Department+of+Materials+Science+and+Engineering
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A KAIST Research Team Develops an Ultra-High Performing “Universal Electrode” for Next-Generation Fuel Cells
Fuel cells are devices that generate electricity with high efficiency using hydrogen, a clean energy source, and are expected to play an important part in the upcoming hydrogen society. The recent development of an excellent universal electrode material that is applicable to all next-generation fuel cells and can withstand 700 hours of operation has therefore garnered a great deal of attention.
On August 9, a joint research team led by Prof. WooChul Jung from the KAIST Department of Materials Science and Engineering, Prof. Kang Taek Lee from the KAIST Department of Mechanical Engineering, and Prof. Jun Hyuk Kim from the Department of Chemical Engineering at Hongik University announced the development of an electrode material that is applicable to both oxygen- and proton-conducting solid oxide cells.
Depending on the type of ion conducted by the electrolyte, ceramic fuel cells are categorized into either solid oxide fuel cells (SOFC) or protonic ceramic fuel cells (PCFC). As they can both convert between electricity and hydrogen production, fuel cells can be categorized into a total of four device types. These devices are applicable in hydrogen fuel cell vehicles, hydrogen charging stations, and power generation systems, and are henceforth emerging as core next-generation technologies for a carbon-neutral society.
However, these devices have a chronic problem where the speed of their slowest reaction would decrease with a drop of driving temperature, which greatly reduces device efficiency. Various studies have been conducted to solve this, but most reported that electrode materials have low catalytic activity and their applications are limited to specific devices, which limits them from being used as SOFCs that require reversible power conversion and hydrogen production.
< Figure 1. Schematic diagram of high-performance oxygen ion conductive solid oxide fuel cell (SOFC) and proton conductive ceramic fuel cell (PCFC) operates with the new universal electrodes >
To solve this issue, the research team doped a perovskite oxide material with Ta5+, a high valence ion that did not receive much attention in the field. Through this, the team successfully stabilized what is usually a highly unstable crystal structure, and confirmed that catalytic activity improved by 100 times.
The electrode material developed by the team was applied to all four of the mentioned device types. Furthermore, their efficiencies were greater than any of the devices reported thus far, and showed excellent performance by stably running for much longer (700 hours) compared to existing materials that deteriorated within the first 100 hours of operation.
< Figure 2. (a) Power conversion and hydrogen production performance chart for the protonic ceramic fuel cell (PCFC) with the new universal electrodes (b) and performance comparison with other reported devices >
This research, in which KAIST’s Ph.D. candidates Dongyeon Kim and Sejong Ahn, and Professor Jun Hyuk Kim from Hongik University contributed as co-first authors, was published in the internationally renowned Energy & Environmental Science under the title, "Oxygen-Electrode for Reversible Solid Oxide Electrochemical Cells at Reduced Temperatures".
Prof. WooChul Jung said, “We broke free from the idea that we must develop a completely new material to solve an existing problem, and instead suggested a way to control the crystal structure of a lesser-known material to develop a high-efficiency fuel cell, and that’s what makes these results more significant.”
Prof. Kang Taek Lee added, “Unlike previously reported materials that could only be applied to one device type at a time, our material has the flexibility of being applicable to all four. We therefore look forward to its contribution in the commercialization of eco-friendly energy technology including fuel cells and water-splitting equipment for hydrogen production.”
This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Ministry of Science and ICT.
2023.08.22 View 4648 -
KAIST Team Develops Highly-Sensitive Wearable Piezoelectric Blood Pressure Sensor for Continuous Health Monitoring
- A collaborative research team led by KAIST Professor Keon Jae Lee verifies the accuracy of the highly-sensitive sensor through clinical trials
- Commercialization of the watch and patch-type sensor is in progress
A KAIST research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering and the College of Medicine of the Catholic University of Korea has developed a highly sensitive, wearable piezoelectric blood pressure sensor.
Blood pressure is a critical indicator for assessing general health and predicting stroke or heart failure. In particular, cardiovascular disease is the leading cause of global death, therefore, periodic measurement of blood pressure is crucial for personal healthcare.
Recently, there has been a growing interest in healthcare devices for continuous blood pressure monitoring. Although smart watches using LED-based photoplethysmography (PPG) technology have been on market, these devices have been limited by the accuracy constraints of optical sensors, making it hard to meet the international standards of automatic sphygmomanometers.
Professor Lee’s team has developed the wearable piezoelectric blood pressure sensor by transferring a highly sensitive, inorganic piezoelectric membrane from bulk sapphire substrates to flexible substrates. Ultrathin piezoelectric sensors with a thickness of several micrometers (one hundredth of the human hair) exhibit conformal contact with the skin to successfully collect accurate blood pressure from the subtle pulsation of the blood vessels.
Clinical trial at the St. Mary’s Hospital of the Catholic University validated the accuracy of blood pressure sensor at par with international standard with errors within ±5 mmHg and a standard deviation under 8 mmHg for both systolic and diastolic blood pressure. In addition, the research team successfully embedded the sensor on a watch-type product to enable continuous monitoring of blood pressure.
Prof. Keon Jae Lee said, “Major target of our healthcare devices is hypertensive patients for their daily medical check-up. We plan to develop a comfortable patch-type sensor to monitor blood pressure during sleep and have a start-up company commercialize these watch and patch-type products soon.”
This result titled “Clinical validation of wearable piezoelectric blood pressure sensor for health monitoring” was published in the online issue of Advanced Materials on March 24th, 2023. (DOI: 10.1002/adma.202301627)
Figure 1. Schematic illustration of the overall concept for a wearable piezoelectric blood pressure sensor (WPBPS).
Figure 2. Wearable piezoelectric blood pressure sensor (WPBPS) mounted on a watch (a) Schematic design of the WPBPS-embedded wristwatch. (b) Block diagram of the wireless communication circuit, which filters, amplifies, and transmits wireless data to portable devices. (c) Pulse waveforms transmitted from the wristwatch to the portable device by the wireless communication circuit. The inset shows a photograph of monitoring a user’s beat-to-beat pulses and their corresponding BP values in real time using the developed WPBPS-mounted wristwatch.
2023.04.17 View 5332 -
KAIST Team Develops Surface-Lighting MicroLED Patch with Significant Melanogenesis Inhibition Effect
A KAIST research team led by Ph.d candidate Jae Hee Lee and Professor Keon Jae Lee from the Department of Materials Science and Engineering has developed a surface-lighting microLED patch for UV-induced melanogenesis inhibition.
Melanin is brown or dark pigments existing in the skin, which can be abnormally synthesized by external UV or stress. Since the excessive melanin leads to skin diseases such as spots and freckles, proper treatment is required to return normal skin condition.
Recently, LED-based photo-stimulators have been released for skin care, however, their therapeutic effect is still controversial. Since conventional LED stimulators cannot conformally attach to the human skin, distance-induced side effects are caused by light loss and high heat transfer. To achieve effective phototreatment, the LED stimulator needs to be irradiated in contact with the human skin surface, enabling proper and uniform light deliver to the dermis with minimal optical loss.
In this work, the research team fabricated skin-attachable surface-lighting microLED (SµLED, 4 × 4 cm2) patch by utilizing a thousand of microLED chips and silica-embedded light diffusion layer. 100 µm-sized LED chips are vertically-interconnected for high flexibility and low heat generation, allowing its long-term operation on the human skin.
< Image 1. The overall concept of SµLED patch. a) SµLED patch operated on the human skin. b) Schematic illustration of SµLED patch structure. c) 4 × 4 cm2-sized SµLED patch. d) Schematic illustration of the advantages of SµLED patch such as efficient light delivery, low heat generation, and surface-lighting irradiation. >
The research team confirmed melanogenesis inhibition by irradiating the SµLED patch and the conventional LED (CLED) on the artificial human skin and mice dorsal skin. The SµLED-treated groups of human cells and mouse tissues showed minimal epidermal photo-toxicity and consistently effective reduction in synthesized melanin, compared to CLED-treated groups. In addition, significant suppression of proteins/catalysts expression involved in melanin synthesis such as MITF (microphthalmia-associated transcription factor), Melan-A and tyrosinase was verified.
< Image 2. The efficacy of melanogenesis inhibition on 3D human skin cells. a). Different irradiation conditions for a-MSH (major factor to stimulate melanin synthesis) treated cells. b) The ratio of pigmented area to total epidermis area. c) Relative variance of melanin level in 1 cm2-sized skin cells. A low variance means that melanin is evenly distributed, and a high variance means that the melanin is irregularly distributed. d) Optical images after in vitro experiments for 12 days. Scale bar, 1cm. e) Histological analysis of 3D skin, showing the greatest reduction in melanin after SµLED irradiation. Scale bar, 20 µm. >
< Image 3. The efficacy of melanogenesis inhibition on mouse dorsal skin. a) Optical images of mice dorsal skin after photo-treatment for 20 days. b) Histological analysis of mice dorsal skin. Less brown color means less expression of protein/catalysis involved in melanin synthesis. Scale bar, 50 µm. >
Prof. Keon Jae Lee said, “Our inorganic-based SµLED patch has outstanding characteristics in light efficiency, reliability, and durability. The SµLED patch is expected to give a great impact on the cosmetic field by reducing side effects and maximizing phototherapeutic effects.” The core technology of cosmetic SµLED has been transferred to Fronics co., Ltd, founded by Prof. Lee. Fronics is building foundry and equipment for mass production of SµLED masks for whole face cover and plans to release the products in March next year.
This paper entitled “Wearable Surface-Lighting Micro-Light-Emitting Diode Patch for Melanogenesis Inhibition” was published in the November 2022 issue of Advanced Healthcare Materials.
2022.11.22 View 7976 -
PICASSO Technique Drives Biological Molecules into Technicolor
The new imaging approach brings current imaging colors from four to more than 15 for mapping overlapping proteins
Pablo Picasso’s surreal cubist artistic style shifted common features into unrecognizable scenes, but a new imaging approach bearing his namesake may elucidate the most complicated subject: the brain. Employing artificial intelligence to clarify spectral color blending of tiny molecules used to stain specific proteins and other items of research interest, the PICASSO technique, allows researchers to use more than 15 colors to image and parse our overlapping proteins.
The PICASSO developers, based in Korea, published their approach on May 5 in Nature Communications.
Fluorophores — the staining molecules — emit specific colors when excited by a light, but if more than four fluorophores are used, their emitted colors overlap and blend. Researchers previously developed techniques to correct this spectral overlap by precisely defining the matrix of mixed and unmixed images. This measurement depends on reference spectra, found by identifying clear images of only one fluorophore-stained specimen or of multiple, identically prepared specimens that only contain a single fluorophore each.
“Such reference spectra measurement could be complicated to perform in highly heterogeneous specimens, such as the brain, due to the highly varied emission spectra of fluorophores depending on the subregions from which the spectra were measured,” said co-corresponding author Young-Gyu Yoon, professor in the School of Electrical Engineering at KAIST. He explained that the subregions would each need their own spectra reference measurements, making for an inefficient, time-consuming process. “To address this problem, we developed an approach that does not require reference spectra measurements.”
The approach is the “Process of ultra-multiplexed Imaging of biomolecules viA the unmixing of the Signals of Spectrally Overlapping fluorophores,” also known as PICASSO. Ultra-multiplexed imaging refers to visualizing the numerous individual components of a unit. Like a cinema multiplex in which each theater plays a different movie, each protein in a cell has a different role. By staining with fluorophores, researchers can begin to understand those roles.
“We devised a strategy based on information theory; unmixing is performed by iteratively minimizing the mutual information between mixed images,” said co-corresponding author Jae-Byum Chang, professor in the Department of Materials Science and Engineering, KAIST. “This allows us to get away with the assumption that the spatial distribution of different proteins is mutually exclusive and enables accurate information unmixing.”
To demonstrate PICASSO’s capabilities, the researchers applied the technique to imaging a mouse brain. With a single round of staining, they performed 15-color multiplexed imaging of a mouse brain. Although small, mouse brains are still complex, multifaceted organs that can take significant resources to map. According to the researchers, PICASSO can improve the capabilities of other imaging techniques and allow for the use of even more fluorophore colors.
Using one such imaging technique in combination with PICASSO, the team achieved 45-color multiplexed imaging of the mouse brain in only three staining and imaging cycles, according to Yoon.
“PICASSO is a versatile tool for the multiplexed biomolecule imaging of cultured cells, tissue slices and clinical specimens,” Chang said. “We anticipate that PICASSO will be useful for a broad range of applications for which biomolecules’ spatial information is important. One such application the tool would be useful for is revealing the cellular heterogeneities of tumor microenvironments, especially the heterogeneous populations of immune cells, which are closely related to cancer prognoses and the efficacy of cancer therapies.”
The Samsung Research Funding & Incubation Center for Future Technology supported this work. Spectral imaging was performed at the Korea Basic Science Institute Western Seoul Center.
-PublicationJunyoung Seo, Yeonbo Sim, Jeewon Kim, Hyunwoo Kim, In Cho, Hoyeon Nam, Yong-Gyu Yoon, Jae-Byum Chang, “PICASSO allows ultra-multiplexed fluorescence imaging of spatiallyoverlapping proteins without reference spectra measurements,” May 5, Nature Communications (doi.org/10.1038/s41467-022-30168-z)
-ProfileProfessor Jae-Byum ChangDepartment of Materials Science and EngineeringCollege of EngineeringKAIST
Professor Young-Gyu YoonSchool of Electrical EngineeringCollege of EngineeringKAIST
2022.06.22 View 7389 -
2022 KAIST Research Day Recognizes 10 Outstanding Researches
On May 31, the 2022 KAIST Research Day was held at the Jeong Geun-mo Conference Hall at KAIST’s main campus.
Since 2016, Research Day has been a yearly festival for researchers at KAIST. By introducing major research achievements and providing opportunities for information exchanges in R&D, it aims to create an atmosphere for mutual cooperation and communication amongst researchers, thereby vitalizing interdisciplinary research.
At this year’s event, 10 faculty members and their representative research achievements were rewarded. As the winner of the Grand Prize for Research, Professor Il-Doo Kim (Department of Materials Science and Engineering) gave a lecture on his topic, “Ultrasensitive flexible chemical sensor”.
With rising attention being given to environmental safety and healthcare, the importance of mobile sensors for trace amounts of molecules that can quickly raise hazard signals and allow early diagnosis from breath analysis have been brought to light. The lecture will break down ultrasensitive chemical sensor development cases, and introduced how gas sensor technologies developed at KAIST in particular are being applied at semiconductor and display fabrication plants for environmental and safety analyses and hazard prevention.
Professor Il-Doo Kim is a recognized researcher for his inventive achievements in the fields of respiratory gas sensor technology for early disease monitoring, and ordered nanofiber membranes for antiviral and fine dust filters.
Professor Kim has so far published 343 international research papers, received 56 journal covers, been awarded 230 domestic and international patents, and completed 12 technology transfers. He has also received a presidential award on the 51st invention day in 2016, Scientist of the Year Award selected by reporters in 2019, and has been named a fellow in the engineering division of the Korean Academy of Science and Technology in 2022.
Professor Kwang-Hyun Cho at the Department of Bio and Brain Engineering and Professor Doh Chang Lee at the Department of Chemical and Biomolecular Engineering were each awarded the Research Award, and Professor Dongsoo Han at the School of Computing received the Innovation Award.
Professors Buhm Soon Park at the Graduate School of Science and Technology Policy, Changick Kim at the School of Electrical Engineering and Hyun Jung Cho at the School of Digital Humanities and Computational Social Sciences received the Interdisciplinary Research Award as a team.
The passion and experiences of the awardees are to be introduced to undergraduate and graduate students as well as fellow researchers through a pre-recorded video lecture, while the lecture of the winner of the grand prize will be delivered on site.
Meanwhile, the top ten R&D achievements of KAIST selected excellent research outcomes from the natural and biological sciences including “Polariton-based PT symmetry laser that turns loss into gain” (Professor Yong-Hoon Cho at the Department of Physics), “Solution to the Riemann Problem including weak shock waves in 1-dimensional space” (Professor Moon-Jin Kang at the Department of Mathematical Sciences), and “Characterization of immune reaction in COVID-19 patients” (Professor Eui-Cheol Shin at the Graduate School of Medical Science and Engineering.)
Awardees from the engineering field included “Fluid surface stabilization technology using plasma jet” (Professor Wonho Choe at the Department of Nuclear and Quantum Engineering, “Visual recognition technology using event-based cameras” (Professor Kuk-Jin Yoon at theDepartment of Mechanical Engineering, “Artificial sensory system development through neural signal mimicry” (Professor Seongjun Park at the Department of Bio and Brain Engineering, “Mott transition material-based ultrahigh speed, low-power, and deformation-resistant true random number generator” (Professor Kyung Min Kim at the Department of Materials Science and Engineering, “Investment service design based on Aline: ESG” (Professor Sangsu Lee at the Department of Industrial Design), “Structural color printing technology without chemical colorings” (Professor Shin-Hyun Kim at the Department of Chemical and Biomolecular Engineering), and “Differentiable transient optical transfer simulation” (Professor Minhyuk Kim at the School of Computing)
To encourage the participation of members of KAIST, all parts of the ceremony will be broadcast live through YouTube in both English and Korean.” He added, “Offline audiences will congratulate the awardees at Fusion Hall in the KI Building and gain research ideas.”
2022.06.10 View 6899 -
Neuromorphic Memory Device Simulates Neurons and Synapses
Simultaneous emulation of neuronal and synaptic properties promotes the development of brain-like artificial intelligence
Researchers have reported a nano-sized neuromorphic memory device that emulates neurons and synapses simultaneously in a unit cell, another step toward completing the goal of neuromorphic computing designed to rigorously mimic the human brain with semiconductor devices.
Neuromorphic computing aims to realize artificial intelligence (AI) by mimicking the mechanisms of neurons and synapses that make up the human brain. Inspired by the cognitive functions of the human brain that current computers cannot provide, neuromorphic devices have been widely investigated. However, current Complementary Metal-Oxide Semiconductor (CMOS)-based neuromorphic circuits simply connect artificial neurons and synapses without synergistic interactions, and the concomitant implementation of neurons and synapses still remains a challenge. To address these issues, a research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering implemented the biological working mechanisms of humans by introducing the neuron-synapse interactions in a single memory cell, rather than the conventional approach of electrically connecting artificial neuronal and synaptic devices.
Similar to commercial graphics cards, the artificial synaptic devices previously studied often used to accelerate parallel computations, which shows clear differences from the operational mechanisms of the human brain. The research team implemented the synergistic interactions between neurons and synapses in the neuromorphic memory device, emulating the mechanisms of the biological neural network. In addition, the developed neuromorphic device can replace complex CMOS neuron circuits with a single device, providing high scalability and cost efficiency.
The human brain consists of a complex network of 100 billion neurons and 100 trillion synapses. The functions and structures of neurons and synapses can flexibly change according to the external stimuli, adapting to the surrounding environment. The research team developed a neuromorphic device in which short-term and long-term memories coexist using volatile and non-volatile memory devices that mimic the characteristics of neurons and synapses, respectively. A threshold switch device is used as volatile memory and phase-change memory is used as a non-volatile device. Two thin-film devices are integrated without intermediate electrodes, implementing the functional adaptability of neurons and synapses in the neuromorphic memory.
Professor Keon Jae Lee explained, "Neurons and synapses interact with each other to establish cognitive functions such as memory and learning, so simulating both is an essential element for brain-inspired artificial intelligence. The developed neuromorphic memory device also mimics the retraining effect that allows quick learning of the forgotten information by implementing a positive feedback effect between neurons and synapses.”
This result entitled “Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse” was published in the May 19, 2022 issue of Nature Communications.
-Publication:Sang Hyun Sung, Tae Jin Kim, Hyera Shin, Tae Hong Im, and Keon Jae Lee (2022) “Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse,” Nature Communications May 19, 2022 (DOI: 10.1038/s41467-022-30432-2)
-Profile:Professor Keon Jae Leehttp://fand.kaist.ac.kr
Department of Materials Science and EngineeringKAIST
2022.05.20 View 10078 -
'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 19561 -
Five Projects Ranked in the Top 100 for National R&D Excellence
Five KAIST research projects were selected as the 2021 Top 100 for National R&D Excellence by the Ministry of Science and ICT and the Korea Institute of Science & Technology Evaluation and Planning.
The five projects are:-The development of E. coli that proliferates with only formic acid and carbon dioxide by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering
-An original reverse aging technology that restores an old human skin cell into a younger one by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering-The development of next-generation high-efficiency perovskite-silicon tandem solar cells by Professor Byungha Shin from the Department of Materials Science and Engineering-Research on the effects of ultrafine dust in the atmosphere has on energy consumption by Professor Jiyong Eom from the School of Business and Technology Management-Research on a molecular trigger that controls the phase transformation of bio materials by Professor Myungchul Kim from the Department of Bio and Brain Engineering
Started in 2006, an Evaluation Committee composed of experts in industries, universities, and research institutes has made the preliminary selections of the most outstanding research projects based on their significance as a scientific and technological development and their socioeconomic effects. The finalists went through an open public evaluation. The final 100 studies are from six fields: 18 from mechanics & materials, 26 from biology & marine sciences, 19 from ICT & electronics, 10 from interdisciplinary research, and nine from natural science and infrastructure.
The selected 100 studies will receive a certificate and an award plaque from the minister of MSIT as well as additional points for business and institutional evaluations according to appropriate regulations, and the selected researchers will be strongly recommended as candidates for national meritorious awards.
In particular, to help the 100 selected research projects become more accessible for the general public, their main contents will be provided in a free e-book ‘The Top 100 for National R&D Excellence of 2021’ that will be available from online booksellers.
2022.02.17 View 8382 -
New Chiral Nanostructures to Extend the Material Platform
Researchers observed a wide window of chiroptical activity from nanomaterials
A research team transferred chirality from the molecular scale to a microscale to extend material platforms and applications. The optical activity from this novel chiral material encompasses to short-wave infrared region.
This platform could serve as a powerful strategy for hierarchical chirality transfer through self-assembly, generating broad optical activity and providing immense applications including bio, telecommunication, and imaging technique. This is the first observation of such a wide window of chiroptical activity from nanomaterials.
“We synthesized chiral copper sulfides using cysteine, as the stabilizer, and transferring the chirality from molecular to the microscale through self-assembly,” explained Professor Jihyeon Yeom from the Department of Materials Science and Engineering, who led the research. The result was reported in ACS Nano on September 14.
Chiral nanomaterials provide a rich platform for versatile applications. Tuning the wavelength of polarization rotation maxima in the broad range is a promising candidate for infrared neural stimulation, imaging, and nanothermometry. However, the majority of previously developed chiral nanomaterials revealed the optical activity in a relatively shorter wavelength range, not in short-wave infrared.
To achieve chiroptical activity in the short-wave infrared region, materials should be in sub-micrometer dimensions, which are compatible with the wavelength of short-wave infrared region light for strong light-matter interaction. They also should have the optical property of short-wave infrared region absorption while forming a structure with chirality.
Professor Yeom’s team induced self-assembly of the chiral nanoparticles by controlling the attraction and repulsion forces between the building block nanoparticles. During this process, molecular chirality of cysteine was transferred to the nanoscale chirality of nanoparticles, and then transferred to the micrometer scale chirality of nanoflowers with 1.5-2 2 μm dimensions formed by the self-assembly.
“We will work to expand the wavelength range of chiroptical activity to the short-wave infrared region, thus reshaping our daily lives in the form of a bio-barcode that can store vast amount of information under the skin,” said Professor Yeom.
This study was funded by the Ministry of Science and ICT, the Ministry of Health and Welfare, the Ministry of Food and Drug Safety, the National Research Foundation of Korea,the KAIST URP Program, the KAIST Creative Challenging Research Program, Samsung and POSCO Science Fellowship.
-PublicationKi Hyun Park, Junyoung Kwon, Uichang Jeong, Ji-Young Kim, Nicholas A.Kotov, Jihyeon Yeom, “Broad Chrioptical Activity from Ultraviolet to Short-Wave Infrared by Chirality Transfer from Molecular to Micrometer Scale," September 14, 2021 ACS Nano (https://doi.org/10.1021/acsnano.1c05888)
-ProfileProfessor Jihyeon YeomNovel Nanomaterials for New Platforms LaboratoryDepartment of Materials Science and EngineeringKAIST
2021.10.22 View 8144 -
Professor Il-Doo Kim Receives the Science Minister’s Award
Professor Il-Doo Kim from the Department of Materials Science and Engineering received the Science and ICT Minister’s Award in recognition of his commercialization and technology transfer achievements during the Day of IP celebration.
Professor Kim, who has made over 222 patents application and registration home and abroad, has advanced toxic gas detection and breath gas sensor technology by arraying nanosensor fibers. His technological advances in micro-electro-mechanical systems (MEMS) helped to advance the commercialization of the MEMS-related sensor and improve its overall competitiveness.
He founded the Il-Doo Kim Research Center in 2019 and focuses on the commercialization of nanofiber manufacturing through electrospinning and highly efficient nanofiber filters. For instance, he succeeded in manufacturing a nano-filter recyclable mask that maintains excellent filtering efficiency even after hand washing through the development of proprietary technology that aligns nanofibers with a diameter of 100~500 nanometers in orthogonal or unidirectional directions.
Professor Kim also serves as an associate editor at ACS Nano. He said, “The importance of IP goes without saying. I look forward to the registration and application of more KAIST patents leading to commercialization, paving the way for national technological competitiveness.”
2021.09.15 View 5724 -
Biomimetic Resonant Acoustic Sensor Detecting Far-Distant Voices Accurately to Hit the Market
A KAIST research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering has developed a bioinspired flexible piezoelectric acoustic sensor with multi-resonant ultrathin piezoelectric membrane mimicking the basilar membrane of the human cochlea. The flexible acoustic sensor has been miniaturized for embedding into smartphones and the first commercial prototype is ready for accurate and far-distant voice detection.
In 2018, Professor Lee presented the first concept of a flexible piezoelectric acoustic sensor, inspired by the fact that humans can accurately detect far-distant voices using a multi-resonant trapezoidal membrane with 20,000 hair cells. However, previous acoustic sensors could not be integrated into commercial products like smartphones and AI speakers due to their large device size.
In this work, the research team fabricated a mobile-sized acoustic sensor by adopting ultrathin piezoelectric membranes with high sensitivity. Simulation studies proved that the ultrathin polymer underneath inorganic piezoelectric thin film can broaden the resonant bandwidth to cover the entire voice frequency range using seven channels. Based on this theory, the research team successfully demonstrated the miniaturized acoustic sensor mounted in commercial smartphones and AI speakers for machine learning-based biometric authentication and voice processing. (Please refer to the explanatory movie KAIST Flexible Piezoelectric Mobile Acoustic Sensor).
The resonant mobile acoustic sensor has superior sensitivity and multi-channel signals compared to conventional condenser microphones with a single channel, and it has shown highly accurate and far-distant speaker identification with a small amount of voice training data. The error rate of speaker identification was significantly reduced by 56% (with 150 training datasets) and 75% (with 2,800 training datasets) compared to that of a MEMS condenser device.
Professor Lee said, “Recently, Google has been targeting the ‘Wolverine Project’ on far-distant voice separation from multi-users for next-generation AI user interfaces. I expect that our multi-channel resonant acoustic sensor with abundant voice information is the best fit for this application. Currently, the mass production process is on the verge of completion, so we hope that this will be used in our daily lives very soon.”
Professor Lee also established a startup company called Fronics Inc., located both in Korea and U.S. (branch office) to commercialize this flexible acoustic sensor and is seeking collaborations with global AI companies.
These research results entitled “Biomimetic and Flexible Piezoelectric Mobile Acoustic Sensors with Multi-Resonant Ultrathin Structures for Machine Learning Biometrics” were published in Science Advances in 2021 (7, eabe5683).
-Publication
“Biomimetic and flexible piezoelectric mobile acoustic sensors with multiresonant ultrathin structures for machine learning biometrics,” Science Advances (DOI: 10.1126/sciadv.abe5683)
-Profile
Professor Keon Jae Lee
Department of Materials Science and Engineering
Flexible and Nanobio Device Lab
http://fand.kaist.ac.kr/
KAIST
2021.06.14 View 8462 -
Professor Byungha Shin Named Scientist of the Month
Professor Byungha Shin from the Department of Materials Science and Engineering won the Scientist of the Month Award presented by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF) on May 4. Professor Shin was recognized for his research in the field of next-generation perovskite solar cells and received 10 million won in prize money.
To achieve ‘carbon neutrality,’ which many countries across the globe including Korea hope to realize, the efficiency of converting renewable energies to electricity must be improved. Solar cells convert solar energy to electricity. Since single solar cells show lower efficiency, the development of ‘tandem solar cells’ that connect two or more cells together has been popular in recent years.
However, although ‘perovskite’ received attention as a next-generation material for tandem solar cells, it is sensitive to the external environment including light and moisture, making it difficult to maintain stability.
Professor Shin discovered that, theoretically, adding certain anion additives to perovskite solar cells would allow the control of the electrical and structural properties of the two-dimensional stabilization layer that forms inside the film. He confirmed this through high-resolution transmission electron microscopy. Controlling the amount of anions in the additives allowed the preservation of over 80% of the initial stability even after 1000 hours of continuous exposure to sunlight.
Based on this discovery, Professor Shin combined silicon with solar cells to create a tandem solar cell with 26.7% energy convergence efficiency. Considering that the highest-efficiency tandem solar cell in existence showed 29.5% efficiency, this figure is quite high. Professor Shin’s perovskite solar cell is also combinable with the CIGS (Cu(In,Ga)Se2) thin-film solar cell composed of copper (Cu), indium (In), gallium (Ga), and selenium (Se2).
Professor Shin’s research results were published in the online edition of the journal Science in April of last year.
“This research is meaningful for having suggested a direction for solar cell material stabilization using additives,” said Professor Shin. “I look forward to this technique being applied to a wide range of photoelectrical devices including solar cells, LEDs, and photodetectors,” he added.
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2021.05.07 View 8133