Research Stories

Search article
  • Extreme Heat May Hasten Cognitive Decline in Vulnerable Populations

    Sociology LEE, HAENA Prof.

    Extreme Heat May Hasten Cognitive Decline in Vulnerable Populations

    Prof. Haena Lee and colleagues published a study in the Journal of Epidemiology and Community Health(IF: 6.3) on the impact of cumulative exposure to extreme heat on cognitive decline among vulnerable groups—particularly Black older adults and those living in poor neighborhoods. The study, one of the first to investigate the decade-long consequences of extreme heat, finds that cumulative exposure to extreme heat can undermine cognitive health, but it does so unequally across the population. July 2023 was the hottest month on record. Extreme heat is the leading cause of weather-related deaths in the U.S., claiming more lives each year than hurricanes, tornadoes, and lightning combined. Young children and older adults are particularly vulnerable to heat-related illnesses such as heat exhaustion and heat stroke. Recent studies suggest that high temperatures may hurt cognitive function, but these studies tend to look at a snapshot of someone’s cognition at a single time point following brief exposure to heat. Less is known about the long-term consequences of heat on cognitive health. As heat waves become more frequent and intense due to climate change and urban heat islands, Lee and her colleagues sought to understand the connection between extreme heat exposure and cognitive decline. They analyzed data from nearly 9,500 U.S. adults ages 52 and older surveyed over a 12-year period (2006-2018) as part of the Health and Retirement Study conducted by the University of Michigan Institute for Social Research, which measures participants’ cognitive function over time. The researchers also looked at socioeconomic measures of the neighborhoods where participants lived. In addition, they calculated participants’ cumulative exposure to extreme heat (the number of days in which the heat index reached or exceeded a location-specific threshold) during this 12-year period based on historical temperature data from the CDC’s National Environmental Public Health Tracking Network. They found that high exposure to extreme heat was associated with faster cognitive decline among residents of poor neighborhoods, but not for those in wealthier neighborhoods. Moreover, cumulative exposure to extreme heat was associated with faster cognitive decline among Black older adults, but not white or Hispanic older adults. One possibility is that affluent neighborhoods tend to have resources that can help in a heat wave—things like well-maintained green spaces, air conditioning, and cooling centers. In disadvantaged neighborhoods, these resources may not exist. Another explanation for this pattern of findings is that Black older adults may have disproportionately experienced systemic disadvantages throughout their lives due to structural racism, segregation, and other discriminatory policies, all of which may affect cognitive reserve. The researchers urge local governments and health officials to develop policies and tools that identify residents who are susceptible to extreme heat, empower at-risk communities, map their specific needs, and develop targeted support and increased communication with these populations. Paper: Cumulative exposure to extreme heat and trajectories of cognitive decline among older adults in the USA •Journal: Journal of Epidemiology and Community Health •Author: Haena Lee, Eunyoung Choi, Virginia Chang •DOI: http://dx.doi.org/10.1136/jech-2023-220675

    • No. 230
    • 2023-09-26
    • 132
  • Development of hyperspectral imaging and multiplexing sensor technology using a metasurface

    Biophysics KIM, INKI Prof. ·Kim Yangkyu, Aleksandr Barulin Researcher

    Development of hyperspectral imaging and multiplexing sensor technology using a metasurface

    Professor Inki Kim's research team from the Department of Biophysics at Sungkyunkwan University, in collaboration with Professor Luke Lee from Harvard Medical School, Professor Junsuk Rho from POSTECH, and Professor Inhee Choi from University of Seoul, has developed a hyperspectral imaging technology using a metasurface chip for real-time monitoring of cells. Hyperspectral imaging technology is a technique that allows the simultaneous observation of both the shape and spectral signals of objects through a microscope. Through this technique, when observing objects, it is possible to obtain both spatial and temporal information about the object's location and chemical composition simultaneously. The ability to detect and monitor various biological processes occurring within cells, as well as chemically related substances directly or indirectly, in real-time from within or outside the cell, is considered a crucial technology for enabling early diagnosis of various diseases and the development of therapeutic agents. In this study, a technology was developed that utilizes Plasmonic Resonance Energy Transfer (PRET) to detect the molecular fingerprint of target chemical substances through a label-free approach. The sensor technology based on Plasmonic Resonance Energy Transfer (PRET) exploits the phenomenon of energy transfer between plasmonic scatterers and absorbing molecules. This allows the extraction of chemical information or electronic state of molecules as a form of plasmonic quenching dips. However, conventional nanoparticle-based PRET sensors have limitations due to the restricted scattering characteristics of nanoparticles, allowing measurement of only one type of molecule at a time. Furthermore, there has been a challenge in real-time monitoring of substance transfer occurring between cells and within cells due to these limitations. Additionally, in order to accurately and in real-time monitor the spatial and temporal changes within cells, a more precise chip-based device implementation is required. The research team has developed hyperspectral imaging and multiplexing sensor technology using a metasurface, an ultra-thin flat optical component with a thickness of just one-thousandth of a hair's width (Figure 1). A metasurface refers to a two-dimensional sub-wavelength structures that can manipulate wave properties at the interfaces. The metasurface chip can manipulate the scattering characteristics of light and has been utilized to create optical components that scatter only the desired wavelengths of light in the visible spectrum (Figure 2). The team confirmed that the metasurface chip can simultaneously detect different types of molecules, such as cytochrome and chlorophyll, which play crucial roles in the metabolism of animal and plant cells, respectively. Cytochrome is a crucial enzyme involved in electron transport chains and serves as an indicator of cellular health, while chlorophyll acts as an optical antenna, absorbing light energy for photosynthesis in plant cells. Furthermore, the research team has introduced a technology that allows real-time quantitative detection of reactive oxygen species secreted by living cells using this metasurface chip. They developed a technique on the metasurface chip where normal cells, cancer cells, and drug-treated cancer cells were cultured, and the reactive oxygen species produced by each cell were monitored using a label-free approach (Figure 3). Over the course of an hour, they monitored the amount of reactive oxygen species emitted from the same positions within the cells, observing a higher secretion of reactive oxygen species in the order of drug-treated cancer cells, general cancer cells, and normal cells. This technology is anticipated to be applied as a drug screening platform in the future. Through this study, the metasurface chip-based hyperspectral imaging and sensor technology that has been implemented hold the potential not only for detecting various chemical substances within cells but also for real-time monitoring of cellular secretions used in intercellular communication. The results of this study were officially published in the highly prestigious journal Advanced Materials (IF = 32.086) on August 10th and have been recognized for the excellence of the research, being selected as the cover article for that issue. ▲ Figure1. hyperspectral imaging and multiplexing sensor technology using a metasurface ▲ Figure4. Advanced Materials Cover Photo

    • No. 229
    • 2023-09-19
    • 552
  • Developing a Deep Learning System for the Detection of Motorbike Helmet Law Violations

    Semiconductor Systems Engineering JEON, JAE WOOK Prof.

    Developing a Deep Learning System for the Detection of Motorbike Helmet Law Violations

    The use of video surveillance to implement automated detection systems for motorcycle helmet usage has the potential to significantly improve the efficacy of educational and enforcement efforts, which will contribute to increase road safety. Even though these systems might offer some benefits, there are other features of the currently used detection methods that still need to be improved. For instance, the technologies that are now in use frequently require additional assistance to localize specific motorcycles within the field of view precisely, and they may not be able to discern between drivers and passengers regarding the use of helmets. FIGURE 1: Detecting Violation of Helmet Rule for Motorcyclists To promote the research and development of such systems, the AI City Challenge provided Detecting Violation of Helmet Rule for Motorcyclists Competition (Track 5) for the first time in its 7th edition in June 2023, in Vancouver, Canada. The challenge was a part of Computer Vision and Pattern Recognition (CVPR), the leading annual academic conference in the field of artificial intelligence and computer vision. Each participant was to build a system that could determine whether individual motorcycle riders were wearing helmets within 5 months. FIGURE 2: The pipeline of the framework A research team led by Professor Jae Wook Jeon competed with 38 other teams worldwide and won the competition with 1st place. The team was able to achieve the highest detection score by developing a two-step artificial intelligence technology. This system first finds the position of many motorcycles and the riders of those motorcycles, then determines whether the riders of each motorcycle are wearing helmets. FIGURE 3: The diagram of data conversion for training both steps. With this success, the technological expertise of Professor Jae Wook Jeon's team can be showcased to the worldwide research community, and the technology that was produced is anticipate to be utilized extensively in the development of future intelligent traffic surveillance systems. Along with automatic detection of regulation violators, many problems such as lane detection, crossroad and joint detection, signal and sign detection, automatic parking, and recognition of surroundings must be solved for future smart cities. Professor Jae Wook Jeon’s team will continue research in the area of intelligent image processing in order to make dreams come true. FIGURE 4: Visualization of results. Various scenes have different outside environments.

    • No. 228
    • 2023-08-24
    • 1480
  • Prof. Woo Jong Yu's research team implements an artificial brain neural circuit that self-learns like the human brain

    Electronic and Electrical Engineering YU, WOOJONG Prof.

    Prof. Woo Jong Yu's research team implements an artificial brain neural circuit that self-learns like the human brain

    Sungkyunkwan University's Department of Electrical Engineering Professor Woo Jong Yu and Department of Energy Sciences Professor Young Hee Lee's joint research team developed an artificial brain cell device that operates equally with human brain cells through collaboration with Senior Researcher Eui Yeon Won of Hyundai Motor Company. A self-learning artificial brain neural network circuit mimicking the neural network structure was implemented. Current artificial intelligence algorithms use hundreds of large-capacity supercomputers and consume enormous amounts of electrical energy. On the other hand, the human brain implements intelligence using very little energy in a small size of about two fists. If an artificial brain that operates on the same principle as the human brain is created, artificial intelligence that operates on a small smartphone will be possible. In fact, ‘neuromorphic systems’ that mimic the human brain are being actively researched, and ‘Memristor’, a new concept memory that can precisely imitate the human brain, is attracting attention. *Neuromorphic Systems: Computers designed in the structure of Neuromorphic systems that imitate the human brain. Conventional computers follow the von Neumann architecture, which is fast for sequential calculations like mathematical operations but not suitable for the parallel calculations required for artificial intelligence. For instance, AlphaGo achieved parallel computation by connecting thousands of computers in a von Neumann structure, leading to high power consumption. In contrast, neuromorphic systems are designed to perform parallel computations efficiently by connecting thousands of small CPUs and memory units, similar to human brain cells, resulting in high AI performance with minimal power consumption. *Memristor: A portmanteau of "memory" and "resistor." Unlike regular resistors, memristors store memory by changing resistance values in response to external input and maintain these changes for a certain period. Unlike conventional memory components that remember only 0/1 states, memristors can remember over 100 states. This property enables a single memristor to replace multiple memory units, significantly reducing the size and power consumption of artificial neural networks. Memristor is a new device that stores memories by changing the resistance value, and is used to mimic ‘synapse’, a connection between brain cells and a memory storage organ. In 2016, Professor Yu's research team first developed a flash memory-based 'two-electrode-floating gate-memristor' with excellent reliability, and used it to simulate the memory storage method of synapses (Nature Commun. Doi:10.1038/ncomms12725 ). However, in order to fully implement the human brain function, a brain cell mimic device that performs calculation is additionally required. *Flash Memory: A non-volatile memory that retains stored information even when power is lost. It's widely used in portable devices like smartphones and digital cameras due to its high reliability. In this study, the research team developed 'multi-electrode-floating gate-memristor' for the first time and succeeded in mimicking the operation of brain cells (neurons) (Figure 1). Electrical signals are exchanged between brain cells connected in various ways. At this time, the brain cells accumulate the electrical signals received in the body (membrane potential) (leakage summation function), and when a certain amount of electrical signals are accumulated, a new electrical signal is generated (ignition). function). The research team first implemented artificial brain cells that perfectly follow the movements of these brain cells by using 'multi-electrode-floating gate-memristor'. The potential of the floating gate of 'multi-electrode-floating gate-memristor' simulated the membrane potential of brain cells to simulate the leaky-integrate function, and measured the amount of accumulation of electrical signals through a comparator connected in a sequential circuit and The electrical signal ignition function (fire) was simulated. 'Multi-electrode-floating gate-memristor' is a structure with several electrodes (multi-electrode), and by using this, it was possible to simulate the multi-pronged connection between brain cells. The research team next connected several 'multiple electrodes-floating gates-memristors' to each other to simulate self (unsupervised) learning of the connection structure between human brain cells (Figure 2). To this end, 'multiple electrodes-floating gates' An artificial brain neural network that connects 12 ‘gate-memristor’-based artificial brain cells (computing elements) (9 inputs, 3 outputs) with 27 ‘floating gate-memristor (2016)’ artificial synapses (memory elements). circuit was constructed. The artificial brain neural network circuit constructed in this way learned the classification of direction lines, which is the primary visual information processing function (vision cortex 1) of the human brain, on its own (unsupervised) without human intervention, and with the learned intelligence, perfectly distinguished the direction of various direction lines. gave In addition, in the learning simulation of human handwritten digit data (MNIST dataset), it self-learned (unsupervised) without human intervention and showed a high accuracy of 83%. Professor Woo Jong Yu said, “This research is significant in that it has realized active artificial intelligence that learns on its own (unsupervised) like humans through the development of artificial brain cells, synapses, and artificial brain neural network circuits that operate in perfect harmony with the human brain. “Unlike conventional supervised learning, which is confined within the boundaries of human knowledge, unsupervised learning learns on its own through data, so it is possible to implement creative artificial intelligence that can derive new knowledge that goes beyond the level of human knowledge.” *The results of this research were published in Nature Communications (IF = 17.694), a world-class science and technology journal, on May 27, and the Devices section of the 'Editors' Highlights page' selects the best papers among the latest was introduced on 14 June. * Multi-neuron connection using multi-terminal floating–gate memristor for unsupervised learning (저널: Nature Communications, DOI: https://doi.org/10.1038/s41467-023-38667-3, Editors’ Highlights page - Devices: https://www.nature.com/collections/bjiiabbacg) ▲ Figure 1. [Left] Connection structure between brain cells in the human brain. [Right] Artificial brain cell structure using ‘multi-terminal floating gate memristor’ that mimics human brain cell and connection structure. ▲ Figure 2. [Upper left] Classification of direction lines, the primary visual information processing function (visure cortex 1) of the brain [Right] 'Artificial brain neural network circuit that connects 12 brain cell (computation) elements and 27 synapse (memory) elements'

    • No. 227
    • 2023-08-11
    • 1437
  • Prior Knowledge Boosts Sensorimotor Reactions through Changes in Sensory Cortical Neural Activity

    Biomedical Engineering LEE, JOONYEOL Prof. ·JeongJun Park, Seolmin Kim

    Prior Knowledge Boosts Sensorimotor Reactions through Changes in Sensory Cortical Neural Activity

    Animals use sensory cues to adapt and excel in their environments. Among them, primates, including us humans, heavily lean on visuals. Even if clear visual information is missing, the brain combines what it sees with memories, guiding the right reactions. Yet, the exact mechanism of how the brain processes this combined info is still under investigation. Joonyeol Lee's team from the Department of Biomedical Engineering has unveiled that our past experiences play a role in adjusting brain activity, particularly in the sensory parts of the cerebral cortex. This adjustment sharpens an animal's ability to trace moving items, an insight derived from studying rhesus monkeys' behaviors and neural patterns. Interestingly, when visuals indicating movement are faint or unclear, monkeys are notably sharper at tracking them when the motion is predictable. However, when the motion's unpredictable, their tracking tends to falter. This behavior matches the shifts in brain activity in a region known as area MT (middle temporal area). In essence, activity in the area MT appears to incorporate both the motion prediction and the actual visuals, echoing the improvement in tracking eye movements. Joonyeol Lee noted that area MT does more than just pass along visual details. It processes this data differently based on past experiences, shaping behavior in the process. This study, funded by the Institute for Basic Science (IBS-R015-D1), was released online in 'Science Advances (IF 13.6)' on July 7th. Both JeongJun Park (now pursuing his PhD at Washington University in St. Louis) and Seolmin Kim were the co-first authors. The research was a collaborative effort with assistant professor HyungGoo R. Kim of the same department.

    • No. 226
    • 2023-08-01
    • 1323
  • Observed a phenomenon of an electron curve like a pitcher control a trajectory of a curved ball

    Energy Science CHOI, GYUNGMIN Prof. ·Kyung-Hun Ko : Young-Gwan Choi

    Observed a phenomenon of an electron curve like a pitcher control a trajectory of a curved ball

    Prof. Gyung-Min Choi’s team (co-1st author: Young-Gwan Choi, co-1st author: Kyung-Hun Ko) at Sungkyunkwan University and Prof. Hyun-Woo Lee’s team (co-1st author: Daegeun Jo) at Postech found that electron has a curved trajectory by the orbital Hall effect. Like a pitcher control a trajectory of a curved ball, scientists want to control a trajectory of electrons inside electronic devices. Prof. Choi’s research team found that the trajectory of electrons can be controlled by the orbital angular momentum. Prof. Choi said that this research demonstrate that angular momentum of electrons can be controlled. And this mechanism can be applied for a low power operation of magnetic memory. This work was supported by the junior researcher support program, and it was published on the Nature at July 6th. Title: Observation of the orbital Hall effect in a light metal T

    • No. 225
    • 2023-07-17
    • 1627
  • IBS Center for Neuroscience Imaging Research develops a predictive model of rumination based on fMRI

    Biomedical Engineering WOO, CHOONG-WAN Prof. ·Jungwoo Kim, a doctoral student at Sungkyunkwan University

    IBS Center for Neuroscience Imaging Research develops a predictive model of rumination based on fMRI

    "Rumination" refers to the state characterized by preoccupation with a specific thought or emotion. It resembles a cow chewing its cud repeatedly, as we "ruminate" over certain thoughts or feelings. Often, this involves incessant dwelling on negative situations, problems, or worries. This repetitive pattern of thought and fixation on negative emotions can contribute to mental health conditions such as depression or anxiety. Thus, researchers have recognized rumination as a significant risk factor for depression. The tendency for rumination varies among individuals. It has been hypothesized that this individual difference of rumination is associated with the unique brain connectivity patterns of each person, although this has not been clearly elucidated. To address this, Prof. Choong-Wan Woo's research team from the Department of Global Biomedical Engineering at Sungkyunkwan University, in collaboration with Prof. Tor Wager's research team developed a predictive model rumination. They applied machine learning to functional magnetic resonance imaging (fMRI) data obtained from healthy individuals during rest. The brain-based predictive model demonstrated its effectiveness in predicting the severity of depression in depression patients as well. Importantly, this study revealed the significant role of the dorsomedial prefrontal cortex within the default mode network in predicting the level of rumination. "The pattern of thought flow during rest reflect important aspect of our mind, and this study shows that the tendency to think negatively can be decoded from brain connectivity," said Prof. Choong-Wan Woo, who led the study. “As these findings build, we hope to use brain imaging to monitor and manage mental health in the future." The first author of this study, Jungwoo Kim, a doctoral student at Sungkyunkwan University, stated, "This study is clinically and scientifically significant as it demonstrates which brain regions and their connectivity underlie individual differences in the process of rumination." This research was supported by the Institute for Basic Science (IBS-R015-D1), the National Research Foundation of Korea (2019R1C1C1004512), and BrainKorea21 Four. This study was published on June 15, 2023 in the world-renowned journal Nature Communications (IF 17.694).

    • No. 224
    • 2023-06-29
    • 1638
  • A state-of-the-art artificial intelligence model for designing the next-generation gene editing tool called Prime Editor

    Integrative Biotechnology KIM, HUI-KWON Prof. ·co-authors Jiyun Kim and Jisung Kim

    A state-of-the-art artificial intelligence model for designing the next-generation gene editing tool called Prime Editor

    The research team, led by Professor Hui Kwon Kim from the Department of Integrative Biotechnology, in collaboration with co-authors Jiyun Kim and Jisung Kim, has successfully developed a state-of-the-art artificial intelligence model for designing the next-generation gene editing tool called Prime editor. This groundbreaking work was conducted in partnership with Professor Hyongbum Henry Kim's team from Yonsei University College of Medicine. The research findings were published in the prestigious scientific journal "Cell" on May 11, 2023. This research received support from the National Research Foundation of Korea (2020R1C1C1003284) and the Korea Drug Development Fund (HN22C0571). CRISPR-Cas9, an artificial enzyme and gene editing tool, allows for targeted cleavage and precise modification of specific genes. It serves as a fundamental technology for gene therapy, genetic modification of plants and animals, and plays a crucial role in genome-wide functional screening. The pioneering scientists responsible for developing gene editing technology using CRISPR-Cas9 were awarded the Nobel Prize in Chemistry in 2020, and this field continues to garner significant attention worldwide. While gene editing using CRISPR-Cas9 is associated with limitations such as cell toxicity, nonspecific gene corrections, and unintended byproduct generation due to double-strand DNA (DSB) cleavage, a groundbreaking technology known as Prime editing has been developed. This innovative approach allows for the introduction of new genetic information into target DNA sites without the need for DSBs (Anzalone et al., 2019, Nature). The Prime editor system consists of a Prime editor, which is a Cas9 nickase fused with a reverse transcriptase, and a prime editing guide RNA (pegRNA) that plays a crucial role in determining the efficiency of prime editing. This advanced approach is considered significantly safer and more efficient compared to traditional gene editing methods. However, the process of prime editing is complex, and the wide range of options for pegRNA designs presents challenges in its application. To address these challenges, the research team has generated a dataset on prime editing efficiency induced by over 330,000 pegRNAs over a period of approximately three years, representing the largest dataset in the field. Through systematic analysis of this dataset, the team has successfully identified key factors that determine prime editing efficiencies. Furthermore, they have developed artificial intelligence models, namely DeepPrime, DeepPrime-FT, and DeepPrime off, which enable the prediction of pegRNA efficiency and accuracy by inputting the target DNA sequence and the desired edit. Previously, the utilization of the prime editor required the synthesis and experimental validation of dozens to hundreds of pegRNAs, which was time-consuming and labor-intensive. With these cutting-edge models, researchers can now easily design the optimal pegRNA without the need for wet experiments, facilitating advancements in the field of genome editing.

    • No. 223
    • 2023-06-09
    • 1974
  • Graphynes and Graphdiynes for Energy Storage and Catalytic Utilization

    Chemistry LEE, JINYONG Prof. ·Dr. Hao Li and Dr. Jong Hyeon Lim

    Graphynes and Graphdiynes for Energy Storage and Catalytic Utilization

    Prof. Jin Yong Lee (Department of Chemistry) and his research team, including Dr. Hao Li and Dr. Jong Hyeon Lim (co-first authors) and Prof. Baotao Kang (University of Jinan, a doctoral student under Prof. Jin Yong Lee's supervision), published a theoretical studies on the application of the Graphyne and Graphdiyne (GDY) materials in various energy storage and catalytic utilization fields in Chemical Reviews (IF: 72.087) on April 26, 2023 under the title "Graphynes and Graphdiynes for Energy Storage and Catalytic Utilization: Theoretical Insights into Recent Advances." This paper explains the structural, optical, and electronic properties of the Graphyne family from the perspective of theoretical chemists. It particularly focuses on the diverse applications of GDY-based materials, which are the only experimentally reported members of the Graphyne family. Various carbon allotropes have made significant contributions to humanity with their unique and outstanding properties. Among them, Graphyne materials, as carbon-based low-dimensional substances, possess excellent physical and chemical characteristics, making them suitable for a wide range of potential applications. Especially, GDY is the only synthesized member and has garnered practical applications in various research fields, such as energy storage and catalytic utilization, due to its large surface area, sp/sp2 hybrid orbitals, and a consistent band gap. However, most of the Graphyne family has only been studied theoretically, and there are still many unsynthesized Graphyne members. Therefore, the diverse theoretical and experimental results covered in this paper can provide important insights not only to researchers in the theoretical field but also to experimental researchers, stimulating more collaborative research between theory and experiments. Efficient development of energy storage materials and catalysts is essential for addressing the global energy crisis. In this field, GDY is a promising material for various energy technologies, including electrocatalysis, photocatalysis, solar energy conversion, hydrogen storage, ion batteries, and supercapacitors. However, research on graphyne-based materials remains limited due to the lack of synthesis. This paper suggests potential applications in the field of energy materials by investigating the structure and electronic properties of graphyne through research. In the past decade, Prof. Jin Yong Lee has published dozens of papers on this topic in journals such as Chem. Eng. J., Energy & Environ. Mater., Carbon, App. Surf. Sci., Phys. Chem. Chem. Phys. *Title:Graphynes and Graphdiynes for Energy Storage and Catalytic Utilization: Theoretical Insights into Recent Advances

    • No. 222
    • 2023-05-31
    • 1356
  • Detection of the ultra-sensitive presence of perfluorooctanoic acid (PFOA)

    Bio-Mechatronic Engineering PARK, JINSUNG Prof.

    Detection of the ultra-sensitive presence of perfluorooctanoic acid (PFOA)

    Professor Jinsung Park's research team has achieved a groundbreaking feat by successfully detecting the ultra-sensitive presence of perfluorooctanoic acid (PFOA) using self-assembled p-Phenylenediamine* nanoparticles (SAp-PD). This marks the first-ever application of SAp-PD nanoparticles for detecting PFOA, which is generated during the cooking process of Teflon-coated frying pans. The team's remarkable accomplishment was published in the esteemed international journal "Journal of Hazardous Materials," known for the top 3% in the field of environmental science. *p-Phenylenediamine(p-PD) : It is an organic compound that is one of the ingredients in dyes, possessing an amino group (-NH2) at both ends. It typically exists as a white powder at room temperature, but it exhibits a characteristic reddish-brown color when oxidized in an aqueous solution. PFOA, widely employed across various industries for its surfactant properties, heat resistance, and non-stick characteristics, has particularly found extensive use as a coating agent for cooking utensils such as frying pans. However, its classification as a Group 2B carcinogen by the International Agency for Research on Cancer (IARC), a subsidiary of the World Health Organization (WHO), has led to restrictions on its utilization. The detection of elevated levels of perfluorinated compounds in Daegu's tap water in 2018 further intensified the significance of detecting PFOA with high sensitivity. The research team employed Raman spectroscopy, a technique that detects and analyzes unique spectral patterns produced when examining specific laser wavelengths on materials. Each substance possesses its distinct spectrum, analogous to individual fingerprints. To maximize the Raman signal, they incorporated the Surface Enhanced Raman Spectroscopy (SERS) method, which relies on the nanostructure of the substrate to enhance the analysis. In this study, the team utilized a silver nanograss substrate, on which they measured the variation in Raman intensity of SAp-PD—exhibiting its own distinct Raman spectrum—before and after exposure to PFOA. . In a significant scientific breakthrough, research team have developed an ultra-sensitive detection sensor technology for perfluorooctanoic acid (PFOA) by harnessing the unique properties of p-Phenylenediamine (p-PD). When p-PD is present in an aqueous solution, it gradually undergoes oxidation over time, transforming into self-assembled spherical nanoparticles while exhibiting distinct Raman spectra. However, when p-PD interacts with PFOA, the nanostructure undergoes collapse, leading to a reduction in Raman spectrum intensity. Leveraging this mechanism, the team successfully developed a pioneering technology for detecting PFOA with exceptional sensitivity, marking a significant advancement in the field. . The research team achieved the detection of PFOA in distilled water at an astonishingly low concentration of 1.28 pM (pico molar, 10^-12 M) using SAp-PD nanoparticles. In real environmental, they detected PFOA at a concentration of 1.6 nM (nano molar, 10^-9 M) in tap water. Impressively, they also succeeded in detecting PFOA concentrations of 1.69 nM and 10.3 μM (micro molar, 10^-6 M) when examining Teflon-coated frying pans scratched with an iron scourer and freshly cooked rice in the same pan, respectively. Professor Jinsung Park expressed his optimism about the potential applications of this technology, stating, "The proposed technology holds significant promise not only for detecting perfluorinated compounds but also for identifying various environmentally toxic substances." He emphasized the prospects of applying the developed technology in future environmental monitoring efforts. Hyunjun Park, the doctoral candidate involved in the research, shared his aspirations, saying, "Building upon this study, I hope to establish a versatile multi-sensing platform capable of highly sensitive detection of toxic substances that pose risks to human health in real environmental." The research was conducted with the support by the Environmental Pollution Management Technology Development Project by the Korea Environmental Industry & Technology Institute and received funding from the Basic Science Research and Mid-career Research Programs of the National Research. Meanwhile, Professor Jinsung Park has been making significant strides in the field of detecting toxic substances in disease-inducing environments. Notably, he recently published his research achievements on the development of a sensor for detecting heavy metals in wastewater in the renowned international academic journal ACS Sustainable Chemistry & Engineering (IF: 9.224) on April 21. This publication further demonstrates Professor Park's dedication to advancing research on the identification of toxic substances linked to the onset of various diseases. ※ Paper: Ultra-sensitive SERS detection of perfluorooctanoic acid based on self-assembled p-phenylenediamine nanoparticle complex ※ Journal: Journal of Hazardous Materials, IF: 14.224 ※ https://www.sciencedirect.com/science/article/abs/pii/S0304389423006672?via%3Dihub

    • No. 221
    • 2023-05-22
    • 1755
  • Development of the artificial chemosensory neuronal synapse that mimic the biological olfactory system

    Advanced Materials Science and Engineering LEE, NAEEUNG Prof.

    Development of the artificial chemosensory neuronal synapse that mimic the biological olfactory system

    Prof. Nae-Eung Lee's lab at the Department of Materials Science and Engineering has been developing artificial sensory systems that can imitate the fundamental functions of the human sensory systems, such as the tactile, auditory, visionary, gustatory, and olfactory systems. The development of an artificial sensory system that imitates the human sensory nervous system and performs intelligent and energy-efficient signal processing is expected to be applied in various autonomous systems, such as future robots and automobiles, as part of artificial intelligence technology. It is surprising that the human body is so sophisticated that it can pre-process all sensory information at the organ level before sending it to higher brain centers. Surprisingly, the human body is highly sophisticated and can pre-process all the sensory information at the organ level before transmitting it to higher brain centers. The lab's recent work on the artificial chemosensory neuronal synapse was inspired by the excitatory and inhibitory synaptic functions of the neurons and synapses in the human olfactory system and was published in "Nature Communications." The team developed a simple device design by combining a chemoreceptive ionogel and a mixed ion-electronic conducting semiconductor channel. This design enables long-term retentive memory in response to chemical stimuli, with the memorized signals being erased by applying electrical stimuli to the device. The overall modulation of the signals is similar to the functions of the human nose, where different neuronal connections help to memorize and inhibit the signals from chemoreceptors. Prof. Lee and their team, including co-corresponding author Dr. Atanu Bag and Ph.D. candidate Hamna Haq Chouhdry, discussed that this proposed device's concept can significantly reduce energy consumption and the amount of data required from sensors for neuromorphic signal processing. The data, pre-processed at the device level, can be directly utilized for software-based or hardware-based neuromorphic processing. This will lead to further research on artificial sensory systems and organs to implement neuromorphic cognitive functions that mimic the human sensory system. This research was conducted at a university-focused research institute supported by the Ministry of Education and the Ministry of Science and ICT through the National Research Foundation of Korea and mid-career researcher support project, respectively. The research was published online in February 2023 in "Nature Communications" with the title "A flexible artificial chemosensory neuronal synapse based on chemoreceptive ionogel-gated electrochemical transistor," which was featured as research under the "Devices" section of Nature Communications Editor's Highlights webpage. Fig. Top panel shows the comparison of the biological and artificial chemosensory synapse. The lower panel shows device operation under excitatory chemical and inhibitory electrical stimulus.

    • No. 220
    • 2023-05-08
    • 2331
  • Wavelength engineerable porous organic polymer photosensitizers with protonation triggered ROS generation

    Chemistry LEE, JINYONG Prof. ·Ph. D. Jong Hyeon Lim

    Wavelength engineerable porous organic polymer photosensitizers with protonation triggered ROS generation

    The research team led by Prof. Jin Yong Lee of the Department of Chemistry (co-first author Ph. D. Jong Hyeon Lim) has developed a new porous organic photosensitizer (PS) system, KUP-1 for photodynamic therapy (PDT) through collaborative research with research teams led by Prof. Sungnam Park (Korea University), Prof. Dokyoung Kim (Kyung Hee University), Prof. Jong Seung Kim (Korea University), and Prof. Chang Seop Hong (Korea University). The research was published online in Nature Communications (IF: 17.694) in March 2023 under the title "Wavelength engineearable porous organic polymer photosensitizers with protonation triggered ROS generation. Although PDT is the most attractive and promising non-invasive therapy for cancer and microbial infection treatment, since several stages are involved in energy transfer process from the excitation of PS to the generation of reactive oxygen species (ROS), its ROS generation efficiency can lead to a significant variation with even slight changes in the excitation wavelength. Therefore, it is a challenge to adjust the excitation wavelength conditions of PS maintaining high ROS generation efficiency, which is also necessary to improve the therapeutic efficiency of PDT. The KUP system developed in this study is an imidazolium-based porous organic polymer material via cost-effective one-pot reactions. Remarkably, the optimal wavelength for maximum performance for generating ROS can be tuned by modifying the linker, despite the absence of metal ions and covalently attached heavy atoms. However, the detailed mechanism was not well understood, so Prof. Jin Yong Lee's group presented a theoretical basis for the experimental results showing that only ROS was generated in the protonated KUP system by comparing the relative rates of reactions that occur during the ROS generation process through non-adiabatic molecular dynamics (NAMD) simulations and density functional theory (DFT) calculations. They also confirmed that the adsorption energy between the protonated KUP system and oxygen molecule was higher than that of the neutral system. It is expected that the new PS developed in this study will contribute to the development of PDT for cancer treatment. *Title: Wavelength engineerable porous organic polymer photosensitizers with protonation triggered ROS generation.

    • No. 219
    • 2023-04-24
    • 2047
  • Content Manager