-light photobiomodulation. These findings suggest that the LED mask may boost mitochondrial ATP production in the skin and activate regenerative responses that PN alone cannot fully induce.
Professor Lee said, “This study clinically demonstrates that home-use LED masks can extend beyond skin rejuvenation to skin brightening and highlights the importance of delivering light in close contact with the skin. When combined with in-clinic skin-rejuvenation injections, the mask can substantially improve skin elasticity and accelerate post-procedure recovery, creating a new clinic-to-home care platform.”
This study was conducted jointly by researchers from KAIST and AMOREPACIFIC. A related product based on the technology is scheduled to launch in Japan in Q4 2026 and enter the U.S. market in Q1 2027.
The resulting paper, titled “Clinical validation of skin brightening and rejuvenation enabled by a skin-conformable surface-emitting micro-LED mask with injection,” was published in Nano Energy
(Vol. 157, Article 112288; DOI: 10.1016/j.nanoen.2026.112288).
2026.09.02 View 439 -
KAIST Develops a Soft 3D-Printed Robotic Hand that Gently Grips Everything from Eggs to a 1 kg Water Bottle
3D printers that once could only produce rigid objects can now create products as soft and stretchable as rubber. A team of Korean researchers used AI to identify the optimal "recipe" for a material that can be printed into complex shapes while stretching to more than six times its original length. The material is expected to expand the range of applications for 3D printing, from robotic hands to form-fitting wearable devices and custom medical devices.
KAIST (President Choong-Sik Bae) announced on September 1 that a research team led by Professor Seungchul Lee from the Department of Mechanical Engineering, working with Dr. Jongbeom Na's team at the Korea Institute of Science and Technology’s (KIST, President Sang-Rok Oh) Extreme Materials Research Center and Professor Bumsoo Park from the Department of Manufacturing Systems and Design Engineering (MSDE) at Seoul National University of Science and Technology (SEOULTECH, President Dong-Hwan Kim), had used AI to develop a material that is both 3D-printable and highly stretchable, like rubber.
The need for such materials — soft, stretchable, and capable of forming complex shapes — has been growing as soft robots that come into direct contact with people, wearable devices worn on the body, and medical devices custom-fitted to patients have drawn increasing attention.
The 3D printing technology the team used, Digital Light Processing (DLP), cures a liquid material into a desired shape by exposing it to light. While DLP can quickly produce complex structures, making a material more stretchable and durable tends to raise its viscosity to the point that it no longer flows well enough to be printed. Conversely, thinning the material to make it easier to print reduces its stretchability and strength. Thus, developing a material that is both easy to print and highly stretchable was the central challenge.
The team used AI to identify the optimal "material recipe" that satisfies both conditions. Notably, the training data included not only materials that print well, but also highly viscous materials that are difficult to print.
The researchers cured various liquid material formulations in small molds and measured how stretchable and hard they were, how quickly they cured under light, and how well they flowed. This produced a dataset linking a wide range of material formulations to their respective properties.
The team then used machine learning to examine the relationship between material formulation and performance. Based on this, the AI identified the optimal material combination that is both 3D-printable and highly stretchable.
The material identified by the AI printed reliably on a DLP 3D printer and showed high stretchability, extending to more than six times its original length when pulled, without easily tearing.
To verify its real-world potential, the team 3D-printed a "soft actuator" using the material. A soft actuator is a device that uses air pressure and other means to create gentle, muscle-like movement. When inflated with air, it expanded like a balloon and bent as naturally as a human finger.
A soft robotic hand made by combining several actuators lifted a 1 kg water bottle and successfully and stably grasped objects of varying shapes and rigidity, from fragile eggs to glass bottles, an egg carton, and a computer mouse.
Beyond developing a single highly stretchable material, this research is significant for presenting an AI-based method for more quickly identifying materials with desired properties.
Previously, researchers had to directly formulate and test countless materials to find the optimal combination. Going forward, AI can first identify promising material combinations based on experimental data, which researchers then verify through testing, thereby reducing trial and error and shortening material development time.
"This research is significant as it shows that combining researchers' experimental data with artificial intelligence can efficiently identify optimal material combinations that were previously difficult to find," explained Professor Seungchul Lee. "We expect it to be used to more rapidly develop 3D-printing materials with the performance needed across a range of fields, including soft robots, wearable devices, and custom medical devices."
The study, with Dr. Younghan Song and Professor Bumsoo Park as co-first authors, was published in the international journal Nature Communications on June 4.
Paper title: Machine learning guided formulation design of digital light processing printable elastomers beyond viscosity stretchability tradeoff
DOI: https://doi.org/10.1038/s41467-026-73735-4
This research was supported by the Ministry of Trade, Industry and Resource's Machinery and Equipment Industry Technology Development Program (20023762), and by the Ministry of Science and ICT's Nano & Material Technology Development Program (RS-2026-25534767) and Excellent New Researcher Program (RS-2024-00350423).
2026.09.01 View 750 -
KAIST Opens the Era of Industrial-Scale Microbial Foods, Proposing Growth Strategies for the Next-Generation Protein Market
The question is no longer whether microbial foods can be made. The question now is who can turn them into an industry first. KAIST researchers have comprehensively analyzed the conditions required for the microbial food industry to succeed across manufacturing, markets, and regulation, and have proposed growth strategies for the next-generation protein industry.
KAIST (President Choongsik Bae) announced on the 31st of July that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, together with researchers from SilicoBio, a KAIST faculty startup, has comprehensively analyzed the conditions needed for the microbial food industry to succeed in terms of manufacturing, market entry, and regulatory readiness, and has presented an industrialization strategy and roadmap.
This study is significant in that it did not develop a new microorganism or production technology, but instead systematically analyzed the key challenges involved in connecting laboratory-based core technologies to real-world industry. In particular, by presenting an integrated perspective that encompasses manufacturing readiness, market entry strategies, and regulatory responses, the study proposes a direction for developing microbial foods beyond the next-generation protein industry into a future biomanufacturing platform. It is expected to serve as an important milestone for strengthening national biomanufacturing competitiveness and fostering the global sustainable food industry.
The researchers analyzed that competition in the microbial food industry is shifting from productivity at the laboratory level to manufacturing readiness. They identified stable raw material supply and quality control, control and safety assurance of non-model microorganisms, reduction of downstream processing costs, and regulatory compliance for byproduct recycling as key factors that will determine the pace of commercialization. Manufacturing Readiness refers to the level at which a laboratory technology can be reliably produced at industrial scale. Non-model microorganisms are microorganisms with high industrial potential but insufficient accumulated research infrastructure. Downstream processing refers to the processes of separating, purifying, concentrating, and drying target components after fermentation.
The researchers particularly emphasized that future competitiveness will depend less on the excellence of any single technology and more on the ability to build integrated manufacturing platforms. An Integrated Manufacturing Platform refers to a production system that operates the entire process as one connected framework, from strain development and large-scale fermentation to purification, quality control, and product formulation. Even for the same microbial food product, the choice of raw material can affect pretreatment costs and quality variability, while the choice of strain and fermentation process can greatly influence production cost, energy use, and product quality. The researchers therefore concluded that future industrial competitiveness will depend on how quickly companies can build manufacturing platforms that optimize these factors in an integrated way.
On the market side, the researchers also identified the conditions needed for the microbial food industry to succeed. Based on consumer surveys and industry cases, they found that microbial foods cannot spread simply by emphasizing environmental sustainability. Consumers place importance on taste, texture, familiarity, and safety, while food manufacturers value functionality that can be applied to actual products. Companies and investors, meanwhile, consider the predictability of regulatory approval procedures and speed of market entry to be especially important. In other words, the microbial food market has entered an industrial stage where not only technology, but also product development capability and regulatory readiness are evaluated together.
The researchers also argued that microbial foods should not be viewed merely as an alternative protein industry. They suggested that microbial foods have the potential to develop into a core platform for precision fermentation-based functional food ingredients, high-value biomaterials, and circular biomanufacturing. Precision Fermentation is a technology that uses microorganisms to selectively produce specific proteins or functional substances. Circular Biomanufacturing refers to a sustainable manufacturing system that uses byproducts and renewable resources to produce new bio-based products. This means that microbial foods could become not only a future food source, but also a new production system connecting the global food, materials, and biomanufacturing industries.
The industrialization strategy proposed in this study is also closely aligned with the business direction of SilicoBio, which participated in the joint research. Based on the manufacturing readiness strategy presented in the study, SilicoBio is working to build a platform that connects microbial proteins and functional food ingredients to industrial-scale fermentation, scale-up, and product development. Scale-up refers to the process of expanding production from laboratory scale to industrial scale.
Distinguished Professor Sang Yup Lee of KAIST said, “As global competition surrounding synthetic biology and biomanufacturing intensifies, microbial foods are growing into a key industry that will shape national biomanufacturing competitiveness beyond future food.” He added, “Going forward, competitiveness will be determined by how quickly we can build an industrialization ecosystem that connects core technologies to real production and markets.”
A SilicoBio representative said, “Our goal is to connect the industrialization strategy proposed in this study to actual production and commercialization,” adding, “We will build a platform capable of stably producing microbial-based next-generation foods and functional biomaterials.”
This study, with Seok Yeong Jung, a doctoral student in the Department of Chemical and Biomolecular Engineering, as first author and researchers from SilicoBio participating as co-authors, was published on July 17 in the international journal One Earth (Impact Factor 15.3, JCR top 2.07%).
Paper title: Microbial foods as scalable platforms toward a circular protein economy for sustainable nutrition
DOI: https://doi.org/10.1016/j.oneear.2026.101772
Authors: Sang Yup Lee (KAIST, corresponding author), Seok Yeong Jung (KAIST, first author), Sol Choi (SilicoBio, second author), Jun-Woo Kim (SilicoBio and Inha University, third author), and two others
SilicoBio is a KAIST faculty startup founded in June 2025 by Distinguished Professor Sang Yup Lee, a world-renowned scholar in synthetic biology. The company focuses on connecting laboratory-level achievements in systems metabolic engineering to real industrialization. By combining KAIST’s core technologies with the industrialization experience of personnel from CJ BIO, SilicoBio has built a team capable of reviewing not only strain design, but also industrial-scale fermentation and scale-up, material purification and product development, pilot production, and process validation. Based on this foundation, SilicoBio is pursuing a phased commercialization strategy, starting with next-generation protein products and expanding into functional ingredients and eventually new drug and novel material candidates.
This research was supported by the “Development of Next-Generation Biorefinery Core Technologies to Lead the Biochemical Industry” project under the Petroleum-Alternative Eco-Friendly Chemical Technology Development Program funded by the Ministry of Science and ICT, and by the “Advancement of a Synthetic Biology-Based Industrial Cell Factory Platform and Commercialization of High-Value Functional Biomaterials” project under the Deep Science Startup Activation Support Program funded by the Commercialization Promotion Agency for R&D Outcome.
2026.08.31 View 456 -
KAIST Develops Smartphone-Based Technology to Detect Hidden Cameras
A smartphone can now be transformed into a “hidden-camera detector.” KAIST researchers have developed an AI technology that can detect hidden cameras using only a smartphone and a low-cost LED device. This new security technology enables users to protect their privacy more easily and is expected to help prevent illegal filming in everyday spaces such as hotels and short-term rentals.
KAIST (President Choongsik Bae) announced on August 30 that a research team led by Professor Jun Han of the School of Computing, in collaboration with the National University of Singapore and Singapore Management University, has developed “SweepLED,” a technology that detects hidden cameras by attaching an LED case to a smartphone.
As hidden cameras are increasingly being installed in everyday spaces such as hotels, short-term rentals, and restrooms, the need is growing for detection technology that everyday users can easily use. However, existing portable detectors require users to visually identify bright reflective spots, which can lead to false positives by mistaking reflections from metal, glass, or glossy plastic surfaces for camera lenses.
SweepLED works by keeping the smartphone camera fixed while changing only the direction of the LED illumination, then analyzing the patterns of reflected light that appear on object surfaces. Reflections from ordinary glossy objects tend to move or disappear depending on the direction of the light. In contrast, camera lenses show distinctive deformation patterns in their reflections due to their internal lens, aperture, and sensor structures.
The research team uses deep learning-based analysis to distinguish these differences in temporal reflection patterns. While conventional detection methods rely on the user’s eyes to simply look for “bright spots,” SweepLED is different in that it analyzes both the movement and shape changes of reflections across multiple lighting angles.
This enables more reliable detection of hidden camera lenses inside various everyday objects commonly found in lodging spaces, such as chargers, clocks, remote controls, and everyday objects.
The research team evaluated SweepLED on 30 objects that may be found in real-world environments and found that it achieved approximately 94% detection accuracy. It also took less than five seconds to inspect a single object.
In addition, the core components of the LED case attached to the smartphone cost less than USD 7, or about KRW 10,000, demonstrating the potential for this technology to be developed into an affordable detection tool that general users can easily access.
Professor Jun Han said, “Hidden cameras pose a serious threat to personal safety and privacy in everyday spaces,” adding, “This research is meaningful in that it combines low-cost smartphone-based hardware with AI analysis to present the possibility of a practical detection technology that even non-experts can use.”
This paper, with KAIST doctoral student Jonghyuk Yun as first author, was presented on June 20 at ACM MobiSys 2026, one of the leading international conferences in the field of mobile computing.
Paper title: Hide-and-Sweep: Detecting Concealed Cameras via LED Illumination Sweeps
https://doi.org/10.1145/3812835.3814866
Author information: Jonghyuk Yun (first author), Jaeyoung Moon, Yunseo Park, Sean Rui Xiang Tan, Byunghyun Kim, Rajesh Krishna Balan, and Professor Jun Han (corresponding author)
This research was supported by the STEAM Global Convergence Research Support Program and the Mid-Career Researcher Program of the Ministry of Science and ICT and the National Research Foundation of Korea.
2026.08.31 View 5636 -
Neural Implant in Korea Remotely Controlled from the United States, Bringing Brain Research into the IoT Era
A researcher in Chicago remotely controls a miniaturized brain implant in Daejeon, Korea — over the internet. Korean researchers have developed a wireless device that can deliver drugs and light to precisely modulate targeted neurons from anywhere in the world. The technology is expected to overcome the constraints of distance and location, supporting long-term studies of brain disorders and the future development of therapeutic devices.
KAIST (President Choongsik Bae) announced on August 27 that a research team led by Professor Jae-Woong Jeong from the School of Electrical Engineering, in collaboration with Professor Wha Young Kim's team at Yonsei University College of Medicine, has developed an IoT-enabled wireless neural implant that integrates drug delivery, optical stimulation, wireless communication, and internet-based remote control into a single miniaturized device.
Conventional studies involving optical stimulation or drug delivery to the brain often required bulky equipment connected by wires, restricting the natural movement of experimental animals. Even wireless devices had their own limitations, often requiring researchers to operate them at close range, thereby restricting experimental flexibility and introducing the so-called “observer effect”.
To overcome these limitations, the research team developed the brain implant with IoT connectivity. Even without being physically present in the laboratory, researchers can remotely administer drugs or stimulate specific brain neurons with light in real time via the internet. The device can also be programmed to operate automatically at a preset time.
The device is about the size of a sugar cube and is designed not to interfere with the animal's natural behavior. Researchers no longer need to repeatedly approach or handle equipment near the animal, reducing the stress caused by a researcher's presence, which can otherwise affect the animal's behavior and bias experimental results.
The implant contains a microfluidic system that precisely delivers drugs to a targeted region of the brain, as well as a micro-LED that enables optical control of specific neurons. Drug delivery and optical stimulation can be controlled independently, or the two functions can be combined.
The drug reservoir is designed to be magnetically detachable. Even after the drug is depleted, researchers can replace or refill the reservoir without the need for additional implantation surgery, enabling long-term, repeated experiments.
The research team implanted the device in rats and verified its performance over a four-week period. In particular, a researcher in Chicago successfully operated the brain implant in Daejeon, Korea, in real time via the internet, demonstrating that the device can operate reliably over intercontinental distances.
The team also conducted an experiment in which cocaine was wirelessly administered to a rat's brain while specific neurons were simultaneously stimulated with light. The results showed that addiction-related behavioral responses could be suppressed, demonstrating the potential of combining drug delivery and optical stimulation for neural circuit research.
By eliminating the need for researchers to operate equipment directly beside experimental animals, this technology enables long-term studies of the relationship between brain circuits and behavior under naturalistic conditions. It is expected to be useful for studying conditions that involve long-term changes in neural circuit function and behavior, such as addiction, depression, and neurodegenerative diseases.
The technology could ultimately pave the way for intelligent implantable medical devices that combine brain-state sensing with AI to deliver drugs or neural stimulation precisely when needed.
Professor Jae-Woong Jeong from KAIST said, “This technology transforms wireless brain implants that use light and drugs from short-range control tools into IoT-based brain engineering platforms capable of long-term, automated, and remote experimentation.” He added, “In the long term, it could contribute to the development of intelligent implantable medical devices for the diagnosis and treatment of brain disorders.”
Professor Wha Young Kim from Yonsei University said, “This platform allows researchers to remotely and precisely control specific brain circuits over extended periods while animals move freely under naturalistic conditions.” She added, “It is expected to become an important tool for identifying causal relationships between neural circuits and behavior in disease models such as addiction, depression, and neurodegenerative disorders.”
Eun Young Jeong, a doctoral student in KAIST's School of Electrical Engineering, and Jong Woo Park, a doctoral student at Yonsei University College of Medicine, served as co-first authors. The study was published on July 29 in the international journal Science Advances.
Paper title: IoT-enabled wireless neural implant for chronic, programmable neuropharmacology and optogenetics,
DOI: 10.1126/sciadv.aee8648
This research was supported by the Mid-Career Researcher Program and Basic Research Laboratory Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT, as well as the Industrial Technology Alchemist Project of the Ministry of Trade, Industry and Energy.
2026.08.27 View 1783 -
KAIST Uses Light to Distinguish Real from Fake with Hard-to-Replicate “Artificial Fingerprints”
Shine a light, and the real can be distinguished from the fake. KAIST researchers have developed a security technology that uses unique “artificial fingerprints” created by the random assembly of nanoparticles. Although extremely difficult to replicate, these fingerprints can be conveniently authenticated using only a smartphone flashlight and a laser pointer, opening up potential applications in anti-counterfeiting and electronic device authentication.
KAIST announced on August 26 that a research team led by Professor Sang Ouk Kim from the Department of Materials Science and Engineering, in collaboration with a team led by Professor Seok Joon Kwon of Sungkyunkwan University, has developed a new foundational security technology based on randomly assembled colloidal nanopatterns—unique microscopic patterns formed by particles too small to be seen with the naked eye. The technology enables authentication using everyday light sources such as smartphone flashlights and laser pointers.
Recent advances in artificial intelligence have made cyberattacks increasingly sophisticated, while future quantum computers may pose a threat to conventional cryptographic systems. As a result, growing attention is being paid to technologies that use the unique physical characteristics of products or devices themselves for security, in addition to software-based encryption.
A physical unclonable function, or PUF, is a security technology that uses minute physical differences naturally generated during the manufacturing process as security information. Just as every person has a unique fingerprint, microscopic particles form a different arrangement each time they assemble. Even when the same materials and process are used, reproducing the exact positions and orientations of the particles is extremely difficult. The researchers used these differences as “artificial fingerprints” for authenticating products and devices.
However, conventional high-security PUFs typically require expensive microscopes, spectroscopic equipment, or imaging systems to read information from their tiny and complex structures, making them difficult to use conveniently in everyday settings.
The research team focused on solving this dilemma between high security and easy authentication. By using the self-assembly of spherical particles hundreds of nanometers in size on a water surface, the team created unique structures composed of many small crystalline domains with different sizes and orientations. These structures are different every time they are made, making them difficult to replicate, while also producing clear optical signals when illuminated.
The research team implemented an authentication method in which the two patterns generated by each product are registered in advance and subsequently compared with those observed from the actual product. In other words, a single “nanofingerprint” is authenticated using two different light sources: a flashlight and a laser. Much like identifying a person using both their face and fingerprint, verifying one nanostructure in two independent ways strengthens security.
When illuminated with ordinary light, such as a smartphone flashlight, the nanostructure produces a unique color and reflection pattern depending on the particle arrangement. When illuminated with a laser pointer, the microscopic particle structure scatters the light in multiple directions, producing a second distinctive optical pattern.
To create a counterfeit, a forger would have to reproduce not only the nanoparticle structure itself, but also the exact color and reflection pattern produced under a flashlight and the optical pattern generated under laser illumination—making replication extremely difficult.
The researchers also successfully transferred the nanostructures onto a variety of surfaces, including flexible plastics, metals, transparent films, and hydrogels—soft, gel-like materials capable of retaining large amounts of water.
The technology could be used to assign a unique “hardware ID” to electronic products and Internet of Things devices for product authentication. It could also serve as an anti-counterfeiting label for luxury goods, artworks, and pharmaceuticals. Because it can be applied to transparent films, it may also be developed into security stickers that do not obscure a product’s design or appearance.
Professor Sang Ouk Kim of KAIST’s Department of Materials Science and Engineering said, “The key achievement of this study is that it combines randomly formed structures that are extremely difficult to replicate with a simple authentication method using readily available tools such as a flashlight or laser pointer.” He added, “We expect the technology to develop into a next-generation security solution that can be readily used in everyday applications, including electronic device authentication and anti-counterfeiting labels.”
Dr. Geon Gug Yang of KAIST’s Department of Materials Science and Engineering and Ph.D. student Seong-Gyun Im of Sungkyunkwan University’s Department of Chemical Engineering contributed equally as co-first authors. Professors Sang Ouk Kim of KAIST and Seok Joon Kwon of Sungkyunkwan University served as co-corresponding authors. The results were published online on July 23 in the international journal Nature Communications.
Paper title: “Dual-space visible light authentication toward high security physical unclonable function”
DOI: https://doi.org/10.1038/s41467-026-75781-4
This research was supported by the Mid-Career Researcher Program and the InnoCORE Program funded by the Ministry of Science and ICT, as well as by the Samsung Research Funding & Incubation Center for Future Technology.
2026.08.26 View 686 -
KAIST Identifies a Route to Faster-Charging, Longer-Lasting EV Batteries
Can electric vehicles charge quickly without sacrificing battery longevity? A KAIST research team has identified a potential solution using a three-dimensional digital twin—a virtual model that recreates the internal microstructure of a real battery electrode. The team found that fast-charging performance and degradation behavior are influenced not only by the amounts of materials and pore space within the electrode, but also by how they are distributed.
KAIST (President Choongsik Bae) announced on August 24 that a research team led by Professor Kang Taek Lee from the Department of Mechanical Engineering, in collaboration with Professor EunAe Cho of the Department of Materials Science and Engineering, constructed a 3D digital twin informed by the microstructure and specifications of a commercial graphite anode. Using the model, the researchers quantitatively analyzed localized degradation mechanisms that arise during fast charging.
A lithium-ion battery anode consists of graphite, which stores lithium; a binder that holds the graphite particles together; and electrolyte-filled pore space where lithium ions travel. When a battery is charged, lithium ions move into the graphite particles in the anode where they are intercalated and stored. But if charging happens too quickly, some lithium ions cannot enter the graphite in time and instead build up as metallic lithium on the surface—a phenomenon called Li plating. It is similar to cars piling up at the entrance of a parking lot when too many arrive at once and cannot get inside fast enough. If this continues, it can degrade both battery performance and lifespan.
During charging, a thin protective film also forms on the graphite surface called the solid electrolyte interphase (SEI) layer. A properly formed SEI layer is necessary, but if it becomes too thick or uneven, it can degrade battery performance. In addition, as lithium enters the graphite particles during charging, the particles expand and push against the surrounding material, creating mechanical stress inside the electrode.
These processes occur simultaneously at the microscale, making their individual effects difficult to distinguish experimentally. Existing computational models have also relied mainly on the electrode's average properties, making it hard to capture the complex internal structure and location-dependent behavior within the electrode.
To address this, the research team built a "3D digital twin" of the battery electrode based on the structure of an actual commercial graphite anode. The team reconstructed the graphite particles, the binder that holds them together, and the electrolyte-filled pores through which lithium ions travel—all in three dimensions.
Using this virtual electrode, the researchers varied the electrode thickness, porosity, and the distribution of the binder, then simulated fast charging to analyze how lithium ions moved. They also examined where Li plating occured, how the protective film formed, and which parts of the anode experienced concentrated stress.
The results showed that even when the overall charge capacities were similar, the internal degradation behavior of the anodes could differ significantly depending on how the binder and pore space were arranged inside the electrode.
In 50-micrometer (μm) anodes, the difference in charge capacities due to binder distribution was within 4%—meaning there was little apparent difference in charging performance. Inside the electrode, however, the locations where lithium was intercalated and where performance-degrading reactions occurred differed clearly.
In particular, when the binder was concentrated near the separator, the available pore space for lithium-ion transport decreased, making it more difficult for lithium ions to move through the anode. It is much like how a narrower road causes traffic congestion. In this case, Li plating near the current collector increased by more than 10% compared to the anode with an evenly distributed binder.
Conversely, when the binder was spread relatively evenly throughout the electrode, lithium-ion transport became more uniform, and the protective film also formed more uniformly.
This difference grew larger as the electrode became thicker. In 83 μm-thick anodes, the charge capacity difference between the two binder distributions widened to about 18%. This suggests that making thicker electrodes to store more energy requires carefully designing not just how much material is used, but exactly how it is arranged inside.
The location of pore space also affected the stress the electrode experienced. Where there was enough pore space, the surrounding area could accommodate the graphite particles as they expanded during charging. Where pore space was insufficient, the graphite particles had no room to expand, concentrating stress in specific areas.
Through this study, the research team proposed a new design direction for fast-charging lithium-ion batteries: rather than simply looking at how much binder and pore space an electrode contains, researchers should also consider where and how they are distributed.
Using a 3D digital twin makes it possible to examine potential problems inside a battery in virtual space before building and testing multiple electrode designs by hand. The approach is expected to help identify optimal electrode structure, contributing to the development of batteries that can charge faster while maintaining longer service life.
"This research is significant in that it used a 3D digital twin to uncover internal battery problems that were difficult to detect from overall charging performance alone," said Professor Lee. He added that properly arranging the binder and pore space inside the electrode could help design batteries that store more energy while charge faster, and last longer.
The study, with KAIST PhD candidate Yejin Kang from the Department of Mechanical Engineering as first author, was published in the international journal InfoMat (Impact Factor 19.6) and was for the journal’s back cover on July 7.
Paper title: Digital twin quantifies spatial-heterogeneity-driven failure in fast-charging lithium-ion battery anodes,
DOI: https://doi.org/10.1002/inf2.70141
This research was supported by the Ministry of Science and ICT's Mid-Career Researcher Support Program, its Convergence Technology Development Program, and the InnoCORE Research Center.
2026.08.24 View 784 -
KAIST Uses Surface ‘Defects’ to Enhance Droplet Formation and Removal, Achieving Up to 5.5 Times the Heat Transfer Performance of Conventional Surfaces
A technology that boosts condensation heat transfer performance by up to 5.5 times that of conventional copper surfaces has been developed by helping water droplets form more readily and detach more quickly. It is expected to help improve the energy efficiency of power plants and desalination facilities and enhance the cooling performance of electronic devices.
KAIST (President Choongsik Bae) announced on August 23 that a joint research team led by Professor Youngsuk Nam from the Department of Mechanical Engineering and Professor Sung Gap Im from the Department of Chemical and Biomolecular Engineering has developed a technology that controls the thickness and structure of an ultrathin polymer coating applied to a surface, allowing more water droplets to form and the resulting droplets to detach more quickly as water vapor turns into liquid water.
Condensation is the process by which water vapor turns into liquid water. It is easy to observe in everyday life, as when droplets form on the surface of a cold beverage cup. In industrial settings, it is widely used to convert steam back into water at power plants, obtain fresh water from seawater, and remove heat generated by electronic devices.
During condensation, rapidly removing water from the surface is essential. On ordinary metal surfaces, small droplets merge to form a thin water film. This water film adds thermal resistance, impeding heat flow and reducing heat transfer efficiency, much like layers of winter clothing that slow the loss of body heat.
By contrast, when water forms as small droplets and continuously detaches, it repeatedly exposes fresh surface area. This phenomenon, in which water condenses as droplets, is known as dropwise condensation. Put simply, instead of water continuously covering the surface, droplets repeatedly form and fall away. This allows heat to be transferred more effectively.
Existing technologies, however, faced a dilemma. Roughening the surface to create more sites where droplets could first form caused the droplets to become caught on the structures and prevented them from detaching easily. Conversely, smoothing the surface helped droplets detach but reduced the number of sites available for new droplets to form. In other words, surface features that promote droplet formation can also make droplets harder to remove, creating a fundamental trade-off between nucleation and droplet mobility.
The research team solved this problem by using nanoscale polymer aggregates that had previously been regarded as ‘defects’ in polymer films. The team used initiated chemical vapor deposition (iCVD), a process that deposits gas-phase precursors onto a surface to create an ultrathin polymer film. When the polymer film was made thinner, small polymer aggregates formed densely across the surface and served as nucleation sites where water droplets could readily begin to form. As a result, approximately three times more droplets formed on the thin polymer films than on the thicker films.
The team then added a heat treatment step to reduce the force holding droplets to the surface. This allowed droplets to detach easily before growing large. In other words, thinning the polymer film increased the number of sites where droplets could form, while heat treatment helped the resulting droplets detach quickly. The key advance was to control these two competing effects separately: film thickness increased droplet nucleation, while thermal treatment promoted droplet removal.
New droplets form again where previous droplets have detached. Much like the next person taking a seat as soon as it becomes vacant, the faster droplets form and detach, the more frequently the surface is renewed, allowing heat to transfer more efficiently during condensation.
The research team coated copper tubes commonly used in actual condensers with the polymer film and evaluated their performance. The maximum condensation heat transfer coefficient, a measure of heat transfer ability, reached approximately 88 kW·m⁻²·K⁻¹. This represented heat transfer performance up to approximately 5.5 times higher than that of a conventional copper surface with a water film. The coating also performed more than 50% better than a conventional hydrophobic coating surface.
Unlike conventional approaches focused on making surfaces smooth or hydrophobic, this study actively used small surface ‘defects.’ The researchers found that nanoscale particles previously regarded as features to be eliminated could instead help droplets form, and they incorporated that finding into a new surface-design strategy.
If applied to power plants or industrial heat exchangers, the technology could help improve energy efficiency by transferring heat more effectively. It is also expected to enable more effective water collection in desalination and water-harvesting devices and faster heat removal for improved cooling of electronic devices.
Professor Nam said, “This research is meaningful because it uses nanostructures previously regarded as defects as features that help droplets form. We have presented a new method for improving heat transfer efficiency by separately controlling droplet formation and removal.” He added, “Because this technology can form extremely thin, uniform coatings even on surfaces with complex shapes, we expect it to be used in various energy and environmental applications, including industrial heat exchangers.”
Jun Soo Kim, a researcher in the Department of Mechanical Engineering, and Minjeong Kang, a researcher in the Department of Chemical and Biomolecular Engineering, co-authored the study as first authors. The results were published online in the international journal Nature Communications on July 16.
Paper title: Rational design of polymer film morphology via structure–performance linkage for enhanced condensation performance
DOI: https://doi.org/10.1038/s41467-026-75621-5
This research was supported by the Mid-Career Researcher Program (Ministry of Science and ICT and the National Research Foundation of Korea), the SME Technology Innovation Development Program (Ministry of SMEs and Startups and the Korea Technology and Information Promotion Agency for SMEs), and the Deep-Tech Startup Activation Support Program (Ministry of Science and ICT and Commercialization Promotion Agency for R&D Outcomes, COMPA).
2026.08.24 View 735 -
KAIST Develops Core Technology to Reverse Biological Changes Once Thought Irreversible, Opening New Possibilities for Aging and Cancer Research
Once a cell has locked into an abnormal state — the way cancer cells do — can it ever be restored back to normal? A KAIST research team has identified the ‘molecular lock’ that keeps cells trapped in an altered state, opening a new path toward releasing that lock and reversing a cell’s fate.
KAIST (President Choongsik Bae) announced on the 21st of August that a research team led by Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering has, for the first time, identified the causal circuits responsible for irreversibility in intracellular molecular networks and developed a fundamental control technology called ROOT that can regulate these circuits and restore biological states to their original condition.
Cells in the human body change their state in response to external stimuli. In many cases, however, these state changes are irreversible, in the sense that cells do not return to their original state even after the stimulus disappears.
Irreversibility is essential for maintaining normal biological processes, such as a cell differentiating into one with a specific function. At the same time, it can also drive disease progression — for example, in epithelial–mesenchymal transition, which gives cancer cells the ability to migrate into and invade surrounding tissue.
Complicating matters, the circuits that maintain these state changes inside a cell are highly intricate: more than a thousand positive feedback loops are woven throughout the network, in which one molecule activates a series of other molecules that in turn reactivate the original molecule. This is similar to the feedback screech produced when a microphone is placed next to a speaker, where a sound repeatedly amplifies itself. Even a change that starts with an external stimulus can persist after the stimulus is gone, simply because the cell’s own molecules keep reinforcing one another. Until now, it has been extremely difficult to determine which of these countless circuits is actually responsible for locking a cell into an irreversible state.
To solve this problem, the team developed ROOT technology, short for Revelation Of the Original circuit of irreversible Transition, which works by representing intracellular regulatory processes as computational logic models and analyzing them through systems biology techniques. Using ROOT, the research team successfully simulated the process in which cells maintain a signal even after an external stimuli is removed, allowing them to identify a set of core circuits that cause irreversibility, which they defined as the “irreversibility kernel.”
Going beyond identifying the cause, the team also proposed two groundbreaking control strategies.
The first, “resetting control,” restores a cell to its state before the change while leaving the cell’s underlying irreversible property intact — comparable to leaving the lock itself in place, but opening the locked door and returning to the starting point.
The second, “reversing control,” removes the source of irreversibility itself, allowing a cell to move freely between different states — comparable to disabling the mechanism that automatically locks a door each time it closes, so that afterward the door can be opened and closed again.
The team applied the new technique to various biological models, including B-cell differentiation, epithelial–mesenchymal transition in lung cancer, and enterocyte and beta-cell differentiation models based on single-cell transcriptome data, in which the ROOT method accurately identified causal circuits that matched known cell-fate determinants. The team also proposed more effective resetting control strategies, demonstrating that the method can be broadly applied even to models built from real experimental data.
Rather than simply removing cells that have become fixed in an abnormal state, as in cancer or aging, the technology is expected to help identify and control the core circuits that keep cells trapped in that state, enabling new treatment strategies that restore cells to a normal condition.
Professor Kwang-Hyun Cho said, “The core achievement of this study is identifying the causal circuits behind cells that, once changed, do not return to their original state, and developing a technology to control these circuits and restore cells to their previous condition.” He added, “We expect this technology to be used in developing new treatment strategies that restore abnormally fixed cell states — such as those seen in cancer and aging — back to normal.”
This study was co-led by Dr. Jongwan Kim and Dr. Seong-Hoon Jang of KAIST’s Department of Bio and Brain Engineering as co-first authors, with participation from Dr. Jonghoon Lee and Ph.D. student Corbin Hopper. The research was published on August 13 in Proceedings of the National Academy of Sciences of the United States of America (PNAS), one of the world’s leading scientific journals.
Paper title: The structural origin of irreversible transitions in biological networks,
DOI: https://doi.org/10.1073/pnas.2600800123
This research was supported by the Mid-Career Researcher Program and the Basic Research Laboratory Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT.
2026.08.21 View 1359