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A Way for Smartwatches to Detect Depression Risks Devised by KAIST and U of Michigan Researchers
- A international joint research team of KAIST and the University of Michigan developed a digital biomarker for predicting symptoms of depression based on data collected by smartwatches
- It has the potential to be used as a medical technology to replace the economically burdensome fMRI measurement test
- It is expected to expand the scope of digital health data analysis
The CORONA virus pandemic also brought about a pandemic of mental illness. Approximately one billion people worldwide suffer from various psychiatric conditions. Korea is one of more serious cases, with approximately 1.8 million patients exhibiting depression and anxiety disorders, and the total number of patients with clinical mental diseases has increased by 37% in five years to approximately 4.65 million. A joint research team from Korea and the US has developed a technology that uses biometric data collected through wearable devices to predict tomorrow's mood and, further, to predict the possibility of developing symptoms of depression.
< Figure 1. Schematic diagram of the research results. Based on the biometric data collected by a smartwatch, a mathematical algorithm that solves the inverse problem to estimate the brain's circadian phase and sleep stages has been developed. This algorithm can estimate the degrees of circadian disruption, and these estimates can be used as the digital biomarkers to predict depression risks. >
KAIST (President Kwang Hyung Lee) announced on the 15th of January that the research team under Professor Dae Wook Kim from the Department of Brain and Cognitive Sciences and the team under Professor Daniel B. Forger from the Department of Mathematics at the University of Michigan in the United States have developed a technology to predict symptoms of depression such as sleep disorders, depression, loss of appetite, overeating, and decreased concentration in shift workers from the activity and heart rate data collected from smartwatches.
According to WHO, a promising new treatment direction for mental illness focuses on the sleep and circadian timekeeping system located in the hypothalamus of the brain, which directly affect impulsivity, emotional responses, decision-making, and overall mood.
However, in order to measure endogenous circadian rhythms and sleep states, blood or saliva must be drawn every 30 minutes throughout the night to measure changes in the concentration of the melatonin hormone in our bodies and polysomnography (PSG) must be performed. As such treatments requires hospitalization and most psychiatric patients only visit for outpatient treatment, there has been no significant progress in developing treatment methods that take these two factors into account. In addition, the cost of the PSG test, which is approximately $1000, leaves mental health treatment considering sleep and circadian rhythms out of reach for the socially disadvantaged.
The solution to overcome these problems is to employ wearable devices for the easier collection of biometric data such as heart rate, body temperature, and activity level in real time without spatial constraints. However, current wearable devices have the limitation of providing only indirect information on biomarkers required by medical staff, such as the phase of the circadian clock.
The joint research team developed a filtering technology that accurately estimates the phase of the circadian clock, which changes daily, such as heart rate and activity time series data collected from a smartwatch. This is an implementation of a digital twin that precisely describes the circadian rhythm in the brain, and it can be used to estimate circadian rhythm disruption.
< Figure 2. The suprachiasmatic nucleus located in the hypothalamus of the brain is the central biological clock that regulates the 24-hour physiological rhythm and plays a key role in maintaining the body’s circadian rhythm. If the phase of this biological clock is disrupted, it affects various parts of the brain, which can cause psychiatric conditions such as depression. >
The possibility of using the digital twin of this circadian clock to predict the symptoms of depression was verified through collaboration with the research team of Professor Srijan Sen of the Michigan Neuroscience Institute and Professor Amy Bohnert of the Department of Psychiatry of the University of Michigan.
The collaborative research team conducted a large-scale prospective cohort study involving approximately 800 shift workers and showed that the circadian rhythm disruption digital biomarker estimated through the technology can predict tomorrow's mood as well as six symptoms, including sleep problems, appetite changes, decreased concentration, and suicidal thoughts, which are representative symptoms of depression.
< Figure 3. The circadian rhythm of hormones such as melatonin regulates various physiological functions and behaviors such as heart rate and activity level. These physiological and behavioral signals can be measured in daily life through wearable devices. In order to estimate the body’s circadian rhythm inversely based on the measured biometric signals, a mathematical algorithm is needed. This algorithm plays a key role in accurately identifying the characteristics of circadian rhythms by extracting hidden physiological patterns from biosignals. >
Professor Dae Wook Kim said, "It is very meaningful to be able to conduct research that provides a clue for ways to apply wearable biometric data using mathematics that have not previously been utilized for actual disease management." He added, "We expect that this research will be able to present continuous and non-invasive mental health monitoring technology. This is expected to present a new paradigm for mental health care. By resolving some of the major problems socially disadvantaged people may face in current treatment practices, they may be able to take more active steps when experiencing symptoms of depression, such as seeking counsel before things get out of hand."
< Figure 4. A mathematical algorithm was devised to circumvent the problems of estimating the phase of the brain's biological clock and sleep stages inversely from the biodata collected by a smartwatch. This algorithm can estimate the degree of daily circadian rhythm disruption, and this estimate can be used as a digital biomarker to predict depression symptoms. >
The results of this study, in which Professor Dae Wook Kim of the Department of Brain and Cognitive Sciences at KAIST participated as the joint first author and corresponding author, were published in the online version of the international academic journal npj Digital Medicine on December 5, 2024. (Paper title: The real-world association between digital markers of circadian disruption and mental health risks) DOI: 10.1038/s41746-024-01348-6
This study was conducted with the support of the KAIST's Research Support Program for New Faculty Members, the US National Science Foundation, the US National Institutes of Health, and the US Army Research Institute MURI Program.
2025.01.20 View 645 -
KAIST Develops CamBio - a New Biotemplating Method
- Professor Jae-Byum Chang and Professor Yeon Sik Jung’s joint research team of the Department of Materials Science and Engineering developed a highly tunable bio-templating method “CamBio” that makes use of intracellular protein structures
- Substrate performance improvement of up to 230% demonstrated via surface-enhanced Raman spectroscopy (SERS)
- Expected to have price competitiveness over bio-templating method as it expands the range of biological samples
- Expected to expand the range of application of nanostructure synthesis technology by utilizing various biological structures
< Photo 1. (From left) Professor Yeon Sik Jung, Ph.D. candidate Dae-Hyeon Song, Professor Jae-Byum Chang, and (from top right) Dr. Chang Woo Song and Dr. Seunghee H. Cho of the Department of Materials Science and Engineering >
Biological structures have complex characteristics that are difficult to replicate artificially, but biotemplating methods* that directly utilize these biological structures have been used in various fields of application. The KAIST research team succeeded in utilizing previously unusable biological structures and expanding the areas in which biotemplate methods can be applied.
*Biotemplating: A method of using biotemplates as a mold to create functional structural materials, utilizing the functions of these biological structures, from viruses to the tissues and organs that make up our bodies
KAIST (President Kwang Hyung Lee) announced on the 10th that a joint research team of Professors Jae-Byum Chang and Professor Yeon Sik Jung of the Department of Materials Science and Engineering developed a biotemplating method that utilizes specific intracellular proteins in biological samples and has high tunability.
Existing biotemplate methods mainly utilize only the external surface of biological samples or have limitations in utilizing the structure-function correlation of various biological structures due to limited dimensions and sample sizes, making it difficult to create functional nanostructures.
To solve this problem, the research team studied a way to utilize various biological structures within the cells while retaining high tunability.
< Figure 1. CamBio utilizing microtubules, a intracellular protein structure. The silver nanoparticle chains synthesized along the microtubules that span the entire cell interior can be observed through an electron microscope, and it is shown that this can be used as a successful SERS substrate. >
As a result of the research, the team developed the “Conversion to advanced materials via labeled Biostructure”, shortened as “CamBio”, which enables the selective synthesis of nanostructures with various characteristics and sizes from specific protein structures composed of diverse proteins within biological specimens.
The CamBio method secures high tunability of functional nanostructures that can be manufactured from biological samples by merging various manufacturing and biological technologies.
Through the technology of repeatedly attaching antibodies, arranging cells in a certain shape, and thinly slicing tissue, the functional nanostructures made with CamBio showed improved performance on the surface-enhanced Raman spectroscopy (SERS)* substrate used for material detection.
*Surface-enhanced Raman spectroscopy (SERS): A technology that can detect very small amounts of substances using light, based on the principle that specific substances react to light and amplifies signals on surfaces of metals such as gold or silver.
The research team found that the nanoparticle chains made using the intracellular protein structures through the process of repeated labeling with antibodies allowed easier control, and improved SERS performance by up to 230%.
In addition, the research team expanded from utilizing the structures inside cells to obtaining samples of muscle tissues inside meat using a cryostat and successfully producing a substrate with periodic bands made of metal particles by performing the CamBio process. This method of producing a substrate not only allows large-scale production using biological samples, but also shows that it is a cost-effective method.
< Figure 2. A method for securing tunability using CamBio at the cell level. Examples of controlling characteristics by integrating iterative labeling and cell pattering techniques with CamBio are shown. >
The CamBio developed by the research team is expected to be used as a way to solve problems faced by various research fields as it is to expand the range of bio-samples that can be produced for various usage.
The first author, Dae-Hyeon Song, a Ph.D. candidate of KAIST Department of Materials Science and Engineering said, “Through CamBio, we have comprehensively accumulated biotemplating methods that can utilize more diverse protein structures,” and “If combined with the state-of-the-art biological technologies such as gene editing and 3D bioprinting and new material synthesis technologies, biostructures can be utilized in various fields of application.”
< Figure 3. A method for securing tunability using CamBio at the tissue level. In order to utilize proteins inside muscle tissue, the frozen tissue sectioning technology is combined, and through this, a substrate with a periodic nanoparticle band pattern is successfully produced, and it is shown that large-area acquisition of samples and price competitiveness can be achieved. >
This study, in which the Ph.D. candidate Dae-Hyeon Song along with Dr. Chang Woo Song, and Dr. Seunghee H. Cho of the same department participated as the first authors, was published online in the international academic journal, Advanced Science, on November 13th, 2024.
(Paper title: Highly Tunable, Nanomaterial-Functionalized Structural Templating of Intracellular Protein Structures Within Biological Species) https://doi.org/10.1002/advs.202406492
This study was conducted with a combination of support from various programs including the National Convergence Research of Scientific Challenges (National Research Foundation of Korea (NRF) 2024), Engineering Reseach Center (ERC) (Wearable Platform Materials Technology Center, NRF 2023), ERC (Global Bio-integrated Materials Center, NRF 2024), and the National Advanced Program for Biological Research Resources (Bioimaging Data Curation Center, NRF 2024) funded by Ministry of Science and ICT.
2025.01.10 View 765 -
“Cross-Generation Collaborative Labs” for Semiconductor, Chemistry, and Computer Science Opened
< Photo of Professor Hoi-Jun Yoo (center) of the School of Electrical Engineering at the signboard unveiling ceremony >
KAIST held a ceremony to mark the opening of three additional ‘Cross-Generation Collaborative Labs’ on the morning of January 7th, 2025.
The “Next-Generation AI Semiconductor System Lab” by Professor Hoi-Jun Yoo of the School of Electrical Engineering, the “Molecular Spectroscopy and Chemical Dynamics Lab” by Professor Sang Kyu Kim of the Department of Chemistry, and the “Advanced Data Computing Lab” by Professor Sue Bok Moon of the School of Computer Science are the three new labs given the honored titled of the “Cross-Generation Collaborative Lab”.
The Cross-Generation Collaborative Lab is KAIST’s unique system that was set up to facilitate the collaboration between retiring professors and junior professors to continue the achievements and know-how the elders have accumulated over their academic career. Since its introduction in 2018, nine labs have been named to be the Cross-Generation Labs, and this year’s new addition brings the total up to twelve.
The ‘Next-Generation AI Semiconductor System Lab’ led by Professor Hoi-Jun Yoo will be operated by Professor Joo-Young Kim of the same school.
Professor Hoi-Jun Yoo is a world-renowned scholar with outstanding research achievements in the field of on-device AI semiconductor design. Professor Joo-Young Kim is an up-and-coming researcher studying large language models and design of AI semiconductors for server computers, and is currently researching technologies to design PIM (Processing-in-Memory), a core technology in the field of AI semiconductors.
Their research goal is to systematically collaborate and transfer next-generation AI semiconductor design technology, including brain-mimicking AI algorithms such as deep neural networks and generative AI, to integrate core technologies, and to maximize the usability of R&D outputs, thereby further solidifying the position of Korean AI semiconductor companies in the global market.
Professor Hoi-Jun Yoo said, “I believe that, we will be able to present a development direction of for the next-generation AI semiconductors industries at home and abroad through collaborative research and play a key role in transferring and expanding global leadership.”
< Professor Sang Kyu Kim of the Department of Chemistry (middle), at the signboard unveiling ceremony for his laboratory >
The “Molecular Spectroscopy and Chemical Dynamics Laboratory”, where Professor Sang Kyu Kim of the Department of Chemistry is in charge, will be operated by Professor Tae Kyu Kim of the same department, and another professor in the field of spectroscopy and dynamics will join in the future.
Professor Sang Kyu Kim has secured technologies for developing unique experimental equipment based on ultrashort lasers and supersonic molecular beams, and is a world leader who has been creatively pioneering new fields of experimental physical chemistry.
The research goal is to describe chemical reactions and verify from a quantum mechanical perspective and introduce new theories and technologies to pursue a complete understanding of the principles of chemical reactions. In addition, the accompanying basic scientific knowledge will be applied to the design of new materials.
Professor Sang Kyu Kim said, “I am very happy to be able to pass on the research infrastructure to the next generation through this system, and I will continue to nurture it to grow into a world-class research lab through trans-generational collaborative research.”
< Photo of Professor Sue Bok Moon (center) at the signboard unveiling ceremony by the School of Computing >
Lastly, the “Advanced Data Computing Lab” led by Professor Sue Bok Moon is joined by Professor Mee Young Cha of the same school and Professor Wonjae Lee of the Graduate School of Culture Technology.
Professor Sue Bok Moon showed the infinite possibilities of large-scale data-based social network research through Cyworld, YouTube, and Twitter, and had a great influence on related fields beyond the field of computer science.
Professor Mee Young Cha is a data scientist who analyzes difficult social issues such as misinformation, poverty, and disaster detection using big data-based AI. She is the first Korean to be recognized for her achievements as the director of the Max Planck Institute in Germany, a world-class basic science research institute. Therefore, there is high expectation for synergy effects from overseas collaborative research and technology transfer and sharing among the participating professors of the collaborative research lab. Professor Wonjae Lee is researching dynamic interaction analysis between science and technology using structural topic models.
They plan to conduct research aimed at improving the analysis and understanding of negative influences occurring online, and in particular, developing a hateful precursor detection model using emotions and morality to preemptively block hateful expressions.
Professor Sue Bok Moon said, “Through this collaborative research lab, we will play a key role in conducting in-depth collaborative research on unexpected negative influences in the AI era so that we can have a high level of competitiveness worldwide.”
The ceremonies for the unveiling of the new Cross-Generation Collaborative Lab signboard were held in front of each lab from 10:00 AM on the 7th, in the attendance of President Kwang Hyung Lee, Senior Vice President for Research Sang Yup Lee, and other key officials of KAIST and the new staff members to join the laboratories.
2025.01.07 View 559 -
KAIST Scientifically Identifies a Method to Prevent Dental Erosion from Carbonated Drinks
A Korean research team, which had previously visualized and scientifically proven the harmful effects of carbonated drinks like cola on dental health using nanotechnology, has now identified a mechanism for an effective method to prevent tooth damage caused by these beverages.
KAIST (represented by President Kwang Hyung Lee) announced on the 5th of December that a team led by Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with Seoul National University's School of Dentistry (Departments of Pediatric Dentistry and Oral Microbiology) and Professor Hye Ryung Byon’s research team from the Department of Chemistry, has revealed through nanotechnology that silver diamine fluoride (SDF)* forms a fluoride-containing protective layer on the tooth surface, effectively inhibiting cola-induced erosion.
*SDF (Silver Diamine Fluoride): A dental agent primarily used for the treatment and prevention of tooth decay. SDF strengthens carious lesions, suppresses bacterial growth, and halts the progression of cavities.
The team analyzed the surface morphology and mechanical properties of tooth enamel on a nanoscale using atomic force microscopy (AFM). They also examined the chemical properties of the nano-film formed by SDF treatment using X-ray photoelectron spectroscopy (XPS)* and Fourier-transform infrared spectroscopy (FTIR)*.
*XPS (X-ray Photoelectron Spectroscopy): A powerful surface analysis technique used to investigate the chemical composition and electronic structure of materials.
*FTIR (Fourier-Transform Infrared Spectroscopy): An analytical method that identifies the molecular structure and composition of materials by analyzing how they absorb or transmit infrared light.
The findings showed significant differences in surface roughness and elastic modulus between teeth exposed to cola with and without SDF treatment. Teeth treated with SDF exhibited minimal changes in surface roughness due to erosion (from 64 nm to 70 nm) and maintained a high elastic modulus (from 215 GPa to 205 GPa).
This was attributed to the formation of a fluoroapatite* layer by SDF, which acted as a protective shield.
*Fluoroapatite: A phosphate mineral with the chemical formula Ca₅(PO₄)₃F (calcium fluoro-phosphate). It can occur naturally or be synthesized biologically/artificially and plays a crucial role in strengthening teeth and bones.
< Figure 1. Schematic of the workflow. Surface morphology and mechanical properties of untreated and treated silver diamine fluoride (SDF) treated enamel exposed to cola were analyzed over time using atomic force microscopy (AFM). >
Professor Young J. Kim from Seoul National University's Department of Pediatric Dentistry noted, "This technology could be applied to prevent dental erosion and strengthen teeth for both children and adults. It is a cost-effective and accessible dental treatment."
< Figure 2. Changes in surface roughness and elastic modulus according to time of exposure to cola for SDF untreated and treated teeth. After 1 hour, the surface roughness of the SDF untreated teeth rapidly became rougher from 83 nm to 287 nm and the elastic modulus weakened from 125 GPa to 13 GPa, whereas the surface roughness of the SDF treated teeth changed only slightly from 64 nm to 70 nm and the elastic modulus barely changed from 215 GPa to 205 GPa, maintaining a similar state to the initial state. >
Professor Seungbum Hong emphasized, "Dental health significantly impacts quality of life. This research offers an effective non-invasive method to prevent early dental erosion, moving beyond traditional surgical treatments. By simply applying SDF, dental erosion can be prevented and enamel strengthened, potentially reducing pain and costs associated with treatment."
This study, led by the first author Aditi Saha, a PhD student in KAIST’s Department of Materials Science and Engineering, was published in the international journal Biomaterials Research on November 7 under the title "Nanoscale Study on Noninvasive Prevention of Dental Erosion of Enamel by Silver Diamine Fluoride". The research was supported by the National Research Foundation of Korea.
2024.12.11 View 1193 -
KAIST Develops Technology for the Precise Diagnosis of Electric Vehicle Batteries Using Small Currents
Accurately diagnosing the state of electric vehicle (EV) batteries is essential for their efficient management and safe use. KAIST researchers have developed a new technology that can diagnose and monitor the state of batteries with high precision using only small amounts of current, which is expected to maximize the batteries’ long-term stability and efficiency.
KAIST (represented by President Kwang Hyung Lee) announced on the 17th of October that a research team led by Professors Kyeongha Kwon and Sang-Gug Lee from the School of Electrical Engineering had developed electrochemical impedance spectroscopy (EIS) technology that can be used to improve the stability and performance of high-capacity batteries in electric vehicles.
EIS is a powerful tool that measures the impedance* magnitude and changes in a battery, allowing the evaluation of battery efficiency and loss. It is considered an important tool for assessing the state of charge (SOC) and state of health (SOH) of batteries. Additionally, it can be used to identify thermal characteristics, chemical/physical changes, predict battery life, and determine the causes of failures. *Battery Impedance: A measure of the resistance to current flow within the battery that is used to assess battery performance and condition.
However, traditional EIS equipment is expensive and complex, making it difficult to install, operate, and maintain. Moreover, due to sensitivity and precision limitations, applying current disturbances of several amperes (A) to a battery can cause significant electrical stress, increasing the risk of battery failure or fire and making it difficult to use in practice.
< Figure 1. Flow chart for diagnosis and prevention of unexpected combustion via the use of the electrochemical impedance spectroscopy (EIS) for the batteries for electric vehicles. >
To address this, the KAIST research team developed and validated a low-current EIS system for diagnosing the condition and health of high-capacity EV batteries. This EIS system can precisely measure battery impedance with low current disturbances (10mA), minimizing thermal effects and safety issues during the measurement process.
In addition, the system minimizes bulky and costly components, making it easy to integrate into vehicles. The system was proven effective in identifying the electrochemical properties of batteries under various operating conditions, including different temperatures and SOC levels.
Professor Kyeongha Kwon (the corresponding author) explained, “This system can be easily integrated into the battery management system (BMS) of electric vehicles and has demonstrated high measurement accuracy while significantly reducing the cost and complexity compared to traditional high-current EIS methods. It can contribute to battery diagnosis and performance improvements not only for electric vehicles but also for energy storage systems (ESS).”
This research, in which Young-Nam Lee, a doctoral student in the School of Electrical Engineering at KAIST participated as the first author, was published in the prestigious international journal IEEE Transactions on Industrial Electronics (top 2% in the field; IF 7.5) on September 5th. (Paper Title: Small-Perturbation Electrochemical Impedance Spectroscopy System With High Accuracy for High-Capacity Batteries in Electric Vehicles, Link: https://ieeexplore.ieee.org/document/10666864)
< Figure 2. Impedance measurement results of large-capacity batteries for electric vehicles. ZEW (commercial EW; MP10, Wonatech) versus ZMEAS (proposed system) >
This research was supported by the Basic Research Program of the National Research Foundation of Korea, the Next-Generation Intelligent Semiconductor Technology Development Program of the Korea Evaluation Institute of Industrial Technology, and the AI Semiconductor Graduate Program of the Institute of Information & Communications Technology Planning & Evaluation.
2024.10.17 View 2209 -
A KAIST research team unveils new path for dense photonic integration
Integrated optical semiconductor (hereinafter referred to as optical semiconductor) technology is a next-generation semiconductor technology for which many researches and investments are being made worldwide because it can make complex optical systems such as LiDAR and quantum sensors and computers into a single small chip. In the existing semiconductor technology, the key was how small it was to make it in units of 5 nanometers or 2 nanometers, but increasing the degree of integration in optical semiconductor devices can be said to be a key technology that determines performance, price, and energy efficiency.
KAIST (President Kwang-Hyung Lee) announced on the 19th that a research team led by Professor Sangsik Kim of the Department of Electrical and Electronic Engineering discovered a new optical coupling mechanism that can increase the degree of integration of optical semiconductor devices by more than 100 times.
The degree of the number of elements that can be configured per chip is called the degree of integration. However, it is very difficult to increase the degree of integration of optical semiconductor devices, because crosstalk occurs between photons between adjacent devices due to the wave nature of light.
In previous studies, it was possible to reduce crosstalk of light only in specific polarizations, but in this study, the research team developed a method to increase the degree of integration even under polarization conditions, which were previously considered impossible, by discovering a new light coupling mechanism.
This study, led by Professor Sangsik Kim as a corresponding author and conducted with students he taught at Texas Tech University, was published in the international journal 'Light: Science & Applications' [IF=20.257] on June 2nd. done. (Paper title: Anisotropic leaky-like perturbation with subwavelength gratings enables zero crosstalk).
Professor Sangsik Kim said, "The interesting thing about this study is that it paradoxically eliminated the confusion through leaky waves (light tends to spread sideways), which was previously thought to increase the crosstalk." He went on to add, “If the optical coupling method using the leaky wave revealed in this study is applied, it will be possible to develop various optical semiconductor devices that are smaller and that has less noise.”
Professor Sangsik Kim is a researcher recognized for his expertise and research in optical semiconductor integration. Through his previous research, he developed an all-dielectric metamaterial that can control the degree of light spreading laterally by patterning a semiconductor structure at a size smaller than the wavelength, and proved this through experiments to improve the degree of integration of optical semiconductors. These studies were reported in ‘Nature Communications’ (Vol. 9, Article 1893, 2018) and ‘Optica’ (Vol. 7, pp. 881-887, 2020). In recognition of these achievements, Professor Kim has received the NSF Career Award from the National Science Foundation (NSF) and the Young Scientist Award from the Association of Korean-American Scientists and Engineers.
Meanwhile, this research was carried out with the support from the New Research Project of Excellence of the National Research Foundation of Korea and and the National Science Foundation of the US.
< Figure 1. Illustration depicting light propagation without crosstalk in the waveguide array of the developed metamaterial-based optical semiconductor >
2023.06.21 View 5060 -
KAIST researchers find the key to overcome the limits in X-ray microscopy
X-ray microscopes have the advantage of penetrating most substances, so internal organs and skeletons can be observed non-invasively through chest X-rays or CT scans. Recently, studies to increase the resolution of X-ray imaging technology are being actively conducted in order to precisely observe the internal structure of semiconductors and batteries at the nanoscale.
KAIST (President Kwang Hyung Lee) announced on April 12th that a joint research team led by Professor YongKeun Park of the Department of Physics and Dr. Jun Lim of the Pohang Accelerator Laboratory has succeeded in developing a core technology that can overcome the resolution limitations of existing X-ray microscopes.
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This study, in which Dr. KyeoReh Lee participated as the first author, was published on 6th of April in “Light: Science and Application”, a world-renowned academic journal in optics and photonics. (Paper title: Direct high-resolution X-ray imaging exploiting pseudorandomness).
X-ray nanomicroscopes do not have refractive lenses. In an X-ray microscope, a circular grating called a concentric zone plate is used instead of a lens. The resolution of an image obtained using the zone plate is determined by the quality of the nanostructure that comprises the plate. There are several difficulties in fabricating and maintaining these nanostructures, which set the limit to the level of resolution for X-ray microscopy.
The research team developed a new X-ray nanomicroscopy technology to overcome this problem. The X-ray lens proposed by the research team is in the form of numerous holes punched in a thin tungsten film, and generates random diffraction patterns by diffracting incident X-rays. The research team mathematically identified that, paradoxically, the high-resolution information of the sample was fully contained in these random diffraction patterns, and actually succeeded in extracting the information and imaging the internal states of the samples.
The imaging method using the mathematical properties of random diffraction was proposed and implemented in the visible light band for the first time by Dr. KyeoReh Lee and Professor YongKeun Park in 2016*. This study uses the results of previous studies to solve the difficult, lingering problem in the field of the X-ray imaging. ※ "Exploiting the speckle-correlation scattering matrix for a compact reference-free holographic image sensor." Nature communications 7.1 (2016): 13359.
The resolution of the image of the constructed sample has no direct correlation with the size of the pattern etched on the random lens used. Based on this idea, the research team succeeded in acquiring images with 14 nm resolution (approximately 1/7 the size of the coronavirus) by using random lenses made in a circular pattern with a diameter of 300 nm.
The imaging technology developed by this research team is a key fundamental technology that can enhance the resolution of X-ray nanomicroscopy, which has been blocked by limitations of the production of existing zone plates.
The first author and one of the co-corresponding author, Dr. KyeoReh Lee of KAIST Department of Physics, said, “In this study, the resolution was limited to 14 nm, but if the next-generation X-ray light source and high-performance X-ray detector are used, the resolution would exceed that of the conventional X-ray nano-imaging and approach the resolution of an electron microscope.” and added, “Unlike an electron microscope, X-rays can observe the internal structure without damaging the sample, so it will be able to present a new standard for non-invasive nanostructure observation processes such as quality inspections for semiconductors.”.
The co-corresponding author, Dr. Jun Lim of the Pohang Accelerator Laboratory, said, “In the same context, the developed image technology is expected to greatly increase the performance in the 4th generation multipurpose radiation accelerator which is set to be established in Ochang of the Northern Chungcheong Province.”
This research was conducted with the support through the Research Leader Program and the Sejong Science Fellowship of the National Research Foundation of Korea.
Fig. 1. Designed diffuser as X-ray imaging lens. a, Schematic of full-field transmission X-ray microscopy. The attenuation (amplitude) map of a sample is measured. The image resolution (dx) is limited by the outermost zone width of the zone plate (D). b, Schematic of the proposed method. A designed diffuser is used instead of a zone plate. The image resolution is finer than the hole size of the diffuser (dx << D).
Fig. 2. The left panel is a surface electron microscopy (SEM) image of the X-ray diffuser used in the experiment. The middle panel shows the design of the X-ray diffuser, and there is an inset in the middle of the panel that shows a corresponding part of the SEM image. The right panel shows an experimental random X-ray diffraction pattern, also known as a speckle pattern, obtained from the X-ray diffuser.
Fig. 3. Images taken from the proposed randomness-based X-ray imaging (bottom) and the corresponding surface electron microscope (SEM) images (top).
2023.04.12 View 5458 -
The cause of disability in aged brain meningeal membranes identified
Due to the increase in average age, studies on changes in the brain following general aging process without serious brain diseases have also become an issue that requires in-depth studies. Regarding aging research, as aging progresses, ‘sugar’ accumulates in the body, and the accumulated sugar becomes a causative agent for various diseases such as aging-related inflammation and vascular disease. In the end, “surplus” sugar molecules attach to various proteins in the body and interfere with their functions.
KAIST (President Kwang Hyung Lee), a joint research team of Professor Pilnam Kim and Professor Yong Jeong of the Department of Bio and Brain Engineering, revealed on the 15th that it was confirmed that the function of being the “front line of defense” for the cerebrocortex of the brain meninges, the layers of membranes that surrounds the brain, is hindered when 'sugar' begins to build up on them as aging progresses.
Professor Kim's research team confirmed excessive accumulation of sugar molecules in the meninges of the elderly and also confirmed that sugar accumulation occurs mouse models in accordance with certain age levels. The meninges are thin membranes that surround the brain and exist at the boundary between the cerebrospinal fluid and the cortex and play an important role in protecting the brain. In this study, it was revealed that the dysfunction of these brain membranes caused by aging is induced by 'excess' sugar in the brain. In particular, as the meningeal membrane becomes thinner and stickier due to aging, a new paradigm has been provided for the discovery of the principle of the decrease in material exchange between the cerebrospinal fluid and the cerebral cortex.
This research was conducted by the Ph.D. candidate Hyo Min Kim and Dr. Shinheun Kim as the co-first authors to be published online on February 28th in the international journal, Aging Cell. (Paper Title: Glycation-mediated tissue-level remodeling of brain meningeal membrane by aging)
The meninges, which are in direct contact with the cerebrospinal fluid, are mainly composed of collagen, an extracellular matrix (ECM) protein, and are composed of fibroblasts, which are cells that produce this protein. The cells that come in contact with collagen proteins that are attached with sugar have a low collagen production function, while the meningeal membrane continuously thins and collapses as the expression of collagen degrading enzymes increases.
Studies on the relationship between excess sugar molecules accumulation in the brain due to continued sugar intake and the degeneration of neurons and brain diseases have been continuously conducted. However, this study was the first to identify meningeal degeneration and dysfunction caused by glucose accumulation with the focus on the meninges itself, and the results are expected to present new ideas for research into approach towards discoveries of new treatments for brain disease.
Researcher Hyomin Kim, the first author, introduced the research results as “an interesting study that identified changes in the barriers of the brain due to aging through a convergent approach, starting from the human brain and utilizing an animal model with a biomimetic meningeal model”.
Professor Pilnam Kim's research team is conducting research and development to remove sugar that accumulated throughout the human body, including the meninges. Advanced glycation end products, which are waste products formed when proteins and sugars meet in the human body, are partially removed by macrophages. However, glycated products bound to extracellular matrix proteins such as collagen are difficult to remove naturally. Through the KAIST-Ceragem Research Center, this research team is developing a healthcare medical device to remove 'sugar residue' in the body.
This study was carried out with the National Research Foundation of Korea's collective research support.
Figure 1. Schematic diagram of proposed mechanism showing aging‐related ECM remodeling through meningeal fibroblasts on the brain leptomeninges. Meningeal fibroblasts in the young brain showed dynamic COL1A1 synthetic and COL1‐interactive function on the collagen membrane. They showed ITGB1‐mediated adhesion on the COL1‐composed leptomeningeal membrane and induction of COL1A1 synthesis for maintaining the collagen membrane. With aging, meningeal fibroblasts showed depletion of COL1A1 synthetic function and altered cell–matrix interaction.
Figure 2. Representative rat meningeal images observed in the study. Compared to young rats, it was confirmed that type 1 collagen (COL1) decreased along with the accumulation of glycated end products (AGE) in the brain membrane of aged rats, and the activity of integrin beta 1 (ITGB1), a representative receptor corresponding to cell-collagen interaction. Instead, it was observed that the activity of discoidin domain receptor 2 (DDR2), one of the tyrosine kinases, increased.
Figure 3. Substance flux through the brain membrane decreases with aging. It was confirmed that the degree of adsorption of fluorescent substances contained in cerebrospinal fluid (CSF) to the brain membrane increased and the degree of entry into the periphery of the cerebral blood vessels decreased in the aged rats. In this study, only the influx into the brain was confirmed during the entry and exit of substances, but the degree of outflow will also be confirmed through future studies.
2023.03.15 View 5435 -
KAIST to showcase a pack of KAIST Start-ups at CES 2023
- KAIST is to run an Exclusive Booth at the Venetian Expo (Hall G) in Eureka Park, at CES 2023, to be held in Las Vegas from Thursday, January 5th through Sunday, the 8th.
- Twelve businesses recently put together by KAIST faculty, alumni, and the start-ups given legal usage of KAIST technologies will be showcased.
- Out of the participating start-ups, the products by Fluiz and Hills Robotics were selected as the “CES Innovation Award 2023 Honoree”, scoring top in their respective categories.
On January 3, KAIST announced that there will be a KAIST booth at Consumer Electronics Show (CES) 2023, the most influential tech event in the world, to be held in Las Vegas from January 3 to 8.
At this exclusive corner, KAIST will introduce the technologies of KAIST start-ups over the exhibition period.
KAIST first started holding its exclusive booth in CES 2019 with five start-up businesses, following up at CES 2020 with 12 start-ups and at CES 2022 with 10 start-ups. At CES 2023, which would be KAIST’s fourth conference, KAIST will be accompanying 12 businesses including start-ups by the faculty members, alumni, and technology transfer companies that just began their businesses with technologies from their research findings that stands a head above others.
To maximize the publicity opportunity, KAIST will support each company’s marketing strategies through cooperation with the Korea International Trade Association (KITA), and provide an opportunity for the school and each startup to create global identity and exhibit the excellence of their technologies at the convention.
The following companies will be at the KAIST Booth in Eureka Park:
The twelve startups mentioned above aim to achieve global technology commecialization in their respective fields of expertise spanning from eXtended Reality (XR) and gaming, to AI and robotics, vehicle and transport, mobile platform, smart city, autonomous driving, healthcare, internet of thing (IoT), through joint research and development, technology transfer and investment attraction from world’s leading institutions and enterprises.
In particular, Fluiz and Hills Robotics won the CES Innovation Award as 2023 Honorees and is expected to attain greater achievements in the future.
A staff member from the KAIST Institute of Technology Value Creation said, “The KAIST Showcase for CES 2023 has prepared a new pitching space for each of the companies for their own IR efforts, and we hope that KAIST startups will actively and effectively market their products and technologies while they are at the convention. We hope it will help them utilize their time here to establish their name in presence here which will eventually serve as a good foothold for them and their predecessors to further global commercialization goals.”
2023.01.04 View 10939 -
Yuji Roh Awarded 2022 Microsoft Research PhD Fellowship
KAIST PhD candidate Yuji Roh of the School of Electrical Engineering (advisor: Prof. Steven Euijong Whang) was selected as a recipient of the 2022 Microsoft Research PhD Fellowship.
< KAIST PhD candidate Yuji Roh (advisor: Prof. Steven Euijong Whang) >
The Microsoft Research PhD Fellowship is a scholarship program that recognizes outstanding graduate students for their exceptional and innovative research in areas relevant to computer science and related fields. This year, 36 people from around the world received the fellowship, and Yuji Roh from KAIST EE is the only recipient from universities in Korea. Each selected fellow will receive a $10,000 scholarship and an opportunity to intern at Microsoft under the guidance of an experienced researcher.
Yuji Roh was named a fellow in the field of “Machine Learning” for her outstanding achievements in Trustworthy AI. Her research highlights include designing a state-of-the-art fair training framework using batch selection and developing novel algorithms for both fair and robust training. Her works have been presented at the top machine learning conferences ICML, ICLR, and NeurIPS among others. She also co-presented a tutorial on Trustworthy AI at the top data mining conference ACM SIGKDD. She is currently interning at the NVIDIA Research AI Algorithms Group developing large-scale real-world fair AI frameworks.
The list of fellowship recipients and the interview videos are displayed on the Microsoft webpage and Youtube.
The list of recipients: https://www.microsoft.com/en-us/research/academic-program/phd-fellowship/2022-recipients/
Interview (Global): https://www.youtube.com/watch?v=T4Q-XwOOoJc
Interview (Asia): https://www.youtube.com/watch?v=qwq3R1XU8UE
[Highlighted research achievements by Yuji Roh: Fair batch selection framework]
[Highlighted research achievements by Yuji Roh: Fair and robust training framework]
2022.10.28 View 9802 -
Atomically-Smooth Gold Crystals Help to Compress Light for Nanophotonic Applications
Highly compressed mid-infrared optical waves in a thin dielectric crystal on monocrystalline gold substrate investigated for the first time using a high-resolution scattering-type scanning near-field optical microscope.
KAIST researchers and their collaborators at home and abroad have successfully demonstrated a new platform for guiding the compressed light waves in very thin van der Waals crystals. Their method to guide the mid-infrared light with minimal loss will provide a breakthrough for the practical applications of ultra-thin dielectric crystals in next-generation optoelectronic devices based on strong light-matter interactions at the nanoscale.
Phonon-polaritons are collective oscillations of ions in polar dielectrics coupled to electromagnetic waves of light, whose electromagnetic field is much more compressed compared to the light wavelength. Recently, it was demonstrated that the phonon-polaritons in thin van der Waals crystals can be compressed even further when the material is placed on top of a highly conductive metal. In such a configuration, charges in the polaritonic crystal are “reflected” in the metal, and their coupling with light results in a new type of polariton waves called the image phonon-polaritons. Highly compressed image modes provide strong light-matter interactions, but are very sensitive to the substrate roughness, which hinders their practical application.
Challenged by these limitations, four research groups combined their efforts to develop a unique experimental platform using advanced fabrication and measurement methods. Their findings were published in Science Advances on July 13.
A KAIST research team led by Professor Min Seok Jang from the School of Electrical Engineering used a highly sensitive scanning near-field optical microscope (SNOM) to directly measure the optical fields of the hyperbolic image phonon-polaritons (HIP) propagating in a 63 nm-thick slab of hexagonal boron nitride (h-BN) on a monocrystalline gold substrate, showing the mid-infrared light waves in dielectric crystal compressed by a hundred times.
Professor Jang and a research professor in his group, Sergey Menabde, successfully obtained direct images of HIP waves propagating for many wavelengths, and detected a signal from the ultra-compressed high-order HIP in a regular h-BN crystals for the first time. They showed that the phonon-polaritons in van der Waals crystals can be significantly more compressed without sacrificing their lifetime.
This became possible due to the atomically-smooth surfaces of the home-grown gold crystals used as a substrate for the h-BN. Practically zero surface scattering and extremely small ohmic loss in gold at mid-infrared frequencies provide a low-loss environment for the HIP propagation. The HIP mode probed by the researchers was 2.4 times more compressed and yet exhibited a similar lifetime compared to the phonon-polaritons with a low-loss dielectric substrate, resulting in a twice higher figure of merit in terms of the normalized propagation length.
The ultra-smooth monocrystalline gold flakes used in the experiment were chemically grown by the team of Professor N. Asger Mortensen from the Center for Nano Optics at the University of Southern Denmark.
Mid-infrared spectrum is particularly important for sensing applications since many important organic molecules have absorption lines in the mid-infrared. However, a large number of molecules is required by the conventional detection methods for successful operation, whereas the ultra-compressed phonon-polariton fields can provide strong light-matter interactions at the microscopic level, thus significantly improving the detection limit down to a single molecule. The long lifetime of the HIP on monocrystalline gold will further improve the detection performance.
Furthermore, the study conducted by Professor Jang and the team demonstrated the striking similarity between the HIP and the image graphene plasmons. Both image modes possess significantly more confined electromagnetic field, yet their lifetime remains unaffected by the shorter polariton wavelength. This observation provides a broader perspective on image polaritons in general, and highlights their superiority in terms of the nanolight waveguiding compared to the conventional low-dimensional polaritons in van der Waals crystals on a dielectric substrate.
Professor Jang said, “Our research demonstrated the advantages of image polaritons, and especially the image phonon-polaritons. These optical modes can be used in the future optoelectronic devices where both the low-loss propagation and the strong light-matter interaction are necessary. I hope that our results will pave the way for the realization of more efficient nanophotonic devices such as metasurfaces, optical switches, sensors, and other applications operating at infrared frequencies.”
This research was funded by the Samsung Research Funding & Incubation Center of Samsung Electronics and the National Research Foundation of Korea (NRF). The Korea Institute of Science and Technology, Ministry of Education, Culture, Sports, Science and Technology of Japan, and The Villum Foundation, Denmark, also supported the work.
Figure. Nano-tip is used for the ultra-high-resolution imaging of the image phonon-polaritons in hBN launched by the gold crystal edge.
Publication:
Menabde, S. G., et al. (2022) Near-field probing of image phonon-polaritons in hexagonal boron nitride on gold crystals. Science Advances 8, Article ID: eabn0627. Available online at https://science.org/doi/10.1126/sciadv.abn0627.
Profile:
Min Seok Jang, MS, PhD
Associate Professor
jang.minseok@kaist.ac.kr
http://janglab.org/
Min Seok Jang Research Group
School of Electrical Engineering
http://kaist.ac.kr/en/
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
2022.07.13 View 10185 -
'Fingerprint' Machine Learning Technique Identifies Different Bacteria in Seconds
A synergistic combination of surface-enhanced Raman spectroscopy and deep learning serves as an effective platform for separation-free detection of bacteria in arbitrary media
Bacterial identification can take hours and often longer, precious time when diagnosing infections and selecting appropriate treatments. There may be a quicker, more accurate process according to researchers at KAIST. By teaching a deep learning algorithm to identify the “fingerprint” spectra of the molecular components of various bacteria, the researchers could classify various bacteria in different media with accuracies of up to 98%.
Their results were made available online on Jan. 18 in Biosensors and Bioelectronics, ahead of publication in the journal’s April issue.
Bacteria-induced illnesses, those caused by direct bacterial infection or by exposure to bacterial toxins, can induce painful symptoms and even lead to death, so the rapid detection of bacteria is crucial to prevent the intake of contaminated foods and to diagnose infections from clinical samples, such as urine. “By using surface-enhanced Raman spectroscopy (SERS) analysis boosted with a newly proposed deep learning model, we demonstrated a markedly simple, fast, and effective route to classify the signals of two common bacteria and their resident media without any separation procedures,” said Professor Sungho Jo from the School of Computing.
Raman spectroscopy sends light through a sample to see how it scatters. The results reveal structural information about the sample — the spectral fingerprint — allowing researchers to identify its molecules. The surface-enhanced version places sample cells on noble metal nanostructures that help amplify the sample’s signals.
However, it is challenging to obtain consistent and clear spectra of bacteria due to numerous overlapping peak sources, such as proteins in cell walls. “Moreover, strong signals of surrounding media are also enhanced to overwhelm target signals, requiring time-consuming and tedious bacterial separation steps,” said Professor Yeon Sik Jung from the Department of Materials Science and Engineering.
To parse through the noisy signals, the researchers implemented an artificial intelligence method called deep learning that can hierarchically extract certain features of the spectral information to classify data. They specifically designed their model, named the dual-branch wide-kernel network (DualWKNet), to efficiently learn the correlation between spectral features. Such an ability is critical for analyzing one-dimensional spectral data, according to Professor Jo.
“Despite having interfering signals or noise from the media, which make the general shapes of different bacterial spectra and their residing media signals look similar, high classification accuracies of bacterial types and their media were achieved,” Professor Jo said, explaining that DualWKNet allowed the team to identify key peaks in each class that were almost indiscernible in individual spectra, enhancing the classification accuracies. “Ultimately, with the use of DualWKNet replacing the bacteria and media separation steps, our method dramatically reduces analysis time.”
The researchers plan to use their platform to study more bacteria and media types, using the information to build a training data library of various bacterial types in additional media to reduce the collection and detection times for new samples.
“We developed a meaningful universal platform for rapid bacterial detection with the collaboration between SERS and deep learning,” Professor Jo said. “We hope to extend the use of our deep learning-based SERS analysis platform to detect numerous types of bacteria in additional media that are important for food or clinical analysis, such as blood.”
The National R&D Program, through a National Research Foundation of Korea grant funded by the Ministry of Science and ICT, supported this research.
-PublicationEojin Rho, Minjoon Kim, Seunghee H. Cho, Bongjae Choi, Hyungjoon Park, Hanhwi Jang, Yeon Sik Jung, Sungho Jo, “Separation-free bacterial identification in arbitrary media via deepneural network-based SERS analysis,” Biosensors and Bioelectronics online January 18, 2022 (doi.org/10.1016/j.bios.2022.113991)
-ProfileProfessor Yeon Sik JungDepartment of Materials Science and EngineeringKAIST
Professor Sungho JoSchool of ComputingKAIST
2022.03.04 View 20065