Kaist

AI Gets a Private Tutor, Learning Human Preference..

< Professor Junmo Kim and Ph.D. candidate Minchan Kwon, School of Electrical Engineering > No matter how much data they learn, why do Artificial Intelligence (AI) models often miss the mark on human intent? Conventional "comparison learning," designed to help AI understand human preferences, has frequently led to confusion rather than clarity. A KAIST research team has now presented a new learning solution that allows AI to accurately learn human preferences even with limited data by assigning it a "private tutor." On December17th, a research team led by Professor Junmo Kim of KAIST School of Electrical Engineering announced the development of "TVKD" (Teacher Value-based Knowledge Distillation), a reinforcement learning framework that significantly improves data efficiency and learning stability while effectively reflecting human preferences. Existing AI training methods typically rely on collecting massive amounts of "preference comparison" data—simple structures like "A is better than B." However, this approach requires vast datasets and often causes the AI to become confused in ambiguous situations where the distinction is unclear. To solve this problem, the research team proposed a method in which a ‘Teacher model’ that has first deeply understood human preferences delivers only the core information to a ‘Student model.’ This can be compared to a private tutor who organizes and teaches complex content, and the research team named this ‘Preference Distillation.’ The biggest feature of this technology is that instead of simply imitating ‘good or bad,’ it is designed so that the teacher model learns a ‘Value Function’ that numerically judges how valuable each situation is, and then delivers this to the student model. Through this, the AI can learn by making comprehensive judgments about ‘why this choice is better’ rather than fragmentary comparisons, even in ambiguous situations. < Conceptual diagram of TVKD: After teaching the human preference dataset to the teacher model, learning proceeds by delivering the teacher's information and the dataset to the student model > The core of this technology is twofold. First, by reflecting value judgments that consider the entire context into the student model, learning that understands the overall flow rather than fragmentary answers has become possible. Second, a technique was introduced to adjust learning importance according to the reliability of preference data. Clear data is significantly reflected in learning, while the influence of ambiguous or noisy data is reduced, allowing the AI to learn stably even in realistic environments. As a result of the research team applying this technology to various AI models and conducting experiments, it showed more accurate and stable performance than methods previously known to have the best performance. In particular, it recorded achievements that stably outperformed existing top technologies in major evaluation indices such as MT-Bench and AlpacaEval. Professor Junmo Kim said, “In reality, human preference data is not always sufficient or perfect,” and added, “This technology will allow AI to learn consistently even under such constraints, so it will be highly practical in various fields.” < Performance comparison results for each task of MT-Bench. It can be confirmed that the proposed TVKD framework records generally higher scores than existing methods. > < Visualization results of the Shaping term. The top tokens (converted into words) judged as important by the teacher model within the response are displayed in red, intuitively showing which tokens have a greater influence during the value-based alignment process. > Ph.D. candidate Minchan Kwon from our university’s School of Electrical Engineering participated as the first author, and the research results were accepted at ‘NeurIPS 2025’, the most prestigious international conference in the field of artificial intelligence. The research was presented at a poster session on December 3, 2025 (US Pacific Time). ※ Paper Title: Preference Distillation via Value based Reinforcement Learning, DOI: https://doi.org/10.48550/arXiv.2509.16965 Meanwhile, this research was carried out with support from the Information & Communications Technology Planning & Evaluation (IITP) funded by the government (Ministry of Science and ICT) in 2024 (No. RS-2024-00439020, Development of Sustainable Real-time Multimodal Interactive Generative AI, SW Star Lab).

Harry Potter–Style ‘Moving Invisibility Cloak’ Tec..

<(Top row, left) Ph.D candidate Hyeonseung Lee, Professor Wonho Choe, (Second row, left) Professor Hyoungsoo Kim, Professor Sanghoo Park,(Top) First author Dr. Jeongsu Pyeon> What do Harry Potter’s invisibility cloak and stealth fighter jets that evade radar have in common? They both make objects invisible despite their physical presence. Building upon this concept, our research team has taken it one step further by developing a “smart invisibility cloak” like technology that hides electromagnetic waves even better as it stretches and moves. This technology is expected to open new possibilities for moving robots, body-mounted wearable devices, and next-generation stealth technologies. On December 16th, research teams led by Professor Hyoungsoo Kim of the Department of Mechanical Engineering and Professor Sanghoo Park of the Department of Nuclear and Quantum Engineering from KAIST announced that they have developed a core enabling technology for next-generation stretchable cloaking＊ based on Liquid Metal Composite Ink (LMCP), which can absorb, modulate, and shield electromagnetic waves. ＊ Cloaking: A technology that makes an object appear as if it does not exist to detection equipment such as radar or sensors, even though it is physically present. To realize cloaking technology, it is necessary to freely control light or electromagnetic waves on the surface of an object. However, conventional metallic materials are rigid and do not stretch well, and when forcibly stretched, they easily break. For this reason, there have been significant difficulties in applying such materials to body-conforming electronic devices or robots that freely change shape. The liquid metal composite ink developed by the research team maintains electrical conductivity even when stretched up to 12 times its original length (1200％), and it demonstrated high stability with little oxidation or performance degradation even after being left in air for nearly a year. Unlike conventional metals, this ink is rubber-like and soft while fully retaining metallic functionality. These properties are possible because, during the drying process, liquid metal particles inside the ink spontaneously connect with one another to form a mesh-like metallic network structure. This structure functions as a “metamaterial”—an artificial structure in which extremely small patterns are repeatedly printed using ink so that electromagnetic waves interact with the structure in a designed manner. As a result, the material simultaneously exhibits liquid-like flexibility and metal-like robustness. The fabrication process is also simple. Without complex procedures such as high-temperature sintering or laser processing, the ink can be printed using a printer or applied with a brush and then simply dried. In addition, common drying issues such as stains or cracking do not occur, enabling smooth and uniform metal patterns. To verify the performance of the ink, the research team became the first in the world to fabricate a “stretchable metamaterial absorber” whose electromagnetic wave absorption characteristics change depending on the degree of stretching. Simply stretching the rubber-like substrate after printing patterns with the ink changes the type (frequency band) of electromagnetic waves that are absorbed. This demonstrates the potential for cloaking technology that can more effectively hide objects from radar or communication signals depending on the situation. <Figure. Comparison of LMCP ink properties, printing process applicability, mechanical/electrical performance, and versatility on various substrates. (a) Comparison results regarding surface tension, viscosity, wettability, and post-processing requirements between conventional liquid metal-based inks and the LMCP ink in this study. The results demonstrate that LMCP ink possesses the advantage of requiring no post-processing while maintaining relatively high viscosity and excellent wettability. (Right radar chart: Qualitative comparison of key performance indicators, including electrical conductivity, surface tension, viscosity, wettability, and post-processing requirements). (b) Various printing methods based on the self-sintering characteristics of LMCP ink: nozzle-based direct writing, brushing, patterning using shadow masks and doctor blade processes, and large-area electrode fabrication via the roll-to-roll method. (c) Stretchability and electrical stability of LMCP electrodes. Results show resistance changes when samples are stretched from 0％ to 1200％, and stable operation is confirmed under 0％–500％ strain through a 3 V LED driving experiment. (d) Examples of various patterns and devices fabricated using LMCP ink. Applicable structures are presented, including large-area uniform coating, precise grid patterns, crack-free metal paths, LED circuits operating under tension, and stretchable spiral electrodes> (e) Examples demonstrating stable printing of LMCP ink on various substrates (SIR, NBR, PVC, PET, WPU, PDMS, Latex), indicating excellent pattern reproducibility and adhesion regardless of the substrate type> This technology is evaluated as a groundbreaking electronic material technology that simultaneously satisfies stretchability, electrical conductivity, long-term stability, process simplicity, and electromagnetic wave control functionality. Professor Hyoungsoo Kim stated, “We have made it possible to implement electromagnetic wave functionality using only printing processes without complex equipment,” adding, “This technology is expected to be utilized in various future technologies such as robotic skin, body-mounted wearable devices, and radar stealth technologies in the defense sector.” This research was recognized as an important fundamental technology in the field of next-generation electronic materials and was published in the October 2025 issue of the international Wiley journal Small on October 16, where it was selected as a cover article. Paper title: J. Pyeon, H. Lee, W. Choe, S. Park, H. Kim, “Versatile Liquid Metal Composite Inks for Printable, Durable, and Ultra-Stretchable Electronics,” Small 2501829 (2025) DOI: https://doi.org/10.1002/smll.202501829 Author information: First author: Dr. Jeongsu Pyeon Co-authors: Doctoral candidate Hyeonseung Lee, Professor Wonho Choe Corresponding authors: Professor Hyoungsoo Kim, Professor Sanghoo Park This work was supported by the National Research Foundation of Korea’s Mid-Career Research Program (MSIT: 2021R1A2C2007835) and the KAIST UP Program. < Selected as the cover article of the October 2025 issue of the international journal Small > < Invisibility cloak technology image (AI-generated image) >

Octopus-Inspired 3D Micro-LEDs Pave the Way for Se..

<(From Left) Professor Keon Jae Lee, Professor Tae-Hyuk Kwon, Ph.D candidate Min Seo Kim, Dr. Jae Hee Lee, Dr. Chae Gyu Lee> -KAIST and UNIST Researchers Develop Shape-Morphing Device to Overcome Pancreatic Tumor Microenvironment Barriers Conventional pancreatic cancer treatments face a critical hurdle due to the dense tumor microenvironment (TME). This biological barrier surrounds the tumor, severely limiting the infiltration of chemotherapy agents and immune cells. While photodynamic therapy (PDT) offers a promising alternative, existing external light sources, such as lasers, fail to penetrate deep tissues effectively and pose risks of thermal damage and inflammation to healthy organs To address these challenges, Professor Keon Jae Lee’s team at KAIST, in collaboration with Professor Tae-Hyuk Kwon at UNIST, developed an implantable, shape-morphing 3D micro-LED device capable of effectively delivering light to deep tissues. The key technology lies in the device’s flexible, octopus-like architecture, which allows it to wrap around the entire pancreatic tumor. This mechanical compliance ensures uniform light delivery to the tumor despite the tumor’s physiological expansion or contraction, enabling continuous, low intensity photostimulation that precisely targets cancer cells while preserving normal tissue. In in-vivo experiments involving mouse models, the device demonstrated remarkable therapeutic efficacy. Within just three days, tumor fibrous tissue was reduced by 64％, and the pancreatic tissue successfully reverted to normal tissue, overcoming the limitations of conventional PDT. Prof. Keon Jae Lee said, "This research presents a new therapeutic paradigm by directly disrupting the tumor microenvironment, the primary obstacle in pancreatic cancer treatment." He added, "We aim to expand this technology into a smart platform integrated with artificial intelligence (AI) for real-time tumor monitoring and personalized treatment. We are currently seeking partners to advance clinical trials and commercialization for human application." <Overall concept of 3D Shape-morphing micro-LEDs (SMLEDs). The 3D long-term, low-intensity photodynamic therapy (PDT) system attaches to the pancreatic surface, ensuring stable and continuous light delivery. Initially maintaining a 2D structure, the system morphs into a 3D structure upon implantation to conform to the shape of the pancreas. In in vivo experiments, the device maintained stable adhesion without detachment for four weeks and reduced the pancreatic tumor size by 64％.> Professor Tae-Hyuk Kwon commented, "While phototherapy is effective for selective cancer treatment, conventional technologies have been limited by the challenges of delivering light to deep tissues and developing suitable photosensitizers." He added, "Building on this breakthrough, we aim to expand effective immune-based therapeutic strategies for targeting intractable cancers." <Cover Image. The 3D long-term, low-intensity photodynamic therapy (PDT) system, developed by Professor Keon Jae Lee's team at the Department of Materials Science and Engineering at KAIST, was featured as the cover article of the international journal Advanced Materials> The result, titled "Deeply Implantable, Shape-Morphing, 3D MicroLEDs for Pancreatic Cancer Therapy," was featured as the cover article in Advanced Materials (Volume 37) on December 10, 2025.

KAIST Predicts Human Group Behavior with AI！ 1st P..

<(From Left) Ph.D candidate Geon Lee, Ph.D candidate Minyoung Choe, M.S candidate Jaewan Chun, Professor Kijung Shin, M.S candidate Seokbum Yoon> KAIST (President Kwang Hyung Lee) announced on the 9th of December that Professor Kijung Shin’s research team at the Kim Jaechul Graduate School of AI has developed a groundbreaking AI technology that predicts complex social group behavior by analyzing how individual attributes such as age and role influence group relationships. With this technology, the research team achieved the remarkable feat of winning the Best Paper Award at the world-renowned data mining conference “IEEE ICDM,” hosted by the Institute of Electrical and Electronics Engineers (IEEE). This is the highest honor awarded to only one paper out of 785 submissions worldwide, and marks the first time in 23 years that a Korean university research team has received this award, once again demonstrating KAIST’s technological leadership on the global research stage. Today, group interactions involving many participants at the same time—such as online communities, research collaborations, and group chats—are rapidly increasing across society. However, there has been a lack of technology that can precisely explain both how such group behavior is structured and how individual characteristics influence it at the same time. To overcome this limitation, Professor Kijung Shin’s research team developed an AI model called “NoAH (Node Attribute-based Hypergraph Generator),” which realistically reproduces the interplay between individual attributes and group structure. NoAH is an artificial intelligence that explains and imitates what kinds of group behaviors emerge when people’s characteristics come together. For example, it can analyze and faithfully reproduce how information such as a person’s interests and roles actually combine to form group behavior. As such, NoAH is an AI that generates “realistic group behavior” by simultaneously reflecting human traits and relationships. It was shown to reproduce various real-world group behaviors—such as product purchase combinations in e-commerce, the spread of online discussions, and co-authorship networks among researchers—far more realistically than existing models. < The process of generating group interactions using NoAH > Professor Kijung Shin stated, “This study opens a new AI paradigm that enables a richer understanding of complex interactions by considering not only the structure of groups but also individual attributes together,” and added, “Analyses of online communities, messengers, and social networks will become far more precise.” This research was conducted by a team consisting of Professor Kijung Shin and KAIST Kim Jaechul Graduate School of AI students: master’s students Jaewan Chun and Seokbum Yoon, and doctoral students Minyoung Choe and Geon Lee, and was presented at IEEE ICDM on November 18. ※ Paper title: “Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes” Original paper: https://arxiv.org/abs/2509.21838 < Photo from the award ceremony held on November 14 at the International Spy Museum in Washington, D.C.> Meanwhile, including this award-winning paper, Professor Shin’s research team presented a total of four papers at IEEE ICDM this year. In addition, in 2023, the team also received the Best Student Paper Runner-up (4th place) at the same conference. This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-202400457882, AI Research Hub Project) (RS-2019-II190075, Artificial Intelligence Graduate School Program (KAIST)) (No. RS-2022-II220871, Development of AI Autonomy and Knowledge Enhancement for AI Agent Collaboration).

KAIST Removes 99.9％ of Ultrafine Dust Using Nano W..

<(From Left) Ph.D candidate Sungyoon Woo, Professor Il-Doo Kim, Professor Seung S.Lee, Ph.D candiate Jihwan Chae, Researcher Jiyeon Yu, (Upper Right) Dr. Yujang Cho> A KAIST research team has drawn attention by developing a new water-based air purification technology that combines “nano water droplets that capture dust” with a “nano sponge structure that autonomously draws up water,” enabling dust removal using nano water droplets without filters, self-supplied water operation, and long-term, quiet, and safe performance. KAIST (President Kwang Hyung Lee) announced on the December 8 that a joint research team led by Professor Il-Doo Kim of the Department of Materials Science and Engineering and Professor Seung S. Lee of the Department of Mechanical Engineering developed a new water electrospray–based air purification device that rapidly removes ultrafine dust without filters, generates no ozone, and operates with ultra-low power consumption. The research team confirmed that this device overcomes the limitations of conventional air purifiers by eliminating the need for filter replacement, producing no ozone, and removing even extremely fine ultrafine dust as small as PM0.3 (diameter 0.3 μm), which is about 1/200 the thickness of a human hair, within a short time. In addition, it demonstrated high stability and durability without performance degradation even during long-term use. This device was created by combining Professor Seung S. Lee’s “ozone-free water electrospray” technology with Professor Il-Doo Kim’s “hygroscopic nanofiber Emitter” technology. Inside the device are a high-voltage electrode, a nanofiber absorber that autonomously draws up water, and polymer microchannels that transport water via capillary action. Thanks to this structure, a self-pumped configuration is achieved in which water is automatically supplied without a pump, enabling stable long-term water electrospray operation. Tests conducted by the research team in a 0.1 m3 experimental chamber showed that the device removed 99.9％ of various particles in the PM0.3–PM10 range within 20 minutes. In particular, it exhibited outstanding performance by removing 97％ of PM0.3 ultrafine dust, which is difficult to eliminate using conventional filter-based air purifiers, within just 5 minutes. Even after 30 consecutive tests and 50 hours of continuous operation, the device operated stably without performance degradation, and its power consumption was approximately 1.3 W, which is lower than that of a smartphone charger and only about 1/20 that of conventional HEPA (High Efficiency Particulate Air) filter–based air purifiers. In addition, because there is no filter, there is no pressure loss in airflow and almost no noise is generated. This technology maintains high-efficiency purification performance while generating no ozone at all, presenting the potential for a next-generation eco-friendly air purification platform. In particular, with advantages such as elimination of filter replacement costs, ultra-low power operation, and secured long-term stability, it is expected to expand into various fields including indoor environments as well as automotive, cleanroom, portable, and wearable air purification modules. Commercialization of this technology is currently underway through A2US Co., Ltd., a university spin-off company from Professor Seung S. Lee’s laboratory. A2US Co., Ltd. won a CES 2025 Innovation Award and plans to launch a portable air purifier product in 2026. The product is equipped not only with fine dust removal using nano water droplets but also with odor removal and pathogen sterilization functions. <Figure1.Design and Operating Mechanism of a Miniature Air-Purification Device Based on Cone-Jet Water Electrospray Using a Self-Pumping Hygroscopic (PVA–PAA–MMT) Nanofiber Membrane (PPM-NFM) Emitter.> <Figure 2. (a) Schematic of the Self-Pumping Hygroscopic Nanofiber Membrane (PPM-NFM) Emitter, and (b) Corresponding Photograph and Surface Scanning Microscopy Images.>

KAIST, Production Temperature ↓ by 500°C, Power Ou..

<(Top row, from left) Professor Kang Taek Lee, Ph.D candidate Yejin Kang, Dr. Dongyeon Kim, (Bottom row, from left) M.S candidate Mincheol Lee, Ph.D candidate Seeun Oh, Ph.D candidate Seungsoo Jang, Ph.D candidate Hyeonggeun Kim> As power demand surges in the AI era, the “protonic ceramic electrochemical cell (PCEC),” which can simultaneously produce electricity and hydrogen, is gaining attention as a next-generation energy technology. However, this cell has faced the technical limitation of requiring an ultra-high production temperature of 1,500°C. A KAIST research team has succeeded in establishing a new manufacturing process that lowers this limit by more than 500°C for the first time in the world. KAIST (President Kwang Hyung Lee) announced on the 4th of December that Professor Kang Taek Lee’s research team in the Department of Mechanical Engineering developed a new process that enables the fabrication of high-performance protonic ceramic electrochemical cells at temperatures more than 500°C lower than before, using “microwave ＋ vapor control technology” that leverages microwave heating principles and the diffusion environment of chemical vapor generated from specific chemical components. The electrolyte—the key material of protonic ceramic electrochemical cells—contains barium (Ba), and barium easily evaporates at temperatures above 1,500°C, which has been the main cause of performance degradation. Therefore, the ability to harden the ceramic electrolyte at a lower temperature has been the core issue that determines cell performance. As power demand surges in the AI era, the “protonic ceramic electrochemical cell (PCEC),” which can simultaneously produce electricity and hydrogen, is gaining attention as a next-generation energy technology. However, this cell has faced the technical limitation of requiring an ultra-high production temperature of 1,500°C. A KAIST research team has succeeded in establishing a new manufacturing process that lowers this limit by more than 500°C for the first time in the world. KAIST (President Kwang Hyung Lee) announced on the 4th of December that Professor Kang Taek Lee’s research team in the Department of Mechanical Engineering developed a new process that enables the fabrication of high-performance protonic ceramic electrochemical cells at temperatures more than 500°C lower than before, using “microwave ＋ vapor control technology” that leverages microwave heating principles and the diffusion environment of chemical vapor generated from specific chemical components. The electrolyte—the key material of protonic ceramic electrochemical cells—contains barium (Ba), and barium easily evaporates at temperatures above 1,500°C, which has been the main cause of performance degradation. Therefore, the ability to harden the ceramic electrolyte at a lower temperature has been the core issue that determines cell performance. To solve this, the research team devised a new heat-treatment method called “vapor-phase diffusion.” This technique places a special auxiliary material (a vapor source) next to the cell and irradiates it with microwaves to quickly diffuse vapor. When the temperature reaches approximately 800°C, the vapor released from the auxiliary material moves toward the electrolyte and tightly bonds the ceramic particles. Thanks to this technology, a process that previously required 1,500°C can now be completed at just 980°C. In other words, a world-first ceramic electrochemical cell fabrication technology has been created that produces high-performance electricity at a “low temperature” without damaging the electrolyte. A cell fabricated with this process produced 2 W of power stably from a 1 cm² cell (roughly the size of a fingernail) at 600°C and generated 205 mL of hydrogen per hour at 600°C (about the volume of a small paper cup, among the highest in the industry). It also maintained stability without performance degradation during 500 hours of continuous operation. In other words, this technology reduces the production temperature (−500°C), lowers the operating temperature (600°C), doubles performance (2 W/cm²), and extends the lifespan (500-hour stability), achieving world-class performance in ceramic cell technology. The research team also enhanced the reliability of the technology by using digital twins (virtual simulations) to analyze gas-transport phenomena occurring in the microscopic internal structure of the cell − phenomena that are difficult to observe in actual experiments. <Figure 1. (a) Schematic of the vapor-diffusion-based process; (b) Surface microstructure of the electrolyte; (c) Internal barium composition ratio of the electrolyte according to processing conditions; (d) Comparison of power-generation performance with previous studies> < Figure 2. (a) Three-dimensional reconstructed image of the protonic ceramic electrochemical cell fuel electrode according to processing conditions (b) Pore structure (c) Gas-transport simulation results > Professor Kang Taek Lee emphasized, “This study is the world’s first case of using vapor to lower the heat-treatment temperature by more than 500°C while still producing a high-performance, high-stability cell.” He added, “It is expected to become a key manufacturing technology that addresses the power challenges of the AI era and accelerates the hydrogen society.” Dongyeon Kim (KAIST PhD) and Yejin Kang (KAIST PhD candidate) participated as co–first authors. The research results were published in Advanced Materials (IF: 26.8), one of the world’s leading journals in energy and materials science, and were selected as the Inside Front Cover article on October 29. (Paper title: “Sub-1000°C Sintering of Protonic Ceramic Electrochemical Cells via Microwave-Driven Vapor Phase Diffusion,” DOI: https://doi.org/10.1002/adma.202506905) This research was supported by the MSIT’s Mid-career Researcher Program and the H2 Next Round Program.

KAIST Unveils Cause of Performance Degradation in ..

<(From left in the front row) Professor Nam-Soon Choi, Professor Dong-Hwa Seo, (back row, from left) Ph.D candidate Gihoon Lee, Ph.D candidate Seung Hee Han, Ph.D candidate Jae-Seung Kim, (top) M.S candidate Junyoung Kim> High-nickel batteries, which are high-energy lithium-ion batteries primarily used in electric vehicles, offer high energy density but suffer from rapid performance degradation. A research team from KAIST has, for the first time globally, identified the fundamental cause of the rapid deterioration (degradation) of high-nickel batteries and proposed a new approach to solve it. KAIST announced on December 3rd that a research team led by Professor Nam-Soon Choi of the Department of Chemical and Biomolecular Engineering, in collaboration with a research team led by Professor Dong-Hwa Seo of the Department of Materials Science and Engineering, has revealed that the electrolyte additive 'succinonitrile (CN4), which has been used to improve battery stability and lifespan, is actually the key culprit causing performance degradation in high-nickel batteries. In a battery, electricity is generated as lithium ions travel between the cathode and the anode. A small amount of CN4 is included in the electrolyte to facilitate the movement of lithium. The research team confirmed through computer calculations that CN4, which has two nitrile (-CN) structures, attaches excessively strongly to the nickel ions on the surface of the high-nickel cathode. The nitrile structure is a 'hook-like' structure, where carbon and nitrogen are bound by a triple bond, making it adhere well to metal ions. This strong bonding destroys the protective electrical double layer (EDL) that should form on the cathode surface. During the charging and discharging process, the cathode structure is distorted (Jahn-Teller distortion), and even electrons from the cathode are drawn out to the CN4, leading to rapid damage of the cathode. Nickel ions that leak out during this process migrate through the electrolyte to the anode surface, where they accumulate. This nickel acts as a 'bad catalyst' that accelerates electrolyte decomposition and wastes lithium, further speeding up battery degradation. Various analyses confirmed that CN4 transforms the high-nickel cathode surface into an abnormal layer deficient in nickel, and changes the normally stable structure into an abnormal 'rock-salt structure'. This proves the dual nature of CN4: while useful in LCO batteries (lithium cobalt oxide), it actually causes the structural collapse in high-nickel batteries with a high nickel ratio. This research holds significant meaning as a precise analysis that goes beyond simple control of charging/discharging conditions, to even elucidating the actual electron transfer occurring between metal ions and electrolyte molecules. Based on this achievement, the research team plans to develop a new electrolyte additive optimized for high-nickel cathodes. <Schematic diagram of the ligand coordination between CN₄ molecules and Ni³⁺ on the high-nickel cathode surface and the cathode structural degradation process> Professor Nam-Soon Choi stated, "A precise, molecular-level understanding is essential to enhance battery lifespan and stability. This research will pave the way for the development of new additives that do not excessively bond with nickel, significantly contributing to the commercialization of next-generation high-capacity batteries." This research, jointly led by Professor Nam-Soon Choi, Seung Hee Han, Junyoung Kim, and Gihoon Lee of the Department of Chemical and Biomolecular Engineering, and Professor Dong-Hwa Seo and Jae-Seung Kim of the Department of Materials Science and Engineering as co-first authors, was published online on November 14th in the prestigious international journal 'ACS Energy Letters' and was selected as the cover article. ※ Paper Title: Unveiling Bidentate Nitrile-Driven Structural Degradation in Ultra-High-Nickel Cathodes, https://doi.org/10.1021/acsenergylett.5c02845 <Cover Page of International Journal(ACS Energy Letters)> The research was supported by Samsung SDI.

AI Technology World No. 1 in Finding the Exact Mom..

< (From left) Professor Joon Hyuk Noh (Assistant Professor, Department of Artificial Intelligence, Ewha Womans University), Seojin Hwan, Yoonki Cho (Ph.D. Candidate), Professor Sung-Eui Yoon (School of Computing, KAIST) > When faced with a complex question like 'What object disappeared while the camera was pointing elsewhere?', a common problem is that AI often relies on language patterns to guess a 'plausible answer,' instead of actually observing the real situation in the video. To overcome this limitation, our university's research team developed a technology that enables the AI to autonomously identify the 'exact critical moment (Trigger moment)' within the video, and the team’s excellence was proven by winning an international AI competition with this technology. The university announced on the 28th that the research team led by Professor Sung-Eui Yoon from the School of Computing, in collaboration with Professor Joon Hyuk Noh's team from Ewha Womans University, took 1st place in the Grounded Video Question Answering track of the Perception Test Challenge held at ICCV 2025, a world-renowned computer vision conference. The Perception Test Challenge held at ICCV 2025 was organized by Google DeepMind with a total prize pool of 50,000 Euros (approximately 83 million KRW). It assesses the cognitive and reasoning abilities of multimodal AI, which must comprehensively understand various data, including video, audio, and text. Crucially, the core evaluation factor is the ability to make judgments based on actual video evidence, moving beyond language-centric bias. Unlike conventional methods that analyze the entire video indiscriminately, our university's research team developed a new technology that instructs the AI to first locate the core scene (Trigger moment) essential for finding the correct answer. Simply put, this technology is designed to make the AI autonomously determine: “This scene is decisive for answering this question！” The research team calls this framework CORTEX (Chain-of-Reasoning for Trigger Moment Extraction). The research team's system consists of a three-stage structure where three models performing different functions operate sequentially. First, the Reasoning AI (Gemini 2.5 Pro) reasons about which moment is required to answer the question and finds candidate Trigger moments. Next, the Object Location Finding Model (Grounding Model, Molmo-7B) accurately identifies the exact location (coordinates) of people, cars, and objects on the screen during the selected moment. Finally, the Tracking Model (SAM2) precisely tracks the movement of objects in the time frame before and after the selected scene, using that scene as a reference, thereby reducing errors. In short, the 'method of accurately pinpointing a key scene and tracking the evidence for the answer centered on that scene' significantly reduced problems like initial misjudgment or occlusion in the video. In the Grounded Video Question Answering (Grounded VideoQA) track, which saw 23 participating teams, the KAIST team SGVR Lab (Scalable Graphics, Vision & Robotics Lab) recorded 0.4968 points in the HOTA (Higher Order Tracking Accuracy) metric, overwhelmingly surpassing the 2nd place score of 0.4304 from Columbia University, USA, to secure 1st place. This achievement is nearly double the previous year's winning score of 0.2704 points. This technology has wide-ranging applications in real-life settings. Autonomous driving vehicles can accurately identify moments of potential accident risk, robots can understand the surrounding environment smarter, security and surveillance systems can rapidly locate critical scenes, and media analysis can precisely track the actions of people or objects in chronological order. This is a core technology that enables AI to judge based on "actual evidence in the video." The ability to accurately pinpoint how objects behave over time in a video is expected to greatly expand the application of AI in real-world scenarios in the future. < Pipeline image of the grounding framework for video question answering proposed by the research team > This research was presented on October 19th at ICCV 2025, the 3rd Perception Test Challenge conference. The achievement was supported by the Ministry of Science and ICT's Basic Research Program (Mid-Career Researcher), the SW Star Lab Project's 'Development of Perception, Action, and Interaction Algorithms for Open-World Robot Services,' and the AGI Project's 'Reality Construction and Bi-directional Capability Approach based on Cognitive Agents for Embodied AGI' tasks."

The World＇s Smallest Fully Wireless Neural Implant..

< (From left) Sunwoo Lee, KAIST Joint Professor, Alyosha Molnar, Cornell University Professor > The human brain contains about 100 billion brain cells, and the chemical and electrical signals they exchange create most mental functions. Neural implant technology for precisely reading these signals is essential for the research and treatment of neurodegenerative diseases. A research team from KAIST and international collaborators has successfully implemented a fully wireless, ultra-small implant, which was previously only a theoretical possibility, going beyond simple miniaturization and weight reduction of neural implants. KAIST announced on the November 27th that a joint research team led by Professor Sunwoo Lee (Joint Professor in Materials Science and Engineering at KAIST and from the School of Electrical and Electronic Engineering at Nanyang Technological University, NTU) and Professor Alyosha Molnar's team from Cornell University in the US has developed 'MOTE (Micro-Scale Opto-Electronic Tetherless Electrode)', an ultra-small wireless neural implant less than 100 micrometers (µm) — smaller than a grain of salt. The team successfully implanted this device into the brains of laboratory mice and stably measured brain waves for one year. In the brain, invisible, minute electrical signals constantly move, creating our various mental activities such as memory, judgment, and emotion. The technology to directly measure these signals outside the body without connecting wires has been highlighted as key for brain research and the treatment of neurological disorders like dementia and Parkinson's disease. However, existing implants have limitations: their thick wired structure causes movement in the brain, leading to inflammation and signal degradation over time, and their size and heat generation restrict long-term use. To overcome these limitations, the research team created an ultra-small circuit based on the existing semiconductor process (CMOS) and combined it with their self-developed ultra-fine Micro-LEDs (µLEDs) to drastically miniaturize the device. They also applied a special surface coating to significantly enhance durability, allowing it to withstand the biological environment for a long time. The resulting MOTE is less than 100 µm thick and has a volume of less than 1 nanoliter, making it thinner than a human hair and smaller than a grain of salt, the world's smallest level among currently reported wireless neural implants. Another key feature of MOTE is that it is a fully wireless system that requires no battery. The device is structured to receive external light to generate power, detect brain waves, and then transmit the information back outside embedded in the light signal using Pulse Position Modulation (PPM). This method drastically reduces energy consumption, minimizes the risk of heat generation, and eliminates the need for battery replacement, enabling long-term use. The research team conducted a one-year long-term experiment by implanting the ultra-small MOTE into the brains of mice. The results showed normal brain wave measurement over the extended period, with almost no inflammation observed around the implant and no degradation in device performance. This is considered the first clear demonstration that an ultra-small wireless implant can maintain normal function for a prolonged time inside a living body. < MOTE neural implant on a salt crystal (left), MOTE neural implants after 296 days of implantation in a laboratory mouse (right) > Professor Sunwoo Lee stated, "The greatest significance of the newly developed neural implant lies in its actual implementation of a fully wireless, ultra-small implant that was previously only anticipated as a possibility, going beyond simple miniaturization and weight reduction." He added, "This proves the technological possibility of resolving not only the known unknowns raised during the development and use of wireless neural implants, but also the unknown unknowns that newly emerge during the actual development process." He further added, "This technology will be broadly applicable not only to brain science research but also to nervous system disease monitoring and the development of long-term recording-based treatment technologies." The research results were published online in the prestigious journal Nature Electronics on November 3rd. ※ Paper Title: A subnanolitre tetherless optoelectronic microsystem for chronic neural recording in awake mice, DOI: https://doi.org/10.1038/s41928-025-01484-1 This research was supported by the US National Institutes of Health (NIH), Nanyang Technological University (Singapore), the Singapore National Research Foundation, the Singapore Ministry of Education, and the ASPIRE League Partnership Seed Fund 2024. The specialized fabrication processes were conducted at the Cornell NanoScale Facility (part of the US National Nanotechnology Coordinated Infrastructure, NNCI) and NTU's Nanyang NanoFabrication Centre.

How Does AI Think？ KAIST Achieves First Visualizat..

<(From Left) Ph.D candidate Daehee Kwon, Ph.D candidate Sehyun lee, Professor Jaesik Choi> Although deep learning–based image recognition technology is rapidly advancing, it still remains difficult to clearly explain the criteria AI uses internally to observe and judge images. In particular, technologies that analyze how large-scale models combine various concepts (e.g., cat ears, car wheels) to reach a conclusion have long been recognized as a major unsolved challenge. KAIST (President Kwang Hyung Lee) announced on the 26th of November that Professor Jaesik Choi’s research team at the Kim Jaechul Graduate School of AI has developed a new explainable AI (XAI) technology that visualizes the concept-formation process inside a model at the level of circuits, enabling humans to understand the basis on which AI makes decisions. The study is evaluated as a significant step forward that allows researchers to structurally examine “how AI thinks.” Inside deep learning models, there exist basic computational units called neurons, which function similarly to those in the human brain. Neurons detect small features within an image—such as the shape of an ear, a specific color, or an outline—and compute a value (signal) that is transmitted to the next layer. In contrast, a circuit refers to a structure in which multiple neurons are connected to jointly recognize a single meaning (concept). For example, to recognize the concept of cat ear, neurons detecting outline shapes, neurons detecting triangular forms, and neurons detecting fur-color patterns must activate in sequence, forming a functional unit (circuit). Up until now, most explanation techniques have taken a neuron-centric approach based on the idea that “a specific neuron detects a specific concept.” However, in reality, deep learning models form concepts through cooperative circuit structures involving many neurons. Based on this observation, the KAIST research team proposed a technique that expands the unit of concept representation from “neuron → circuit.” The research team’s newly developed technology, Granular Concept Circuits (GCC), is a novel method that analyzes and visualizes how an image-classification model internally forms concepts at the circuit level. GCC automatically traces circuits by computing Neuron Sensitivity and Semantic Flow. Neuron Sensitivity indicates how strongly a neuron responds to a particular feature, while Semantic Flow measures how strongly that feature is passed on to the next concept. Using these metrics, the system can visualize, step-by-step, how basic features such as color and texture are assembled into higher-level concepts. The team conducted experiments in which specific circuits were temporarily disabled (ablation). As a result, when the circuit responsible for a concept was deactivated, the AI’s predictions actually changed. In other words, the experiment directly demonstrated that the corresponding circuit indeed performs the function of recognizing that concept. This study is regarded as the first to reveal, at a fine-grained circuit level, the actual structural process by which concepts are formed inside complex deep learning models. Through this, the research suggests practical applicability across the entire explainable AI (XAI) domain—including strengthening transparency in AI decision-making, analyzing the causes of misclassification, detecting bias, improving model debugging and architecture, and enhancing safety and accountability. The research team stated, “This technology shows the concept structures that AI forms internally in a way that humans can understand,” adding that “this study provides a scientific starting point for researching how AI thinks.” Professor Jaesik Choi emphasized, “Unlike previous approaches that simplified complex models for explanation, this is the first approach to precisely interpret the model’s interior at the level of fine-grained circuits,” and added, “We demonstrated that the concepts learned by AI can be automatically traced and visualized.” < external_image > < Overview of the Conceptual Circuit Proposed by the Research Team > This study, with Ph.D. candidates Dahee Kwon and Sehyun Lee from KAIST Kim Jaechul Graduate School of AI as co–first authors, was presented on October 21 at the International Conference on Computer Vision (ICCV). Paper title: Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations Paper link: https://openaccess.thecvf.com/content/ICCV2025/papers/Kwon_Granular_Concept_Circuits_Toward_a_Fine-Grained_Circuit_Discovery_for_Concept_ICCV_2025_paper.pdf This research was supported by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP) under the “Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation” project, the AI Research Hub Project, and the KAIST AI Graduate School Program, and was carried out with support from the Defense Acquisition Program Administration (DAPA) and the Agency for Defense Development (ADD) at the KAIST Center for Applied Research in Artificial Intelligence.

KAIST Confirms Reduction of Amyloid-β Using Red OL..

<Professor Kyung Cheol Choi, Dr. Byeongju Noh, Ph.D candidate Young-Hun Jung, Ph.D candidate Minwoo Park, Dr.Ja Wook Koo, Researcher Jiyun Lee, Researcher Ji-Eun Lee, Dr. Hyang Sook Hoe, Dr. Hyun-Ju Lee, Dr. Sora Kang, Researcher Seokjun Oh> A Korean research team, raising the question “Which OLED light color can actually improve memory and pathological markers in Alzheimer’s patients?”, has identified the most effective OLED color capable of enhancing cognitive function using only light—with no drugs involved. The OLED platform developed for this study can precisely control color, brightness, flicker frequency, and exposure duration, suggesting potential future development into personalized OLED-based electroceuticals. On the 24th, KAIST (President Kwang Hyung Lee) announced that a joint research team led by Professor Kyung Cheol Choi from the School of Electrical Engineering at KAIST and Dr. Ja Wook Koo and Dr. Hyang Sook Hoe from the Korea Brain Research Institute (KBRI) developed a uniform-illuminance, three-color OLED photostimulation technology and confirmed that “red 40-Hz light” was the most effective among blue, green, and red in improving Alzheimer's pathology and memory function. To overcome the structural limitations of conventional LEDs—such as brightness imbalance, heat generation risk, and variability caused by animal movement—the researchers developed an OLED-based photostimulation platform that emits light uniformly. Using this platform, they compared white, red, green, and blue light under identical conditions (40-Hz frequency, brightness, and exposure time) and found that red 40-Hz light produced the most significant improvement. In an early-stage (3-month-old) Alzheimer’s animal model, improvement in pathology and memory was observed after only two days of stimulation. When early Alzheimer’s model mice were exposed to one hour of light per day for two days, both white and red light improved long-term memory. Additionally, the amount of amyloid-β (Aβ) plaques—protein aggregates known as a major factor in Alzheimer’s disease—was reduced in key brain regions such as the hippocampus, and levels of the plaque-clearing enzyme ADAM17 increased. This indicates that even very short periods of light stimulation can reduce harmful proteins in the brain and improve memory function. In particular, with red light, the inflammatory cytokine IL-1β, known to exacerbate inflammation and contribute to Alzheimer’s progression, decreased significantly, demonstrating an anti-inflammatory effect. Moreover, the more plaque was reduced, the greater the improvement in memory—direct evidence that pathological improvement leads to cognitive enhancement. In the mid-stage (6-month-old) Alzheimer’s model, statistically significant pathological improvement was seen only with red light. In a two-week long-term stimulation experiment under the same conditions, both white and red light improved memory, but a statistically meaningful reduction in plaques appeared only under red light. < The mechanism by which red OLED stimulation of neurons reduces amyloid-β in Alzheimer’s model mice > Differences at the molecular level were also clear. Under red light, levels of ADAM17 (which helps remove plaques) increased, while levels of BACE1, an enzyme responsible for producing plaques, decreased—demonstrating a dual effect of both inhibiting plaque formation and promoting plaque removal. In contrast, white light only lowered BACE1, showing more limited therapeutic effects compared to red light. This scientifically identifies that the color of light is a key factor determining therapeutic efficacy. To determine which neural circuits were activated by light stimulation, the team analyzed the expression of c-Fos, an immediate-early gene that is activated when neurons fire. They found activation throughout the visual–memory circuit, extending from the visual cortex → thalamus → hippocampus, providing direct neurological evidence that light stimulation awakens the visual pathway, enhancing hippocampal function and memory. Thanks to the uniform-illuminance OLED platform, light was evenly delivered regardless of animal movement, ensuring stable experimental results and high reproducibility across repeated tests. This study is the first to demonstrate that cognitive function can be improved using only light, without drugs, and that Alzheimer’s pathological markers can be regulated through combinations of light color, frequency, and duration. The OLED platform developed in this study allows fine control over color, brightness, flicker ratio, and exposure time, making it suitable for personalized stimulation design in future human clinical research. The research team plans to expand conditions such as stimulation intensity, energy, duration, and combined visual–auditory stimulation, aiming toward clinical-stage development. < Graphical abstract for the journal ACS Biomaterials Science & Engineering – Illustration of the mechanism by which red OLED stimulation reduces amyloid-β > Dr. Byeongju Noh (from Professor Kyung Cheol Choi’s research team) said, “This study experimentally demonstrates the importance of color standardization and confirms that red OLED is the key color that activates ADAM17 and suppresses BACE1 across disease stages.” Professor Kyung Cheol Choi emphasized, “Our uniform-illuminance OLED platform overcomes the structural limitations of traditional LEDs and enables high reproducibility and safe evaluation. We expect wearable RED OLED electroceuticals for everyday use to present a new therapeutic paradigm for Alzheimer’s disease.” The research findings were published online on October 25 in ACS Biomaterials Science & Engineering, a leading international journal in biomedical and materials science. Paper Title: Color Dependence of OLED Phototherapy for Cognitive Function and Beta-Amyloid Reduction through ADAM17 and BACE1 DOI: https://pubs.acs.org/doi/full/10.1021/acsbiomaterials.5c01162 Co-authors: Byeongju Noh, Hyun-Ju Lee, Jiyun Lee, Jiyun Lee, Ji-Eun Lee, Bitna Joo, Young-Hun Jung, Minwoo Park, Sora Kang, Seokjun Oh, Jeong-Woo Hwang, Dae-Si Kang, Yongmin Jeon, So-Min Lee, Hyang Sook Hoe, Ja Wook Koo, Kyung Cheol Choi This research was supported by the National Research Foundation of Korea and the National IT Industry Promotion Agency under the Ministry of Science and ICT, and the Korea Brain Research Institute Basic Research Program. (2017R1A5A1014708, 2022M3E5E9018226, H0501-25-1001, 25-BR-02-02, 25-BR-02-04)

A KAIST team develops the world＇s first modular co..

<(From Left) Distinguished Professor Sang Yup Lee, Ph.D candidate Pingxin Lin, Ph.D candiate Zhou Hengrui> The integration of systems metabolic engineering with co-culture strategies that couples bacterial cellulose production with natural colorant biosynthesis enabled the one-pot generation of rainbow-colored bacterial cellulose, establishing a sustainable biomanufacturing platform that can replace petroleum-based textiles and eliminate chemical dyeing processes. A research group at KAIST has successfully developed a modular co-culture platform for the one-pot production of rainbow-colored bacterial cellulose. The team, led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, engineered Komagataeibacter xylinus for bacterial cellulose synthesis and Escherichia coli for natural colorants overproduction. A co-culture of these engineered strains enabled the in situ coloration of bacterial cellulose. This research offers a versatile platform for producing living materials in multiple colors, and provides new opportunities for sustainable textiles, wearable biomaterials, and functional living materials that combine optical and structural properties beyond the reach of conventional textile technologies. Bacterial cellulose is an attractive and biodegradable alternative to petroleum-derived fabrics due to its high purity, mechanical strength, and water-retention properties. However, the limited color range of bacterial cellulose, which is typically white, has limited its broader application in the textile industry, where more vibrant colored fabrics are increasingly desired. Conventional dyeing methods rely on petroleum-based colorants and toxic reagents, creating environmental and processing challenges. These challenges have driven the demand for alternative production methods. To address these issues, KAIST researchers, including Ph.D. Candidate Hengrui Zhou, Ph.D. Candidate Pingxin Lin, Professor Ki Jun Jeong, and Distinguished Professor Sang Yup Lee, combined systems metabolic engineering with co-culture strategies to develop a bio-based route that integrates bacterial cellulose formation with natural pigment synthesis, enabling the production of colored living materials in a single step without additional chemical processing. The team’s work, entitled “One-pot production of colored bacterial cellulose,” was published in Trends in Biotechnology on November 12,2025. This research details the one-pot production of multicolored bacterial cellulose using a modular co-culture platform that integrates a bacterial cellulose-overproducing K. xylinus strain with natural colorant-producing E. coli strains. The team focused on addressing the limitations in bacterial cellulose coloration caused by environmental challenges and complex processing requirements. By employing vesicle engineering and optimizing co-culture parameters, the researchers achieved one-pot production of red, orange, yellow, green, blue, navy, and purple bacterial cellulose, eliminating the need for external dyes and toxic chemical treatments. To enhance dyeing efficiency, E. coli strains were engineered for the overproduction and secretion of natural colorants. It was determined that the intracellular accumulation of these pigments disrupts cellular metabolism and physiology, thereby inhibiting their production. To overcome this limitation, vesicle engineering has emerged as a key strategy to mitigate these cytotoxic effects, including the induction of inner- and outer-membrane vesicles and the modulation of cell morphology, enabling the more efficient secretion of colorants and increased overall production. The engineered E. coli strains were optimized in fed-batch fermentation, achieving record-breaking production of 16.92 ± 0.10 g/L of deoxyviolacein, 8.09 ± 0.17 g/L of violacein, 1.82 ± 0.07 g/L of proviolacein, and 936.25 ± 9.70 mg/L of prodeoxyviolacein, the highest reported titers to date for all four violacein derivatives. < Figure 1. Rainbow-colored bacterial cellulose (microbial fiber) with applied color > A co-culture platform combining the K. xylinus with E. coli strains was further developed and optimized, enabling the in situ one-pot coloration of bacterial cellulose in vibrant green, blue, navy, and purple. Fed-batch fermentation further improved the performance of the platform, achieving the world-first one-pot production of multicolored bacterial cellulose on a larger scale. To expand the bacterial cellulose color palette, engineered carotenoid-producing E. coli strains were incorporated, enabling the successful synthesis of red, orange, and yellow bacterial cellulose. This milestone demonstrates the potential of microbial fermentation as a sustainable alternative to petroleum-based textile processes. “We can anticipate that this microbial cell factory-based one-pot production of rainbow-colored bacterial cellulose has the potential to replace current petroleum-based textile processes,” said Ph.D. Candidate Hengrui Zhou. “The systems metabolic engineering strategies developed in this study could be broadly applied for the production of diverse sustainable textiles, wearable biomaterials, and functional living materials that combine optical and structural properties beyond the capabilities of conventional textile technologies.” He added, “This platform reduces the environmental impact while greatly expanding design possibilities. Beyond serving as a proof-of-concept, this technology offers a promising route toward scalable, eco-friendly fabrics with in situ coloration. Its modular design allows the incorporation of diverse natural colorant pathways, enabling the creation of living materials in multiple colors.” < Figure 2. Schematic of a microbe-based platform for one-step production of rainbow-colored bacterial cellulose > “As demand for sustainable textiles and living materials continues to grow, we expect that the integrated biomanufacturing platform developed here will play a pivotal role in producing diverse functional biomaterials with additional design possibilities in a single step, without additional chemical processing,” explained Distinguished Professor Sang Yup Lee. This work was supported by the Development of Next-generation Biorefinery Platform Technologies for Leading Bio-based Chemicals Industry project (2022M3J5A1056072) and the Development of Platform Technologies of Microbial Cell Factories for the Next-generation Biorefineries project (2022M3J5A1056117) from the National Research Foundation supported by the Korean Ministry of Science and ICT. Source: Hengrui Zhou (1st), Pingxin Lin (2nd), Ki Jun Jeong (3rd), and Sang Yup Lee (Corresponding). “One-pot production of colored bacterial cellulose”. Trends in Biotechnology (Published) doi: 10.1016/j.tibtech.2025.09.019