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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).
2024.07.05 View 6394 -
The 3rd Global Entrepreneurship Summer School (GESS 2024) Successfully Completed in Silicon Valley
The 2024 Global Entrepreneurship Summer School (2024 KAIST GESS), hosted by the Office of Global Initiatives under the KAIST International Office (Director Man-Sung Yim), was held for the third time. This program allows students to visit Silicon Valley, a global startup hub, to directly experience its famous startup ecosystem and develop their capabilities for global expansion. A total of 20 students were selected through applications, interviews, final presentations, mentoring, and peer evaluations. Additionally, 17 students from the KAIST Impact MBA course at the KAIST Business School also participated.
Before starting the Silicon Valley program, participants received mentoring on business model development and pitching advice from a senior entrepreneur at KAIST for about two months, beginning last May. Afterward, they developed business items for each team at KAIST’s main campus in Daejeon. For seven days, starting from June 23rd, workshops were held under the themes of global entrepreneurship, learning through failure, capital and network, and startup culture at KOTRA Silicon Valley Trade Center, JP Morgan, and Plug and Play Tech Center. This program's lecture series provided prospective entrepreneurs with the opportunity to systematically learn the mindset and gain the experience needed to start a global business.
The participants also visited local companies and gained experience in the field of global technology startups. Visits included Bear Robotics (CEO John Ha), Soundable Health (CEO Cathering Song), ImpriMed (CEO Sungwon Lim), Phantom AI (CEO Hyunggi Cho), B Garage (CEO Aiden Kim), and Simple Steps (CEO Doyeon Kim). Lectures contained vivid experiences from Silicon Valley CEOs and company tours boosted the students' passion for entrepreneurship. In particular, Doyeon Kim, CEO of Simple Steps, which helps prevent career breaks for Korean female immigrants in Silicon Valley and allows talented female immigrants to demonstrate their abilities in society, said, “As a KAIST alumna entrepreneur, it was meaningful to share my experience with this generation of students who dream of starting a global business and creating social enterprises in the United States.”
This program also included a tour of Silicon Valley's big tech companies that have made a significant impact on the digital ecosystem through technological advancement and innovation. This included Broadcom, which maintains a strong global presence in the semiconductor and infrastructure software technology fields. At the invitation of Chairman Hock Tan, GESS participants had the opportunity to attend his lecture and ask questions. Chairman Tan, who received an honorary doctorate in engineering from KAIST last February, emphasized that experiencing failure and giving consistent effort over a long period of time are more important than anything else in order to grow as a global entrepreneur, and that technologies influencing the global market evolve over generations.
< Photo. Group photo of GESS 2024 participants at Broadcom with Chairman Hock Tan (center) ⓒBroadcom>
As part of this program, participants conducted a volunteer program called 'Let's play with AI+ Tech' with the Sunnyvale community in Silicon Valley and Foothill College to help grow together with the community. Through this program, GESS participants cultivated the virtues of a global leader. In this volunteer activity, low-income elementary school students and parents from the Sunnyvale community participated in chatbot training led by KAIST students, providing an opportunity to work with underprivileged groups in the local community.
In the final pitching event, the highlight of the program, local venture investors from Silicon Valley were invited as judges and evaluated the pitches for each team's business items. The participating students, who developed their own business models while receiving advice through face-to-face mentoring from a professional accelerator in Silicon Valley, showcased their creative and innovative ideas, presenting themselves as future global entrepreneurs. Merey Makhmutova (BS in Civil and Environmental Engineering) from the K-Bridge team, who won the final pitch, expressed her ambition: “Even before GESS pitch day, our team kept refining the pitch deck as we attended the lectures and benefitted from the mentoring. Our intense teamwork was a significant reason why we ultimately won first prize.” She added that K-Bridge aims to win an award at the upcoming UKC Pitching Competition and expressed her gratitude for being able to participate in this program. Arseniy Kan (BS in Electrical Engineering) from the KAIST Enablers team, who took second place, said, “The 2024 KAIST GESS Program became the most unforgettable and precious opportunity of my lifetime, and I dream of using this opportunity as a stepping stone to becoming a global entrepreneur.“ Additionally, Kangster (CEO Kang Kim), who won the Impact MBA final pitching session, had the opportunity to secure a meeting with a local investment company after their GESS final pitch.
The 2024 KAIST GESS was held in cooperation with the KAIST International Office, the KAIST College of Business, and Startup KAIST. Director Man-Sung Yim from the Office of Global Initiatives, who hosted the event, said, “KAIST students will grow into leaders with global influence and contribute to the international community by creating global value. At the same time, we hope to raise the international status of our university.” Professor Sangchan Park, who led the 17 Impact MBA students in this educational program, added, “Meeting with companies leading the global market and visiting Silicon Valley has been a valuable learning experience for students aiming to start a global startup.”
KAIST plans to continue promoting its global entrepreneurship education program by enriching its curriculum each year and helping students grow into entrepreneurs with the virtues of global leaders.
2024.07.03 View 8642 -
KAIST President Kwang-Hyung Lee receives honorary doctorate from Université de Montréal
KAIST (President Kwang-Hyung Lee) announced on June 16th that President Kwang-Hyung Lee received an honorary doctorate on the 15th, local time, from the Université de Montréal in Canada, one of the largest French-speaking universities in North America.
< Image. (from left) Mr. Pierre Lassonde, Chairman of the Board of Polytechnique Montréal, President Maud Cohen of Polytechnique Montréal, President Kwang-Hyung Lee of KAIST, Chancellor Frantz Saintellemy of Université de Montréal and Mr. Alexandre Chabot, Secretary General of Université de Montéal. >
President Lee was selected as the recipient of the honorary doctorate from the Université de Montréal upon the recommendation of Polytechnique Montréal in recognition of his contributions in pioneering the multidisciplinary approach to integrate a number of fields studies including computer science, biology, and nanotechnology.
Polytechnique Montréal is a university in affiliation with the University of Montréal and is one of the largest engineering education and research institutions of Canada. President Lee's honorary doctorate was awarded at the Convocation Ceremony of Polytechnique Montréal held for the Class of 2024. On this day, Mr. Serge Gendron, a businessman, a philanthropist and an alum of Polytechnique Montréal, also had the honor of receiving an honorary doctorate along with President Lee.
President Kwang-Hyung Lee is internationally recognized for his contributions in various fields, including engineering education, multidisciplinary research, strategy establishment, and future prospects. President Lee is also well known to have had significant influence on the first-generation venture entrepreneurs, a large portion of which are from KAIST, who have now grown into full-fledged entrepreneurs. For these activities, President Lee received numerous decorations and commendations within Korea, including the National Order of Civil Merit - Camellia Medal, and in 2003, he received the ‘Légion d’Honneur Chevalier’ from the French government as well.
Through his speech at the ceremony, KAIST President Kwang-Hyung Lee expressed his gratitude to the Université de Montréal and Polytechnique Montréal, while congratulating and encouraging the graduates who are poised to start anew as they part from the school. “Hold on to your dreams, try looking at the world from a different perspective, and enjoy the challenges without being afraid of failures.” With these three pieces of advice, President Lee cheered on the graduates saying, “The future belongs to those of you who challenge them.”
Maud Cohen, the President of Polytechnique Montréal, commented on President Kwang-Hyung Lee's honorary doctorate, that Polytechnique Montréal is proud to award an honorary doctorate to Mr. Lee for his exceptional career path, his holistic, multidisciplinary and undeniably forward-looking vision, which strongly echoes the values of Polytechnique Montréal, and for his involvement in and commitment to education, research and the future of the next generation.
* Established in 1873, Polytechnique Montréal is one of Canada’s largest engineering education and research universities, and is located on the Université de Montréal campus – North America’s largest Francophone university campus. Joshua Bengio, who won the Turing Award for establishing the foundations of deep learning, is gaining international recognition in artificial intelligence and other related fields at Polytechnique Montréal. Polytechnique Montréal chose KAIST as the first Korean university establish partnership with and has continued to build up close cooperative relationship since 1998.
* The Université de Montréal (UdeM) is a public university founded in 1878. It is located in Montréal, in the French-speaking province of Québec, Canada. It is one of Canada's five major universities, and the second largest in terms of student enrollment. The Université de Montréal is the largest in the French-speaking world in terms of both student enrollment and research. The Université de Montréal enjoys an excellent reputation as one of the best French-language post-secondary institutions. Its rector is Mr. Daniel Jutras.
2024.06.16 View 6175 -
Revolutionary 'scLENS' Unveiled to Decode Complex Single-Cell Genomic Data
Unlocking biological information from complex single-cell genomic data has just become easier and more precise, thanks to the innovative 'scLENS' tool developed by the Biomedical Mathematics Group within the IBS Center for Mathematical and Computational Sciences led by Chief Investigator Jae Kyoung Kim, who is also a professor at KAIST. This new finding represents a significant leap forward in the field of single-cell transcriptomics.
Single-cell genomic analysis is an advanced technique that measures gene expression at the individual cell level, revealing cellular changes and interactions that are not observable with traditional genomic analysis methods. When applied to cancer tissues, this analysis can delineate the composition of diverse cell types within a tumor, providing insights into how cancer progresses and identifying key genes involved during each stage of progression.
Despite the immense potential of single-cell genomic analysis, handling the vast amount of data that it generates has always been challenging. The amount of data covers the expression of tens of thousands of genes across hundreds to thousands of individual cells. This not only results in large datasets but also introduces noise-related distortions, which arise in part due to current measurement limitations.
< Figure 1. Overview of scLENS (single-cell Low-dimensional embedding using the effective Noise Subtract) >
(Left) Current dimensionality reduction methods for scRNA-seq data involve conventional data preprocessing steps, such as log normalization, followed by manual selection of signals from the scaled data. However, this study reveals that the high levels of sparsity and variability in scRNA-seq data can lead to signal distortion during the data preprocessing, compromising the accuracy of downstream analyses.
(Right) To address this issue, the researchers integrated L2 normalization into the conventional preprocessing pipeline, effectively mitigating signal distortion. Moreover, they developed a novel signal detection algorithm that eliminates the need for user intervention by leveraging random matrix theory-based noise filtering and signal robustness testing. By incorporating these techniques, scLENS enables accurate and automated analysis of scRNA-seq data, overcoming the limitations of existing dimensionality reduction methods.
Corresponding author Jae Kyoung Kim highlighted, “There has been a remarkable advancement in experimental technologies for analyzing single-cell transcriptomes over the past decade. However, due to limitations in data analysis methods, there has been a struggle to fully utilize valuable data obtained through extensive cost and time."
Researchers have developed numerous analysis methods over the years to discern biological signals from this noise. However, the accuracy of these methods has been less than satisfactory. A critical issue is that determining signal and noise thresholds often depends on subjective decisions from the users.
The newly developed scLENS tool harnesses Random Matrix Theory and Signal robustness test to automatically differentiate signals from noise without relying on subjective user input.
First author Hyun Kim stated, "Previously, users had to arbitrarily decide the threshold for signal and noise, which compromised the reproducibility of analysis results and introduced subjectivity. scLENS eliminates this problem by automatically detecting signals using only the inherent structure of the data."
During the development of scLENS, researchers identified the fundamental reasons for inaccuracies in existing analysis methods. They found that commonly used data preprocessing methods distort both biological signals and noise. The new preprocessing approach that scLENS offers is free from such distortions.
By resolving issues related to noise threshold determined by subjective user choice and signal distortion in conventional data preprocessing, scLENS significantly outperforms existing methods in accuracy. Additionally, scLENS automates the laborious process of signal dimension selection, allowing researchers to extract biological signals conveniently and automatically.
CI Kim added, "scLENS solves major issues in single-cell transcriptome data analysis, substantially improving the accuracy and efficiency throughout the analysis process. This is a prime example of how fundamental mathematical theories can drive innovation in life sciences research, allowing researchers to more quickly and accurately answer biological questions and uncover secrets of life that were previously hidden."
This research was published in the international journal 'Nature Communications' on April 27.
Terminology
* Single-cell RNA sequencing (scRNA-seq): A technique used to measure gene expression levels in individual cells, providing insights into cell heterogeneity and rare cell types.
* Dimensionality reduction: A method to reduce the number of features or variables in a dataset while preserving the most important information, making data analysis more manageable and interpretable.
* Random matrix theory: A mathematical framework used to model and analyze the properties of large, random matrices, which can be applied to filter out noise in high-dimensional data.
* Signal robustness test: Among the signals, this test selects signals that are robust to the slight perturbation in data because real biological signals should be invariant for such slight modification in the data.
2024.05.09 View 5672 -
KAIST Develops Sodium Battery Capable of Rapid Charging in Just a Few Seconds
Sodium (Na), which is over 500 times more abundant than lithium (Li), has recently garnered significant attention for its potential in sodium-ion battery technologies. However, existing sodium-ion batteries face fundamental limitations, including lower power output, constrained storage properties, and longer charging times, necessitating the development of next-generation energy storage materials.
On the 11th of April, KAIST (represented by President Kwang Hyung Lee) announced that a research team led by Professor Jeung Ku Kang from the Department of Materials Science and Engineering had developed a high-energy, high-power hybrid sodium-ion battery capable of rapid charging.
The innovative hybrid energy storage system integrates anode materials typically used in batteries with cathodes suitable for supercapacitors. This combination allows the device to achieve both high storage capacities and rapid charge-discharge rates, positioning it as a viable next-generation alternative to lithium-ion batteries.
However, the development of a hybrid battery with high energy and high power density requires an improvement to the slow energy storage rate of battery-type anodes as well as the enhancement of the relatively low capacity of supercapacitor-type cathode materials.
< Figure 1. Schematic synthetic procedures of high-capacity/high-rate anode and cathode materials for a sodium-ion hybrid energy storages (SIHES) and their proposed energy storage mechanisms. Synthetic procedures for (a) ultrafine iron sulfide-embedded S-doped carbon/graphene (FS/C/G) anode and (b) zeolitic imidazolate framework-derived porous carbon (ZDPC) cathode materials. (c) Proposed energy storage mechanisms of Na+ ions in FS/C/G anode and ClO-4 ions in ZDPC cathode for an SIHES. >
To account for this, Professor Kang's team utilized two distinct metal-organic frameworks for the optimized synthesis of hybrid batteries. This approach led to the development of an anode material with improved kinetics through the inclusion of fine active materials in porous carbon derived from metal-organic frameworks. Additionally, a high-capacity cathode material was synthesized, and the combination of the cathode and anode materials allowed for the development of a sodium-ion storage system optimizing the balance and minimizing the disparities in energy storage rates between the electrodes.
The assembled full cell, comprising the newly developed anode and cathode, forms a high-performance hybrid sodium-ion energy storage device. This device surpasses the energy density of commercial lithium-ion batteries and exhibits the characteristics of supercapacitors' power density. It is expected to be suitable for rapid charging applications ranging from electric vehicles to smart electronic devices and aerospace technologies.
< Figure 2. Electrochemical characterizations of FS/C/G-20//ZDPC SIHES full cells (left). Ragone plots for FS/C/G-20//ZDPC (this work) and other previously reported sodium-ion electrochemical energy storage devices (right). >
Professor Kang noted that the hybrid sodium-ion energy storage device, capable of rapid charging and achieving an energy density of 247 Wh/kg and a power density of 34,748 W/kg, represents a breakthrough in overcoming the current limitations of energy storage systems. He anticipates broader applications across various electronic devices, including electric vehicles.
This research, co-authored by KAIST doctoral candidates Jong Hui Choi and Dong Won Kim, was published in the international journal Energy Storage Materials on March 29 with the title "Low-crystallinity conductive multivalence iron sulfide-embedded S-doped anode and high-surface-area O-doped cathode of 3D porous N-rich graphitic carbon frameworks for high-performance sodium-ion hybrid energy storages."
The study was conducted with support from the Ministry of Science and ICT and the National Research Foundation of Korea through the Nanomaterial Technology Development Project.
2024.04.18 View 17854 -
A KAIST-SNUH Team Devises a Way to Make Mathematical Predictions to find Metabolites Related to Somatic Mutations in Cancers
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".
2024.03.18 View 6492 -
KAIST Develops Healthcare Device Tracking Chronic Diabetic Wounds
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.
2024.03.11 View 7134 -
KAIST Team Develops an Insect-Mimicking Semiconductor to Detect Motion
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.
2024.02.29 View 7369 -
KAIST Research Team Develops Sweat-Resistant Wearable Robot Sensor
New electromyography (EMG) sensor technology that allows the long-term stable control of wearable robots and is not affected by the wearer’s sweat and dead skin has gained attention recently. Wearable robots are devices used across a variety of rehabilitation treatments for the elderly and patients recovering from stroke or trauma.
A joint research team led by Professor Jae-Woong Jung from the KAIST School of Electrical Engineering (EE) and Professor Jung Kim from the KAIST Department of Mechanical Engineering (ME) announced on January 23rd that they have successfully developed a stretchable and adhesive microneedle sensor that can electrically sense physiological signals at a high level without being affected by the state of the user’s skin.
For wearable robots to recognize the intentions behind human movement for their use in rehabilitation treatment, they require a wearable electrophysiological sensor that gives precise EMG measurements. However, existing sensors often show deteriorating signal quality over time and are greatly affected by the user’s skin conditions. Furthermore, the sensor’s higher mechanical hardness causes noise since the contact surface is unable to keep up with the deformation of the skin. These shortcomings limit the reliable, long-term control of wearable robots.
< Figure 1. Design and working concept of the Stretchable microNeedle Adhesive Patch (SNAP). (A) Schematic illustration showing the overall system configuration and application of SNAP. (B) Exploded view schematic diagram of a SNAP, consisting of stretchable serpentine interconnects, Au-coated Si microneedle, and ECA made of Ag flakes–silicone composite. (C) Optical images showing high mechanical compliance of SNAP. >
However, the recently developed technology is expected to allow long-term and high-quality EMG measurements as it uses a stretchable and adhesive conducting substrate integrated with microneedle arrays that can easily penetrate the stratum corneum without causing discomfort. Through its excellent performance, the sensor is anticipated to be able to stably control wearable robots over a long period of time regardless of the wearer’s changing skin conditions and without the need for a preparation step that removes sweat and dead cells from the surface of their skin.
The research team created a stretchable and adhesive microneedle sensor by integrating microneedles into a soft silicon polymer substrate. The hard microneedles penetrate through the stratum corneum, which has high electrical resistance. As a result, the sensor can effectively lower contact resistance with the skin and obtain high-quality electrophysiological signals regardless of contamination. At the same time, the soft and adhesive conducting substrate can adapt to the skin’s surface that stretches with the wearer’s movement, providing a comfortable fit and minimizing noise caused by movement.
< Figure 2. Demonstration of the wireless Stretchable microNeedle Adhesive Patch (SNAP) system as an Human-machine interfaces (HMI) for closed-loop control of an exoskeleton robot. (A) Illustration depicting the system architecture and control strategy of an exoskeleton robot. (B) The hardware configuration of the pneumatic back support exoskeleton system. (C) Comparison of root mean square (RMS) of electromyography (EMG) with and without robotic assistance of pretreated skin and non-pretreated skin. >
To verify the usability of the new patch, the research team conducted a motion assistance experiment using a wearable robot. They attached the microneedle patch on a user’s leg, where it could sense the electrical signals generated by the muscle. The sensor then sent the detected intention to a wearable robot, allowing the robot to help the wearer lift a heavy object more easily.
Professor Jae-Woong Jung, who led the research, said, “The developed stretchable and adhesive microneedle sensor can stability detect EMG signals without being affected by the state of a user’s skin. Through this, we will be able to control wearable robots with higher precision and stability, which will help the rehabilitation of patients who use robots.”
The results of this research, written by co-first authors Heesoo Kim and Juhyun Lee, who are both Ph.D. candidates in the KAIST School of EE, were published in Science Advances on January 17th under the title “Skin-preparation-free, stretchable microneedle adhesive patches for reliable electrophysiological sensing and exoskeleton robot control”.
This research was supported by the Bio-signal Sensor Integrated Technology Development Project by the National Research Foundation of Korea, the Electronic Medicinal Technology Development Project, and the Step 4 BK21 Project.
2024.01.30 View 8012 -
KAIST Professor Jiyun Lee becomes the first Korean to receive the Thurlow Award from the American Institute of Navigation
< Distinguished Professor Jiyun Lee from the KAIST Department of Aerospace Engineering >
KAIST (President Kwang-Hyung Lee) announced on January 27th that Distinguished Professor Jiyun Lee from the KAIST Department of Aerospace Engineering had won the Colonel Thomas L. Thurlow Award from the American Institute of Navigation (ION) for her achievements in the field of satellite navigation.
The American Institute of Navigation (ION) announced Distinguished Professor Lee as the winner of the Thurlow Award at its annual awards ceremony held in conjunction with its international conference in Long Beach, California on January 25th. This is the first time a person of Korean descent has received the award.
The Thurlow Award was established in 1945 to honor Colonel Thomas L. Thurlow, who made significant contributions to the development of navigation equipment and the training of navigators. This award aims to recognize an individual who has made an outstanding contribution to the development of navigation and it is awarded to one person each year. Past recipients include MIT professor Charles Stark Draper, who is well-known as the father of inertial navigation and who developed the guidance computer for the Apollo moon landing project.
Distinguished Professor Jiyun Lee was recognized for her significant contributions to technological advancements that ensure the safety of satellite-based navigation systems for aviation. In particular, she was recognized as a world authority in the field of navigation integrity architecture design, which is essential for ensuring the stability of intelligent transportation systems and autonomous unmanned systems. Distinguished Professor Lee made a groundbreaking contribution to help ensure the safety of satellite-based navigation systems from ionospheric disturbances, including those affected by sudden changes in external factors such as the solar and space environment.
She has achieved numerous scientific discoveries in the field of ionospheric research, while developing new ionospheric threat modeling methods, ionospheric anomaly monitoring and mitigation techniques, and integrity and availability assessment techniques for next-generation augmented navigation systems. She also contributed to the international standardization of technology through the International Civil Aviation Organization (ICAO).
Distinguished Professor Lee and her research group have pioneered innovative navigation technologies for the safe and autonomous operation of unmanned aerial vehicles (UAVs) and urban air mobility (UAM). She was the first to propose and develop a low-cost navigation satellite system (GNSS) augmented architecture for UAVs with a near-field network operation concept that ensures high integrity, and a networked ground station-based augmented navigation system for UAM. She also contributed to integrity design techniques, including failure monitoring and integrity risk assessment for multi-sensor integrated navigation systems.
< Professor Jiyoon Lee upon receiving the Thurlow Award >
Bradford Parkinson, professor emeritus at Stanford University and winner of the 1986 Thurlow Award, who is known as the father of GPS, congratulated Distinguished Professor Lee upon hearing that she was receiving the Thurlow Award and commented that her innovative research has addressed many important topics in the field of navigation and her solutions are highly innovative and highly regarded.
Distinguished Professor Lee said, “I am very honored and delighted to receive this award with its deep history and tradition in the field of navigation.” She added, “I will strive to help develop the future mobility industry by securing safe and sustainable navigation technology.”
2024.01.26 View 6997 -
KAIST and Hyundai Motors Collaborate to Develop Ultra-Fast Hydrogen Leak Detection within 0.6 Seconds
Recently, as the spread of eco-friendly hydrogen cars increases, the importance of hydrogen sensors is also on the rise. In particular, achieving technology to detect hydrogen leaks within one second remains a challenging task. Accordingly, the development of the world's first hydrogen sensor that meets the performance standards of the U.S. Department of Energy has become a hot topic.
A team at KAIST led by Dr. Min-Seung Jo from Professor Jun-Bo Yoon's team in the Department of Electrical and Electronic Engineering has successfully achieved all of its desired performance indicators, meeting globally recognized standards through collaboration with the Electromagnetic Energy Materials Research Team at Hyundai Motor Company's Basic Materials Research Center and Professor Min-Ho Seo of Pusan National University. On January 10th, the research group announced that the world's first hydrogen sensor with a speed of less than 0.6 seconds had been developed.
In order to secure faster and more stable hydrogen detection technology than existing commercialized hydrogen sensors, the KAIST team began developing a next-generation hydrogen sensor in 2021 together with Hyundai Motor Company, and succeeded after two years of development.
< Figure 1. (Left) The conceptual drawing of the structure of the coplanar heater-integrated hydrogen sensor. Pd nanowire is stably suspended in the air even with its thickness of 20 nm. (Right) A graph of hydrogen sensor performance operating within 0.6 seconds for hydrogen at a concentration of 0.1 to 4% >
Existing hydrogen sensor research has mainly focused on sensing materials, such as catalytic treatments or the alloying of palladium (Pd) materials, which are widely used in hydrogen sensors. Although these studies showed excellent performance with certain performance indicators, they did not meet all of the desired performance indicators and commercialization was limited due to the difficulty of batch processing.
To overcome this, the research team developed a sensor that satisfied all of the performance indicators by combining independent micro/nano structure design and process technology based on pure palladium materials. In addition, considering future mass production, pure metal materials with fewer material restrictions were used rather than synthetic materials, and a next-generation hydrogen sensor was developed that can be mass-produced based on a semiconductor batch process.
The developed device is a differential coplanar device in which the heater and sensing materials are integrated side by side on the same plane to overcome the uneven temperature distribution of existing gas sensors, which have a structure where the heater, insulating layer, and sensing materials are stacked vertically. The palladium nanomaterial, which is a sensing material, has a completely floating structure and is exposed to air from beneath, maximizing the reaction area with a gas to ensure a fast reaction speed. In addition, the palladium sensing material operates at a uniform temperature throughout the entire area, and the research team was able to secure a fast operation speed, wide sensing concentration, and temperature/humidity insensitivity by accurately controlling temperature-sensitive sensing performance.
< Figure 2. Electron microscopy of the coplanar heater-integrated hydrogen sensor (left) Photo of the entire device (top right) Pd nanowire suspended in the air (bottom right) Cross section of Pd nanowire >
The research team packaged the fabricated device with a Bluetooth module to create an integrated module that wirelessly detects hydrogen leaks within one second and then verified its performance. Unlike existing high-performance optical hydrogen sensors, this one is highly portable and can be used in a variety of applications where hydrogen energy is used.
Dr. Min-Seung Jo, who led the research, said, “The results of this research are of significant value as they not only operate at high speeds by exceeding the performance limits of existing hydrogen sensors, but also secure the reliability and stability necessary for actual use, and can be used in various places such as automobiles, hydrogen charging stations, and homes.” He also revealed his future plans, saying, “Through the commercialization of this hydrogen sensor technology, I would like to contribute to advancing the safe and eco-friendly use of hydrogen energy.”
< Figure 3. (Left) Real-time hydrogen detection results from the coplanar heater-integrated hydrogen sensor integrated and packaged in wireless communication and an app for mobile phone. (Middle) LED blinking cycle control in accordance with the hydrogen concentration level. (Right) Results of performance confirmation of the detection within 1 second in a real-time hydrogen leak demo >
The research team is currently working with Hyundai Motor Company to manufacture the device on a wafer scale and then mount it on a vehicle module to further verify detection and durability performance.
This research, conducted by Dr. Min-Seung Jo as the first author, has three patent applications filed in the U.S. and Korea, and was published in the renowned international academic journal 'ACS Nano'. (Paper title: Ultrafast (∼0.6 s), Robust, and Highly Linear Hydrogen Detection up to 10% Using Fully Suspended Pure Pd Nanowire). (Impact Factor: 18.087). ( https://pubs.acs.org/doi/10.1021/acsnano.3c06806?fig=fig1&ref=pdf )
The research was conducted through support from the National Research Foundation of Korea's Nano and Materials Technology Development Project and support and joint development efforts from Hyundai Motor Company's Basic Materials Research Center.
2024.01.25 View 6383 -
KAIST Research Team Breaks Down Musical Instincts with AI
Music, often referred to as the universal language, is known to be a common component in all cultures. Then, could ‘musical instinct’ be something that is shared to some degree despite the extensive environmental differences amongst cultures?
On January 16, a KAIST research team led by Professor Hawoong Jung from the Department of Physics announced to have identified the principle by which musical instincts emerge from the human brain without special learning using an artificial neural network model.
Previously, many researchers have attempted to identify the similarities and differences between the music that exist in various different cultures, and tried to understand the origin of the universality. A paper published in Science in 2019 had revealed that music is produced in all ethnographically distinct cultures, and that similar forms of beats and tunes are used. Neuroscientist have also previously found out that a specific part of the human brain, namely the auditory cortex, is responsible for processing musical information.
Professor Jung’s team used an artificial neural network model to show that cognitive functions for music forms spontaneously as a result of processing auditory information received from nature, without being taught music. The research team utilized AudioSet, a large-scale collection of sound data provided by Google, and taught the artificial neural network to learn the various sounds. Interestingly, the research team discovered that certain neurons within the network model would respond selectively to music. In other words, they observed the spontaneous generation of neurons that reacted minimally to various other sounds like those of animals, nature, or machines, but showed high levels of response to various forms of music including both instrumental and vocal.
The neurons in the artificial neural network model showed similar reactive behaviours to those in the auditory cortex of a real brain. For example, artificial neurons responded less to the sound of music that was cropped into short intervals and were rearranged. This indicates that the spontaneously-generated music-selective neurons encode the temporal structure of music. This property was not limited to a specific genre of music, but emerged across 25 different genres including classic, pop, rock, jazz, and electronic.
< Figure 1. Illustration of the musicality of the brain and artificial neural network (created with DALL·E3 AI based on the paper content) >
Furthermore, suppressing the activity of the music-selective neurons was found to greatly impede the cognitive accuracy for other natural sounds. That is to say, the neural function that processes musical information helps process other sounds, and that ‘musical ability’ may be an instinct formed as a result of an evolutionary adaptation acquired to better process sounds from nature.
Professor Hawoong Jung, who advised the research, said, “The results of our study imply that evolutionary pressure has contributed to forming the universal basis for processing musical information in various cultures.” As for the significance of the research, he explained, “We look forward for this artificially built model with human-like musicality to become an original model for various applications including AI music generation, musical therapy, and for research in musical cognition.” He also commented on its limitations, adding, “This research however does not take into consideration the developmental process that follows the learning of music, and it must be noted that this is a study on the foundation of processing musical information in early development.”
< Figure 2. The artificial neural network that learned to recognize non-musical natural sounds in the cyber space distinguishes between music and non-music. >
This research, conducted by first author Dr. Gwangsu Kim of the KAIST Department of Physics (current affiliation: MIT Department of Brain and Cognitive Sciences) and Dr. Dong-Kyum Kim (current affiliation: IBS) was published in Nature Communications under the title, “Spontaneous emergence of rudimentary music detectors in deep neural networks”.
This research was supported by the National Research Foundation of Korea.
2024.01.23 View 7193