Kaist

KAIST Develops Microbial Liquid Egg Substitute​

A team of researchers published a paper on developing a substitute for eggs using microorganisms, grabbing international attention. It is expected that the development of egg substitutes using non-animal raw materials will solve the problems of factory farming, which causes problems like increased emission of greenhouse gas and waste, and contribute to building a sustainable food system that allows easy protein intake.   KAIST (President Kwang-Hyung Lee) announced that Research Professor Kyeong Rok Choi from the Biological Process Research Center and Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering have published a paper on the development of an "Eco-Friendly Liquid Egg Substitute Derived from Microorganisms."   Eggs play a crucial role in various culinary applications due to their unique physicochemical properties such as gelling, foaming, and emulsifying, while also providing essential nutrients. However, traditional egg production is not only unethical and resource-intensive but also has significant environmental impacts such as greenhouse gas emissions and waste issues. Additionally, factors such as wars and trade regulations have led to significant increases in egg prices, highlighting food security concerns. In response to these issues, there has been growing interest in egg substitutes made from non-animal sources to establish a sustainable food system.   Although there has been progress in developing non-animal protein-based egg substitutes, no substitute has been able to fully replicate the essential functional properties of liquid eggs, such as gelling and foaming, while also providing complete nutrition. In this context, the research team aimed to develop a liquid egg substitute using microbial biomass, which has a protein content comparable to that of meat per unit dry mass. Various microorganisms, such as yeast, Bacillus, lactic acid bacteria, and other probiotics, have been proven safe through long-term human consumption. Microbial biomass requires fewer resources like water and land during production, and possesses high-quality nutrients, making it a promising sustainable food resource.       < Figure 1. Comparison of heat treatment results of microbial pellets and microbial lysates >   However, the semi-solid microbial biomass recovered through microbial cultivation was observed to turn liquid upon heating, unlike liquid egg. To address this, the research team devised a microbial lysate by breaking down the cell walls and cell membranes of microorganisms, which correspond to the eggshell. They found that the microbial lysate's proteins coagulated when heated and formed a gel similar to that of liquid egg. The gel formed from the heated microbial lysate was found to have microscopic structures and physical properties similar to those of boiled eggs. The addition of microbial-derived edible enzymes or plant-based materials allowed for the adjustment of its properties, enabling the creation of various textures.   Furthermore, the researchers demonstrated that the microbial lysate could form stable foams widely used in baking, such as meringues (made from egg whites). They successfully baked meringue cookies using this lysate, showing its potential as a functional liquid egg substitute.   Distinguished Professor Sang Yup Lee stated, "This substitute has excellent nutritional components, making it suitable for regular food consumption. It is especially promising as emergency food for long-term space travel, wartime situations, and other emergencies. More importantly, it contributes to securing a sustainable food system."       < Figure 2. Example of foaming ability of microbial lysate and meringue cookie production >       < Figure 3. Example of foaming ability of microbial lysate and meringue cookie production >     The paper was published online in the journal npj Science of Food, issued by Nature.   - Paper Title: Microbial lysates repurposed as liquid egg substitutes   - Authors: Kyeong Rok Choi (first author), Da-Hee Ahn, Seok Yeong Jung, YuHyun Lee, and Sang Yup Lee (corresponding author)     This research was supported by the Ministry of Science and ICT's project for developing eco-friendly chemical technologies to replace petroleum (Project Leader: Distinguished Professor Sang Yup Lee, KAIST) and the Rural Development Administration's Agricultural Microorganisms Project Group (Director: Professor Pan-sik Jang, Seoul National University) for developing protein production technology from inorganic substances through microbial metabolic system control (Project Leader: Research Professor Kyeong Rok Choi, KAIST).

KAIST Employs Image-recognition AI to Determine Ba..

An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.   KAIST (President Kwang-Hyung Lee) announced on July 2nd that Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the Electronics and Telecommunications Research Institute (ETRI) and Drexel University in the United States, has developed a method to predict the major elemental composition and charge-discharge state of NCM cathode materials with 99.6％ accuracy using convolutional neural networks (CNN)＊.   ＊Convolutional Neural Network (CNN): A type of multi-layer, feed-forward, artificial neural network used for analyzing visual images.   The research team noted that while scanning electron microscopy (SEM) is used in semiconductor manufacturing to inspect wafer defects, it is rarely used in battery inspections. SEM is used for batteries to analyze the size of particles only at research sites, and reliability is predicted from the broken particles and the shape of the breakage in the case of deteriorated battery materials.   The research team decided that it would be groundbreaking if an automated SEM can be used in the process of battery production, just like in the semiconductor manufacturing, to inspect the surface of the cathode material to determine whether it was synthesized according to the desired composition and that the lifespan would be reliable, thereby reducing the defect rate.       < Figure 1. Example images of true cases and their grad-CAM overlays from the best trained network. >   The researchers trained a CNN-based AI applicable to autonomous vehicles to learn the surface images of battery materials, enabling it to predict the major elemental composition and charge-discharge cycle states of the cathode materials. They found that while the method could accurately predict the composition of materials with additives, it had lower accuracy for predicting charge-discharge states. The team plans to further train the AI with various battery material morphologies produced through different processes and ultimately use it for inspecting the compositional uniformity and predicting the lifespan of next-generation batteries.   Professor Joshua C. Agar, one of the collaborating researchers of the project from the Department of Mechanical Engineering and Mechanics of Drexel University, said, "In the future, artificial intelligence is expected to be applied not only to battery materials but also to various dynamic processes in functional materials synthesis, clean energy generation in fusion, and understanding foundations of particles and the universe."   Professor Seungbum Hong from KAIST, who led the research, stated, "This research is significant as it is the first in the world to develop an AI-based methodology that can quickly and accurately predict the major elemental composition and the state of the battery from the structural data of micron-scale SEM images. The methodology developed in this study for identifying the composition and state of battery materials based on microscopic images is expected to play a crucial role in improving the performance and quality of battery materials in the future."       < Figure 2. Accuracies of CNN Model predictions on SEM images of NCM cathode materials with additives under various conditions. >     This research was conducted by KAIST’s Materials Science and Engineering Department graduates Dr. Jimin Oh and Dr. Jiwon Yeom, the co-first authors, in collaboration with Professor Josh Agar and Dr. Kwang Man Kim from ETRI. It was supported by the National Research Foundation of Korea, the KAIST Global Singularity project, and international collaboration with the US research team. The results were published in the international journal npj Computational Materials on May 4. (Paper Title: “Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images”)

Professor Sung-Hyon Myaeng from School of Computin..

  Professor Sung-Hyon Myaeng from KAIST has published a book "The Age of AGI and the Future of Humanity."   Professor Sung-Hyon Myaeng of KAIST (Korea Advanced Institute of Science and Technology) has published a new book titled "The Age of AGI and the Future of Humanity," which analyzes the latest trends in artificial intelligence research and its potential impact on future society. This book deeply examines the various possibilities of the AGI era and the challenges humans need to prepare concrete strategies for coexistence and advancement with AI. Additionally, it discusses ethical, social, and economic issues related to coexistence with AGI and offers specific survival guidelines to ensure humans can thrive in the AGI era.   The author, Professor Sung-Hyon Myaeng, conducts research on artificial intelligence and machine learning at the School of Computing at KAIST and is recognized as an authority in the field of AI through numerous academic papers and books. His research has received international attention, and he said "I want to share in-depth knowledge of AGI with the public through this new book"   "The Age of AGI and the Future of Humanity" is an essential read for understanding the AGI era and preparing for coexistence with AI. It is highly recommended for anyone interested in artificial intelligence.   Author: Sung-Hyon Myaeng (Professor at the School of Computing, KAIST)   Publisher: 헤이 북스   Purchase Link:   https://www.yes24.com/Product/Goods/126794729 https://product.kyobobook.co.kr/detail/S000213398879  

Novel High-performance and Sustainable Paper Coati..

What if there is a biodegradable packaging material with high performance without leaving microplastics?   Plastic pollution presents a global challenge that must be solved. In particular, packaging accounts for 30-50％ of the total plastic consumption. While paper packaging is eco-friendly, it lacks crucial functionalities like moisture resistance and strength. Traditional coating materials exacerbate plastic pollution, prompting the need for sustainable alternatives.   Polyethylene (PE) and ethylene vinyl alcohol (EVOH) are typically used as coating materials to improve the low barrier properties of paper packaging, but these substances do not decompose and worsen microplastic pollution when disposed of in the natural environment. In response to this problem, packaging materials made from bio-based substances and biodegradable plastics have been developed, but in most cases, as the packaging performance improves, the biodegradability diminishes rapidly.   KAIST announced that a joint research team led by Professor Jaewook Myung of the Department of Civil and Environmental Engineering, Professor Hanseul Yang of the Department of Life Sciences, and Professor Jongcheol Seo of the Department of Packaging and Logistics

KAIST introduces microbial food as a strategy food..

The global food crisis is increasing due to rapid population growth and declining food productivity to climate change. Moreover, today's food production and supply system emit a huge amount of carbon dioxide, reaching 30％ of the total amount emitted by humanity, aggravating climate change. Sustainable and nutritious microbial food is attracting attention as a key to overcoming this impasse.   KAIST (President Kwang Hyung Lee) announced on April 12th that Research Professor Kyeong Rok Choi of the BioProcess Engineering Research Center and Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering published a paper that proposes a direction of research on ‘microbial food production from sustainable raw materials.’   Microbial food refers to various foods and food ingredients produced using microorganisms. Microbial biomass contains a large amount of protein per unit in dry mass, comparable to that of meat, and emits the smallest amount of carbon dioxide and is required to produce a unit mass compared to various livestock, fish, shellfish, and crops. Since the amount of water and space requirement is small, it can be an eco-friendly, sustainable and highly nutritious food resource.   Fermented foods are the most readily available microbial foods around us. Although the proportion of microbial biomass in fermented foods is small, compounds with relatively low nutritional value, such as carbohydrates, are consumed during the fermentation process, and as microorganisms proliferate, the content of nutrients with higher nutritional value, such as proteins and vitamins, increases.   Various food compounds isolated and purified from biomass or culture media obtained through microbial culture are also a branch of microbial food. Examples that can be found around us include various amino acids, including monosodium glutamate, food proteins, enzymes, flavoring compounds, food colorings, and bioactive substances.       < Figure 1. Schematic diagram portraying various microbial biomass production strategies utlizing sustainable feedstocks >   Lastly, the most ultimate and fundamental form of microbial food can be said to be microbial biomass or extracts produced through microbial culture and foods cooked using them. A representative example is single-cell protein, which collectively refers to microbial biomass or microbial proteins extracted from it.   In this paper, the researchers comprehensively covered various non-edible raw materials and strategies for using them that can be used to produce microbial food in a more sustainable way. Furthermore, it covers various microbial foods that are actually produced in the industry using the relevant raw materials and their characteristics, as well as prospects for the production and generalization of sustainable microbial foods.   Research Professor Kyeong Rok Choi, the first author of this paper, said, “Microbial foods produced from various sustainable raw materials will soon be commonly encountered at our tables.” Second author Seok Yeong Jung, a doctoral student, also said, “Microbial foods of the future will not be limited foods consumed only out of a sense of obligation to the environment, but will be complete foods that are consumed by choice because of their nutritional value and taste.” In addition, Distinguished Professor Sang Yup Lee said, “It is time for the industry and academia, as well as the public and private sectors, to cooperate more closely so that more diverse microbial foods can be developed and supplied in order to create a sustainable society for ourselves and our descendants.”       < Figure 2. Compositions and environmental footprints of animal, plant and microbial biomass. >   This paper was published online on April 9 in ‘Nature Microbiology’ published by Nature.   ※ Paper title: From sustainable feedstocks to microbial foods   ※ Author information: Kyeong Rok Choi (first author), Seok Yeong Jung (second author) and Sang Yup Lee (corresponding author)   This research was conducted under the development of platform technologies of microbial cell factories for the next-generation biorefineries project (project leader KAIST Distinguished Professor Sang Yup Lee) supported by the Ministry of Science and ICT and the Cooperative Research Program for Agriculture Science and Technology Development (Project leader KAIST Research Professor Kyeong Rok Choi) of the Agricultural Microbiology Project Group (Director, Professor Pahn-Shick Chang) supported by the Rural Development Administration.

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