Kaist

A KAIST-SNUH Team Devises a Way to Make Mathematic..

Cancer is characterized by abnormal metabolic processes different from those of normal cells. Therefore, cancer metabolism has been extensively studied to develop effective diagnosis and treatment strategies. Notable achievements of cancer metabolism studies include the discovery of oncometabolites＊ and the approval of anticancer drugs by the U.S. Food and Drug Administration (FDA) that target enzymes associated with oncometabolites. Approved anticancer drugs such as ‘Tibsovo (active ingredient: ivosidenib)’ and ‘Idhifa (active ingredient: enasidenib)’ are both used for the treatment of acute myeloid leukemia. Despite such achievements, studying cancer metabolism, especially oncometabolites, remains challenging due to time-consuming and expensive methodologies such as metabolomics. Thus, the number of confirmed oncometabolites is very small although a relatively large number of cancer-associated gene mutations have been well studied. ＊Oncometabolite: A metabolite that shows pro-oncogenic function when abnormally accumulated in cancer cells. An oncometabolite is often generated as a result of gene mutations, and this accumulation promotes the growth and survival of cancer cells. Representative oncometabolites include 2-hydroxyglutarate, succinate, and fumarate. On March 18th, a KAIST research team led by Professor Hyun Uk Kim from the Department of Chemical and Biomolecular Engineering developed a computational workflow that systematically predicts metabolites and metabolic pathways associated with somatic mutations in cancer through collaboration with research teams under Prof Youngil Koh, Prof. Hongseok Yun, and Prof. Chang Wook Jeong from Seoul National University Hospital. The research teams have successfully reconstructed patient-specific genome-scale metabolic models (GEMs)＊ for 1,043 cancer patients across 24 cancer types by integrating publicly available cancer patients’ transcriptome data (i.e., from international cancer genome consortiums such as PCAWG and TCGA) into a generic human GEM. The resulting patient-specific GEMs make it possible to predict each patient’s metabolic phenotypes. ＊Genome-scale metabolic model (GEM): A computational model that mathematically describes all of the biochemical reactions that take place inside a cell. It allows for the prediction of the cell’s metabolic phenotypes under various genetic and/or environmental conditions. < Figure 1. Schematic diagram of a computational methodology for predicting metabolites and metabolic pathways associated with cancer somatic mutations. of a computational methodology for predicting metabolites and metabolic pathways associated with cancer somatic mutations. > The team developed a four-step computational workflow using the patient-specific GEMs from 1,043 cancer patients and somatic mutation data obtained from the corresponding cancer patients. This workflow begins with the calculation of the flux-sum value of each metabolite by simulating the patient-specific GEMs. The flux-sum value quantifies the intracellular importance of a metabolite. Next, the workflow identifies metabolites that appear to be significantly associated with specific gene mutations through a statistical analysis of the predicted flux-sum data and the mutation data. Finally, the workflow selects altered metabolic pathways that significantly contribute to the biosynthesis of the predicted oncometabolite candidates, ultimately generating metabolite-gene-pathway sets as an output. The two co-first authors, Dr. GaRyoung Lee (currently a postdoctoral fellow at the Dana-Farber Cancer Institute and Harvard Medical School) and Dr. Sang Mi Lee (currently a postdoctoral fellow at Harvard Medical School) said, “The computational workflow developed can systematically predict how genetic mutations affect cellular metabolism through metabolic pathways. Importantly, it can easily be applied to different types of cancer based on the mutation and transcriptome data of cancer patient cohorts.” Prof. Kim said, “The computational workflow and its resulting prediction outcomes will serve as the groundwork for identifying novel oncometabolites and for facilitating the development of various treatment and diagnosis strategies”. This study, which was supported by the National Research Foundation of Korea, has been published online in Genome Biology, a representative journal in the field of biotechnology and genetics, under the title "Prediction of metabolites associated with somatic mutations in cancers by using genome‑scale metabolic models and mutation data".

KAIST Develops Healthcare Device Tracking Chronic ..

A KAIST research team has developed an effective wireless system that monitors the wound healing process by tracking the spatiotemporal temperature changes and heat transfer characteristics of damaged areas such as diabetic wounds. On the 5th of March, KAIST (represented by President Kwang Hyung Lee) announced that the research team led by Professor Kyeongha Kwon from KAIST’s School of Electrical Engineering, in association with Chung-Ang University professor Hanjun Ryu, developed digital healthcare technology that tracks the wound healing process in real time, which allows appropriate treatments to be administered. < Figure 1. Schematic illustrations and diagrams of real-time wound monitoring systems. > The skin serves as a barrier protecting the body from harmful substances, therefore damage to the skin may cause severe health risks to patients in need of intensive care. Especially in the case of diabetic patients, chronic wounds are easily formed due to complications in normal blood circulation and the wound healing process. In the United States alone, hundreds of billions of dollars of medical costs stem from regenerating the skin from such wounds. While various methods exist to promote wound healing, personalized management is essential depending on the condition of each patient's wounds. Accordingly, the research team tracked the heating response within the wound by utilizing the differences in temperature between the damaged area and the surrounding healthy skin. They then measured heat transfer characteristics to observe moisture changes near the skin surface, ultimately establishing a basis for understanding the formation process of scar tissue. The team conducted experiments using diabetic mice models regarding the delay in wound healing under pathological conditions, and it was demonstrated that the collected data accurately tracks the wound healing process and the formation of scar tissue. To minimize the tissue damage that may occur in the process of removing the tracking device after healing, the system integrates biodegradable sensor modules capable of natural decomposition within the body. These biodegradable modules disintegrate within the body after use, thus reducing the risk of additional discomfort or tissue damage upon device removal. Furthermore, the device could one day be used for monitoring inside the wound area as there is no need for removal. Professor Kyeongha Kwon, who led the research, anticipates that continuous monitoring of wound temperature and heat transfer characteristics will enable medical professionals to more accurately assess the status of diabetic patients' wounds and provide appropriate treatment. He further predicted that the implementation of biodegradable sensors allows for the safe decomposition of the device after wound healing without the need for removal, making live monitoring possible not only in hospitals but also at home. The research team plans to integrate antimicrobial materials into this device, aiming to expand its technological capabilities to enable the observation and prevention of inflammatory responses, bacterial infections, and other complications. The goal is to provide a multi-purpose wound monitoring platform capable of real-time antimicrobial monitoring in hospitals or homes by detecting changes in temperature and heat transfer characteristics indicative of infection levels. < Image 1. Image of the bioresorbable temperature sensor > The results of this study were published on February 19th in the international journal Advanced Healthcare Materials and selected as the inside back cover article, titled "Materials and Device Designs for Wireless Monitoring of Temperature and Thermal Transport Properties of Wound Beds during Healing." This research was conducted with support from the Basic Research Program, the Regional Innovation Center Program, and the BK21 Program.

The World’s First Hacking-preventing Cryptographic..

With the dramatic increase in the amount of information exchanged between components or devices in the 5G/6G era, such as for the Internet of Things (IoT) and autonomous driving, hacking attacks are becoming more sophisticated. Consequently, enhancing security functions is essential for safely transmitting data between and among devices. On February 29th, a KAIST research team led by Professors Yang-gyu Choi and Seung-tak Ryu from the School of Electrical Engineering announced the successful development of the world's first security cryptographic semiconductor. The team has developed the Cryptoristor, a cryptographic transistor based on FinFET technology, produced through a 100％ silicon-compatible process, for the first time in the world. Cryptoristor is a random number generator (RNG) with unparalleled characteristics, featuring a unique structure comprising a single transistor and a distinctive mechanism. In all security environments, including artificial intelligence, the most crucial element is the RNG. In the most commonly used security chip, the Advanced Encryption Standard (AES), the RNG is a core component, occupying approximately 75％ of the total chip area and more than 85％ of its energy consumption. Hence, there is an urgent need for the development of low-power/ultra-small RNGs suitable for mobile or IoT devices. Existing RNGs come with limitations as they lack compatibility with silicon CMOS processes and circuit-based RNGs occupy a large surface area. In contrast, the team’s newly developed Cryptoristor, a cryptographic semiconductor based on a single-component structure, consumes and occupies less than .001 of the power and area compared to the current chips being used. Utilizing the inherent randomness of FinFETs, fabricated on a Silicon-on-Insulator (SOI) substrate with an insulating layer formed beneath the silicon, the team developed an RNG that unpredictably produces zeroes and ones. < Figure 1. Conceptual diagram of the security cryptographic transistor device. > Generally speaking, preventing hackers from predicting the encrypted algorithms during data exchanges through mobile devices is pivotal. Therefore, this method ensures unpredictability by generating random sequences of zeroes and ones that change every time. Moreover, while the Cryptoristor-based RNG research is the world's first of its kind without any international implementation cases, it shares the same transistor structure as existing logic or memory components. This enables 100％ production through rapid mass production processes using existing semiconductor facilities at a low cost. Seung-il Kim, a PhD student who led the research, explained the significance of the study, stating, "As a cryptographic semiconductor, the ultra-small/low-power random number generator enhances security through its distinctive unpredictability, supporting safe hyperconnectivity with secure transmissions between chips or devices. Particularly, compared to previous research, it offers excellent advantages in terms of energy consumption, integration density, and cost, making it suitable for IoT device environments." This research, with master’s student Hyung-jin Yoo as the co-author, was officially published in the online edition of Science Advances, a sister journal of Science, in February 2024 (research paper title: Cryptographic transistor for true random number generator with low power consumption). This research received support from the Next-Generation Intelligent Semiconductor Technology Development Project and the Core Technology Development Project for the National Semiconductor Research Laboratory.

KAIST Research Team Creates the Scent of Jasmine f..

The fragrance of jasmine and ylang-ylang, used widely in the manufacturing of cosmetics, foods, and beverages, can be produced by direct extraction from their respective flowers. In reality, this makes it difficult for production to meet demand, so companies use benzyl acetate, a major aromatic component of the two fragrances that is chemically synthesized from raw materials derived from petroleum. On February 26, a KAIST research team led by Research Professor Kyeong Rok Choi from the BioProcess Engineering Research Center and Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering revealed the development of the first microbial process to effectively produce benzyl acetate, an industrially useful compound, from renewable carbon sources such as glucose. The results were published in their paper titled “A microbial process for the production of benzyl acetate”. < Figure 1. Production of benzyl acetate through co-culture of upstream and downstream strains harboring the benzoic acid-dependent pathway. > The team, led by Distinguished Professor Lee, aimed to produce benzyl acetate through an environmentally friendly and sustainable method, and developed an Escherichia coli strand to convert glucose into benzyl acetate through system metabolic engineering＊. ＊System metabolic engineering: a field of research founded by Distinguished Professor Lee to effectively develop microbial cell plants, a core component of the bio-industry that will replace the existing chemical industry, which is highly dependent on petroleum. The research team developed a metabolic pathway that biosynthesizes benzyl acetate from benzoic acid derived from glucose, and successfully produced benzyl acetate by co-culturing＊＊ the strain. ＊＊co-culture: simultaneously synthesizing two or more types of microorganisms in a mixture However, it has been confirmed that the enzyme used to convert benzoic acid into benzyl acetate in this co-culturing technique acts non-specifically on an intermediate product during benzoic acid biosynthesis, producing a by-product called cinnamyl acetate. This process consumes the intermediate product needed for benzoic acid biosynthesis, thereby reducing the production efficiency of the target compound, benzyl acetate. To overcome this problem, Distinguished Professor Lee and his team devised a delayed co-culture method, where they first produced benzoic acid in the earlier stages of fermentation by only culturing the top strain that produces benzoic acid from glucose, and later inoculated the bottom strain to convert the accumulated benzoic acid in the culture medium into benzyl acetate. By applying this co-culture technique, the team suppressed the formation of by-products without further strain improvement or applying additional enzymes, and multiplied the concentration of the target compound by 10 times, producing 2.2 g/L of benzyl acetate. In addition, the team confirmed its potential for the commercial production of benzyl acetate through a technical economic analysis on this microbial process. < Figure 2. Delayed co-culture of the Bn1 and Bn-BnAc3 strains for improved production of benzyl acetate through the benzoic acid-independent pathway.> Research Professor Keyong Rok Choi, who was the first author of this paper, said, “This work is significant in that we have developed an effective microbial process to produce the industrially useful compound benzyl acetate, and also in that we have suggested a new approach to overcome the target chemical efficiency diminution and by-product formation issues caused commonly through non-specific enzyme activities during metabolic engineering.” Distinguished Professor Lee said, “If we can increase the variety and number of microbial processes that produce useful chemicals through sustainable methods and at the same time develop effective strategies to solve chronic and inevitable problems that arise during microbial strain development, we will be able to accelerate the transition from the petrochemical industry into the eco-friendly and sustainable bio-industry. This work was published online in Nature Chemical Engineering, issued by Nature. This research was supported by the ‘Implementation of Intelligent Cell Factory Technology (PI: Distinguished Professor Sang Yup Lee) Project by the Ministry of Science and ICT, and the ‘Development of Protein Production Technology from Inorganic Substances through Microbiological Metabolic System Control’ (PI: Research Professor Kyeong Rok Choi) by the Agricultural Microbiological Project Group at the Rural Development Administration.

KAIST Team Develops an Insect-Mimicking Semiconduc..

The recent development of an “intelligent sensor” semiconductor that mimics the optic nerve of insects while operating at ultra-high speeds and low power offers extensive expandability into various innovative technologies. This technology is expected to be applied to various fields including transportation, safety, and security systems, contributing to both industry and society. On February 19, a KAIST research team led by Professor Kyung Min Kim from the Department of Materials Science and Engineering (DMSE) announced the successful developed an intelligent motion detector by merging various memristor＊ devices to mimic the visual intelligence＊＊ of the optic nerve of insects. ＊Memristor: a “memory resistor” whose state of resistance changes depending on the input signal ＊＊Visual intelligence: the ability to interpret visual information and perform calculations within the optic nerve With the recent advances in AI technology, vision systems are being improved by utilizing AI in various tasks such as image recognition, object detection, and motion analysis. However, existing vision systems typically recognize objects and their behaviour from the received image signals using complex algorithms. This method requires a significant amount of data traffic and higher power consumption, making it difficult to apply in mobile or IoT devices. Meanwhile, insects are known to be able to effectively process visual information through an optic nerve circuit called the elementary motion detector, allowing them to detect objects and recognize their motion at an advanced level. However, mimicking this pathway using conventional silicon integrated circuit (CMOS) technology requires complex circuits, and its implementation into actual devices has thus been limited. < Figure 1. Working principle of a biological elementary motion detection system. > Professor Kyung Min Kim’s research team developed an intelligent motion detecting sensor that operates at a high level of efficiency and ultra-high speeds. The device has a simple structure consisting of only two types of memristors and a resistor developed by the team. The two different memristors each carry out a signal delay function and a signal integration and ignition function, respectively. Through them, the team could directly mimic the optic nerve of insects to analyze object movement. < Figure 2. (Left) Optical image of the M-EMD device in the left panel (scale bar 200 μm) and SEM image of the device in the right panel (scale bar: 20 μm). (Middle) Responses of the M-EMD in positive direction. (Right) Responses of the M-EMD in negative direction. > To demonstrate its potential for practical applications, the research team used the newly developed motion detector to design a neuromorphic computing system that can predict the path of a vehicle. The results showed that the device used 92.9％ less energy compared to existing technology and predicted motion with more accuracy. < Figure 3. Neuromorphic computing system configuration based on motion recognition devices > Professor Kim said, “Insects make use of their very simple visual intelligence systems to detect the motion of objects at a surprising high speed. This research is significant in that we could mimic the functions of a nerve using a memristor device.” He added, “Edge AI devices, such as AI-topped mobile phones, are becoming increasingly important. This research can contribute to the integration of efficient vision systems for motion recognition, so we expect it to be applied to various fields such as autonomous vehicles, vehicle transportation systems, robotics, and machine vision.” This research, conducted by co-first authors Hanchan Song and Min Gu Lee, both Ph.D. candidates at KAIST DMSE, was published in the online issue of Advanced Materials on January 29. This research was supported by the Mid-Sized Research Project by the National Research Foundation of Korea, the Next-Generation Intelligent Semiconductor Technology Development Project, the PIM Artificial Intelligence Semiconductor Core Technology Development Project, the National Nano Fab Center, and the Leap Research Project by KAIST.

Team KAIST placed among top two at MBZIRC Maritime..

Representing Korean Robotics at Sea: KAIST’s 26-month strife rewarded Team KAIST placed among top two at MBZIRC Maritime Grand Challenge - Team KAIST, composed of students from the labs of Professor Jinwhan Kim of the Department of Mechanical Engineering and Professor Hyunchul Shim of the School of Electrical and Engineering, came through the challenge as the first runner-up winning the prize money totaling up to ＄650,000 (KRW 860 million). - Successfully led the autonomous collaboration of unmanned aerial and maritime vehicles using cutting-edge robotics and AI technology through to the final round of the competition held in Abu Dhabi from January 10 to February 6, 2024. KAIST (President Kwang-Hyung Lee), reported on the 8th that Team KAIST, led by students from the labs of Professor Jinwhan Kim of the Department of Mechanical Engineering and Professor Hyunchul Shim of the School of Electrical Engineering, with Pablo Aviation as a partner, won a total prize money of ＄650,000 (KRW 860 million) at the Maritime Grand Challenge by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC), finishing first runner-up. This competition, which is the largest ever robotics competition held over water, is sponsored by the government of the United Arab Emirates and organized by ASPIRE, an organization under the Abu Dhabi Ministry of Science, with a total prize money of ＄3 million. In the competition, which started at the end of 2021, 52 teams from around the world participated and five teams were selected to go on to the finals in February 2023 after going through the first and second stages of screening. The final round was held from January 10 to February 6, 2024, using actual unmanned ships and drones in a secluded sea area of 10 km2 off the coast of Abu Dhabi, the capital of the United Arab Emirates. A total of 18 KAIST students and Professor Jinwhan Kim and Professor Hyunchul Shim took part in this competition at the location at Abu Dhabi. Team KAIST will receive ＄500,000 in prize money for taking second place in the final, and the team’s prize money totals up to ＄650,000 including ＄150,000 that was as special midterm award for finalists. The final mission scenario is to find the target vessel on the run carrying illegal cargoes among many ships moving within the GPS-disabled marine surface, and inspect the deck for two different types of stolen cargo to recover them using the aerial vehicle to bring the small cargo and the robot manipulator topped on an unmanned ship to retrieve the larger one. The true aim of the mission is to complete it through autonomous collaboration of the unmanned ship and the aerial vehicle without human intervention throughout the entire mission process. In particular, since GPS cannot be used in this competition due to regulations, Professor Jinwhan Kim's research team developed autonomous operation techniques for unmanned ships, including searching and navigating methods using maritime radar, and Professor Hyunchul Shim's research team developed video-based navigation and a technology to combine a small autonomous robot with a drone. The final mission is to retrieve cargo on board a ship fleeing at sea through autonomous collaboration between unmanned ships and unmanned aerial vehicles without human intervention. The overall mission consists the first stage of conducting the inspection to find the target ship among several ships moving at sea and the second stage of conducting the intervention mission to retrieve the cargoes on the deck of the ship. Each team was given a total of three opportunities, and the team that completed the highest-level mission in the shortest time during the three attempts received the highest score. In the first attempt, KAIST was the only team to succeed in the first stage search mission, but the competition began in earnest as the Croatian team also completed the first stage mission in the second attempt. As the competition schedule was delayed due to strong winds and high waves that continued for several days, the organizers decided to hold the finals with the three teams, including the Team KAIST and the team from Croatia’s the University of Zagreb, which completed the first stage of the mission, and Team Fly-Eagle, a team of researcher from China and UAE that partially completed the first stage. The three teams were given the chance to proceed to the finals and try for the third attempt, and in the final competition, the Croatian team won, KAIST took the second place, and the combined team of UAE-China combined team took the third place. The final prize to be given for the winning team is set at ＄2 million with ＄500,000 for the runner-up team, and ＄250,000 for the third-place. Professor Jinwhan Kim of the Department of Mechanical Engineering, who served as the advisor for Team KAIST, said, “I would like to express my gratitude and congratulations to the students who put in a huge academic and physical efforts in preparing for the competition over the past two years. I feel rewarded because, regardless of the results, every bit of efforts put into this up to this point will become the base of their confidence and a valuable asset in their growth into a great researcher.” Sol Han, a doctoral student in mechanical engineering who served as the team leader, said, “I am disappointed of how narrowly we missed out on winning at the end, but I am satisfied with the significance of the output we’ve got and I am grateful to the team members who worked hard together for that.” HD Hyundai, Rainbow Robotics, Avikus, and FIMS also participated as sponsors for Team KAIST's campaign.

KAIST develops an artificial muscle device that pr..

- Professor IlKwon Oh’s research team in KAIST’s Department of Mechanical Engineering developed a soft fluidic switch using an ionic polymer artificial muscle that runs with ultra-low power to lift objects 34 times greater than its weight. - Its light weight and small size make it applicable to various industrial fields such as soft electronics, smart textiles, and biomedical devices by controlling fluid flow with high precision, even in narrow spaces. Soft robots, medical devices, and wearable devices have permeated our daily lives. KAIST researchers have developed a fluid switch using ionic polymer artificial muscles that operates at ultra-low power and produces a force 34 times greater than its weight. Fluid switches control fluid flow, causing the fluid to flow in a specific direction to invoke various movements. KAIST (President Kwang-Hyung Lee) announced on the 4th of January that a research team under Professor IlKwon Oh from the Department of Mechanical Engineering has developed a soft fluidic switch that operates at ultra-low voltage and can be used in narrow spaces. Artificial muscles imitate human muscles and provide flexible and natural movements compared to traditional motors, making them one of the basic elements used in soft robots, medical devices, and wearable devices. These artificial muscles create movements in response to external stimuli such as electricity, air pressure, and temperature changes, and in order to utilize artificial muscles, it is important to control these movements precisely. Switches based on existing motors were difficult to use within limited spaces due to their rigidity and large size. In order to address these issues, the research team developed an electro-ionic soft actuator that can control fluid flow while producing large amounts of force, even in a narrow pipe, and used it as a soft fluidic switch. < Figure 1. The separation of fluid droplets using a soft fluid switch at ultra-low voltage. > The ionic polymer artificial muscle developed by the research team is composed of metal electrodes and ionic polymers, and it generates force and movement in response to electricity. A polysulfonated covalent organic framework (pS-COF) made by combining organic molecules on the surface of the artificial muscle electrode was used to generate an impressive amount of force relative to its weight with ultra-low power (~0.01V). As a result, the artificial muscle, which was manufactured to be as thin as a hair with a thickness of 180 µm, produced a force more than 34 times greater than its light weight of 10 mg to initiate smooth movement. Through this, the research team was able to precisely control the direction of fluid flow with low power. < Figure 2. The synthesis and use of pS-COF as a common electrode-electrolyte host for electroactive soft fluid switches. A) The synthesis schematic of pS-COF. B) The schematic diagram of the operating principle of the electrochemical soft switch. C) The schematic diagram of using a pS-COF-based electrochemical soft switch to control fluid flow in dynamic operation. > Professor IlKwon Oh, who led this research, said, “The electrochemical soft fluidic switch that operate at ultra-low power can open up many possibilities in the fields of soft robots, soft electronics, and microfluidics based on fluid control.” He added, “From smart fibers to biomedical devices, this technology has the potential to be immediately put to use in a variety of industrial settings as it can be easily applied to ultra-small electronic systems in our daily lives.” The results of this study, in which Dr. Manmatha Mahato, a research professor in the Department of Mechanical Engineering at KAIST, participated as the first author, were published in the international academic journal Science Advances on December 13, 2023. (Paper title: Polysulfonated Covalent Organic Framework as Active Electrode Host for Mobile Cation Guests in Electrochemical Soft Actuator) This research was conducted with support from the National Research Foundation of Korea's Leader Scientist Support Project (Creative Research Group) and Future Convergence Pioneer Project. ＊ Paper DOI: https://www.science.org/doi/abs/10.1126/sciadv.adk9752

A KAIST Research Team Develops High-Performance St..

With the market for wearable electric devices growing rapidly, stretchable solar cells that can function under strain have received considerable attention as an energy source. To build such solar cells, it is necessary that their photoactive layer, which converts light into electricity, shows high electrical performance while possessing mechanical elasticity. However, satisfying both of these two requirements is challenging, making stretchable solar cells difficult to develop. On December 26, a KAIST research team from the Department of Chemical and Biomolecular Engineering (CBE) led by Professor Bumjoon Kim announced the development of a new conductive polymer material that achieved both high electrical performance and elasticity while introducing the world’s highest-performing stretchable organic solar cell. Organic solar cells are devices whose photoactive layer, which is responsible for the conversion of light into electricity, is composed of organic materials. Compared to existing non-organic material-based solar cells, they are lighter and flexible, making them highly applicable for wearable electrical devices. Solar cells as an energy source are particularly important for building electrical devices, but high-efficiency solar cells often lack flexibility, and their application in wearable devices have therefore been limited to this point. The team led by Professor Kim conjugated a highly stretchable polymer to an electrically conductive polymer with excellent electrical properties through chemical bonding, and developed a new conductive polymer with both electrical conductivity and mechanical stretchability. This polymer meets the highest reported level of photovoltaic conversion efficiency (19％) using organic solar cells, while also showing 10 times the stretchability of existing devices. The team thereby built the world’s highest performing stretchable solar cell that can be stretched up to 40％ during operation, and demonstrated its applicability for wearable devices. < Figure 1. Chemical structure of the newly developed conductive polymer and performance of stretchable organic solar cells using the material. > Professor Kim said, “Through this research, we not only developed the world’s best performing stretchable organic solar cell, but it is also significant that we developed a new polymer that can be applicable as a base material for various electronic devices that needs to be malleable and/or elastic.” < Figure 2. Photovoltaic efficiency and mechanical stretchability of newly developed polymers compared to existing polymers. > This research, conducted by KAIST researchers Jin-Woo Lee and Heung-Goo Lee as first co-authors in cooperation with teams led by Professor Taek-Soo Kim from the Department of Mechanical Engineering and Professor Sheng Li from the Department of CBE, was published in Joule on December 1 (Paper Title: Rigid and Soft Block-Copolymerized Conjugated Polymers Enable High-Performance Intrinsically-Stretchable Organic Solar Cells). This research was supported by the National Research Foundation of Korea.

A KAIST Team Develops Selective Transfer Printing ..

- A KAIST research team led by Professor Keon Jae Lee demonstrates the transfer printing of a large number of micro-sized inorganic semiconductor chips via the selective modulation of micro-vacuum force. MicroLEDs are a light source for next-generation displays that utilize inorganic LED chips with a size of less than 100 μm. MicroLEDs have attracted a great deal of attention due to their superior electrical/optical properties, reliability, and stability compared to conventional displays such as LCD, OLED, and QD. To commercialize microLEDs, transfer printing technology is essential for rearranging microLED dies from a growth substrate onto the final substrate with a desired layout and precise alignment. However, previous transfer methods still have many challenges such as the need for additional adhesives, misalignment, low transfer yield, and chip damage. Professor Lee’s research team has developed a micro-vacuum assisted selective transfer printing (µVAST) technology to transfer a large number of microLED chips by adjusting the micro-vacuum suction force. The key technology relies on a laser-induced etching (LIE) method for forming 20 μm-sized micro-hole arrays with a high aspect ratio on glass substrates at fabrication speed of up to 7,000 holes per second. The LIE-drilled glass is connected to the vacuum channels, controlling the micro-vacuum force at desired hole arrays to selectively pick up and release the microLEDs. The micro-vacuum assisted transfer printing accomplishes a higher adhesion switchability compared to previous transfer methods, enabling the assembly of micro-sized semiconductors with various heterogeneous materials, sizes, shapes, and thicknesses onto arbitrary substrates with high transfer yields. < Figure 01. Concept of micro-vacuum assisted selective transfer printing (μVAST). > Professor Keon Jae Lee said, “The micro-vacuum assisted transfer provides an interesting tool for large-scale, selective integration of microscale high-performance inorganic semiconductors. Currently, we are investigating the transfer printing of commercial microLED chips with an ejector system for commercializing next-generation displays (Large screen TVs, flexible/stretchable devices) and wearable phototherapy patches.” This result titled “Universal selective transfer printing via micro-vacuum force” was published in Nature Communications on November 26th, 2023. (DOI: 10.1038/S41467-023-43342-8) < Figure 02. Universal transfer printing of thin-film semiconductors via μVAST. > < Figure 03. Flexible devices fabricated by μVAST. > Title: Entire process including LIE and µVAST Vimeo link: https://vimeo.com/894430416?share=copy

KAIST presents strategies for environmentally frie..

- Provides current research trends in bio-based polyamide production - Research on bio-based polyamides production gains importance for achieving a carbon-neutral society Global industries focused on carbon neutrality, under the slogan "Net-Zero," are gaining increasing attention. In particular, research on microbial production of polymers, replacing traditional chemical methods with biological approaches, is actively progressing. Polyamides, represented by nylon, are linear polymers widely used in various industries such as automotive, electronics, textiles, and medical fields. They possess beneficial properties such as high tensile strength, electrical insulation, heat resistance, wear resistance, and biocompatibility. Since the commercialization of nylon in 1938, approximately 7 million tons of polyamides are produced worldwide annually. Considering their broad applications and significance, producing polyamides through bio-based methods holds considerable environmental and industrial importance. KAIST (President Kwang-Hyung Lee) announced that a research team led by Distinguished Professor Sang Yup Lee, including Dr. Jong An Lee and doctoral candidate Ji Yeon Kim from the Department of Chemical and Biomolecular Engineering, published a paper titled "Current Advancements in Bio-Based Production of Polyamides”. The paper was featured on the cover of the monthly issue of "Trends in Chemistry” by Cell Press. As part of climate change response technologies, bio-refineries involve using biotechnological and chemical methods to produce industrially important chemicals and biofuels from renewable biomass without relying on fossil resources. Notably, systems metabolic engineering, pioneered by KAIST's Distinguished Professor Sang Yup Lee, is a research field that effectively manipulates microbial metabolic pathways to produce valuable chemicals, forming the core technology for bio-refineries. The research team has successfully developed high-performance strains producing a variety of compounds, including succinic acid, biodegradable plastics, biofuels, and natural products, using systems metabolic engineering tools and strategies. The research team predicted that if bio-based polyamide production technology, which is widely used in the production of clothing and textiles, becomes widespread, it will attract attention as a future technology that can respond to the climate crisis due to its environment-friendly production technology. In this study, the research team comprehensively reviewed the bio-based polyamide production strategies. They provided insights into the advancements in polyamide monomer production using metabolically engineered microorganisms and highlighted the recent trends in bio-based polyamide advancements utilizing these monomers. Additionally, they reviewed the strategies for synthesizing bio-based polyamides through chemical conversion of natural oils and discussed the biodegradability and recycling of the polyamides. Furthermore, the paper presented the future direction in which metabolic engineering can be applied for the bio-based polyamide production, contributing to environmentally friendly and sustainable society. Ji Yeon Kim, the co-first author of this paper from KAIST, stated "The importance of utilizing systems metabolic engineering tools and strategies for bio-based polyamides production is becoming increasingly prominent in achieving carbon neutrality." Professor Sang Yup Lee emphasized, "Amid growing concerns about climate change, the significance of environmentally friendly and sustainable industrial development is greater than ever. Systems metabolic engineering is expected to have a significant impact not only on the chemical industry but also in various fields." < [Figure 1] A schematic overview of the overall process for polyamides production > This paper by Dr. Jong An Lee, PhD student Ji Yeon Kim, Dr. Jung Ho Ahn, and Master Yeah-Ji Ahn from the Department of Chemical and Biomolecular Engineering at KAIST was published in the December issue of 'Trends in Chemistry', an authoritative review journal in the field of chemistry published by Cell. It was published on December 7 as the cover paper and featured review. ※ Paper title: Current advancements in the bio-based production of polyamides ※ Author information: Jong An Lee, Ji Yeon Kim, Jung Ho Ahn, Yeah-Ji Ahn, and Sang Yup Lee This research was conducted with the support from the development of platform technologies of microbial cell factories for the next-generation biorefineries project and C1 gas refinery program by Korean Ministry of Science and ICT. < [Figure 2] Cover paper of the December issue of Trends in Chemistry >

KAIST introduces eco-friendly technologies for pla..

- A research team under Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering published a paper in Nature Microbiology on the overview and trends of plastic production and degradation technology using microorganisms. - Eco-friendly and sustainable plastic production and degradation technology using microorganisms as a core technology to achieve a plastic circular economy was presented. Plastic is one of the important materials in modern society, with approximately 460 million tons produced annually and with expected production reaching approximately 1.23 billion tons in 2060. However, since 1950, plastic waste totaling more than 6.3 billion tons has been generated, and it is believed that more than 140 million tons of plastic waste has accumulated in the aquatic environment. Recently, the seriousness of microplastic pollution has emerged, not only posing a risk to the marine ecosystem and human health, but also worsening global warming by inhibiting the activity of marine plankton, which play an important role in lowering the Earth's carbon dioxide concentration. KAIST President Kwang-Hyung Lee announced on December 11 that a research team under Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering had published a paper titled 'Sustainable production and degradation of plastics using microbes', which covers the latest technologies for producing plastics and processing waste plastics in an eco-friendly manner using microorganisms. As the international community moves to solve this plastic problem, various efforts are being made, including 175 countries participating to conclude a legally binding agreement with the goal of ending plastic pollution by 2024. Various technologies are being developed for sustainable plastic production and processing, and among them, biotechnology using microorganisms is attracting attention. Microorganisms have the ability to naturally produce or decompose certain compounds, and this ability is maximized through biotechnologies such as metabolic engineering and enzyme engineering to produce plastics from renewable biomass resources instead of fossil raw materials and to decompose waste plastics. Accordingly, the research team comprehensively analyzed the latest microorganism-based technologies for the sustainable production and decomposition of plastics and presented how they actually contribute to solving the plastic problem. Based on this, they presented limitations, prospects, and research directions of the technologies for achieving a circular economy for plastics. Microorganism-based technologies for various plastics range from widely used synthetic plastics such as polyethylene (PE) to promising bioplastics such as natural polymers derived from microorganisms (polyhydroxyalkanoate (PHA)) that are completely biodegradable in the natural environment and do not pose a risk of microplastic generation. Commercialization statuses and latest technologies were also discussed. In addition, the technology to decompose these plastics using microorganisms and their enzymes and the upcycling technology to convert them into other useful compounds after decomposition were introduced, highlighting the competitiveness and potential of technology using microorganisms. First author So Young Choi, a research assistant professor in the Department of Chemical and Biomolecular Engineering at KAIST, said, “In the future, we will be able to easily find eco-friendly plastics made using microorganisms all around us,” and corresponding author Distinguished Professor Sang Yup Lee said, “Plastic can be made more sustainable. It is important to use plastics responsibly to protect the environment and simultaneously achieve economic and social development through the new plastics industry, and we look forward to the improved performance of microbial metabolic engineering technology.” This paper was published on November 30th in the online edition of Nature Microbiology. ※ Paper Title : Sustainable production and degradation of plastics using microbes Authors: So Young Choi, Youngjoon Lee, Hye Eun Yu, In Jin Cho, Minju Kang & Sang Yup Lee < Life cycle of plastics produced using microbial biotechnologies > This research was conducted with the support from the Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries Project (2022M3J5A1056117) and the Development of Platform Technology for the Production of Novel Aromatic Bioplastic Using Microbial Cell Factories Project (2022M3J4A1053699) by the Korean Ministry of Science and ICT.

KAIST-UCSD researchers build an enzyme discovering..

- A joint research team led by Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering and Bernhard Palsson of UCSD developed ‘DeepECtransformer’, an artificial intelligence that can predict Enzyme Commission (EC) number of proteins. - The AI is tasked to discover new enzymes that have not been discovered yet, which would allow prediction for a total of 5,360 types of Enzyme Commission (EC) numbers - It is expected to be used in the development of microbial cell factories that produce environmentally friendly chemicals as a core technology for analyzing the metabolic network of a genome. While E. coli is one of the most studied organisms, the function of 30％ of proteins that make up E. coli has not yet been clearly revealed. For this, an artificial intelligence was used to discover 464 types of enzymes from the proteins that were unknown, and the researchers went on to verify the predictions of 3 types of proteins were successfully identified through in vitro enzyme assay. KAIST (President Kwang-Hyung Lee) announced on the 24th that a joint research team comprised of Gi Bae Kim, Ji Yeon Kim, Dr. Jong An Lee and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering at KAIST, and Dr. Charles J. Norsigian and Professor Bernhard O. Palsson of the Department of Bioengineering at UCSD has developed DeepECtransformer, an artificial intelligence that can predict the enzyme functions from the protein sequence, and has established a prediction system by utilizing the AI to quickly and accurately identify the enzyme function. Enzymes are proteins that catalyze biological reactions, and identifying the function of each enzyme is essential to understanding the various chemical reactions that exist in living organisms and the metabolic characteristics of those organisms. Enzyme Commission (EC) number is an enzyme function classification system designed by the International Union of Biochemistry and Molecular Biology, and in order to understand the metabolic characteristics of various organisms, it is necessary to develop a technology that can quickly analyze enzymes and EC numbers of the enzymes present in the genome. Various methodologies based on deep learning have been developed to analyze the features of biological sequences, including protein function prediction, but most of them have a problem of a black box, where the inference process of AI cannot be interpreted. Various prediction systems that utilize AI for enzyme function prediction have also been reported, but they do not solve this black box problem, or cannot interpret the reasoning process in fine-grained level (e.g., the level of amino acid residues in the enzyme sequence). The joint team developed DeepECtransformer, an AI that utilizes deep learning and a protein homology analysis module to predict the enzyme function of a given protein sequence. To better understand the features of protein sequences, the transformer architecture, which is commonly used in natural language processing, was additionally used to extract important features about enzyme functions in the context of the entire protein sequence, which enabled the team to accurately predict the EC number of the enzyme. The developed DeepECtransformer can predict a total of 5360 EC numbers. The joint team further analyzed the transformer architecture to understand the inference process of DeepECtransformer, and found that in the inference process, the AI utilizes information on catalytic active sites and/or the cofactor binding sites which are important for enzyme function. By analyzing the black box of DeepECtransformer, it was confirmed that the AI was able to identify the features that are important for enzyme function on its own during the learning process. "By utilizing the prediction system we developed, we were able to predict the functions of enzymes that had not yet been identified and verify them experimentally," said Gi Bae Kim, the first author of the paper. "By using DeepECtransformer to identify previously unknown enzymes in living organisms, we will be able to more accurately analyze various facets involved in the metabolic processes of organisms, such as the enzymes needed to biosynthesize various useful compounds or the enzymes needed to biodegrade plastics." he added. "DeepECtransformer, which quickly and accurately predicts enzyme functions, is a key technology in functional genomics, enabling us to analyze the function of entire enzymes at the systems level," said Professor Sang Yup Lee. He added, “We will be able to use it to develop eco-friendly microbial factories based on comprehensive genome-scale metabolic models, potentially minimizing missing information of metabolism.” The joint team’s work on DeepECtransformer is described in the paper titled "Functional annotation of enzyme-encoding genes using deep learning with transformer layers" written by Gi Bae Kim, Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering of KAIST and their colleagues. The paper was published via peer-review on the 14th of November on “Nature Communications”. This research was conducted with the support by “the Development of next-generation biorefinery platform technologies for leading bio-based chemicals industry project (2022M3J5A1056072)” and by “Development of platform technologies of microbial cell factories for the next-generation biorefineries project (2022M3J5A1056117)” from National Research Foundation supported by the Korean Ministry of Science and ICT (Project Leader: Distinguished Professor Sang Yup Lee, KAIST). < Figure 1. The structure of DeepECtransformer's artificial neural network >