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COVID-19 Update: Fall Semester to Continue Offering Classes Online
KAIST announced that the university would continue online classes through the fall semester. However, the university will conduct additional in-person classes for upper-level undergraduate lab classes and some graduate courses where on-site interaction was deemed to be highly necessary. Some 600-level graduate courses at the Daejeon campus and graduate courses at the Seoul campus will carry out both in-person and online classes. The fall semester will start from August 31.
Provost and Executive Vice President Kwang Hyung Lee announced the fall semester plan in his letter to the entire student body on July 9. He said that the university decided to continue with online classes in consideration of the safety of KAIST community members and the current status of the COVID-19 spread. However, he said the new plan will help students choose class options between in-person and online classes.
“Although the number of classes with two versions is limited, we believe this will help many students continue learning without the sustained face-to-face contact that is inherent in residential education,” Provost Lee said.
In-person classes conducted in the fall semester will also be provided online for students who are not available for in-person classes. Students may choose the type of the classes they prefer according to their situation, among only the courses that will offer two versions. Professors will decide if they will conduct two versions of their classes. The Office of Academic Affairs is collecting the professors’ applications for conducting both versions until July 24.
KAIST offered real-time online classes and pre-recorded KLMS (KAIST Learning Management System) classes during the spring semester with a very limited number of in-person lab classes for graduate courses and these two versions of online class will continue for fall semester.
Provost Lee asked the students who will take the in-person classes to strictly observe all precaution measures as the university will do its best to abide by the government guidelines against the Covid-19 in preparation for the fall semester.
“We will continue to make appropriate and safe accommodations for them,” said Provost Lee.
Those who need to reside in on-campus dormitories are required to be approved for moving. The applications will open after all the in-person class schedules are fixed next month. However, students who were approved for staying in the dormitories last semester can move in without additional approval procedures for the fall semester.
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2020.07.10 View 7233 -
X-ray Scattering Shines Light on Protein Folding
- Multiple forms of a non-functional, unfolded protein follow different pathways and timelines to reach its folded, functional state, a study reveals. -
KAIST researchers have used an X-ray method to track how proteins fold, which could improve computer simulations of this process, with implications for understanding diseases and improving drug discovery. Their findings were reported in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) on June 30.
When proteins are translated from their DNA codes, they quickly transform from a non-functional, unfolded state into their folded, functional state. Problems in folding can lead to diseases like Alzheimer’s and Parkinson’s.
“Protein folding is one of the most important biological processes, as it forms the functioning 3D protein structure,” explained the physical chemist Hyotcherl Ihee of the Department of Chemistry at KAIST. Dr. Tae Wu Kim, the lead author of this research from Ihee’s group, added, “Understanding the mechanisms of protein folding is important, and could pave the way for disease study and drug development.”
Ihee’s team developed an approach using an X-ray scattering technique to uncover how the protein cytochrome c folds from its initial unfolded state. This protein is composed of a chain of 104 amino acids with an iron-containing heme molecule. It is often used for protein folding studies.
The researchers placed the protein in a solution and shined ultraviolet light on it. This process provides electrons to cytochrome c, reducing the iron within it from the ferric to the ferrous form, which initiates folding. As this was happening, the researchers beamed X-rays at very short intervals onto the sample. The X-rays scattered off all the atomic pairs in the sample and a detector continuously recorded the X-ray scattering patterns. The X-ray scattering patterns provided direct information regarding the 3D protein structure and the changes made in these patterns over time showed real-time motion of the protein during the folding process.
The team found cytochrome c proteins initially exist in a wide variety of unfolded states. Once the folding process is triggered, they stop by a group of intermediates within 31.6 microseconds, and then those intermediates follow different pathways with different folding times to reach an energetically stable folded state.
“We don’t know if this diversity in folding paths can be generalized to other proteins,” Ihee confessed. He continued, “However, we believe that our approach can be used to study other protein folding systems.”
Ihee hopes this approach can improve the accuracy of models that simulate protein interactions by including information on their unstructured states. These simulations are important as they can help identify barriers to proper folding and predict a protein’s folded state given its amino acid sequence. Ultimately, the models could help clarify how some diseases develop and how drugs interact with various protein structures.
Ihee’s group collaborated with Professor Young Min Rhee at the KAIST Department of Chemistry, and this work was supported by the National Research Foundation of Korea (NRF) and the Institute for Basic Science (IBS).
Figure. The scientists found that non-functional unfolded forms of the protein cytochrome c follow different pathways and timelines to reach a stable functional folded state.
Publications:
Kim, T. W., et al. (2020) ‘Protein folding from heterogeneous unfolded state revealed by time-resolved X-ray solution scattering’. PNAS. Volume 117. Issue 26. Page 14996-15005. Available online at https://doi.org/10.1073/pnas.1913442117
Profile: Hyotcherl Ihee, Ph.D.
Professor
hyotcherl.ihee@kaist.ac.kr
http://time.kaist.ac.kr/
Ihee Laboratory
Department of Chemistry
KAIST
https://www.kaist.ac.kr
Daejeon 34141, Korea
Profile: Young Min Rhee, Ph.D.
Professor
ymrhee@kaist.ac.kr
http://singlet.kaist.ac.kr
Rhee Research Group
Department of Chemistry
KAIST
https://www.kaist.ac.kr
Daejeon 34141, Korea
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2020.07.09 View 12746 -
Quantum Classifiers with Tailored Quantum Kernel
Quantum information scientists have introduced a new method for machine learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology.
The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space.
Quantum machine learning holds promise as one of the imperative applications for quantum computing. In machine learning, one fundamental problem for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data.
More recently, the kernel method has been introduced in quantum machine learning with great success. The ability of quantum computers to efficiently access and manipulate data in the quantum feature space can open opportunities for quantum techniques to enhance various existing machine learning methods.
The idea of the classification algorithm with a nonlinear kernel is that given a quantum test state, the protocol calculates the weighted power sum of the fidelities of quantum data in quantum parallel via a swap-test circuit followed by two single-qubit measurements (see Figure 1). This requires only a small number of quantum data operations regardless of the size of data. The novelty of this approach lies in the fact that labeled training data can be densely packed into a quantum state and then compared to the test data.
The KAIST team, in collaboration with researchers from the University of KwaZulu-Natal (UKZN) in South Africa and Data Cybernetics in Germany, has further advanced the rapidly evolving field of quantum machine learning by introducing quantum classifiers with tailored quantum kernels.This study was reported at npj Quantum Information in May.
The input data is either represented by classical data via a quantum feature map or intrinsic quantum data, and the classification is based on the kernel function that measures the closeness of the test data to training data.
Dr. Daniel Park at KAIST, one of the lead authors of this research, said that the quantum kernel can be tailored systematically to an arbitrary power sum, which makes it an excellent candidate for real-world applications.
Professor Rhee said that quantum forking, a technique that was invented by the team previously, makes it possible to start the protocol from scratch, even when all the labeled training data and the test data are independently encoded in separate qubits.
Professor Francesco Petruccione from UKZN explained, “The state fidelity of two quantum states includes the imaginary parts of the probability amplitudes, which enables use of the full quantum feature space.”
To demonstrate the usefulness of the classification protocol, Carsten Blank from Data Cybernetics implemented the classifier and compared classical simulations using the five-qubit IBM quantum computer that is freely available to public users via cloud service. “This is a promising sign that the field is progressing,” Blank noted.
Link to download the full-text paper:
https://www.nature.com/articles/s41534-020-0272-6
-Profile
Professor June-Koo Kevin Rhee
rhee.jk@kaist.ac.kr
Professor, School of Electrical Engineering
Director, ITRC of Quantum Computing for AIKAIST
Daniel Kyungdeock Parkkpark10@kaist.ac.krResearch Assistant ProfessorSchool of Electrical EngineeringKAIST
2020.07.07 View 10578 -
Education, a Silver Lining in the Dark COVID-19 Cloud
If there is a silver lining behind the COVID-19 pandemic clouds engulfing the world in darkness, it would be ‘education’. The disruption caused by the pandemic has reminded us of the skills that students need in this unpredictable world and raised public awareness of guaranteeing continuous, fair, and quality learning opportunities. Educational innovation can become a positive and powerful catalyst to transform the world for a better future in the post-COVID era.
According to the speakers at the virtual forum co-hosted by the Global Strategy Institute (GSI) and Korea Policy Center for the Fourth Industrial Revolution (KPC4IR) at KAIST on June 24, the recent transition to remote education amplifies the existing socio-economic disparities between the haves and the have-nots, and narrowing the digital divide is the most urgent challenge that should be addressed in this ever-evolving technology-dominating era. They also called for students to be resilient despite the numerous uncertainties ahead of them and prepare new skill sets to better adjust to new environments.
KAIST launched the GSI as its think tank in February of this year. The GSI aims to identify global issues proactively and help make breakthroughs well aligned with solid science and technology-based policies. The second forum of the KAIST GSI, following its inaugural forum in April, was held under the theme “Envisioning the Future of Education for a Non-Contact Society in the Post-Coronavirus Era”.
In his opening remarks, KAIST President Sung-Chul Shin stressed that “distance teaching and learning will eventually become integral components of our future education system”. He then called for close collaboration between the public and private sectors to better shape the future of digital education. President Shin said that global cooperation is also needed to continue offering inclusive, quality education that can equally benefit every student around the world. “We should never let a crisis go to waste, and the COVID-19 pandemic is no exception,” he added.
CEO of Minerva Schools Ben Nelson described the current coronavirus crisis as “an earthquake happening deep down on the ocean floor – we don’t feel it, but it can cause a devastating tsunami.” He continued, “Online learning can totally change the current education system forever.”
Saying that blended education, which combines online and offline classes, will be the new norm in the post-coronavirus era, Coursera CEO Jeff Maggioncalda anticipates that institutions will have to offer more and more online courses and credentials, and should at the same time prepare to drive down the cost of education as students expect to pay much less in tuition and fees for online learning options. “With the economy slumping and unemployment soaring, job-relevant education will also be a must,” Maggioncalda said.
National University of Singapore President Tan Eng Chye further pointed out that future education systems should prepare students to be creative lifelong learners. President Tan encouraged students to be able to integrate knowledge and technical skills from multiple disciplines for complex problem solving, and be adaptable and resilient with bigger appetites for risks and a higher tolerance for failures. He also mentioned digital competency, empathy, and social responsibility as virtues that students in the post-coronavirus era should possess.
Rebecca Winthrop, Co-Director of the Center for Universal Education at the Brookings Institution, raised concerns over the ever-growing digital disparities caused by the recent shift to online teaching and learning, claiming that insufficient infrastructures for low-income families in developing nations are already causing added educational disparities and provoking the inequity issue around the world. “New approaches to leapfrog inequality and provide quality education equally through faster and more effective means should be studied,” she said.
In response to this, Vice President of Microsoft Anthony Salcito introduced the Microsoft Education Transformation Framework, which provides practical advice to develop strategies for digital education transformation with a holistic, long-term view implemented in discrete phases that the global community can begin today. The Framework reportedly shows how emerging technologies, such as artificial intelligence, support new approaches to building efficient and effective physical and digital infrastructure, modernizing teaching and learning, empowering research, and managing student success.
The GSI will host two more forums in September and November.
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2020.06.24 View 12083 -
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 11362 -
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 10998 -
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 7547 -
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 8557 -
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 6086 -
Professor Sue-Hyun Lee Listed Among WEF 2020 Young Scientists
Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering joined the World Economic Forum (WEF)’s Young Scientists Community on May 26. The class of 2020 comprises 25 leading researchers from 14 countries across the world who are at the forefront of scientific problem-solving and social change. Professor Lee was the only Korean on this year’s roster.
The WEF created the Young Scientists Community in 2008 to engage leaders from the public and private sectors with science and the role it plays in society. The WEF selects rising-star academics, 40 and under, from various fields every year, and helps them become stronger ambassadors for science, especially in tackling pressing global challenges including cybersecurity, climate change, poverty, and pandemics.
Professor Lee is researching how memories are encoded, recalled, and updated, and how emotional processes affect human memory, in order to ultimately direct the development of therapeutic methods to treat mental disorders. She has made significant contributions to resolving ongoing debates over the maintenance and changes of memory traces in the brain.
In recognition of her research excellence, leadership, and commitment to serving society, the President and the Dean of the College of Engineering at KAIST nominated Professor Lee to the WEF’s Class of 2020 Young Scientists Selection Committee. The Committee also acknowledged Professor Lee’s achievements and potential for expanding the boundaries of knowledge and practical applications of science, and accepted her into the Community.
During her three-year membership in the Community, Professor Lee will be committed to participating in WEF-initiated activities and events related to promising therapeutic interventions for mental disorders and future directions of artificial intelligence.
Seven of this year’s WEF Young Scientists are from Asia, including Professor Lee, while eight are based in Europe. Six study in the Americas, two work in South Africa, and the remaining two in the Middle East. Fourteen, more than half, of the newly announced 25 Young Scientists are women.
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2020.05.26 View 10142 -
Antivirus Industry the Centerpiece of New Deal R&D Initiatives
- KAIST launches post-COVID-19 R&D initiatives for smart mobile medical systems. -
KAIST will make the antivirus industry the centerpiece of what it is touting as the KAIST New Deal R&D initiative, which will drive new growth engines for preparing for the post-coronavirus era.
According to the new initiative, KAIST will concentrate on creating antivirus technologies, infectious disease-related big data management, and non-contact services platforms as key future R&D projects.
President Sung-Chul Shin launched the COVID-19 R&D Initiative task force last month, composed of more than 50 professors from the Graduate School of Medical Science and Engineering, the Department of Biological Sciences, the College of Engineering, and the Department of Industrial Design. The task force came up with key research agendas that will promote smart mobile medical systems in the years ahead.
“We will devote all of our R&D capacities to pursue a smart healthcare society,” said President Shin. “Our competitiveness in the fields of AI, ICT, materials, and bio-technology holds significant potential for building a healthy society powered by smart medical systems in Korea,” he added.
The smart medical systems focus mainly on building an Epidemic Mitigating Mobile Module (EMMM). The EMMM will manage epidemics via the three phases of prevention, emergency response, and treatment, with the development of each phase’s technological modules. The EMMM will also build an AI big data platform to assist with clinical applications and epidemic management.
Technologies applicable for the prevention phase include developing recyclable antivirus masks, plasma virus sterilizers, and smart breathable protective gowns. KAIST researchers will also focus on developing diagnosis modules that will identify epidemics more quickly and accurately.
Most significantly, KAIST aims to develop technologies for anti-infection medical services such as the transformable negative pressure ambulance module and negative pressure room, which are specially developed for respiratory infections.
The new R&D initiatives will center on virus therapies and treatments, specifically pushing forward vaccine and robotics studies. As caring robots and delivery robots will become common as main caregivers via noncontact services, research focusing on robotics will be significantly enhanced.
Even before launching the new R&D initiatives, researchers have started to present new technologies to help address the pandemic. Professor Il-Doo Kim’s team in the Department of Materials Science and Engineering developed a washable nano-fiber filtered face mask that is preparing for commercialization.
GPS tracking of infections has expanded comprehensively to detect both indoor and outdoor activities of infected patients. Professor Dong-Soo Han from the School of Computing developed Wi-Fi positioning software built into mobile phones that can trace both activities and is now preparing to roll it out.
Virologist Ui-Cheol Shin from the Graduate School of Medical Science and Engineering is carrying out research on a universal T-cell vaccine that can block the Betacoronaviruses. It is reported that that new epidemics such as SARS, MERS, and COVID-19 carry Betacoronaviruses.
Research teams in the Graduate School of AI are conducting various research projects on building prediction models for outbreaks and spreads using big data.
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2020.05.20 View 10915 -
Highly Efficient Charge-to-Spin Interconversion in Graphene Heterostructures
Researchers present a new route for designing a graphene-based active spintronic component
KAIST physicists described a route to design the energy-efficient generation, manipulation and detection of spin currents using nonmagnetic two-dimensional materials. The research team, led by Professor Sungjae Cho, observed highly efficient charge-to-spin interconversion via the gate-tunable Rashba-Edelstien effect (REE) in graphene heterostructures.
This research paves the way for the application of graphene as an active spintronic component for generating, controlling, and detecting spin current without ferromagnetic electrodes or magnetic fields.
Graphene is a promising spintronic component owing to its long spin diffusion length. However, its small spin-orbit coupling limits the potential of graphene in spintronic applications since graphene cannot be used to generate, control, or detect spin current.
“We successfully increased the spin-orbit coupling of graphene by stacking graphene on top of 2H-TaS2, which is one of the transition metal dichalcogenide materials with the largest spin-orbit coupling. Graphene now can be used to generate, control, and detect spin current,” Professor Cho said.
The Rashba-Edelstein effect is a physical mechanism that enables charge current-to-spin current interconversion by spin-dependent band structure induced by the Rashba effect, a momentum-dependent splitting of spin bands in low-dimensional condensed matter systems.
Professor Cho’s group demonstrated the gate-tunable Rashba-Edelstein effect in a multilayer graphene for the first time. The Rahsba-Edelstein effect allows the two-dimensional conduction electrons of graphene to be magnetized by an applied charge current and form a spin current. Furthermore, as the Fermi level of graphene, tuned by gate voltage, moves from the valence to conduction band, the spin current generated by graphene reversed its spin direction.
This spin reversal is useful in the design of low-power-consumption transistors utilizing spins in that it provides the carrier “On” state with spin up holes (or spin down electrons) and the "Off" state with zero net spin polarization at so called “charge neutrality point” where numbers of electrons and holes are equal.
“Our work is the first demonstration of charge-to-spin interconversion in a metallic TMD (transition-metal dichalcogenides) and graphene heterostructure with a spin polarization state controlled by a gate. We expect that the all-electrical spin-switching effect and the reversal of non-equilibrium spin polarization by the application of gate voltage is applicable for the energy-efficient generation and manipulation of spin currents using nonmagnetic van der Waals materials,” explained Professor Cho.
This study (https://pubs.acs.org/doi/10.1021/acsnano.0c01037) was supported by the National Research Foundation of Korea.
Publication:
Lijun Li, Jin Zhang, Gyuho Myeong, Wongil Shin, Hongsik Lim, Boram Kim, Seungho Kim, Taehyeok Jin, Stuart Cavill, Beom Seo Kim, Changyoung Kim, Johannes Lischner, Aires Ferreira, and Sungjae Cho, Gate-Tunable Reversible Rashba−Edelstein Effect in a Few-Layer Graphene/2H-TaS2 Heterostructure at Room Temperature. ACS Nano 2020. Link to download the paper: https://pubs.acs.org/doi/10.1021/acsnano.0c01037
Profile:
Professor Sungjae Cho, PhD
sungjae.cho@kaist.ac.kr
http://qtak.kaist.ac.kr
Department of Physics
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr
Daejeon 34141, Korea
2020.05.18 View 8515