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A New Strategy for Early Evaluations of CO2 Utilization Technologies
- A three-step evaluation procedure based on technology readiness levels helps find the most efficient technology before allocating R&D manpower and investments in CO2 utilization technologies. -
Researchers presented a unified framework for early-stage evaluations of a variety of emerging CO2 utilization (CU) technologies. The three-step procedure allows a large number of potential CU technologies to be screened in order to identify the most promising ones, including those at low level of technical maturity, before allocating R&D manpower and investments.
When evaluating new technology, various aspects of the new technology should be considered. Its feasibility, efficiency, economic competitiveness, and environmental friendliness are crucial, and its level of technical maturity is also an important component for further consideration. However, most technology evaluation procedures are data-driven, and the amount of reliable data in the early stages of technology development has been often limited.
A research team led by Professor Jay Hyung Lee from the Department of Chemical and Biomolecular Engineering at KAIST proposed a new procedure for evaluating the early development stages of emerging CU technologies which are applicable at various technology readiness levels (TRLs).
The procedure obtains performance indicators via primary data preparation, secondary data calculation, and performance indicator calculation, and the lead author of the study Dr. Kosan Roh and his colleagues presented a number of databases, methods, and computer-aided tools that can effectively facilitate the procedure.
The research team demonstrated the procedure through four case studies involving novel CU technologies of different types and at various TRLs. They confirmed the electrochemical CO2 reduction for the production of ten chemicals, the co-electrolysis of CO2 and water for ethylene production, the direct oxidation of CO2 -based methanol for oxymethylene dimethyl production, and the microalgal biomass co-firing for power generation.
The expected range of the performance indicators for low TRL technologies is broader than that for high TRL technologies, however, it is not the case for high TRL technologies as they are already at an optimized state. The research team believes that low TRL technologies will be significantly improved through future R&D until they are commercialized. “We plan to develop a systematic approach for such a comparison to help avoid misguided decision-making,” Professor Lee explained.
Professor Lee added, “This procedure allows us to conduct a comprehensive and systematic evaluation of new technology. On top of that, it helps make efficient and reliable assessment possible.”
The research team collaborated with Professor Alexander Mitsos, Professor André Bardow, and Professor Matthias Wessling at RWTH Aachen University in Germany. Their findings were reported in Green Chemistry on May 21. This work was supported by the Korea Carbon Capture and Sequestration R&D Center (KCRC).
Publications:
Roh, K., et al. (2020) ‘Early-stage evaluation of emerging CO2 utilization technologies at low technology readiness levels’ Green Chemistry. Available online at https://doi.org/10.1039/c9gc04440j
Profile: Jay Hyung Lee, Ph.D.
Professor
jayhlee@kaist.ac.kr
http://lense.kaist.ac.kr/
Laboratory for Energy System Engineering (LENSE)
Department of Chemical and Biomolecular Engineering
KAIST
https://www.kaist.ac.kr
Daejeon 34141, Korea
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2020.06.22 View 8953 -
New Nanoparticle Drug Combination For Atherosclerosis
Physicochemical cargo-switching nanoparticles (CSNP) designed by KAIST can help significantly reduce cholesterol and macrophage foam cells in arteries, which are the two main triggers for atherosclerotic plaque and inflammation.
The CSNP-based combination drug delivery therapy was proved to exert cholesterol-lowering, anti-inflammatory, and anti-proliferative functions of two common medications for treating and preventing atherosclerosis that are cyclodextrin and statin. Professor Ji-Ho Park and Dr. Heegon Kim from KAIST’s Department of Bio and Brain Engineering said their study has shown great potential for future applications with reduced side effects.
Atherosclerosis is a chronic inflammatory vascular disease that is characterized by the accumulation of cholesterol and cholesterol-loaded macrophage foam cells in the intima. When this atherosclerotic plaque clogs and narrows the artery walls, they restrict blood flow and cause various cardiovascular conditions such as heart attacks and strokes. Heart attacks and strokes are the world’s first and fifth causes of death respectively.
Oral statin administration has been used in clinics as a standard care for atherosclerosis, which is prescribed to lower blood cholesterol and inhibit its accumulation within the plaque. Although statins can effectively prevent the progression of plaque growth, they have only shown modest efficacy in eliminating the already-established plaque. Therefore, patients are required to take statin drugs for the rest of their lives and will always carry the risk of plaque ruptures that can trigger a blood clot.
To address these issues, Professor Park and Dr. Kim exploited another antiatherogenic agent called cyclodextrin. In their paper published in the Journal of Controlled Release on March 10, Professor Park and Dr. Kim reported that the polymeric formulation of cyclodextrin with a diameter of approximately 10 nanometers(nm) can accumulate within the atherosclerotic plaque 14 times more and effectively reduce the plaque even at lower doses, compared to cyclodextrin in a non-polymer structure.
Moreover, although cyclodextrin is known to have a cytotoxic effect on hair cells in the cochlea, which can lead to hearing loss, cyclodextrin polymers developed by Professor Park’s research group exhibited a varying biodistribution profile and did not have this side effect.
In the follow-up study reported in ACS Nano on April 28, the researchers exploited both cyclodextrin and statin and form the cyclodextrin-statin self-assembly drug complex, based on previous findings that each drug can exert local anti-atherosclerosis effect within the plaque. The complex formation processes were optimized to obtain homogeneous and stable nanoparticles with a diameter of about 100 nm for systematic injection.
The therapeutic synergy of cyclodextrin and statin could reportedly enhance plaque-targeted drug delivery and anti-inflammation. Cyclodextrin led to the regression of cholesterol in the established plaque, and the statins were shown to inhibit the proliferation of macrophage foam cells. The study suggested that combination therapy is required to resolve the complex inflammatory cholesterol-rich microenvironment within the plaque.
Professor Park said, “While nanomedicine has been mainly developed for the treatment of cancers, our studies show that nanomedicine can also play a significant role in treating and preventing atherosclerosis, which causes various cardiovascular diseases that are the leading causes of death worldwide.”
This work was supported by KAIST and the National Research Foundation (NRF) of Korea.
Publications:
1. Heegon Kim, Junhee Han, and Ji-Ho Park. (2020) ‘Cyclodextrin polymer improves atherosclerosis therapy and reduces ototoxicity’ Journal of Controlled Release. Volume 319. Page 77-86. Available online at https://doi.org/10.1016/j.jconrel.2019.12.021
2. Kim, H., et al. (2020) ‘Affinity-Driven Design of Cargo-Switching Nanoparticles to Leverage a Cholesterol-Rich Microenvironment for Atherosclerosis Therapy’ ACS Nano. Available online at https://doi.org/10.1021/acsnano.9b08216
Profile: Ji-Ho Park, Ph.D.
Associate Professor
jihopark@kaist.ac.kr
http://openwetware.org/wiki/Park_Lab
Biomaterials Engineering Laboratory (BEL)
Department of Bio and Brain Engineering (BIOENG)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr
Daejeon 34141, Korea
Profile: Heegon Kim, Ph.D.
Postdoctoral Researcher
heegon@kaist.ac.kr
BEL, BIOENG, KAIST
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2020.06.16 View 11756 -
Energy Storage Using Oxygen to Boost Battery Performance
Researchers have presented a novel electrode material for advanced energy storage device that is directly charged with oxygen from the air. Professor Jeung Ku Kang’s team synthesized and preserved the sub-nanometric particles of atomic cluster sizes at high mass loadings within metal-organic frameworks (MOF) by controlling the behavior of reactants at the molecular level. This new strategy ensures high performance for lithium-oxygen batteries, acclaimed as a next-generation energy storage technology and widely used in electric vehicles.
Lithium-oxygen batteries in principle can generate ten times higher energy densities than conventional lithium-ion batteries, but they suffer from very poor cyclability. One of the methods to improve cycle stability is to reduce the overpotential of electrocatalysts in cathode electrodes. When the size of an electrocatalyst material is reduced to the atomic level, the increased surface energy leads to increased activity while significantly accelerating the material’s agglomeration.
As a solution to this challenge, Professor Kang from the Department of Materials Science and Engineering aimed to maintain the improved activity by stabilizing atomic-scale sized electrocatalysts into the sub-nanometric spaces. This is a novel strategy for simultaneously producing and stabilizing atomic-level electrocatalysts within metal-organic frameworks (MOFs).
Metal-organic frameworks continuously assemble metal ions and organic linkers.
The team controlled hydrogen affinities between water molecules to separate them and transfer the isolated water molecules one by one through the sub-nanometric pores of MOFs. The transferred water molecules reacted with cobalt ions to form di-nuclear cobalt hydroxide under precisely controlled synthetic conditions, then the atomic-level cobalt hydroxide is stabilized inside the sub-nanometric pores.
The di-nuclear cobalt hydroxide that is stabilized in the sub-nanometric pores of metal-organic frameworks (MOFs) reduced the overpotential by 63.9% and showed ten-fold improvements in the life cycle.
Professor Kang said, “Simultaneously generating and stabilizing atomic-level electrocatalysts within MOFs can diversify materials according to numerous combinations of metal and organic linkers. It can expand not only the development of electrocatalysts, but also various research fields such as photocatalysts, medicine, the environment, and petrochemicals.”
This study was reported in Advanced Science (Title: Autogenous Production and Stabilization of Highly Loaded Sub-Nanometric Particles within Multishell Hollow Metal-Organic Frameworks and Their Utilization for High Performance in Li-O2 Batteries).
This research was mainly supported by the Global Frontier R&D Program of the Ministry of Science, ICT & Planning (Grant No. 2013M3A6B1078884) funded by the Ministry of Science, ICT & Future Planning, and the National Research Foundation of Korea (Grant No. 2019M3E6A1104196).
Profile:Professor Jeung Ku Kang
jeungku@kaist.ac.kr
http://nanosf.kaist.ac.kr/
Nano Materials Simulation and Fabrication Laboratory
Department of Materials Science and Engineering
KAIST
2020.06.15 View 11192 -
A Deep-Learned E-Skin Decodes Complex Human Motion
A deep-learning powered single-strained electronic skin sensor can capture human motion from a distance. The single strain sensor placed on the wrist decodes complex five-finger motions in real time with a virtual 3D hand that mirrors the original motions. The deep neural network boosted by rapid situation learning (RSL) ensures stable operation regardless of its position on the surface of the skin.
Conventional approaches require many sensor networks that cover the entire curvilinear surfaces of the target area. Unlike conventional wafer-based fabrication, this laser fabrication provides a new sensing paradigm for motion tracking.
The research team, led by Professor Sungho Jo from the School of Computing, collaborated with Professor Seunghwan Ko from Seoul National University to design this new measuring system that extracts signals corresponding to multiple finger motions by generating cracks in metal nanoparticle films using laser technology. The sensor patch was then attached to a user’s wrist to detect the movement of the fingers.
The concept of this research started from the idea that pinpointing a single area would be more efficient for identifying movements than affixing sensors to every joint and muscle. To make this targeting strategy work, it needs to accurately capture the signals from different areas at the point where they all converge, and then decoupling the information entangled in the converged signals. To maximize users’ usability and mobility, the research team used a single-channeled sensor to generate the signals corresponding to complex hand motions.
The rapid situation learning (RSL) system collects data from arbitrary parts on the wrist and automatically trains the model in a real-time demonstration with a virtual 3D hand that mirrors the original motions. To enhance the sensitivity of the sensor, researchers used laser-induced nanoscale cracking.
This sensory system can track the motion of the entire body with a small sensory network and facilitate the indirect remote measurement of human motions, which is applicable for wearable VR/AR systems.
The research team said they focused on two tasks while developing the sensor. First, they analyzed the sensor signal patterns into a latent space encapsulating temporal sensor behavior and then they mapped the latent vectors to finger motion metric spaces.
Professor Jo said, “Our system is expandable to other body parts. We already confirmed that the sensor is also capable of extracting gait motions from a pelvis. This technology is expected to provide a turning point in health-monitoring, motion tracking, and soft robotics.”
This study was featured in Nature Communications.
Publication:
Kim, K. K., et al. (2020) A deep-learned skin sensor decoding the epicentral human motions. Nature Communications. 11. 2149. https://doi.org/10.1038/s41467-020-16040-y29
Link to download the full-text paper:
https://www.nature.com/articles/s41467-020-16040-y.pdf
Profile: Professor Sungho Jo
shjo@kaist.ac.kr
http://nmail.kaist.ac.kr
Neuro-Machine Augmented Intelligence Lab
School of Computing
College of Engineering
KAIST
2020.06.10 View 10834 -
Professor Jee-Hwan Ryu Receives IEEE ICRA 2020 Outstanding Reviewer Award
Professor Jee-Hwan Ryu from the Department of Civil and Environmental Engineering was selected as this year’s winner of the Outstanding Reviewer Award presented by the Institute of Electrical and Electronics Engineers International Conference on Robotics and Automation (IEEE ICRA). The award ceremony took place on June 5 during the conference that is being held online May 31 through August 31 for three months.
The IEEE ICRA Outstanding Reviewer Award is given every year to the top reviewers who have provided constructive and high-quality thesis reviews, and contributed to improving the quality of papers published as results of the conference.
Professor Ryu was one of the four winners of this year’s award. He was selected from 9,425 candidates, which was approximately three times bigger than the candidate pool in previous years. He was strongly recommended by the editorial committee of the conference.
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2020.06.10 View 7401 -
Unravelling Complex Brain Networks with Automated 3-D Neural Mapping
-Automated 3-D brain imaging data analysis technology offers more reliable and standardized analysis of the spatial organization of complex neural circuits.-
KAIST researchers developed a new algorithm for brain imaging data analysis that enables the precise and quantitative mapping of complex neural circuits onto a standardized 3-D reference atlas.
Brain imaging data analysis is indispensable in the studies of neuroscience. However, analysis of obtained brain imaging data has been heavily dependent on manual processing, which cannot guarantee the accuracy, consistency, and reliability of the results.
Conventional brain imaging data analysis typically begins with finding a 2-D brain atlas image that is visually similar to the experimentally obtained brain image. Then, the region-of-interest (ROI) of the atlas image is matched manually with the obtained image, and the number of labeled neurons in the ROI is counted.
Such a visual matching process between experimentally obtained brain images and 2-D brain atlas images has been one of the major sources of error in brain imaging data analysis, as the process is highly subjective, sample-specific, and susceptible to human error. Manual analysis processes for brain images are also laborious, and thus studying the complete 3-D neuronal organization on a whole-brain scale is a formidable task.
To address these issues, a KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering developed new brain imaging data analysis software named 'AMaSiNe (Automated 3-D Mapping of Single Neurons)', and introduced the algorithm in the May 26 issue of Cell Reports.
AMaSiNe automatically detects the positions of single neurons from multiple brain images, and accurately maps all the data onto a common standard 3-D reference space. The algorithm allows the direct comparison of brain data from different animals by automatically matching similar features from the images, and computing the image similarity score.
This feature-based quantitative image-to-image comparison technology improves the accuracy, consistency, and reliability of analysis results using only a small number of brain slice image samples, and helps standardize brain imaging data analyses.
Unlike other existing brain imaging data analysis methods, AMaSiNe can also automatically find the alignment conditions from misaligned and distorted brain images, and draw an accurate ROI, without any cumbersome manual validation process.
AMaSiNe has been further proved to produce consistent results with brain slice images stained utilizing various methods including DAPI, Nissl, and autofluorescence.
The two co-lead authors of this study, Jun Ho Song and Woochul Choi, exploited these benefits of AMaSiNe to investigate the topographic organization of neurons that project to the primary visual area (VISp) in various ROIs, such as the dorsal lateral geniculate nucleus (LGd), which could hardly be addressed without proper calibration and standardization of the brain slice image samples.
In collaboration with Professor Seung-Hee Lee's group of the Department of Biological Science, the researchers successfully observed the 3-D topographic neural projections to the VISp from LGd, and also demonstrated that these projections could not be observed when the slicing angle was not properly corrected by AMaSiNe. The results suggest that the precise correction of a slicing angle is essential for the investigation of complex and important brain structures.
AMaSiNe is widely applicable in the studies of various brain regions and other experimental conditions. For example, in the research team’s previous study jointly conducted with Professor Yang Dan’s group at UC Berkeley, the algorithm enabled the accurate analysis of the neuronal subsets in the substantia nigra and their projections to the whole brain. Their findings were published in Science on January 24.
AMaSiNe is of great interest to many neuroscientists in Korea and abroad, and is being actively used by a number of other research groups at KAIST, MIT, Harvard, Caltech, and UC San Diego.
Professor Paik said, “Our new algorithm allows the spatial organization of complex neural circuits to be found in a standardized 3-D reference atlas on a whole-brain scale. This will bring brain imaging data analysis to a new level.”
He continued, “More in-depth insights for understanding the function of brain circuits can be achieved by facilitating more reliable and standardized analysis of the spatial organization of neural circuits in various regions of the brain.”
This work was supported by KAIST and the National Research Foundation of Korea (NRF).
Figure and Image Credit: Professor Se-Bum Paik, KAIST
Figure and Image Usage Restrictions: News organizations may use or redistribute these figures and images, with proper attribution, as part of news coverage of this paper only.
Publication:
Song, J. H., et al. (2020). Precise Mapping of Single Neurons by Calibrated 3D Reconstruction of Brain Slices Reveals Topographic Projection in Mouse Visual Cortex. Cell Reports. Volume 31, 107682. Available online at https://doi.org/10.1016/j.celrep.2020.107682
Profile:
Se-Bum Paik
Assistant Professor
sbpaik@kaist.ac.kr
http://vs.kaist.ac.kr/
VSNN Laboratory
Department of Bio and Brain Engineering
Program of Brain and Cognitive Engineering
http://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
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2020.06.08 View 11940 -
‘Mole-bot’ Optimized for Underground and Space Exploration
Biomimetic drilling robot provides new insights into the development of efficient drilling technologies
Mole-bot, a drilling biomimetic robot designed by KAIST, boasts a stout scapula, a waist inclinable on all sides, and powerful forelimbs. Most of all, the powerful torque from the expandable drilling bit mimicking the chiseling ability of a mole’s front teeth highlights the best feature of the drilling robot.
The Mole-bot is expected to be used for space exploration and mining for underground resources such as coalbed methane and Rare Earth Elements (REE), which require highly advanced drilling technologies in complex environments.
The research team, led by Professor Hyun Myung from the School of Electrical Engineering, found inspiration for their drilling bot from two striking features of the African mole-rat and European mole.
“The crushing power of the African mole-rat’s teeth is so powerful that they can dig a hole with 48 times more power than their body weight. We used this characteristic for building the main excavation tool. And its expandable drill is designed not to collide with its forelimbs,” said Professor Myung.
The 25-cm wide and 84-cm long Mole-bot can excavate three times faster with six times higher directional accuracy than conventional models. The Mole-bot weighs 26 kg.
After digging, the robot removes the excavated soil and debris using its forelimbs. This embedded muscle feature, inspired by the European mole’s scapula, converts linear motion into a powerful rotational force. For directional drilling, the robot’s elongated waist changes its direction 360° like living mammals.
For exploring underground environments, the research team developed and applied new sensor systems and algorithms to identify the robot’s position and orientation using graph-based 3D Simultaneous Localization and Mapping (SLAM) technology that matches the Earth’s magnetic field sequence, which enables 3D autonomous navigation underground.
According to Market & Market’s survey, the directional drilling market in 2016 is estimated to be 83.3 billion USD and is expected to grow to 103 billion USD in 2021. The growth of the drilling market, starting with the Shale Revolution, is likely to expand into the future development of space and polar resources. As initiated by Space X recently, more attention for planetary exploration will be on the rise and its related technology and equipment market will also increase.
The Mole-bot is a huge step forward for efficient underground drilling and exploration technologies. Unlike conventional drilling processes that use environmentally unfriendly mud compounds for cleaning debris, Mole-bot can mitigate environmental destruction. The researchers said their system saves on cost and labor and does not require additional pipelines or other ancillary equipment.
“We look forward to a more efficient resource exploration with this type of drilling robot. We also hope Mole-bot will have a very positive impact on the robotics market in terms of its extensive application spectra and economic feasibility,” said Professor Myung.
This research, made in collaboration with Professor Jung-Wuk Hong and Professor Tae-Hyuk Kwon’s team in the Department of Civil and Environmental Engineering for robot structure analysis and geotechnical experiments, was supported by the Ministry of Trade, Industry and Energy’s Industrial Technology Innovation Project.
Profile
Professor Hyun Myung
Urban Robotics Lab
http://urobot.kaist.ac.kr/
School of Electrical Engineering
KAIST
2020.06.05 View 9194 -
From Dark to Light in a Flash: Smart Film Lets Windows Switch Autonomously
Researchers have developed a new easy-to-use smart optical film technology that allows smart window devices to autonomously switch between transparent and opaque states in response to the surrounding light conditions.
The proposed 3D hybrid nanocomposite film with a highly periodic network structure has empirically demonstrated its high speed and performance, enabling the smart window to quantify and self-regulate its high-contrast optical transmittance. As a proof of concept, a mobile-app-enabled smart window device for Internet of Things (IoT) applications has been realized using the proposed smart optical film with successful expansion to the 3-by-3-inch scale. This energy-efficient and cost-effective technology holds great promise for future use in various applications that require active optical transmission modulation.
Flexible optical transmission modulation technologies for smart applications including privacy-protection windows, zero-energy buildings, and beam projection screens have been in the spotlight in recent years. Conventional technologies that used external stimuli such as electricity, heat, or light to modulate optical transmission had only limited applications due to their slow response speeds, unnecessary color switching, and low durability, stability, and safety.
The optical transmission modulation contrast achieved by controlling the light scattering interfaces on non-periodic 2D surface structures that often have low optical density such as cracks, wrinkles, and pillars is also generally low. In addition, since the light scattering interfaces are exposed and not subject to any passivation, they can be vulnerable to external damage and may lose optical transmission modulation functions. Furthermore, in-plane scattering interfaces that randomly exist on the surface make large-area modulation with uniformity difficult.
Inspired by these limitations, a KAIST research team led by Professor Seokwoo Jeon from the Department of Materials Science and Engineering and Professor Jung-Wuk Hong of the Civil and Environmental Engineering Department used proximity-field nanopatterning (PnP) technology that effectively produces highly periodic 3D hybrid nanostructures, and an atomic layer deposition (ALD) technique that allows the precise control of oxide deposition and the high-quality fabrication of semiconductor devices.
The team then successfully produced a large-scale smart optical film with a size of 3 by 3 inches in which ultrathin alumina nanoshells are inserted between the elastomers in a periodic 3D nanonetwork.
This “mechano-responsive” 3D hybrid nanocomposite film with a highly periodic network structure is the largest smart optical transmission modulation film that exists. The film has been shown to have state-of-the-art optical transmission modulation of up to 74% at visible wavelengths from 90% initial transmission to 16% in the scattering state under strain. Its durability and stability were proved by more than 10,000 tests of harsh mechanical deformation including stretching, releasing, bending, and being placed under high temperatures of up to 70°C. When this film was used, the transmittance of the smart window device was adjusted promptly and automatically within one second in response to the surrounding light conditions. Through these experiments, the underlying physics of optical scattering phenomena occurring in the heterogeneous interfaces were identified. Their findings were reported in the online edition of Advanced Science on April 26. KAIST Professor Jong-Hwa Shin’s group and Professor Young-Seok Shim at Silla University also collaborated on this project.
Donghwi Cho, a PhD candidate in materials science and engineering at KAIST and co-lead author of the study, said, “Our smart optical film technology can better control high-contrast optical transmittance by relatively simple operating principles and with low energy consumption and costs.”
“When this technology is applied by simply attaching the film to a conventional smart window glass surface without replacing the existing window system, fast switching and uniform tinting are possible while also securing durability, stability, and safety. In addition, its wide range of applications for stretchable or rollable devices such as wall-type displays for a beam projection screen will also fulfill aesthetic needs,” he added.
This work was supported by the National Research Foundation of Korea (NRF), and the Korean Ministries of Science, ICT and Future Planning (MSIP), and Science and ICT (MSIT).
Publication:
Cho, D, et al. (2020) ‘High-Contrast Optical Modulation from Strain-Indicated Nanogaps at 3D Heterogeneous Interfaces’ Advanced Science, 1903708. Available online at https://doi.org/10.1002/advs.201903708
Profile: Seokwoo Jeon, PhD
Professor
jeon39@kaist.ac.kr
https://fdml.kaist.ac.kr/
Flexible Device and Metamaterials Lab (FDML)
Department of Materials Science and Engineering (MSE)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.krDaejeon 34141, Korea
Profile: Jung-Wuk Hong, PhD
Associate Professor
j.hong@kaist.ac.kr
http://aaml.kaist.ac.kr
Advanced Applied Mechanics Laboratory (AAML)
Department of Civil and Environmental Engineering
KAIST
Profile: Donghwi Cho
PhD Candidate
roy0202@kaist.ac.krFDML, MSE, KAIST
Profile: Young-Seok Shim, PhD
Assistant Professor
ysshim@silla.ac.kr
Division of Materials Science and Engineering Silla University
https://www.silla.ac.kr
Busan 46958, Korea
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2020.06.02 View 11606 -
Professor Dongsu Han Named Program Chair for ACM CoNEXT 2020
Professor Dongsu Han from the School of Electrical Engineering has been appointed as the program chair for the 16th Association for Computing Machinery’s International Conference on emerging Networking EXperiments and Technologies (ACM CoNEXT 2020). Professor Han is the first program chair to be appointed from an Asian institution.
ACM CoNEXT is hosted by ACM SIGCOMM, ACM's Special Interest Group on Data Communications, which specializes in the field of communication and computer networks.
Professor Han will serve as program co-chair along with Professor Anja Feldmann from the Max Planck Institute for Informatics. Together, they have appointed 40 world-leading researchers as program committee members for this conference, including Professor Song Min Kim from KAIST School of Electrical Engineering.
Paper submissions for the conference can be made by the end of June, and the event itself is to take place from the 1st to 4th of December.
Conference Website: https://conferences2.sigcomm.org/co-next/2020/#!/home
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2020.06.02 View 8440 -
KAIST Elected to Universities Space Research Association Membership
KAIST joined the Universities Space Research Association (USRA) on May 4, and brought the Association to a total of 113 member universities. The expertise KAIST brings will broaden the Association’s collective strength in space-related science, technology, and engineering worldwide. Professor Hyosang Yoon from the Department of Aerospace Engineering will serve as the representative of KAIST to USRA.
KAIST was selected by USRA’s current university members, in recognition of its significant commitment in, and contributions to, the fields of space and aerospace research. Especially, KAIST have developed Korea's first satellite, KITSAT-1 in 1992, which paved the way for space research in Korea and helped the nation strengthen technological competitiveness in that field.
USRA was established in 1969 under the auspices of the National Academy of Sciences (NAS) of the United States. It is a non-profit corporation chartered to advance space-related science, technology, and engineering. USRA operates scientific institutes and facilities, and conducts other major research and educational programs, using federal funding. USRA also engages the university community and employs in-house scientific leadership, innovative research and development, and project management expertise.
USRA’s President and CEO Dr. Jeffrey A. Isaacson said in his announcement, “We are delighted to welcome these two renowned universities as members. We look forward to their active engagement with, and contributions to, our Association.” President Isaacson visited KAIST on December 10 last year to discuss possible collaborations between two organizations.
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2020.05.29 View 5964 -
Professor Tek-jin Nam Elected to DSR Int’l Advisory Council
Professor Tek-jin Nam from the Department of Industrial Design was elected to serve on the first International Advisory Council (IAC) of the Design Research Society (DRS).
The DRS, an academic society in the field of design research, was founded in the UK in 1966 with the mission of developing and promoting design research. The IAC is newly established under the new DRS governance structure, and its members are selected from distinguished design researchers recommended by DRS members around the globe.
The new IAC members will carry out various activities offered by the DRS, which include innovating design research, strengthening the design researchers’ network and developing policies to nurture new researchers.
2020.05.22 View 4498 -
Visualization of Functional Components to Characterize Optimal Composite Electrodes
Researchers have developed a visualization method that will determine the distribution of components in battery electrodes using atomic force microscopy. The method provides insights into the optimal conditions of composite electrodes and takes us one step closer to being able to manufacture next-generation all-solid-state batteries.
Lithium-ion batteries are widely used in smart devices and vehicles. However, their flammability makes them a safety concern, arising from potential leakage of liquid electrolytes.
All-solid-state lithium ion batteries have emerged as an alternative because of their better safety and wider electrochemical stability. Despite their advantages, all-solid-state lithium ion batteries still have drawbacks such as limited ion conductivity, insufficient contact areas, and high interfacial resistance between the electrode and solid electrolyte.
To solve these issues, studies have been conducted on composite electrodes in which lithium ion conducting additives are dispersed as a medium to provide ion conductive paths at the interface and increase the overall ionic conductivity.
It is very important to identify the shape and distribution of the components used in active materials, ion conductors, binders, and conductive additives on a microscopic scale for significantly improving the battery operation performance.
The developed method is able to distinguish regions of each component based on detected signal sensitivity, by using various modes of atomic force microscopy on a multiscale basis, including electrochemical strain microscopy and lateral force microscopy.
For this research project, both conventional electrodes and composite electrodes were tested, and the results were compared. Individual regions were distinguished and nanoscale correlation between ion reactivity distribution and friction force distribution within a single region was determined to examine the effect of the distribution of binder on the electrochemical strain.
The research team explored the electrochemical strain microscopy amplitude/phase and lateral force microscopy friction force dependence on the AC drive voltage and the tip loading force, and used their sensitivities as markers for each component in the composite anode.
This method allows for direct multiscale observation of the composite electrode in ambient condition, distinguishing various components and measuring their properties simultaneously.
Lead author Dr. Hongjun Kim said, “It is easy to prepare the test sample for observation while providing much higher spatial resolution and intensity resolution for detected signals.” He added, “The method also has the advantage of providing 3D surface morphology information for the observed specimens.”
Professor Seungbum Hong from the Department of Material Sciences and Engineering said, “This analytical technique using atomic force microscopy will be useful for quantitatively understanding what role each component of a composite material plays in the final properties.”
“Our method not only will suggest the new direction for next-generation all-solid-state battery design on a multiscale basis but also lay the groundwork for innovation in the manufacturing process of other electrochemical materials.”
This study is published in ACS Applied Energy Materials and supported by the Big Science Research and Development Project under the Ministry of Science and ICT and the National Research Foundation of Korea, the Basic Research Project under the Wearable Platform Materials Technology Center, and KAIST Global Singularity Research Program for 2019 and 2020.
Publication:Kim, H, et al. (2020) ‘Visualization of Functional Components in a Lithium Silicon Titanium Phosphate-Natural Graphite Composite Anode’. ACS Applied Energy Materials, Volume 3, Issue 4, pp. 3253-3261. Available online at https://doi.org/10.1021/acsaem.9b02045
Profile:
Seungbum Hong
Professor
seungbum@kaist.ac.kr
http://mii.kaist.ac.kr/
Materials Imaging and Integration Laboratory
Department of Material Sciences and Engineering
KAIST
2020.05.22 View 8725