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KAIST presents strategies for environmentally friendly and sustainable polyamides production
- Provides current research trends in bio-based polyamide production
- Research on bio-based polyamides production gains importance for achieving a carbon-neutral society
Global industries focused on carbon neutrality, under the slogan "Net-Zero," are gaining increasing attention. In particular, research on microbial production of polymers, replacing traditional chemical methods with biological approaches, is actively progressing.
Polyamides, represented by nylon, are linear polymers widely used in various industries such as automotive, electronics, textiles, and medical fields. They possess beneficial properties such as high tensile strength, electrical insulation, heat resistance, wear resistance, and biocompatibility. Since the commercialization of nylon in 1938, approximately 7 million tons of polyamides are produced worldwide annually. Considering their broad applications and significance, producing polyamides through bio-based methods holds considerable environmental and industrial importance.
KAIST (President Kwang-Hyung Lee) announced that a research team led by Distinguished Professor Sang Yup Lee, including Dr. Jong An Lee and doctoral candidate Ji Yeon Kim from the Department of Chemical and Biomolecular Engineering, published a paper titled "Current Advancements in Bio-Based Production of Polyamides”. The paper was featured on the cover of the monthly issue of "Trends in Chemistry” by Cell Press.
As part of climate change response technologies, bio-refineries involve using biotechnological and chemical methods to produce industrially important chemicals and biofuels from renewable biomass without relying on fossil resources. Notably, systems metabolic engineering, pioneered by KAIST's Distinguished Professor Sang Yup Lee, is a research field that effectively manipulates microbial metabolic pathways to produce valuable chemicals, forming the core technology for bio-refineries. The research team has successfully developed high-performance strains producing a variety of compounds, including succinic acid, biodegradable plastics, biofuels, and natural products, using systems metabolic engineering tools and strategies.
The research team predicted that if bio-based polyamide production technology, which is widely used in the production of clothing and textiles, becomes widespread, it will attract attention as a future technology that can respond to the climate crisis due to its environment-friendly production technology. In this study, the research team comprehensively reviewed the bio-based polyamide production strategies. They provided insights into the advancements in polyamide monomer production using metabolically engineered microorganisms and highlighted the recent trends in bio-based polyamide advancements utilizing these monomers. Additionally, they reviewed the strategies for synthesizing bio-based polyamides through chemical conversion of natural oils and discussed the biodegradability and recycling of the polyamides. Furthermore, the paper presented the future direction in which metabolic engineering can be applied for the bio-based polyamide production, contributing to environmentally friendly and sustainable society.
Ji Yeon Kim, the co-first author of this paper from KAIST, stated "The importance of utilizing systems metabolic engineering tools and strategies for bio-based polyamides production is becoming increasingly prominent in achieving carbon neutrality."
Professor Sang Yup Lee emphasized, "Amid growing concerns about climate change, the significance of environmentally friendly and sustainable industrial development is greater than ever. Systems metabolic engineering is expected to have a significant impact not only on the chemical industry but also in various fields."
< [Figure 1] A schematic overview of the overall process for polyamides production >
This paper by Dr. Jong An Lee, PhD student Ji Yeon Kim, Dr. Jung Ho Ahn, and Master Yeah-Ji Ahn from the Department of Chemical and Biomolecular Engineering at KAIST was published in the December issue of 'Trends in Chemistry', an authoritative review journal in the field of chemistry published by Cell. It was published on December 7 as the cover paper and featured review.
※ Paper title: Current advancements in the bio-based production of polyamides
※ Author information: Jong An Lee, Ji Yeon Kim, Jung Ho Ahn, Yeah-Ji Ahn, and Sang Yup Lee
This research was conducted with the support from the development of platform technologies of microbial cell factories for the next-generation biorefineries project and C1 gas refinery program by Korean Ministry of Science and ICT.
< [Figure 2] Cover paper of the December issue of Trends in Chemistry >
2023.12.21 View 5451 -
A KAIST Team Develops Selective Transfer Printing Technology for MicroLEDs
- A KAIST research team led by Professor Keon Jae Lee demonstrates the transfer printing of a large number of micro-sized inorganic semiconductor chips via the selective modulation of micro-vacuum force.
MicroLEDs are a light source for next-generation displays that utilize inorganic LED chips with a size of less than 100 μm. MicroLEDs have attracted a great deal of attention due to their superior electrical/optical properties, reliability, and stability compared to conventional displays such as LCD, OLED, and QD. To commercialize microLEDs, transfer printing technology is essential for rearranging microLED dies from a growth substrate onto the final substrate with a desired layout and precise alignment. However, previous transfer methods still have many challenges such as the need for additional adhesives, misalignment, low transfer yield, and chip damage.
Professor Lee’s research team has developed a micro-vacuum assisted selective transfer printing (µVAST) technology to transfer a large number of microLED chips by adjusting the micro-vacuum suction force.
The key technology relies on a laser-induced etching (LIE) method for forming 20 μm-sized micro-hole arrays with a high aspect ratio on glass substrates at fabrication speed of up to 7,000 holes per second. The LIE-drilled glass is connected to the vacuum channels, controlling the micro-vacuum force at desired hole arrays to selectively pick up and release the microLEDs. The micro-vacuum assisted transfer printing accomplishes a higher adhesion switchability compared to previous transfer methods, enabling the assembly of micro-sized semiconductors with various heterogeneous materials, sizes, shapes, and thicknesses onto arbitrary substrates with high transfer yields.
< Figure 01. Concept of micro-vacuum assisted selective transfer printing (μVAST). >
Professor Keon Jae Lee said, “The micro-vacuum assisted transfer provides an interesting tool for large-scale, selective integration of microscale high-performance inorganic semiconductors. Currently, we are investigating the transfer printing of commercial microLED chips with an ejector system for commercializing next-generation displays (Large screen TVs, flexible/stretchable devices) and wearable phototherapy patches.”
This result titled “Universal selective transfer printing via micro-vacuum force” was published in Nature Communications on November 26th, 2023. (DOI: 10.1038/S41467-023-43342-8)
< Figure 02. Universal transfer printing of thin-film semiconductors via μVAST. >
< Figure 03. Flexible devices fabricated by μVAST. >
Title: Entire process including LIE and µVAST
Vimeo link: https://vimeo.com/894430416?share=copy
2023.12.19 View 3626 -
KAIST introduces eco-friendly technologies for plastic production and biodegradation
- A research team under Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering published a paper in Nature Microbiology on the overview and trends of plastic production and degradation technology using microorganisms.
- Eco-friendly and sustainable plastic production and degradation technology using microorganisms as a core technology to achieve a plastic circular economy was presented.
Plastic is one of the important materials in modern society, with approximately 460 million tons produced annually and with expected production reaching approximately 1.23 billion tons in 2060. However, since 1950, plastic waste totaling more than 6.3 billion tons has been generated, and it is believed that more than 140 million tons of plastic waste has accumulated in the aquatic environment. Recently, the seriousness of microplastic pollution has emerged, not only posing a risk to the marine ecosystem and human health, but also worsening global warming by inhibiting the activity of marine plankton, which play an important role in lowering the Earth's carbon dioxide concentration.
KAIST President Kwang-Hyung Lee announced on December 11 that a research team under Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering had published a paper titled 'Sustainable production and degradation of plastics using microbes', which covers the latest technologies for producing plastics and processing waste plastics in an eco-friendly manner using microorganisms.
As the international community moves to solve this plastic problem, various efforts are being made, including 175 countries participating to conclude a legally binding agreement with the goal of ending plastic pollution by 2024. Various technologies are being developed for sustainable plastic production and processing, and among them, biotechnology using microorganisms is attracting attention.
Microorganisms have the ability to naturally produce or decompose certain compounds, and this ability is maximized through biotechnologies such as metabolic engineering and enzyme engineering to produce plastics from renewable biomass resources instead of fossil raw materials and to decompose waste plastics.
Accordingly, the research team comprehensively analyzed the latest microorganism-based technologies for the sustainable production and decomposition of plastics and presented how they actually contribute to solving the plastic problem. Based on this, they presented limitations, prospects, and research directions of the technologies for achieving a circular economy for plastics.
Microorganism-based technologies for various plastics range from widely used synthetic plastics such as polyethylene (PE) to promising bioplastics such as natural polymers derived from microorganisms (polyhydroxyalkanoate (PHA)) that are completely biodegradable in the natural environment and do not pose a risk of microplastic generation. Commercialization statuses and latest technologies were also discussed. In addition, the technology to decompose these plastics using microorganisms and their enzymes and the upcycling technology to convert them into other useful compounds after decomposition were introduced, highlighting the competitiveness and potential of technology using microorganisms.
First author So Young Choi, a research assistant professor in the Department of Chemical and Biomolecular Engineering at KAIST, said, “In the future, we will be able to easily find eco-friendly plastics made using microorganisms all around us,” and corresponding author Distinguished Professor Sang Yup Lee said, “Plastic can be made more sustainable. It is important to use plastics responsibly to protect the environment and simultaneously achieve economic and social development through the new plastics industry, and we look forward to the improved performance of microbial metabolic engineering technology.”
This paper was published on November 30th in the online edition of Nature Microbiology.
※ Paper Title : Sustainable production and degradation of plastics using microbes
Authors: So Young Choi, Youngjoon Lee, Hye Eun Yu, In Jin Cho, Minju Kang & Sang Yup Lee
2023.12.11 View 5488 -
North Korea and Beyond: AI-Powered Satellite Analysis Reveals the Unseen Economic Landscape of Underdeveloped Nations
- A joint research team in computer science, economics, and geography has developed an artificial intelligence (AI) technology to measure grid-level economic development within six-square-kilometer regions.
- This AI technology is applicable in regions with limited statistical data (e.g., North Korea), supporting international efforts to propose policies for economic growth and poverty reduction in underdeveloped countries.
- The research team plans to make this technology freely available for use to contribute to the United Nations' Sustainable Development Goals (SDGs).
The United Nations reports that more than 700 million people are in extreme poverty, earning less than two dollars a day. However, an accurate assessment of poverty remains a global challenge. For example, 53 countries have not conducted agricultural surveys in the past 15 years, and 17 countries have not published a population census. To fill this data gap, new technologies are being explored to estimate poverty using alternative sources such as street views, aerial photos, and satellite images.
The paper published in Nature Communications demonstrates how artificial intelligence (AI) can help analyze economic conditions from daytime satellite imagery. This new technology can even apply to the least developed countries - such as North Korea - that do not have reliable statistical data for typical machine learning training.
The researchers used Sentinel-2 satellite images from the European Space Agency (ESA) that are publicly available. They split these images into small six-square-kilometer grids. At this zoom level, visual information such as buildings, roads, and greenery can be used to quantify economic indicators. As a result, the team obtained the first ever fine-grained economic map of regions like North Korea. The same algorithm was applied to other underdeveloped countries in Asia: North Korea, Nepal, Laos, Myanmar, Bangladesh, and Cambodia (see Image 1).
The key feature of their research model is the "human-machine collaborative approach," which lets researchers combine human input with AI predictions for areas with scarce data. In this research, ten human experts compared satellite images and judged the economic conditions in the area, with the AI learning from this human data and giving economic scores to each image. The results showed that the Human-AI collaborative approach outperformed machine-only learning algorithms.
< Image 1. Nightlight satellite images of North Korea (Top-left: Background photo provided by NASA's Earth Observatory). South Korea appears brightly lit compared to North Korea, which is mostly dark except for Pyongyang. In contrast, the model developed by the research team uses daytime satellite imagery to predict more detailed economic predictions for North Korea (top-right) and five Asian countries (Bottom: Background photo from Google Earth). >
The research was led by an interdisciplinary team of computer scientists, economists, and a geographer from KAIST & IBS (Donghyun Ahn, Meeyoung Cha, Jihee Kim), Sogang University (Hyunjoo Yang), HKUST (Sangyoon Park), and NUS (Jeasurk Yang). Dr Charles Axelsson, Associate Editor at Nature Communications, handled this paper during the peer review process at the journal.
The research team found that the scores showed a strong correlation with traditional socio-economic metrics such as population density, employment, and number of businesses. This demonstrates the wide applicability and scalability of the approach, particularly in data-scarce countries. Furthermore, the model's strength lies in its ability to detect annual changes in economic conditions at a more detailed geospatial level without using any survey data (see Image 2).
< Image 2. Differences in satellite imagery and economic scores in North Korea between 2016 and 2019. Significant development was found in the Wonsan Kalma area (top), one of the tourist development zones, but no changes were observed in the Wiwon Industrial Development Zone (bottom). (Background photo: Sentinel-2 satellite imagery provided by the European Space Agency (ESA)). >
This model would be especially valuable for rapidly monitoring the progress of Sustainable Development Goals such as reducing poverty and promoting more equitable and sustainable growth on an international scale. The model can also be adapted to measure various social and environmental indicators. For example, it can be trained to identify regions with high vulnerability to climate change and disasters to provide timely guidance on disaster relief efforts.
As an example, the researchers explored how North Korea changed before and after the United Nations sanctions against the country. By applying the model to satellite images of North Korea both in 2016 and in 2019, the researchers discovered three key trends in the country's economic development between 2016 and 2019. First, economic growth in North Korea became more concentrated in Pyongyang and major cities, exacerbating the urban-rural divide. Second, satellite imagery revealed significant changes in areas designated for tourism and economic development, such as new building construction and other meaningful alterations. Third, traditional industrial and export development zones showed relatively minor changes.
Meeyoung Cha, a data scientist in the team explained, "This is an important interdisciplinary effort to address global challenges like poverty. We plan to apply our AI algorithm to other international issues, such as monitoring carbon emissions, disaster damage detection, and the impact of climate change."
An economist on the research team, Jihee Kim, commented that this approach would enable detailed examinations of economic conditions in the developing world at a low cost, reducing data disparities between developed and developing nations. She further emphasized that this is most essential because many public policies require economic measurements to achieve their goals, whether they are for growth, equality, or sustainability.
The research team has made the source code publicly available via GitHub and plans to continue improving the technology, applying it to new satellite images updated annually. The results of this study, with Ph.D. candidate Donghyun Ahn at KAIST and Ph.D. candidate Jeasurk Yang at NUS as joint first authors, were published in Nature Communications under the title "A human-machine collaborative approach measures economic development using satellite imagery."
< Photos of the main authors. 1. Donghyun Ahn, PhD candidate at KAIST School of Computing 2. Jeasurk Yang, PhD candidate at the Department of Geography of National University of Singapore 3. Meeyoung Cha, Professor of KAIST School of Computing and CI at IBS 4. Jihee Kim, Professor of KAIST School of Business and Technology Management 5. Sangyoon Park, Professor of the Division of Social Science at Hong Kong University of Science and Technology 6. Hyunjoo Yang, Professor of the Department of Economics at Sogang University >
2023.12.07 View 6515 -
KAIST-UCSD researchers build an enzyme discovering AI
- A joint research team led by Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering and Bernhard Palsson of UCSD developed ‘DeepECtransformer’, an artificial intelligence that can predict Enzyme Commission (EC) number of proteins.
- The AI is tasked to discover new enzymes that have not been discovered yet, which would allow prediction for a total of 5,360 types of Enzyme Commission (EC) numbers
- It is expected to be used in the development of microbial cell factories that produce environmentally friendly chemicals as a core technology for analyzing the metabolic network of a genome.
While E. coli is one of the most studied organisms, the function of 30% of proteins that make up E. coli has not yet been clearly revealed. For this, an artificial intelligence was used to discover 464 types of enzymes from the proteins that were unknown, and the researchers went on to verify the predictions of 3 types of proteins were successfully identified through in vitro enzyme assay.
KAIST (President Kwang-Hyung Lee) announced on the 24th that a joint research team comprised of Gi Bae Kim, Ji Yeon Kim, Dr. Jong An Lee and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering at KAIST, and Dr. Charles J. Norsigian and Professor Bernhard O. Palsson of the Department of Bioengineering at UCSD has developed DeepECtransformer, an artificial intelligence that can predict the enzyme functions from the protein sequence, and has established a prediction system by utilizing the AI to quickly and accurately identify the enzyme function.
Enzymes are proteins that catalyze biological reactions, and identifying the function of each enzyme is essential to understanding the various chemical reactions that exist in living organisms and the metabolic characteristics of those organisms. Enzyme Commission (EC) number is an enzyme function classification system designed by the International Union of Biochemistry and Molecular Biology, and in order to understand the metabolic characteristics of various organisms, it is necessary to develop a technology that can quickly analyze enzymes and EC numbers of the enzymes present in the genome.
Various methodologies based on deep learning have been developed to analyze the features of biological sequences, including protein function prediction, but most of them have a problem of a black box, where the inference process of AI cannot be interpreted. Various prediction systems that utilize AI for enzyme function prediction have also been reported, but they do not solve this black box problem, or cannot interpret the reasoning process in fine-grained level (e.g., the level of amino acid residues in the enzyme sequence).
The joint team developed DeepECtransformer, an AI that utilizes deep learning and a protein homology analysis module to predict the enzyme function of a given protein sequence. To better understand the features of protein sequences, the transformer architecture, which is commonly used in natural language processing, was additionally used to extract important features about enzyme functions in the context of the entire protein sequence, which enabled the team to accurately predict the EC number of the enzyme. The developed DeepECtransformer can predict a total of 5360 EC numbers.
The joint team further analyzed the transformer architecture to understand the inference process of DeepECtransformer, and found that in the inference process, the AI utilizes information on catalytic active sites and/or the cofactor binding sites which are important for enzyme function. By analyzing the black box of DeepECtransformer, it was confirmed that the AI was able to identify the features that are important for enzyme function on its own during the learning process.
"By utilizing the prediction system we developed, we were able to predict the functions of enzymes that had not yet been identified and verify them experimentally," said Gi Bae Kim, the first author of the paper. "By using DeepECtransformer to identify previously unknown enzymes in living organisms, we will be able to more accurately analyze various facets involved in the metabolic processes of organisms, such as the enzymes needed to biosynthesize various useful compounds or the enzymes needed to biodegrade plastics." he added.
"DeepECtransformer, which quickly and accurately predicts enzyme functions, is a key technology in functional genomics, enabling us to analyze the function of entire enzymes at the systems level," said Professor Sang Yup Lee. He added, “We will be able to use it to develop eco-friendly microbial factories based on comprehensive genome-scale metabolic models, potentially minimizing missing information of metabolism.”
The joint team’s work on DeepECtransformer is described in the paper titled "Functional annotation of enzyme-encoding genes using deep learning with transformer layers" written by Gi Bae Kim, Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering of KAIST and their colleagues. The paper was published via peer-review on the 14th of November on “Nature Communications”.
This research was conducted with the support by “the Development of next-generation biorefinery platform technologies for leading bio-based chemicals industry project (2022M3J5A1056072)” and by “Development of platform technologies of microbial cell factories for the next-generation biorefineries project (2022M3J5A1056117)” from National Research Foundation supported by the Korean Ministry of Science and ICT (Project Leader: Distinguished Professor Sang Yup Lee, KAIST).
< Figure 1. The structure of DeepECtransformer's artificial neural network >
2023.11.24 View 4601 -
An intravenous needle that irreversibly softens via body temperature on insertion
- A joint research team at KAIST developed an intravenous (IV) needle that softens upon insertion, minimizing risk of damage to blood vessels and tissues.
- Once used, it remains soft even at room temperature, preventing accidental needle stick injuries and unethical multiple use of needle.
- A thin-film temperature sensor can be embedded with this needle, enabling real-time monitoring of the patient's core body temperature, or detection of unintended fluid leakage, during IV medication.
Intravenous (IV) injection is a method commonly used in patient’s treatment worldwide as it induces rapid effects and allows treatment through continuous administration of medication by directly injecting drugs into the blood vessel. However, medical IV needles, made of hard materials such as stainless steel or plastic which do not mechanically match the soft biological tissues of the body, can cause critical problems in healthcare settings, starting from minor tissue damages in the injection sites to serious inflammations.
The structure and dexterity of rigid medical IV devices also enable unethical reuse of needles for reduction of injection costs, leading to transmission of deadly blood-borne disease infections such as human immunodeficiency virus (HIV) and hepatitis B/C viruses. Furthermore, unintended needlestick injuries are frequently occurring in medical settings worldwide, that are viable sources of such infections, with IV needles having the greatest susceptibility of being the medium of transmissible diseases. For these reasons, the World Health Organization (WHO) in 2015 launched a policy on safe injection practices to encourage the development and use of “smart” syringes that have features to prevent re-use, after a tremendous increase in the number of deadly infectious disease worldwide due to medical-sharps related issues.
KAIST announced on the 13th that Professor Jae-Woong Jeong and his research team of its School of Electrical Engineering succeeded in developing the Phase-Convertible, Adapting and non-REusable (P-CARE) needle with variable stiffness that can improve patient health and ensure the safety of medical staff through convergent joint research with another team led by Professor Won-Il Jeong of the Graduate School of Medical Sciences.
The new technology is expected to allow patients to move without worrying about pain at the injection site as it reduces the risk of damage to the wall of the blood vessel as patients receive IV medication. This is possible with the needle’s stiffness-tunable characteristics which will make it soft and flexible upon insertion into the body due to increased temperature, adapting to the movement of thin-walled vein. It is also expected to prevent blood-borne disease infections caused by accidental needlestick injuries or unethical re-using of syringes as the deformed needle remains perpetually soft even after it is retracted from the injection site.
The results of this research, in which Karen-Christian Agno, a doctoral researcher of the School of Electrical Engineering at and Dr. Keungmo Yang of the Graduate School of Medical Sciences participated as co-first authors, was published in Nature Biomedical Engineering on October 30. (Paper title: A temperature-responsive intravenous needle that irreversibly softens on insertion)
< Figure 1. Disposable variable stiffness intravenous needle. (a) Conceptual illustration of the key features of the P-CARE needle whose mechanical properties can be changed by body temperature, (b) Photograph of commonly used IV access devices and the P-CARE needle, (c) Performance of common IV access devices and the P-CARE needle >
“We’ve developed this special needle using advanced materials and micro/nano engineering techniques, and it can solve many global problems related to conventional medical needles used in healthcare worldwide”, said Jae-Woong Jeong, Ph.D., an associate professor of Electrical Engineering at KAIST and a lead senior author of the study.
The softening IV needle created by the research team is made up of liquid metal gallium that forms the hollow, mechanical needle frame encapsulated within an ultra-soft silicone material. In its solid state, gallium has sufficient hardness that enables puncturing of soft biological tissues. However, gallium melts when it is exposed to body temperature upon insertion, and changes it into a soft state like the surrounding tissue, enabling stable delivery of the drug without damaging blood vessels. Once used, a needle remains soft even at room temperature due to the supercooling phenomenon of gallium, fundamentally preventing needlestick accidents and reuse problems.
Biocompatibility of the softening IV needle was validated through in vivo studies in mice. The studies showed that implanted needles caused significantly less inflammation relative to the standard IV access devices of similar size made of metal needles or plastic catheters. The study also confirmed the new needle was able to deliver medications as reliably as commercial injection needles.
< Photo 1. Photo of the P-CARE needle that softens with body temperature. >
Researchers also showed possibility of integrating a customized ultra-thin temperature sensor with the softening IV needle to measure the on-site temperature which can further enhance patient’s well-being. The single assembly of sensor-needle device can be used to monitor the core body temperature, or even detect if there is a fluid leakage on-site during indwelling use, eliminating the need for additional medical tools or procedures to provide the patients with better health care services.
The researchers believe that this transformative IV needle can open new opportunities for wide range of applications particularly in clinical setups, in terms of redesigning other medical needles and sharp medical tools to reduce muscle tissue injury during indwelling use. The softening IV needle may become even more valuable in the present times as there is an estimated 16 billion medical injections administered annually in a global scale, yet not all needles are disposed of properly, based on a 2018 WHO report.
< Figure 2. Biocompatibility test for P-CARE needle: Images of H&E stained histology (the area inside the dashed box on the left is provided in an expanded view in the right), TUNEL staining (green), DAPI staining of nuclei (blue) and co-staining (TUNEL and DAPI) of muscle tissue from different organs. >
< Figure 3. Conceptual images of potential utilization for temperature monitoring function of P-CARE needle integrated with a temperature sensor. >
(a) Schematic diagram of injecting a drug through intravenous injection into the abdomen of a laboratory mouse (b) Change of body temperature upon injection of drug (c) Conceptual illustration of normal intravenous drug injection (top) and fluid leakage (bottom) (d) Comparison of body temperature during normal drug injection and fluid leakage: when the fluid leak occur due to incorrect insertion, a sudden drop of temperature is detected.
This work was supported by grants from the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.
2023.11.13 View 7616 -
KAIST proposes alternatives to chemical factories through “iBridge”
- A computer simulation program “iBridge” was developed at KAIST that can put together microbial cell factories quickly and efficiently to produce cosmetics and food additives, and raw materials for nylons
- Eco-friendly and sustainable fermentation process to establish an alternative to chemical plants
As climate change and environmental concerns intensify, sustainable microbial cell factories garner significant attention as candidates to replace chemical plants. To develop microorganisms to be used in the microbial cell factories, it is crucial to modify their metabolic processes to induce efficient target chemical production by modulating its gene expressions. Yet, the challenge persists in determining which gene expressions to amplify and suppress, and the experimental verification of these modification targets is a time- and resource-intensive process even for experts. The challenges were addressed by a team of researchers at KAIST (President Kwang-Hyung Lee) led by Distinguished Professor Sang Yup Lee.
It was announced on the 9th by the school that a method for building a microbial factory at low cost, quickly and efficiently, was presented by a novel computer simulation program developed by the team under Professor Lee’s guidance, which is named “iBridge”. This innovative system is designed to predict gene targets to either overexpress or downregulate in the goal of producing a desired compound to enable the cost-effective and efficient construction of microbial cell factories specifically tailored for producing the chemical compound in demand from renewable biomass.
Systems metabolic engineering is a field of research and engineering pioneered by KAIST’s Distinguished Professor Sang Yup Lee that seeks to produce valuable compounds in industrial demands using microorganisms that are re-configured by a combination of methods including, but not limited to, metabolic engineering, synthetic biology, systems biology, and fermentation engineering.
In order to improve microorganisms’ capability to produce useful compounds, it is essential to delete, suppress, or overexpress microbial genes. However, it is difficult even for the experts to identify the gene targets to modify without experimental confirmations for each of them, which can take up immeasurable amount of time and resources.
The newly developed iBridge identifies positive and negative metabolites within cells, which exert positive and/or negative impact on formation of the products, by calculating the sum of covariances of their outgoing (consuming) reaction fluxes for a target chemical. Subsequently, it pinpoints "bridge" reactions responsible for converting negative metabolites into positive ones as candidates for overexpression, while identifying the opposites as targets for downregulation.
The research team successfully utilized the iBridge simulation to establish E. coli microbial cell factories each capable of producing three of the compounds that are in high demands at a production capacity that has not been reported around the world. They developed E. coli strains that can each produce panthenol, a moisturizing agent found in many cosmetics, putrescine, which is one of the key components in nylon production, and 4-hydroxyphenyllactic acid, an anti-bacterial food additive. In addition to these three compounds, the study presents predictions for overexpression and suppression genes to construct microbial factories for 298 other industrially valuable compounds.
Dr. Youngjoon Lee, the co-first author of this paper from KAIST, emphasized the accelerated construction of various microbial factories the newly developed simulation enabled. He stated, "With the use of this simulation, multiple microbial cell factories have been established significantly faster than it would have been using the conventional methods. Microbial cell factories producing a wider range of valuable compounds can now be constructed quickly using this technology."
Professor Sang Yup Lee said, "Systems metabolic engineering is a crucial technology for addressing the current climate change issues." He added, "This simulation could significantly expedite the transition from resorting to conventional chemical factories to utilizing environmentally friendly microbial factories."
< Figure. Conceptual diagram of the flow of iBridge simulation >
The team’s work on iBridge is described in a paper titled "Genome-Wide Identification of Overexpression and Downregulation Gene Targets Based on the Sum of Covariances of the Outgoing Reaction Fluxes" written by Dr. Won Jun Kim, and Dr. Youngjoon Lee of the Bioprocess Research Center and Professors Hyun Uk Kim and Sang Yup Lee of the Department of Chemical and Biomolecular Engineering of KAIST. The paper was published via peer-review on the 6th of November on “Cell Systems” by Cell Press.
This research was conducted with the support from the Development of Platform Technologies of Microbial Cell Factories for the Next-generation Biorefineries Project (Project Leader: Distinguished Professor Sang Yup Lee, KAIST) and Development of Platform Technology for the Production of Novel Aromatic Bioplastic using Microbial Cell Factories Project (Project Leader: Research Professor So Young Choi, KAIST) of the Korean Ministry of Science and ICT.
2023.11.09 View 6375 -
A KAIST Research Team Develops a Smart Color-Changing Flexible Battery with Ultra-high Efficiency
With the rapid growth of the smart and wearable electronic devices market, smart next-generation energy storage systems that have energy storage functions as well as additional color-changing properties are receiving a great deal of attention. However, existing electrochromic devices have low electrical conductivity, leading to low efficiency in electron and ion mobility, and low storage capacities. Such batteries have therefore been limited to use in flexible and wearable devices.
On August 21, a joint research team led by Professor Il-Doo Kim from the KAIST Department of Materials Science and Engineering (DMSE) and Professor Tae Gwang Yun from the Myongji University Department of Materials Science and Engineering announced the development of a smart electrochromic Zn-ion battery that can visually represent its charging and discharging processes using an electrochromic polymer anode incorporated with a “π-bridge spacer”, which increases electron and ion mobility efficiency.
Batteries topped with electrochromic properties are groundbreaking inventions that can visually represent their charged and discharged states using colors, and can be used as display devices that cut down energy consumption for indoor cooling by controlling solar absorbance. The research team successfully built a flexible and electrochromic smart Zn-ion battery that can maintain its excellent electrochromic and electrochemical properties, even under long-term exposure to the atmosphere and mechanical deformations.
< Figure 1. Electrochromic zinc ion battery whose anode is made of a polymer that turns dark blue when charged and transparent when discharged. >
To maximize the efficiency of electron and ion mobility, the team modelled and synthesized the first π-bridge spacer-incorporated polymer anode in the world. π-bonds can improve the mobility of electrons within a structure to speed up ion movement and maximize ion adsorption efficiency, which improves its energy storage capacity.
In anode-based batteries with a π-bridge spacer, the spacer provides room for quicker ion movement. This allows fast charging, an improved zinc-ion discharging capacity of 110 mAh/g, which is 40% greater than previously reported, and a 30% increase in electrochromic function that switches from dark blue to transparent when the device is charged/discharged. In addition, should the transparent flexible battery technology be applied to smart windows, they would display darker colors during the day while they absorb solar energy, and function as a futuristic energy storage technique that can block out UV radiation and replace curtains.
< Figure 2. A schematic diagram of the structure of the electrochromic polymer with π-π spacer and the operation of a smart flexible battery using this cathode material. >
< Figure 3. (A) Density Functional Theory (DFT) theory-based atomic and electronic structure analysis. (B) Comparison of rate characteristics for polymers with and without π-bridge spacers. (C) Electrochemical performance comparison graph with previously reported zinc ion batteries. The anode material, which has an electron donor-acceptor structure with a built-in π-bridge spacer, shows better electrochemical performance and electrochromic properties than existing zinc ion batteries and electrochromic devices. >
Professor Il-Doo Kim said, “We have developed a polymer incorporated with a π-bridge spacer and successfully built a smart Zn-ion battery with excellent electrochromic efficiency and high energy storage capacity.” He added, “This technique goes beyond the existing concept of batteries that are used simply as energy storage devices, and we expect this technology to be used as a futuristic energy storage system that accelerates innovation in smart batteries and wearable technologies.”
This research, co-first authored by the alums of KAIST Departments of Material Sciences of Engineering, Professor Tae Gwang Yun of Myongji University, Dr. Jiyoung Lee, a post-doctoral associate at Northwestern University, and Professor Han Seul Kim at Chungbuk National University, was published as an inside cover article for Advanced Materials on August 3 under the title, “A π-Bridge Spacer Embedded Electron Donor-Acceptor Polymer for Flexible Electrochromic Zn-Ion Batteries”.
< Figure 4. Advanced Materials Inside Cover (August Issue) >
This research was supported by the Nanomaterial Technology Development Project under the Korean Ministry of Science and ICT, the Nano and Material Technology Development Project under the National Research Foundation of Korea, the Successive Academic Generation Development Project under the Korean Ministry of Education, and the Alchemist Project under the Korean Ministry of Trade, Industry & Energy.
2023.09.01 View 6702 -
A KAIST Research Team Produces Eco-Friendly Nylon with Engineered Bacterium
With worsening climate change and environmental issues, in recent years, there has been increased interest in the eco-friendly production of polymers like nylon.
On August 10, Dr. Taehee Han from a KAIST research team led by Distinguished Professor Sang Yup Lee in the Department of Chemical and Biomolecular Engineering revealed the successful development of a microbial strain that produces valerolactam, a monomer of nylon-5.
Valerolactam is an important monomer that constitutes nylon-5 and nylon-6,5. Nylon is the oldest synthetic polymer, and nylon-5 is one of its derivatives composed of monomers with five carbons, while nylon-5,6 is composed of two types of monomers with either five or six carbons. They not only have excellent processability, but are also light and tough, which allows them to be applied in a wide range of industrial sectors including clothing, badminton rackets, fishing nets, tents, and gear parts. Monomers are materials that can be built into polymers, and synthetic processes are what connects them into a polymer.
The chemical production of valerolactam, however, is based on petrochemistry, where extreme reaction conditions are required and toxic waste is produced. To solve these problems, efforts are being made to develop environmentally friendly and highly efficient microbial cell factories for lactam production. Systems metabolic engineering, a key strategy for effective microbial strain development, is a research field pioneered by Professor Sang Yup Lee.
Professor Lee’s team used metabolic engineering, a technique for manipulating microbial metabolic pathways, to construct a synthetic metabolic pathway for valerolactam production in Corynebacteriam glutamicum, a bacterium commonly used for amino acid production. With this, they successfully developed a microbial strain that utilizes biomass-derived glucose as a carbon source to produce high-value valerolactam.
In 2017, the team suggested a novel method that metabolically manipulates Escherichia coli to produce valerolactam. However, there were several limitations at the time including low producibility and the generation of harmful byproducts.
< Figure 1. Schematic graphical representation of the development of microorganisms that produce valerolactam, a nylon-5 monomer >
In this research, the team improved valerolactam producibility and incorporated an additional systems metabolic strategy to the developed microbial strain while eliminating the harmful byproducts. By removing the gene involved in the production of the main byproduct and through gene screening, the team successfully converted 5-aminovaleric acid, a byproduct and a precursor, into valerolactam.
Furthermore, by employing a strategy where the 5-aminovaleric acid-converting gene is inserted multiple times into the genome, the team strengthened the metabolic flux for valerolactam production. As a result, they reached a world-record concentration of 76.1 g/L, which is 6.17 times greater than what was previously reported.
This study was published in Metabolic Engineering on July 12, under the title, “Metabolic engineering of Corynebacterium glutamicum for the high-level production of valerolactam, a nylon-5 monomer”.
Dr. Taehee Han, the first author of the paper, said, “The significance of this research lies in our development of an environmentally friendly technology that efficiently produces monomer lactam for nylon production using microorganisms.” She added, “Through this technology, we will be able to take a step forward in replacing the petrochemical industry with a microorganism-based biopolymer industry.”
This work was supported by the “Development of Next-Generation Biofinery Platform Technologies for Leading Bio-based Chemicals Industry Project” funded by the Korean Ministry of Science and ICT.
2023.08.24 View 5013 -
A KAIST Research Team Develops an Ultra-High Performing “Universal Electrode” for Next-Generation Fuel Cells
Fuel cells are devices that generate electricity with high efficiency using hydrogen, a clean energy source, and are expected to play an important part in the upcoming hydrogen society. The recent development of an excellent universal electrode material that is applicable to all next-generation fuel cells and can withstand 700 hours of operation has therefore garnered a great deal of attention.
On August 9, a joint research team led by Prof. WooChul Jung from the KAIST Department of Materials Science and Engineering, Prof. Kang Taek Lee from the KAIST Department of Mechanical Engineering, and Prof. Jun Hyuk Kim from the Department of Chemical Engineering at Hongik University announced the development of an electrode material that is applicable to both oxygen- and proton-conducting solid oxide cells.
Depending on the type of ion conducted by the electrolyte, ceramic fuel cells are categorized into either solid oxide fuel cells (SOFC) or protonic ceramic fuel cells (PCFC). As they can both convert between electricity and hydrogen production, fuel cells can be categorized into a total of four device types. These devices are applicable in hydrogen fuel cell vehicles, hydrogen charging stations, and power generation systems, and are henceforth emerging as core next-generation technologies for a carbon-neutral society.
However, these devices have a chronic problem where the speed of their slowest reaction would decrease with a drop of driving temperature, which greatly reduces device efficiency. Various studies have been conducted to solve this, but most reported that electrode materials have low catalytic activity and their applications are limited to specific devices, which limits them from being used as SOFCs that require reversible power conversion and hydrogen production.
< Figure 1. Schematic diagram of high-performance oxygen ion conductive solid oxide fuel cell (SOFC) and proton conductive ceramic fuel cell (PCFC) operates with the new universal electrodes >
To solve this issue, the research team doped a perovskite oxide material with Ta5+, a high valence ion that did not receive much attention in the field. Through this, the team successfully stabilized what is usually a highly unstable crystal structure, and confirmed that catalytic activity improved by 100 times.
The electrode material developed by the team was applied to all four of the mentioned device types. Furthermore, their efficiencies were greater than any of the devices reported thus far, and showed excellent performance by stably running for much longer (700 hours) compared to existing materials that deteriorated within the first 100 hours of operation.
< Figure 2. (a) Power conversion and hydrogen production performance chart for the protonic ceramic fuel cell (PCFC) with the new universal electrodes (b) and performance comparison with other reported devices >
This research, in which KAIST’s Ph.D. candidates Dongyeon Kim and Sejong Ahn, and Professor Jun Hyuk Kim from Hongik University contributed as co-first authors, was published in the internationally renowned Energy & Environmental Science under the title, "Oxygen-Electrode for Reversible Solid Oxide Electrochemical Cells at Reduced Temperatures".
Prof. WooChul Jung said, “We broke free from the idea that we must develop a completely new material to solve an existing problem, and instead suggested a way to control the crystal structure of a lesser-known material to develop a high-efficiency fuel cell, and that’s what makes these results more significant.”
Prof. Kang Taek Lee added, “Unlike previously reported materials that could only be applied to one device type at a time, our material has the flexibility of being applicable to all four. We therefore look forward to its contribution in the commercialization of eco-friendly energy technology including fuel cells and water-splitting equipment for hydrogen production.”
This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Ministry of Science and ICT.
2023.08.22 View 6344 -
KAIST Research Team Develops World’s First Humanoid Pilot, PIBOT
In the Spring of last year, the legendary, fictional pilot “Maverick” flew his plane in the film “Top Gun: Maverick” that drew crowds to theatres around the world. This year, the appearance of a humanoid pilot, PIBOT, has stolen the spotlight at KAIST.
< Photo 1. Humanoid pilot robot, PIBOT >
A KAIST research team has developed a humanoid robot that can understand manuals written in natural language and fly a plane on its own. The team also announced their plans to commercialize the humanoid pilot.
< Photo 2. PIBOT on flight simulator (view from above) >
The project was led by KAIST Professor David Hyunchul Shim, and was conducted as a joint research project with Professors Jaegul Choo, Kuk-Jin Yoon, and Min Jun Kim. The study was supported by Future Challenge Funding under the project title, “Development of Human-like Pilot Robot based on Natural Language Processing”. The team utilized AI and robotics technologies, and demonstrated that the humanoid could sit itself in a real cockpit and operate the various pieces of equipment without modifying any part of the aircraft. This is a fundamental difference that distinguishes this technology from existing autopilot functions or unmanned aircrafts.
< Photo 3. PIBOT operating a flight simulator (side) >
The KAIST team’s humanoid pilot is still under development but it can already remember Jeppeson charts from all around the world, which is impossible for human pilots to do, and fly without error. In particular, it can make use of recent ChatGPT technology to remember the full Quick Reference Handbook (QRF) and respond immediately to various situations, as well as calculate safe routes in real time based on the flight status of the aircraft, with emergency response times quicker than human pilots.
Furthermore, while existing robots usually carry out repeated motions in a fixed position, PIBOT can analyze the state of the cockpit as well as the situation outside the aircraft using an embedded camera. PIBOT can accurately control the various switches in the cockpit and, using high-precision control technology, it can accurately control its robotic arms and hands even during harsh turbulence.
< Photo 4. PIBOT on-board KLA-100, Korea’s first light aircraft >
The humanoid pilot is currently capable of carrying out all operations from starting the aircraft to taxiing, takeoff and landing, cruising, and cycling using a flight control simulator. The research team plans to use the humanoid pilot to fly a real-life light aircraft to verify its abilities. Prof. Shim explained, “Humanoid pilot robots do not require the modification of existing aircrafts and can be applied immediately to automated flights. They are therefore highly applicable and practical. We expect them to be applied into various other vehicles like cars and military trucks since they can control a wide range of equipment. They will particularly be particularly helpful in situations where military resources are severely depleted.”
This research was supported by Future Challenge Funding (total: 5.7 bn KRW) from the Agency for Defense Development. The project started in 2022 as a joint research project by Prof. David Hyunchul Shim (chief of research) from the KAIST School of Electrical Engineering (EE), Prof. Jaegul Choo from the Kim Jaechul Graduate School of AI at KAIST, Prof. Kuk-Jin Yoon from the KAIST Department of Mechanical Engineering, and Prof. Min Jun Kim from the KAIST School of EE. The project is to be completed by 2026 and the involved researchers are also considering commercialization strategies for both military and civil use.
2023.08.03 View 12520 -
Professor Joseph J. Lim of KAIST receives the Best System Paper Award from RSS 2023, First in Korea
- Professor Joseph J. Lim from the Kim Jaechul Graduate School of AI at KAIST and his team receive an award for the most outstanding paper in the implementation of robot systems.
- Professor Lim works on AI-based perception, reasoning, and sequential decision-making to develop systems capable of intelligent decision-making, including robot learning
< Photo 1. RSS2023 Best System Paper Award Presentation >
The team of Professor Joseph J. Lim from the Kim Jaechul Graduate School of AI at KAIST has been honored with the 'Best System Paper Award' at "Robotics: Science and Systems (RSS) 2023".
The RSS conference is globally recognized as a leading event for showcasing the latest discoveries and advancements in the field of robotics. It is a venue where the greatest minds in robotics engineering and robot learning come together to share their research breakthroughs. The RSS Best System Paper Award is a prestigious honor granted to a paper that excels in presenting real-world robot system implementation and experimental results.
< Photo 2. Professor Joseph J. Lim of Kim Jaechul Graduate School of AI at KAIST >
The team led by Professor Lim, including two Master's students and an alumnus (soon to be appointed at Yonsei University), received the prestigious RSS Best System Paper Award, making it the first-ever achievement for a Korean and for a domestic institution.
< Photo 3. Certificate of the Best System Paper Award presented at RSS 2023 >
This award is especially meaningful considering the broader challenges in the field. Although recent progress in artificial intelligence and deep learning algorithms has resulted in numerous breakthroughs in robotics, most of these achievements have been confined to relatively simple and short tasks, like walking or pick-and-place. Moreover, tasks are typically performed in simulated environments rather than dealing with more complex, long-horizon real-world tasks such as factory operations or household chores. These limitations primarily stem from the considerable challenge of acquiring data required to develop and validate learning-based AI techniques, particularly in real-world complex tasks.
In light of these challenges, this paper introduced a benchmark that employs 3D printing to simplify the reproduction of furniture assembly tasks in real-world environments. Furthermore, it proposed a standard benchmark for the development and comparison of algorithms for complex and long-horizon tasks, supported by teleoperation data. Ultimately, the paper suggests a new research direction of addressing complex and long-horizon tasks and encourages diverse advancements in research by facilitating reproducible experiments in real-world environments.
Professor Lim underscored the growing potential for integrating robots into daily life, driven by an aging population and an increase in single-person households. As robots become part of everyday life, testing their performance in real-world scenarios becomes increasingly crucial. He hoped this research would serve as a cornerstone for future studies in this field.
The Master's students, Minho Heo and Doohyun Lee, from the Kim Jaechul Graduate School of AI at KAIST, also shared their aspirations to become global researchers in the domain of robot learning. Meanwhile, the alumnus of Professor Lim's research lab, Dr. Youngwoon Lee, is set to be appointed to the Graduate School of AI at Yonsei University and will continue pursuing research in robot learning.
Paper title: Furniture Bench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation. Robotics: Science and Systems.
< Image. Conceptual Summary of the 3D Printing Technology >
2023.07.31 View 7712