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KAIST to Foster a 'Robot Valley' in Daejeon with $10 Million Initiative
<Group Photo of Kick-off Meeting>
On September 3, KAIST announced the official launch of the "2025 Deep Tech Scale-up Valley Nurturing Project" with a kick-off meeting at the KAIST Department of Mechanical Engineering.
KAIST was selected for this project by the Ministry of Science and ICT and the Research and Development Special District Foundation. With this selection, the university plans to create a "Robot Valley".
Over the next three and a half years, KAIST will receive a total of 13.65 billion won (approximately $10 million) in funding. The university's goal is to intensively nurture globally competitive, innovative robotics companies based on foundational technologies and to develop Daejeon into a global hub for the robotics industry.
The initiative will leverage Daejeon's exceptional research talent and its startup and investment ecosystem to create a model for regional revitalization and to cultivate the robotics industry as a next-generation strategic sector.
KAIST's vision for this project is to develop "Human-Friendly Robots (HFR)" that are more than just automated machines; they are collaborative partners that share space, roles, and emotions with people.
The project will implement a multi-stage strategy that includes promoting the commercialization of robotics technology, supporting the startup ecosystem, securing global technological competitiveness, and developing robot commercialization platforms. This will establish a virtuous cycle of technology development, startup and investment growth, and reinvestment.
Unlike traditional startup support and scale-up programs, this project aims for the simultaneous growth of the entire robotics industry, not just individual companies. A key element is an open innovation model where leading robotics firms like Angel Robotics Inc. and EuRoBotics Inc. (led by Professor Byung-ho Yu and Professor Hyun Myung) will share common core technologies related to actuators, circuits, AI, and standardized data. This will allow startups to focus on developing robot products that directly meet customer needs.
The project team includes key KAIST robotics researchers. The project leader is Professor Jung Kim (President of the Korea Robotics Society) from the Department of Mechanical Engineering. Other participating professors include Geon-Jae Lee from the Department of Materials Science and Engineering (human augmentation sensors), Hyun Myung from the School of Electrical Engineering (winner of the QRC 2023 quadruped robot autonomous walking competition at IEEE ICRA), Kyung-Chul Kong from the Department of Mechanical Engineering (two-time champion of the Cybathlon International Competition and founder of Angel Robotics), and Suk-Hyung Bae from the Department of Industrial Design (winner of the ACM SIGGRAPH robot sketching competition).
In addition, the KAIST Technology Commercialization Office, KAIST Holdings, Global Techno Valley Lab (GTLAB), and the Daejeon Center for Creative Economy and Innovation will manage technology commercialization and valley construction. The Daejeon Technopark will also participate to provide comprehensive commercialization support.
"The strategic cooperation between Daejeon City's robotics industry nurturing plan and KAIST was the driving force behind the selection for this project," said Geon-Jae Lee, Director of the KAIST Technology Commercialization Office. "We will create a robotics innovation ecosystem based in Daejeon and systematically foster global companies to rival the likes of ABB in Switzerland and KUKA in Germany, which are considered among the top three robotics companies in the world."
< Kick-off Meeting Scene>
Project leader Jung Kim stated, "We will spearhead efforts to discover and nurture over 15 future unicorn companies by promoting the commercialization of deep-tech robotics developed at KAIST. The entire KAIST robotics research team will dedicate its full efforts to ensure that our research and development achievements lead to real-world industries and startups."
KAIST President Kwang-Hyung Lee emphasized, "As Korea's leading research-oriented university, KAIST will actively support Daejeon's growth into a global robotics hub. This project is more than just research and development; it will be a turning point for KAIST to stand at the center of the global robotics ecosystem and create a new growth engine for the region and the nation."
In collaboration with Daejeon City, KAIST plans to form an "HFR Valley Innovation Council" to share and review project outcomes, ultimately building a self-sustaining ecosystem. This initiative aims to establish Daejeon as a world-class robotics industry hub.
2025.09.03 View 2444 -
KAIST provides a comprehensive resource on microbial cell factories for sustainable chemical production
In silico analysis of five industrial microorganisms identifies optimal strains and metabolic engineering strategies for producing 235 valuable chemicals
Climate change and the depletion of fossil fuels have raised the global need for sustainable chemical production. In response to these environmental challenges, microbial cell factories are gaining attention as eco-friendly platforms for producing chemicals using renewable resources, while metabolic engineering technologies to enhance these cell factories are becoming crucial tools for maximizing production efficiency. However, difficulties in selecting suitable microbial strains and optimizing complex metabolic pathways continue to pose significant obstacles to practical industrial applications.
KAIST (President Kwang-Hyung Lee) announced on 27th of March that Distinguished Professor Sang Yup Lee’s research team in the Department of Chemical and Biomolecular Engineering comprehensively evaluated the production capabilities of various industrial microbial cell factories using in silico simulations and, based on these findings, identified the most suitable microbial strains for producing specific chemicals as well as optimal metabolic engineering strategies.
Previously, researchers attempted to determine the best strains and efficient metabolic engineering strategies among numerous microbial candidates through extensive biological experiments and meticulous verification processes. However, this approach required substantial time and costs. Recently, the introduction of genome-scale metabolic models (GEMs), which reconstruct the metabolic networks within an organism based on its entire genome information, has enabled systematic analysis of metabolic fluxes via computer simulations. This development offers a new way to overcome limitations of conventional experimental approaches, revolutionizing both strain selection and metabolic pathway design.
Accordingly, Professor Lee’s team at the Department of Chemical and Biomolecular Engineering, KAIST, evaluated the production capabilities of five representative industrial microorganisms—Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, Corynebacterium glutamicum, and Pseudomonas putida—for 235 bio-based chemicals. Using GEMs, the researchers calculated both the maximum theoretical yields and the maximum achievable yields under industrial conditions for each chemical, thereby establishing criteria to identify the most suitable strains for each target compound.
< Figure 1. Outline of the strategy for improving microbial cell factories using a genome-scale metabolic model (GEM) >
The team specifically proposed strategies such as introducing heterologous enzyme reactions derived from other organisms and exchanging cofactors used by microbes to expand metabolic pathways. These strategies were shown to increase yields beyond the innate metabolic capacities of the microorganisms, resulting in higher production of industrially important chemicals such as mevalonic acid, propanol, fatty acids, and isoprenoids.
Moreover, by applying a computational approach to analyze metabolic fluxes in silico, the researchers suggested strategies for improving microbial strains to maximize the production of various chemicals. They quantitatively identified the relationships between specific enzyme reactions and target chemical production, as well as the relationships between enzymes and metabolites, determining which enzyme reactions should be up- or down-regulated. Through this, the team presented strategies not only to achieve high theoretical yields but also to maximize actual production capacities.
< Figure 2. Comparison of production routes and maximum yields of useful chemicals using representative industrial microorganisms >
Dr. Gi Bae Kim, the first author of this paper from the KAIST BioProcess Engineering Research Center, explained, “By introducing metabolic pathways derived from other organisms and exchanging cofactors, it is possible to design new microbial cell factories that surpass existing limitations. The strategies presented in this study will play a pivotal role in making microbial-based production processes more economical and efficient.” In addition, Distinguished Professor Sang Yup Lee noted, “This research serves as a key resource in the field of systems metabolic engineering, reducing difficulties in strain selection and pathway design, and enabling more efficient development of microbial cell factories. We expect it to greatly contribute to the future development of technologies for producing various eco-friendly chemicals, such as biofuels, bioplastics, and functional food materials.”
This research was conducted with the support from the Development of platform technologies of microbial cell factories for the next-generation biorefineries project and Development of advanced synthetic biology source technologies for leading the biomanufacturing industry project (Project Leader: Distinguished Professor Sang Yup Lee, KAIST) from National Research Foundation supported by the Korean Ministry of Science and ICT.
2025.03.27 View 8108 -
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 10736 -
KAIST holds its first ‘KAIST Tech Fair’ in New York, USA
< Photo 1. 2023 KAIST Tech Fair in New York >
KAIST (President Kwang-Hyung Lee) announced on the 11th that it will hold the ‘2023 KAIST Tech Fair in New York’ at the Kimmel Center at New York University in Manhattan, USA, on the 22nd of this month. It is an event designed to be the starting point for KAIST to expand its startup ecosystem into the global stage, and it is to attract investments and secure global customers in New York by demonstrating the technological value of KAIST startup companies directly at location.
< Photo 2. President Kwang Hyung Lee at the 2023 KAIST Tech Fair in New York >
KAIST has been holding briefing sessions for technology transfer in Korea every year since 2018, and this year is the first time to hold a tech fair overseas for global companies.
KAIST Institute of Technology Value Creation (Director Sung-Yool Choi) has prepared for this event over the past six months with the Korea International Trade Association (hereinafter KITA, CEO Christopher Koo) to survey customer base and investment companies to conduct market analysis.
Among the companies founded with the technologies developed by the faculty and students of KAIST and their partners, 7 companies were selected to be matched with companies overseas that expressed interests in these technologies. Global multinational companies in the fields of IT, artificial intelligence, environment, logistics, distribution, and retail are participating as demand agencies and are testing the marketability of the start-up's technology as of September.
Daim Research, founded by Professor Young Jae Jang of the Department of Industrial and Systems Engineering, is a company specializing in smart factory automation solutions and is knocking on the door of the global market with a platform technology optimized for automated logistics systems.
< Photo 3. Presentation by Professor Young Jae Jang for DAIM Research >
It is a ‘collaborative intelligence’ solution that maximizes work productivity by having a number of robots used in industrial settings collaborate with one another. The strength of their solution is that logistics robots equipped with AI reinforced learning technology can respond to processes and environmental changes on their own, minimizing maintenance costs and the system can achieve excellent performance even with a small amount of data when it is combined with the digital twin technology the company has developed on its own.
A student startup, ‘Aniai’, is entering the US market, the home of hamburgers, with hamburger patty automation equipments and solutions. This is a robot kitchen startup founded by its CEO Gunpil Hwang, a graduate of KAIST’s School of Electrical Engineering which gathered together the experts in the fields of robot control, design, and artificial intelligence and cognitive technology to develop technology to automatically cook hamburger patties.
At the touch of a button, both sides of the patty are cooked simultaneously for consistent taste and quality according to the set condition. Since it can cook about 200 dishes in an hour, it is attracting attention as a technology that can not only solve manpower shortages but also accelerate the digital transformation of the restaurant industry.
Also, at the tech fair to be held at the Kimmel Center of New York University on the 22nd, the following startups who are currently under market verification in the U.S. will be participating: ▴'TheWaveTalk', which developed a water quality management system that can measure external substances and metal ions by transferring original technology from KAIST; ▴‘VIRNECT’, which helps workers improve their skills by remotely managing industrial sites using XR*; ▴‘Datumo’, a solution that helps process and analyze artificial intelligence big data, ▴‘VESSL AI’, the provider of a solution to eliminate the overhead** of machine learning systems; and ▴ ‘DolbomDream’, which developed an inflatable vest that helps the psychological stability of people with developmental disabilities.
* XR (eXtended Reality): Ultra-realistic technology that enhances immersion by utilizing augmented reality, virtual reality, and mixed reality technologies
** Overhead: Additional time required for stable processing of the program
In addition, two companies (Plasmapp and NotaAI) that are participating in the D-Unicorn program with the support of the Daejeon City and two companies (Enget and ILIAS Biologics) that are receiving support from the Scale Up Tips of the Ministry of SMEs and Startups, three companies (WiPowerOne, IDK Lab, and Artificial Photosynthesis Lab) that are continuing to realize the sustainable development goals for a total of 14 KAIST startups, will hold a corporate information session with about 100 invited guests from global companies and venture capital.
< Photo 4. Presentation for AP Lab >
Prior to this event, participating startups will be visiting the New York Economic Development Corporation and large law firms to receive advice on U.S. government support programs and on their attemps to enter the U.S. market. In addition, the participating companies plan to visit a startup support investment institution pursuing sustainable development goals and the Leslie eLab, New York University's one-stop startup support space, to lay the foundation for KAIST's leap forward in global technology commercialization.
< Photo 5. Sung-Yool Choi, the Director of KAIST Institute of Technology Value Creation (left) at the 2023 KAIST Tech Fair in New York with the key participants >
Sung-Yool Choi, the Director of KAIST Institute of Technology Value Creation, said, “KAIST prepared this event to realize its vision of being a leading university in creating global value.” He added, “We hope that our startups founded with KAIST technology would successfully completed market verification to be successful in securing global demands and in attracting investments for their endeavors.”
2023.09.11 View 27456 -
Quantum Technology: the Next Game Changer?
The 6th KAIST Global Strategy Institute Forum explores how quantum technology has evolved into a new growth engine for the future
The participants of the 6th KAIST Global Strategy Institute (GSI) Forum on April 20 agreed that the emerging technology of quantum computing will be a game changer of the future. As KAIST President Kwang Hyung Lee said in his opening remarks, the future is quantum and that future is rapidly approaching. Keynote speakers and panelists presented their insights on the disruptive innovations we are already experiencing.
The three keynote speakers included Dr. Jerry M. Chow, IBM fellow and director of quantum infrastructure, Professor John Preskill from Caltech, and Professor Jungsang Kim from Duke University. They discussed the academic impact and industrial applications of quantum technology, and its prospects for the future.
Dr. Chow leads IBM Quantum’s hardware system development efforts, focusing on research and system deployment. Professor Preskill is one of the leading quantum information science and quantum computation scholars. He coined the term “quantum supremacy.” Professor Kim is the co-founder and CTO of IonQ Inc., which develops general-purpose trapped ion quantum computers and software to generate, optimize, and execute quantum circuits.
Two leading quantum scholars from KAIST, Professor June-Koo Kevin Rhee and Professor Youngik Sohn, and Professor Andreas Heinrich from the IBS Center for Quantum Nanoscience also participated in the forum as panelists. Professor Rhee is the founder of the nation’s first quantum computing software company and leads the AI Quantum Computing IT Research Center at KAIST.
During the panel session, Professor Rhee said that although it is undeniable the quantum computing will be a game changer, there are several challenges. For instance, the first actual quantum computer is NISQ (Noisy intermediate-scale quantum era), which is still incomplete. However, it is expected to outperform a supercomputer. Until then, it is important to advance the accuracy of quantum computation in order to offer super computation speeds.
Professor Sohn, who worked at PsiQuantum, detailed how quantum computers will affect our society. He said that PsiQuantum is developing silicon photonics that will control photons. We can’t begin to imagine how silicon photonics will transform our society. Things will change slowly but the scale would be massive.
The keynote speakers presented how quantum cryptography communications and its sensing technology will serve as disruptive innovations. Dr. Chow stressed that to realize the potential growth and innovation, additional R&D is needed. More research groups and scholars should be nurtured. Only then will the rich R&D resources be able to create breakthroughs in quantum-related industries. Lastly, the commercialization of quantum computing must be advanced, which will enable the provision of diverse services. In addition, more technological and industrial infrastructure must be built to better accommodate quantum computing.
Professor Preskill believes that quantum computing will benefit humanity. He cited two basic reasons for his optimistic prediction: quantum complexity and quantum error corrections. He explained why quantum computing is so powerful: quantum computer can easily solve the problems classically considered difficult, such as factorization. In addition, quantum computer has the potential to efficiently simulate all of the physical processes taking place in nature.
Despite such dramatic advantages, why does it seem so difficult? Professor Preskill believes this is because we want qubits (quantum bits or ‘qubits’ are the basic unit of quantum information) to interact very strongly with each other. Because computations can fail, we don’t want qubits to interact with the environment unless we can control and predict them.
As for quantum computing in the NISQ era, he said that NISQ will be an exciting tool for exploring physics. Professor Preskill does not believe that NISQ will change the world alone, rather it is a step forward toward more powerful quantum technologies in the future. He added that a potentially transformable, scalable quantum computer could still be decades away.
Professor Preskill said that a large number of qubits, higher accuracy, and better quality will require a significant investment. He said if we expand with better ideas, we can make a better system. In the longer term, quantum technology will bring significant benefits to the technological sectors and society in the fields of materials, physics, chemistry, and energy production.
Professor Kim from Duke University presented on the practical applications of quantum computing, especially in the startup environment. He said that although there is no right answer for the early applications of quantum computing, developing new approaches to solve difficult problems and raising the accessibility of the technology will be important for ensuring the growth of technology-based startups.
2022.04.21 View 17188 -
Deep Learning Framework to Enable Material Design in Unseen Domain
Researchers propose a deep neural network-based forward design space exploration using active transfer learning and data augmentation
A new study proposed a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods.
Professor Seungwha Ryu believes that this study will help address a variety of optimization problems that have an astronomical number of possible design configurations. For the grid composite optimization problem, the proposed framework was able to provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training data-set size. This study was reported in npj Computational Materials last month.
“We wanted to mitigate the limitation of the neural network, weak predictive power beyond the training set domain for the material or structure design,” said Professor Ryu from the Department of Mechanical Engineering.
Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome this limitation also suffer from weak predictive power for the unseen domain.
Professor Ryu’s team, in collaboration with researchers from Professor Grace Gu’s group at UC Berkeley, devised a design method that simultaneously expands the domain using the strong predictive power of a deep neural network and searches for the optimal design by repetitively performing three key steps.
First, it searches for few candidates with improved properties located close to the training set via genetic algorithms, by mixing superior designs within the training set. Then, it checks to see if the candidates really have improved properties, and expands the training set by duplicating the validated designs via a data augmentation method. Finally, they can expand the reliable prediction domain by updating the neural network with the new superior designs via transfer learning. Because the expansion proceeds along relatively narrow but correct routes toward the optimal design (depicted in the schematic of Fig. 1), the framework enables an efficient search.
As a data-hungry method, a deep neural network model tends to have reliable predictive power only within and near the domain of the training set. When the optimal configuration of materials and structures lies far beyond the initial training set, which frequently is the case, neural network-based design methods suffer from weak predictive power and become inefficient.
Researchers expect that the framework will be applicable for a wide range of optimization problems in other science and engineering disciplines with astronomically large design space, because it provides an efficient way of gradually expanding the reliable prediction domain toward the target design while avoiding the risk of being stuck in local minima. Especially, being a less-data-hungry method, design problems in which data generation is time-consuming and expensive will benefit most from this new framework.
The research team is currently applying the optimization framework for the design task of metamaterial structures, segmented thermoelectric generators, and optimal sensor distributions. “From these sets of on-going studies, we expect to better recognize the pros and cons, and the potential of the suggested algorithm. Ultimately, we want to devise more efficient machine learning-based design approaches,” explained Professor Ryu.This study was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research Project.
-Publication
Yongtae Kim, Youngsoo, Charles Yang, Kundo Park, Grace X. Gu, and Seunghwa Ryu, “Deep learning framework for material design space exploration using active transfer learning and data augmentation,” npj Computational Materials (https://doi.org/10.1038/s41524-021-00609-2)
-Profile
Professor Seunghwa Ryu
Mechanics & Materials Modeling Lab
Department of Mechanical Engineering
KAIST
2021.09.29 View 17788 -
Professor Il-Doo Kim Named Scientist of the Year by the Journalists
Professor Il-Doo Kim from the Department of Materials Science and Engineering was named the 2019 Scientist of the Year by Korean science journalists. The award was conferred at the 2019 Science Press Night ceremony of the Korea Science Journalists Association (KSJA) on November 29.
Professor Kim focuses on developing nanofiber gas sensors for diagnosing diseases in advance by analyzing exhaled biomarkers with electrospinning technology. His outstanding research was praised and selected as one of the top 10 nanotechnology of 2019 by the Korea Nano Technology Research Society (KoNTRS), the Ministry of Science and ICT (MSIT), and the Ministry of Trade, Industry and Energy (MOTIE).
Professor Kim was honored with the QIAN Baojun Fiber Award, which is awarded every two years by Donghua University in Shanghai, China to recognize outstanding contributions in fiber science and technology. Professor Kim was also elected as an academician of the Asia Pacific Academy of Materials (APAM) on November 21 in Guangzhou, China.
In May, Professor Kim was appointed as an associate editor of ACS Nano, a leading international research journal in the field of nanoscience. In his editorial published in the May issue of ACS Nano, Professor Kim introduced and shared the history of KAIST and its vision for the future with other members of the journal. He hopes this will help with promoting a closer relationship between the members of the journal and KAIST moving forward.
“Above all,” he said in his acceptance speech, “the greatest news for me as an educator is that the first PhD graduate from our lab, Dr. Seonjin Choi, was appointed as the youngest professor in the Division of Materials Science and Engineering at Hanyang University on September 1.”
2019.12.17 View 16537 -
Ultrafast Quantum Motion in a Nanoscale Trap Detected
< Professor Heung-Sun Sim (left) and Co-author Dr. Sungguen Ryu (right) >
KAIST researchers have reported the detection of a picosecond electron motion in a silicon transistor. This study has presented a new protocol for measuring ultrafast electronic dynamics in an effective time-resolved fashion of picosecond resolution. The detection was made in collaboration with Nippon Telegraph and Telephone Corp. (NTT) in Japan and National Physical Laboratory (NPL) in the UK and is the first report to the best of our knowledge.
When an electron is captured in a nanoscale trap in solids, its quantum mechanical wave function can exhibit spatial oscillation at sub-terahertz frequencies. Time-resolved detection of such picosecond dynamics of quantum waves is important, as the detection provides a way of understanding the quantum behavior of electrons in nano-electronics. It also applies to quantum information technologies such as the ultrafast quantum-bit operation of quantum computing and high-sensitivity electromagnetic-field sensing. However, detecting picosecond dynamics has been a challenge since the sub-terahertz scale is far beyond the latest bandwidth measurement tools.
A KAIST team led by Professor Heung-Sun Sim developed a theory of ultrafast electron dynamics in a nanoscale trap, and proposed a scheme for detecting the dynamics, which utilizes a quantum-mechanical resonant state formed beside the trap. The coupling between the electron dynamics and the resonant state is switched on and off at a picosecond so that information on the dynamics is read out on the electric current being generated when the coupling is switched on.
NTT realized, together with NPL, the detection scheme and applied it to electron motions in a nanoscale trap formed in a silicon transistor. A single electron was captured in the trap by controlling electrostatic gates, and a resonant state was formed in the potential barrier of the trap.
The switching on and off of the coupling between the electron and the resonant state was achieved by aligning the resonance energy with the energy of the electron within a picosecond. An electric current from the trap through the resonant state to an electrode was measured at only a few Kelvin degrees, unveiling the spatial quantum-coherent oscillation of the electron with 250 GHz frequency inside the trap.
Professor Sim said, “This work suggests a scheme of detecting picosecond electron motions in submicron scales by utilizing quantum resonance. It will be useful in dynamical control of quantum mechanical electron waves for various purposes in nano-electronics, quantum sensing, and quantum information”.
This work was published online at Nature Nanotechnology on November 4. It was partly supported by the Korea National Research Foundation through the SRC Center for Quantum Coherence in Condensed Matter. For more on the NTT news release this article, please visit https://www.ntt.co.jp/news2019/1911e/191105a.html
-ProfileProfessor Heung-Sun Sim
Department of PhysicsDirector, SRC Center for Quantum Coherence in Condensed Matterhttps://qet.kaist.ac.kr KAIST
-Publication:Gento Yamahata, Sungguen Ryu, Nathan Johnson, H.-S. Sim, Akira Fujiwara, and Masaya Kataoka. 2019. Picosecond coherent electron motion in a silicon single-electron source. Nature Nanotechnology (Online Publication). 6 pages. https://doi.org/10.1038/s41565-019-0563-2
2019.11.05 View 23688 -
Enhanced Natural Gas Storage to Help Reduce Global Warming
< Professor Atilhan (left) and Professor Yavuz (right) >
Researchers have designed plastic-based materials that can store natural gas more effectively. These new materials can not only make large-scale, cost-effective, and safe natural gas storage possible, but further hold a strong promise for combating global warming.
Natural gas (predominantly methane) is a clean energy alternative. It is stored by compression, liquefaction, or adsorption. Among these, adsorbed natural gas (ANG) storage is a more efficient, cheaper, and safer alternative to conventional compressed natural gas (CNG) and liquefied natural gas (LNG) storage approaches that have drawbacks such as low storage efficiency, high costs, and safety concerns. However, developing adsorptive materials that can more fully exploit the advantages of ANG storage has remained a challenging task.
A KAIST research team led by Professor Cafer T. Yavuz from the Graduate School of Energy, Environment, Water, and Sustainability (EEWS), in collaboration with Professor Mert Atilhan’s group from Texas A&M University, synthesized 29 unique porous polymeric structures with inherent flexibility, and tested their methane gas uptake capacity at high pressures. These porous polymers had varying synthetic complexities, porosities, and morphologies, and the researchers subjected each porous polymer to pure methane gas under various conditions to study the ANG performances.
Of these 29 distinct chemical structures, COP-150 was particularly noteworthy as it achieved a high deliverable gravimetric methane working capacity when cycled between 5 and 100 bar at 273 K, which is 98% of the total uptake capacity. This result surpassed the target set by the United States Department of Energy (US DOE).
COP-150 is the first ever structure to fulfil both the gravimetric and volumetric requirements of the US DOE for successful vehicular use, and the total cost to produce the COP-150 adsorbent was only 1 USD per kilogram.
COP-150 can be produced using freely available and easily accessible plastic materials, and moreover, its synthesis takes place at room temperature, open to the air, and no previous purification of the chemicals is required. The pressure-triggered flexible structure of COP-150 is also advantageous in terms of the total working capacity of deliverable methane for real applications.
The research team believed that the increased pressure flexes the network structure of COP-150 showing “swelling” behavior, and suggested that the flexibility provides rapid desorption and thermal management, while the hydrophobicity and the nature of the covalently bonded framework allow these promising materials to tolerate harsh conditions.
This swelling mechanism of expansion-contraction solves two other major issues, the team noted. Firstly, when using adsorbents based on such a mechanism, unsafe pressure spikes that may occur due to temperature swings can be eliminated. In addition, contamination can also be minimized, since the adsorbent remains contracted when no gas is stored.
Professor Yavuz said, “We envision a whole host of new designs and mechanisms to be developed based on our concept. Since natural gas is a much cleaner fuel than coal and petroleum, new developments in this realm will help switching to the use of less polluting fuels.”
Professor Atilhan agreed the most important impact of their research is on the environment. “Using natural gas more than coal and petroleum will significantly reduce greenhouse gas emissions. We believe, one day, we might see vehicles equipped with our materials that are run by a cleaner natural gas fuel,” he added.
This study, reported in Nature Energy on July 8, was supported by National Research Foundation of Korea (NRF) grants ( NRF-2016R1A2B4011027, NRF-2017M3A7B4042140, and NRF-2017M3A7B4042235).
< Suggested chemical structure of COP-150 >
< Initial ingredients (left) and final product (right) of COP-150 synthesis >
< Comparison of highest reported volumetric working capacities >
(END)
2019.08.09 View 30872 -
Undergrad's Paper Chosen as the Cover Article in Soft Matter
(from left: Research Professor KyuHan Kim and Undergrad Student Subeen Kim)
A KAIST undergraduate student, Subeen Kim, had his paper chosen as the cover article in an international journal during his senior year.
There have been an increasing number of undergraduate students who were published as the first author because the KAIST Undergraduate Research Participation program allows more active research participation by undergraduate students.
Through URP, Kim successfully published his paper in the internationally-renowned journal, Soft Matter, which is published by the Royal Society of Chemistry, and it was chosen as the cover article of that journal in February 2018.
This publication means a lot to him because he designed the cover image himself, based on his imagination and observations.
His research is about controllable one-step double emulsion formation. Double emulsion is a system in which dispersed droplets contain additional immiscible liquid droplets.
Having great retention ability, double emulsion has been used in various applications in the food industry, in cosmetics, and for drug delivery. Nevertheless, two-step emulsification is a conventional approach to produce double emulsions that typically leads to partial destabilization of the emulsion formed during the initial stage. Hence, it does not ensure the stability of a double emulsion. On the other hand, a microfluidic approach with various flow-focusing techniques has been developed, but it has low production efficiency and thus limited industrial applications.
Kim’s results came from the process of phase inversion to solve this problem. He identified the instant formation of double emulsions during the process of phase inversion. Based on this finding, he proposed criteria to achieve high stability of double emulsion.
Through constant research, he developed a quite general method using a combination of an oil soluble poly methyl methacrylate (PMMA) and hydrophobic silica nanoparticle (HDK H18). This new method enables one-step and stable production of double emersions in a stable manner. It also allows control of the number and the volume of inner oil droplets inside the outer water droplets by adjusting PMMA and HDK H18.
Kim enrolled at KAIST as a KAIST Presidential Fellowship and Presidential Science Scholarship in 2014. While studying both chemical and biomolecular engineering and chemistry he has been developing his hypothesis and conducting research.
He was able to begin conducting research because he has taken part in URP projects twice. In his sophomore year, he studied the formation of high internal phase double emulsions. After one year, he conducted research to produce superabsorbent resins, which are the base material for diapers, by using colloid particles. Using partial research outcomes, he published his paper in Nature Communications as a second author.
Kim said, “Double majoring the chemical and biomolecular engineering and chemistry has helped me producing this outcome. I hope that this research contributes to commercializing double emulsions. I will continue to identify accurate principles to produce chemicals that can be controlled exquisitely.”
Figure 1. The cover article of Soft Matter
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Scientist of November, Professor Hyung Jin Sung
Professor Hyung Jin Sung from the Department of Mechanical Engineering at KAIST received a ‘Science and Technology Award of the Month’ given by the Ministry of ICT and Science and the National Research Foundation of Korea for November 2017. He developed technology that can exquisitely control a micrometer-scaled liquid drop on a dime-sized lab-on-a-chip. With his work, he was recognized for reinforcing research capability on microfluidics.
Lab-on-a-chip is an emerging experiment and diagnostic technology in the form of a bio-microchip that facilitates complex and various experiments with only a minimal sample size required. This technology draws a lot of attention not only from medical and pharmaceutical areas, but also the health and environmental field. The biggest problem was that technology for the temperature control of a fluid sample, which is one of the core technologies in microfluidics, has low accuracy. This limit had to be overcome in order to use the lab-on-a-chip more widely.
Professor Sung developed an acoustic and thermal method which controls the temperature of a droplet quickly and meticulously by using sound and energy. This is a thermal method that uses heat generated during the absorption of an acoustic wave into viscoelastic substances. It facilitates a rapid heating rate and spatial-temporal temperature control, allowing heating in desired areas. In addition, Professor Sung applied his technology to polymerase chain reactions, which are used to amplify DNA.
Through this experiment, he successfully shortened the reaction time from 1-2 hours to only three minutes, making this a groundbreaking achievement.
Professor Sung said, “My research is significant for enhancing the applicability of microfluidics. I expect that it will lead to technological innovations in healthcare fields including biochemistry, medical checkups, and new medicine development.”
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EEWS Graduate School Team Receives the S-Oil Best Paper Award
Professor Hyungjun Kim and Dr. He-Young Shin from the EEWS (Energy, Environment, Water and Sustainability) Graduate School at KAIST received the Best Paper Award in Chemistry from S-Oil, a Korean petroleum and refinery company, on November 29, 2016.
Established in 2011, the S-Oil Best Paper Awards are bestowed annually upon ten young scientists in the fields of five basic sciences: mathematics, physics, chemistry, biology, and earth science. The scientists are selected at the recommendation of the Korean Academy of Science and Technology and the Association of Korean Universities. The awards grant a total of USD 230,000 for research funding.
Dr. Shin, the lead author of the awarded research paper, said, “My research interest has been catalyst studies based on theoretical chemistry. I am pleased to accept this award that will support my studies, and will continue to research catalyst design that can predict parameters and integrate them into catalytic systems.”
Professor Hyungjun Kim (left) and Dr. He-Young Shin (right)
2016.12.23 View 15223