materials
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Professor Jimin Park and Dr. Inho Kim join the ranks of the 2024 "35 Innovators Under 35" by the MIT Technology Review
< (From left) Professor Jimin Park of the Department of Chemical and Biomolecular Engineering and Dr. Inho Kim, a graduate of the Department of Materials Science and Engineering >
KAIST (represented by President Kwang-Hyung Lee) announced on the 13th of September that Professor Jimin Park from KAIST’s Department of Chemical and Biomolecular Engineering and Dr. Inho Kim, a graduate from the Department of Materials Science and Engineering (currently a postdoctoral researcher at Caltech), were selected by the MIT Technology Review as the 2024 "35 Innovators Under 35”.
The MIT Technology Review, first published in 1899 by the Massachusetts Institute of Technology, is the world’s oldest and most influential magazine on science and technology, offering in-depth analysis across various technology fields, expanding knowledge and providing insights into cutting-edge technology trends.
Since 1999, the magazine has annually named 35 innovators under the age of 35, recognizing young talents making groundbreaking contributions in modern technology fields. The recognition is globally considered a prestigious honor and a dream for young researchers in the science and technology community.
< Image 1. Introduction for Professor Jimin Park at the Meet 35 Innovators Under 35 Summit 2024 >
Professor Jimin Park is developing next-generation bio-interfaces that link artificial materials with living organisms, and is engaged in advanced research in areas such as digital healthcare and carbon-neutral compound manufacturing technologies. In 2014, Professor Park was also recognized as one of the ‘Asia Pacific Innovators Under 35’ by the MIT Technology Review, which highlights young scientists in the Asia-Pacific region.
Professor Park responded, “It’s a great honor to be named as one of the young innovators by the MIT Technology Review, a symbol of innovation with a long history. I will continue to pursue challenging, interdisciplinary research to develop next-generation interfaces that seamlessly connect artificial materials and living organisms, from atomic to system levels.”
< Image 2. Introduction for Dr. Inho Kim as the 2024 Innovator of Materials Science for 35 Innovators Under 35 >
Dr. Inho Kim, who earned his PhD from KAIST in 2020 under the supervision of Professor Sang Ouk Kim from the Department of Materials Science and Engineering, recently succeeded in developing a new artificial muscle using composite fibers. This new material is considered the most human-like muscle ever reported in scientific literature, while also being 17 times stronger than natural human muscle.
Dr. Kim is researching the application of artificial muscle fibers in next-generation wearable assistive devices that move more naturally, like humans or animals, noting that the fibers are lightweight, flexible, and exhibit conductivity during contraction, enabling real-time feedback. Recognized for this potential, Dr. Inho Kim was named one of the '35 Innovators Under 35' this year, making him the first researcher to win the honor with the research conducted at KAIST and a PhD earned from Korea.
Dr. Kim stated, “I aim to develop robots using these new materials that can replace today’s expensive and heavy exoskeleton suits by eliminating motors and rigid frames. This will significantly reduce costs and allow for better customization, making cutting-edge technology more accessible to those who need it most, like children with cerebral palsy.”
2024.09.13 View 1643 -
KAIST Employs Image-recognition AI to Determine Battery Composition and Conditions
An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.
KAIST (President Kwang-Hyung Lee) announced on July 2nd that Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the Electronics and Telecommunications Research Institute (ETRI) and Drexel University in the United States, has developed a method to predict the major elemental composition and charge-discharge state of NCM cathode materials with 99.6% accuracy using convolutional neural networks (CNN)*.
*Convolutional Neural Network (CNN): A type of multi-layer, feed-forward, artificial neural network used for analyzing visual images.
The research team noted that while scanning electron microscopy (SEM) is used in semiconductor manufacturing to inspect wafer defects, it is rarely used in battery inspections. SEM is used for batteries to analyze the size of particles only at research sites, and reliability is predicted from the broken particles and the shape of the breakage in the case of deteriorated battery materials.
The research team decided that it would be groundbreaking if an automated SEM can be used in the process of battery production, just like in the semiconductor manufacturing, to inspect the surface of the cathode material to determine whether it was synthesized according to the desired composition and that the lifespan would be reliable, thereby reducing the defect rate.
< Figure 1. Example images of true cases and their grad-CAM overlays from the best trained network. >
The researchers trained a CNN-based AI applicable to autonomous vehicles to learn the surface images of battery materials, enabling it to predict the major elemental composition and charge-discharge cycle states of the cathode materials. They found that while the method could accurately predict the composition of materials with additives, it had lower accuracy for predicting charge-discharge states. The team plans to further train the AI with various battery material morphologies produced through different processes and ultimately use it for inspecting the compositional uniformity and predicting the lifespan of next-generation batteries.
Professor Joshua C. Agar, one of the collaborating researchers of the project from the Department of Mechanical Engineering and Mechanics of Drexel University, said, "In the future, artificial intelligence is expected to be applied not only to battery materials but also to various dynamic processes in functional materials synthesis, clean energy generation in fusion, and understanding foundations of particles and the universe."
Professor Seungbum Hong from KAIST, who led the research, stated, "This research is significant as it is the first in the world to develop an AI-based methodology that can quickly and accurately predict the major elemental composition and the state of the battery from the structural data of micron-scale SEM images. The methodology developed in this study for identifying the composition and state of battery materials based on microscopic images is expected to play a crucial role in improving the performance and quality of battery materials in the future."
< Figure 2. Accuracies of CNN Model predictions on SEM images of NCM cathode materials with additives under various conditions. >
This research was conducted by KAIST’s Materials Science and Engineering Department graduates Dr. Jimin Oh and Dr. Jiwon Yeom, the co-first authors, in collaboration with Professor Josh Agar and Dr. Kwang Man Kim from ETRI. It was supported by the National Research Foundation of Korea, the KAIST Global Singularity project, and international collaboration with the US research team. The results were published in the international journal npj Computational Materials on May 4. (Paper Title: “Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images”)
2024.07.02 View 2532 -
KAIST Develops Sodium Battery Capable of Rapid Charging in Just a Few Seconds
Sodium (Na), which is over 500 times more abundant than lithium (Li), has recently garnered significant attention for its potential in sodium-ion battery technologies. However, existing sodium-ion batteries face fundamental limitations, including lower power output, constrained storage properties, and longer charging times, necessitating the development of next-generation energy storage materials.
On the 11th of April, KAIST (represented by President Kwang Hyung Lee) announced that a research team led by Professor Jeung Ku Kang from the Department of Materials Science and Engineering had developed a high-energy, high-power hybrid sodium-ion battery capable of rapid charging.
The innovative hybrid energy storage system integrates anode materials typically used in batteries with cathodes suitable for supercapacitors. This combination allows the device to achieve both high storage capacities and rapid charge-discharge rates, positioning it as a viable next-generation alternative to lithium-ion batteries.
However, the development of a hybrid battery with high energy and high power density requires an improvement to the slow energy storage rate of battery-type anodes as well as the enhancement of the relatively low capacity of supercapacitor-type cathode materials.
< Figure 1. Schematic synthetic procedures of high-capacity/high-rate anode and cathode materials for a sodium-ion hybrid energy storages (SIHES) and their proposed energy storage mechanisms. Synthetic procedures for (a) ultrafine iron sulfide-embedded S-doped carbon/graphene (FS/C/G) anode and (b) zeolitic imidazolate framework-derived porous carbon (ZDPC) cathode materials. (c) Proposed energy storage mechanisms of Na+ ions in FS/C/G anode and ClO-4 ions in ZDPC cathode for an SIHES. >
To account for this, Professor Kang's team utilized two distinct metal-organic frameworks for the optimized synthesis of hybrid batteries. This approach led to the development of an anode material with improved kinetics through the inclusion of fine active materials in porous carbon derived from metal-organic frameworks. Additionally, a high-capacity cathode material was synthesized, and the combination of the cathode and anode materials allowed for the development of a sodium-ion storage system optimizing the balance and minimizing the disparities in energy storage rates between the electrodes.
The assembled full cell, comprising the newly developed anode and cathode, forms a high-performance hybrid sodium-ion energy storage device. This device surpasses the energy density of commercial lithium-ion batteries and exhibits the characteristics of supercapacitors' power density. It is expected to be suitable for rapid charging applications ranging from electric vehicles to smart electronic devices and aerospace technologies.
< Figure 2. Electrochemical characterizations of FS/C/G-20//ZDPC SIHES full cells (left). Ragone plots for FS/C/G-20//ZDPC (this work) and other previously reported sodium-ion electrochemical energy storage devices (right). >
Professor Kang noted that the hybrid sodium-ion energy storage device, capable of rapid charging and achieving an energy density of 247 Wh/kg and a power density of 34,748 W/kg, represents a breakthrough in overcoming the current limitations of energy storage systems. He anticipates broader applications across various electronic devices, including electric vehicles.
This research, co-authored by KAIST doctoral candidates Jong Hui Choi and Dong Won Kim, was published in the international journal Energy Storage Materials on March 29 with the title "Low-crystallinity conductive multivalence iron sulfide-embedded S-doped anode and high-surface-area O-doped cathode of 3D porous N-rich graphitic carbon frameworks for high-performance sodium-ion hybrid energy storages."
The study was conducted with support from the Ministry of Science and ICT and the National Research Foundation of Korea through the Nanomaterial Technology Development Project.
2024.04.18 View 12162 -
KAIST Team Develops an Insect-Mimicking Semiconductor to Detect Motion
The recent development of an “intelligent sensor” semiconductor that mimics the optic nerve of insects while operating at ultra-high speeds and low power offers extensive expandability into various innovative technologies. This technology is expected to be applied to various fields including transportation, safety, and security systems, contributing to both industry and society.
On February 19, a KAIST research team led by Professor Kyung Min Kim from the Department of Materials Science and Engineering (DMSE) announced the successful developed an intelligent motion detector by merging various memristor* devices to mimic the visual intelligence** of the optic nerve of insects.
*Memristor: a “memory resistor” whose state of resistance changes depending on the input signal
**Visual intelligence: the ability to interpret visual information and perform calculations within the optic nerve
With the recent advances in AI technology, vision systems are being improved by utilizing AI in various tasks such as image recognition, object detection, and motion analysis. However, existing vision systems typically recognize objects and their behaviour from the received image signals using complex algorithms. This method requires a significant amount of data traffic and higher power consumption, making it difficult to apply in mobile or IoT devices.
Meanwhile, insects are known to be able to effectively process visual information through an optic nerve circuit called the elementary motion detector, allowing them to detect objects and recognize their motion at an advanced level. However, mimicking this pathway using conventional silicon integrated circuit (CMOS) technology requires complex circuits, and its implementation into actual devices has thus been limited.
< Figure 1. Working principle of a biological elementary motion detection system. >
Professor Kyung Min Kim’s research team developed an intelligent motion detecting sensor that operates at a high level of efficiency and ultra-high speeds. The device has a simple structure consisting of only two types of memristors and a resistor developed by the team. The two different memristors each carry out a signal delay function and a signal integration and ignition function, respectively. Through them, the team could directly mimic the optic nerve of insects to analyze object movement.
< Figure 2. (Left) Optical image of the M-EMD device in the left panel (scale bar 200 μm) and SEM image of the device in the right panel (scale bar: 20 μm). (Middle) Responses of the M-EMD in positive direction. (Right) Responses of the M-EMD in negative direction. >
To demonstrate its potential for practical applications, the research team used the newly developed motion detector to design a neuromorphic computing system that can predict the path of a vehicle. The results showed that the device used 92.9% less energy compared to existing technology and predicted motion with more accuracy.
< Figure 3. Neuromorphic computing system configuration based on motion recognition devices >
Professor Kim said, “Insects make use of their very simple visual intelligence systems to detect the motion of objects at a surprising high speed. This research is significant in that we could mimic the functions of a nerve using a memristor device.” He added, “Edge AI devices, such as AI-topped mobile phones, are becoming increasingly important. This research can contribute to the integration of efficient vision systems for motion recognition, so we expect it to be applied to various fields such as autonomous vehicles, vehicle transportation systems, robotics, and machine vision.”
This research, conducted by co-first authors Hanchan Song and Min Gu Lee, both Ph.D. candidates at KAIST DMSE, was published in the online issue of Advanced Materials on January 29.
This research was supported by the Mid-Sized Research Project by the National Research Foundation of Korea, the Next-Generation Intelligent Semiconductor Technology Development Project, the PIM Artificial Intelligence Semiconductor Core Technology Development Project, the National Nano Fab Center, and the Leap Research Project by KAIST.
2024.02.29 View 3964 -
A KAIST Research Team Develops a Novel “Bone Bandage” Material for Cracked Bones
Bone regeneration is a complex process, and existing methods to aid regeneration including transplants and growth factor transmissions face limitations such as the high cost. But recently, a piezoelectric material that can promote the growth of bone tissue has been developed.
A KAIST research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering (DMSE) announced on January 25 the development of a biomimetic scaffold that generates electrical signals upon the application of pressure by utilizing the unique osteogenic ability of hydroxyapatite (HAp). This research was conducted in collaboration with a team led by Professor Jangho Kim from the Department of Convergence Biosystems Engineering at Chonnam National University.
HAp is a basic calcium phosphate material found in bones and teeth. This biocompatible mineral substance is also known to prevent tooth decay and is often used in toothpaste.
Previous studies on piezoelectric scaffolds confirmed the effects of piezoelectricity on promoting bone regeneration and improving bone fusion in various polymer-based materials, but were limited in simulating the complex cellular environment required for optimal bone tissue regeneration. However, this research suggests a new method for utilizing the unique osteogenic abilities of HAp to develop a material that mimics the environment for bone tissue in a living body.
< Figure 1. Design and characterization of piezoelectrically and topographically originated biomimetic scaffolds. (a) Schematic representation of the enhanced bone regeneration mechanism through electrical and topographical cues provided by HAp-incorporated P(VDF-TrFE) scaffolds. (b) Schematic diagram of the fabrication process. >
The research team developed a manufacturing process that fuses HAp with a polymer film. The flexible and free-standing scaffold developed through this process demonstrated its remarkable potential for promoting bone regeneration through in-vitro and in-vivo experiments in rats.
The team also identified the principles of bone regeneration that their scaffold is based on. Using atomic force microscopy (AFM), they analysed the electrical properties of the scaffold and evaluated the detailed surface properties related to cell shape and cell skeletal protein formation. They also investigated the effects of piezoelectricity and surface properties on the expression of growth factors.
Professor Hong from KAIST’s DMSE said, “We have developed a HAp-based piezoelectric composite material that can act like a ‘bone bandage’ through its ability to accelerate bone regeneration.” He added, “This research not only suggests a new direction for designing biomaterials, but is also significant in having explored the effects of piezoelectricity and surface properties on bone regeneration.”
This research, conducted by co-first authors Soyun Joo and Soyeon Kim from Professor Hong’s group, was published on ACS Applied Materials & Interfaces on January 4 under the title “Piezoelectrically and Topographically Engineered Scaffolds for Accelerating Bone Regeneration”. From Professor Kim’s group, Ph.D. candidate Yonghyun Gwon also participated as co-first author, and Professor Kim himself as a corresponding author.
< Figure 2. Analysis of piezoelectric and surface properties of the biomimetic scaffolds using atomic force microscopy. (a) PFM amplitude and phase images of box-poled composite scaffolds. The white bar represents 2 μm. (b) 3D representations of composite scaffolds paired with typical 2D line sections. (c) In vivo bone regeneration micro-CT analysis, (d) schematic representation of filler-derived electrical origins in bone regeneration. >
This research was supported by the KAIST Research and Development Team, the KUSTAR-KAIST Joint Research Center, the KAIST Global Singularity Project, and the government-funded Basic Research Project by the National Research Foundation of Korea.
2024.02.01 View 4237 -
KAIST Research team develops anti-icing film that only requires sunlight
A KAIST research team has developed an anti-icing and de-icing film coating technology that can apply the photothermal effect of gold nanoparticles to industrial sites without the need for heating wires, periodic spray or oil coating of anti-freeze substances, and substrate design alterations.
The group led by Professor Hyoungsoo Kim from the Department of Mechanical Engineering (Fluid & Interface Laboratory) and Professor Dong Ki Yoon from the Department of Chemistry (Soft Material Assembly Group) revealed on January 3 to have together developed an original technique that can uniformly pattern gold nanorod (GNR) particles in quadrants through simple evaporation, and have used this to develop an anti-icing and de-icing surface.
Many scientists in recent years have tried to control substrate surfaces through various coating techniques, and those involving the patterning of functional nanomaterials have gained special attention. In particular, GNR is considered a promising candidate nanomaterial for its biocompatibility, chemical stability, relatively simple synthesis, and its stable and unique property of surface plasmon resonance. To maximize the performance of GNR, it is important to achieve a high uniformity during film deposition, and a high level of rod alignment. However, achieving both criteria has thus far been a difficult challenge.
< Figure 1. Conceptual image to display Hydrodynamic mechanisms for the formation of a homogeneous quadrant cellulose nanocrystal(CNC) matrix. >
To solve this, the joint research team utilized cellulose nanocrystal (CNC), a next-generation functional nanomaterial that can easily be extracted from nature. By co-assembling GNR on CNC quadrant templates, the team could uniformly dry the film and successfully obtain a GNR film with a uniform alignment in a ring-shape. Compared to existing coffee-ring films, the highly uniform and aligned GNR film developed through this research showed enhanced plasmonic photothermal properties, and the team showed that it could carry out anti-icing and de-icing functions by simply irradiating light in the visible wavelength range.
< Figure 2. Optical and thermal performance evaluation results of gold nanorod film and demonstration of plasmonic heater for anti-icing and de-icing. >
Professor Hyoungsoo Kim said, “This technique can be applied to plastic, as well as flexible surfaces. By using it on exterior materials and films, it can generate its own heat energy, which would greatly save energy through voluntary thermal energy harvesting across various applications including cars, aircrafts, and windows in residential or commercial spaces, where frosting becomes a serious issue in the winter.” Professor Dong Ki Yoon added, “This research is significant in that we can now freely pattern the CNC-GNR composite, which was previously difficult to create into films, over a large area. We can utilize this as an anti-icing material, and if we were to take advantage of the plasmonic properties of gold, we can also use it like stained-glass to decorate glass surfaces.”
This research was conducted by Ph.D. candidate Jeongsu Pyeon from the Department of Mechanical Engineering, and his co-first author Dr. Soon Mo Park (a KAIST graduate, currently a post-doctoral associate at Cornell University), and was pushed in the online volume of Nature Communication on December 8, 2023 under the title “Plasmonic Metasurfaces of Cellulose Nanocrystal Matrices with Quadrants of Aligned Gold Nanorods for Photothermal Anti-Icing." Recognized for its achievement, the research was also selected as an editor’s highlight for the journals Materials Science and Chemistry, and Inorganic and Physical Chemistry.
This research was supported by the Individual Basic Mid-Sized Research Fund from the National Research Foundation of Korea and the Center for Multiscale Chiral Architectures.
2024.01.16 View 6308 -
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 7723 -
KAIST develops biocompatible adhesive applicable to hair transplants
Aside from being used as a new medical adhesive, the new material can be applied to developing a new method of hair transplants, which cannot be repeated multiple times using current method of implanting the wholly intact follicles into the skin.
Medical adhesives are materials that can be applied to various uses such as wound healing, hemostasis, vascular anastomosis, and tissue engineering, and is expected to contribute greatly to the development of minimally invasive surgery and organ transplants. However, adhesives with high adhesion, low toxicity, and capable of decomposing in the body are rare. Adhesives based on natural proteins, such as fibrin and collagen, have high biocompatibility but insufficient adhesive strength. Synthetic polymer adhesives based on urethane or acrylic have greater adhesion but do not decompose well and may cause an inflammatory reaction in the body.
A joint research team led by Professor Myungeun Seo and Professor Haeshin Lee from the KAIST Department of Chemistry developed a bio-friendly adhesive from biocompatible polymers using tannic acid, the source of astringency in wine.
The research team focused on tannic acid, a natural polyphenolic product. Tannic acid is a polyphenol present in large amounts in fruit peels, nuts, and cacao. It has a high affinity and coating ability on other substances, and we sense the astringent taste in wine when tannic acid sticks to the surface of our tongue. When tannic acid is mixed with hydrophilic polymers, they form coacervates, or small droplets of jelly-like fluids that sink. If the polymers used are biocompatible, the mixture can be applied as a medical adhesive with low toxicity. However, coacervates are fundamentally fluid-like and cannot withstand large forces, which limits their adhesive capabilities. Thus, while research to utilize it as an adhesive has been actively discussed, a biodegradable material exhibiting strong adhesion due to its high shear strength has not yet been developed.
The research team figured out a way to enhance adhesion by mixing two biocompatible FDA-approved polymers, polyethylene glycol (PEG) and polylactic acid (PLA). While PEG, which is used widely in eyedrops and cream, is hydrophilic, PLA, a well-known bioplastic derived from lactic acid, is insoluble in water. The team combined the two into a block copolymer, which forms hydrophilic PLA aggregates in water with PEG blocks surrounding them. A coacervate created by mixing the micelles and tannic acid would behave like a solid due to the hard PLA components, and show an elastic modulus improved by a thousand times compared to PEG, enabling it to withstand much greater force as an adhesive.
Figure 1. (Above) Principle of biodegradable adhesive made by mixing poly(ethylene glycol)-poly(lactic acid) diblock copolymer and tannic acid in water. Yellow coacervate is precipitated through hydrogen bonding between the block copolymer micelles and tannic acid, and exhibits adhesion. After heat treatment, hydrogen bonds are rearranged to further improve adhesion. (Bottom) Adhesion comparison. Compared to using poly(ethylene glycol) polymer (d), it can support 10 times more weight when using block copolymer (e) and 60 times more weight after heat treatment (f). The indicated G' values represent the elastic modulus of the material.
Furthermore, the research team observed that the material’s mechanical properties can be improved by over a hundred times through a heating and cooling process that is used to heat-treat metals. They also discovered that this is due to the enforced interactions between micelle and tannic acid arrays.
The research team used the fact that the material shows minimal irritation to the skin and decomposes well in the body to demonstrate its possible application as an adhesive for hair transplantation through an animal experiment. Professor Haeshin Lee, who has pioneered various application fields including medical adhesives, hemostatic agents, and browning shampoo, focused on the adhesive capacities and low toxicity of polyphenols like tannic acid, and now looks forward to it improving the limitations of current hair transplant methods, which still involve follicle transfer and are difficult to be repeated multiple times.
Figure 2. (a) Overview of a hair transplantation method using a biodegradable adhesive (right) compared to a conventional hair transplantation method (left) that transplants hair containing hair follicles. After applying an adhesive to the tip of the hair, it is fixed to the skin by implanting it through a subcutaneous injection, and repeated treatment is possible. (b) Initial animal test results. One day after 15 hair transplantation, 12 strands of hair remain. If you pull the 3 strands of hair, you can see that the whole body is pulled up, indicating that it is firmly implanted into the skin. All strands of hair applied without the new adhesive material fell off, and in the case of adhesive without heat treatment, the efficiency was 1/7.
This research was conducted by first co-authors Dr. Jongmin Park (currently a senior researcher at the Korea Research Institute of Chemical Technology) from Professor Myeongeun Seo’s team and Dr. Eunsook Park from Professor Haeshin Lee’s team in the KAIST Department of Chemistry, and through joint research with the teams led by Professor Hyungjun Kim from the KAIST Department of Chemistry and Professor Siyoung Choi from the Department of Chemical and Biomolecular Engineering. The research was published online on August 22 in the international journal Au (JACS Au) under the title Biodegradable Block Copolymer-Tannic Acid Glue.
This study was funded by the Support Research Under Protection Project of the National Research Foundation (NRF), Leading Research Center Support Project (Research Center for Multiscale Chiral Structure), Biodegradable Plastics Commercialization and Demonstration Project by the Ministry of Trade and Industry, and institutional funding from the Korea Research Institute of Chemical Technology.
2022.10.07 View 7596 -
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 7980 -
Deep Learning Framework to Enable Material Design in Unseen Domain
Researchers propose a deep neural network-based forward design space exploration using active transfer learning and data augmentation
A new study proposed a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods.
Professor Seungwha Ryu believes that this study will help address a variety of optimization problems that have an astronomical number of possible design configurations. For the grid composite optimization problem, the proposed framework was able to provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training data-set size. This study was reported in npj Computational Materials last month.
“We wanted to mitigate the limitation of the neural network, weak predictive power beyond the training set domain for the material or structure design,” said Professor Ryu from the Department of Mechanical Engineering.
Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome this limitation also suffer from weak predictive power for the unseen domain.
Professor Ryu’s team, in collaboration with researchers from Professor Grace Gu’s group at UC Berkeley, devised a design method that simultaneously expands the domain using the strong predictive power of a deep neural network and searches for the optimal design by repetitively performing three key steps.
First, it searches for few candidates with improved properties located close to the training set via genetic algorithms, by mixing superior designs within the training set. Then, it checks to see if the candidates really have improved properties, and expands the training set by duplicating the validated designs via a data augmentation method. Finally, they can expand the reliable prediction domain by updating the neural network with the new superior designs via transfer learning. Because the expansion proceeds along relatively narrow but correct routes toward the optimal design (depicted in the schematic of Fig. 1), the framework enables an efficient search.
As a data-hungry method, a deep neural network model tends to have reliable predictive power only within and near the domain of the training set. When the optimal configuration of materials and structures lies far beyond the initial training set, which frequently is the case, neural network-based design methods suffer from weak predictive power and become inefficient.
Researchers expect that the framework will be applicable for a wide range of optimization problems in other science and engineering disciplines with astronomically large design space, because it provides an efficient way of gradually expanding the reliable prediction domain toward the target design while avoiding the risk of being stuck in local minima. Especially, being a less-data-hungry method, design problems in which data generation is time-consuming and expensive will benefit most from this new framework.
The research team is currently applying the optimization framework for the design task of metamaterial structures, segmented thermoelectric generators, and optimal sensor distributions. “From these sets of on-going studies, we expect to better recognize the pros and cons, and the potential of the suggested algorithm. Ultimately, we want to devise more efficient machine learning-based design approaches,” explained Professor Ryu.This study was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research Project.
-Publication
Yongtae Kim, Youngsoo, Charles Yang, Kundo Park, Grace X. Gu, and Seunghwa Ryu, “Deep learning framework for material design space exploration using active transfer learning and data augmentation,” npj Computational Materials (https://doi.org/10.1038/s41524-021-00609-2)
-Profile
Professor Seunghwa Ryu
Mechanics & Materials Modeling Lab
Department of Mechanical Engineering
KAIST
2021.09.29 View 9350 -
Streamlining the Process of Materials Discovery
The materials platform M3I3 reduces the time for materials discovery by reverse engineering future materials using multiscale/multimodal imaging and machine learning of the processing-structure-properties relationship
Developing new materials and novel processes has continued to change the world. The M3I3 Initiative at KAIST has led to new insights into advancing materials development by implementing breakthroughs in materials imaging that have created a paradigm shift in the discovery of materials. The Initiative features the multiscale modeling and imaging of structure and property relationships and materials hierarchies combined with the latest material-processing data.
The research team led by Professor Seungbum Hong analyzed the materials research projects reported by leading global institutes and research groups, and derived a quantitative model using machine learning with a scientific interpretation. This process embodies the research goal of the M3I3: Materials and Molecular Modeling, Imaging, Informatics and Integration.
The researchers discussed the role of multiscale materials and molecular imaging combined with machine learning and also presented a future outlook for developments and the major challenges of M3I3. By building this model, the research team envisions creating desired sets of properties for materials and obtaining the optimum processing recipes to synthesize them.
“The development of various microscopy and diffraction tools with the ability to map the structure, property, and performance of materials at multiscale levels and in real time enabled us to think that materials imaging could radically accelerate materials discovery and development,” says Professor Hong.
“We plan to build an M3I3 repository of searchable structural and property maps using FAIR (Findable, Accessible, Interoperable, and Reusable) principles to standardize best practices as well as streamline the training of early career researchers.”
One of the examples that shows the power of structure-property imaging at the nanoscale is the development of future materials for emerging nonvolatile memory devices. Specifically, the research team focused on microscopy using photons, electrons, and physical probes on the multiscale structural hierarchy, as well as structure-property relationships to enhance the performance of memory devices.
“M3I3 is an algorithm for performing the reverse engineering of future materials. Reverse engineering starts by analyzing the structure and composition of cutting-edge materials or products. Once the research team determines the performance of our targeted future materials, we need to know the candidate structures and compositions for producing the future materials.”
The research team has built a data-driven experimental design based on traditional NCM (nickel, cobalt, and manganese) cathode materials. With this, the research team expanded their future direction for achieving even higher discharge capacity, which can be realized via Li-rich cathodes.
However, one of the major challenges was the limitation of available data that describes the Li-rich cathode properties. To mitigate this problem, the researchers proposed two solutions: First, they should build a machine-learning-guided data generator for data augmentation. Second, they would use a machine-learning method based on ‘transfer learning.’ Since the NCM cathode database shares a common feature with a Li-rich cathode, one could consider repurposing the NCM trained model for assisting the Li-rich prediction. With the pretrained model and transfer learning, the team expects to achieve outstanding predictions for Li-rich cathodes even with the small data set.
With advances in experimental imaging and the availability of well-resolved information and big data, along with significant advances in high-performance computing and a worldwide thrust toward a general, collaborative, integrative, and on-demand research platform, there is a clear confluence in the required capabilities of advancing the M3I3 Initiative.
Professor Hong said, “Once we succeed in using the inverse “property−structure−processing” solver to develop cathode, anode, electrolyte, and membrane materials for high energy density Li-ion batteries, we will expand our scope of materials to battery/fuel cells, aerospace, automobiles, food, medicine, and cosmetic materials.”
The review was published in ACS Nano in March. This study was conducted through collaborations with Dr. Chi Hao Liow, Professor Jong Min Yuk, Professor Hye Ryung Byon, Professor Yongsoo Yang, Professor EunAe Cho, Professor Pyuck-Pa Choi, and Professor Hyuck Mo Lee at KAIST, Professor Joshua C. Agar at Lehigh University, Dr. Sergei V. Kalinin at Oak Ridge National Laboratory, Professor Peter W. Voorhees at Northwestern University, and Professor Peter Littlewood at the University of Chicago (Article title: Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration).This work was supported by the KAIST Global Singularity Research Program for 2019 and 2020.
Publication:
“Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics and Integration,” S. Hong, C. H. Liow, J. M. Yuk, H. R. Byon, Y. Yang, E. Cho, J. Yeom, G. Park, H. Kang, S. Kim, Y. Shim, M. Na, C. Jeong, G. Hwang, H. Kim, H. Kim, S. Eom, S. Cho, H. Jun, Y. Lee, A. Baucour, K. Bang, M. Kim, S. Yun, J. Ryu, Y. Han, A. Jetybayeva, P.-P. Choi, J. C. Agar, S. V. Kalinin, P. W. Voorhees, P. Littlewood, and H. M. Lee, ACS Nano 15, 3, 3971–3995 (2021) https://doi.org/10.1021/acsnano.1c00211
Profile:
Seungbum Hong, PhD
Associate Professor
seungbum@kaist.ac.kr
http://mii.kaist.ac.kr
Department of Materials Science and Engineering
KAIST
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2021.04.05 View 11125 -
ACS Nano Special Edition Highlights Innovations at KAIST
- The collective intelligence and technological innovation of KAIST was highlighted with case studies including the Post-COVID-19 New Deal R&D Initiative Project. -
KAIST’s innovative academic achievements and R&D efforts for addressing the world’s greatest challenges such as the COVID-19 pandemic were featured in ACS Nano as part of its special virtual issue commemorating the 50th anniversary of KAIST. The issue consisted of 14 review articles contributed by KAIST faculty from five departments, including two from Professor Il-Doo Kim from the Department of Materials Science and Engineering, who serves as an associate editor of the ACS Nano.
ACS Nano, the leading international journal in nanoscience and nanotechnology, published a special virtual issue last month, titled ‘Celebrating 50 Years of KAIST: Collective Intelligence and Innovation for Confronting Contemporary Issues.’
This special virtual issue introduced KAIST’s vision of becoming a ‘global value-creative leading university’ and its progress toward this vision over the last 50 years. The issue explained how KAIST has served as the main hub for advanced scientific research and technological innovation in South Korea since its establishment in 1971, and how its faculty and over 69,000 graduates played a key role in propelling the nation’s rapid industrialization and economic development.
The issue also emphasized the need for KAIST to enhance global cooperation and the exchange of ideas in the years to come, especially during the post-COVID era intertwined with the Fourth Industrial Revolution (4IR). In this regard, the issue cited the first ‘KAIST Emerging Materials e-Symposium (EMS)’, which was held online for five days in September of last year with a global audience of over 10,000 participating live via Zoom and YouTube, as a successful example of what academic collaboration could look like in the post-COVID and 4IR eras.
In addition, the “Science & Technology New Deal Project for COVID-19 Response,” a project conducted by KAIST with support from the Ministry of Science and ICT (MSIT) of South Korea, was also introduced as another excellent case of KAIST’s collective intelligence and technological innovation. The issue highlighted some key achievements from this project for overcoming the pandemic-driven crisis, such as: reusable anti-virus filters, negative-pressure ambulances for integrated patient transport and hospitalization, and movable and expandable negative-pressure ward modules.
“We hold our expectations high for the outstanding achievements and progress KAIST will have made by its centennial,” said Professor Kim on the background of curating the 14 review articles contributed by KAIST faculty from the fields of Materials Science and Engineering (MSE), Chemical and Biomolecular Engineering (CBE), Nuclear and Quantum Engineering (NQE), Electrical Engineering (EE), and Chemistry (Chem).
Review articles discussing emerging materials and their properties covered photonic carbon dots (Professor Chan Beum Park, MSE), single-atom and ensemble catalysts (Professor Hyunjoo Lee, CBE), and metal/metal oxide electrocatalysts (Professor Sung-Yoon Chung, MSE).
Review articles discussing materials processing covered 2D layered materials synthesis based on interlayer engineering (Professor Kibum Kang, MSE), eco-friendly methods for solar cell production (Professor Bumjoon J. Kim, CBE), an ex-solution process for the synthesis of highly stable catalysts (Professor WooChul Jung, MSE), and 3D light-patterning synthesis of ordered nanostructures (Professor Seokwoo Jeon, MSE, and Professor Dongchan Jang, NQE).
Review articles discussing advanced analysis techniques covered operando materials analyses (Professor Jeong Yeong Park, Chem), graphene liquid cell transmission electron microscopy (Professor Jong Min Yuk, MSE), and multiscale modeling and visualization of materials systems (Professor Seungbum Hong, MSE).
Review articles discussing practical state-of-the-art devices covered chemiresistive hydrogen sensors (Professor Il-Doo Kim, MSE), patient-friendly diagnostics and implantable treatment devices (Professor Steve Park, MSE), triboelectric nanogenerators (Professor Yang-Kyu Choi, EE), and next-generation lithium-air batteries (Professor Hye Ryung Byon, Chem, and Professor Il-Doo Kim, MSE).
In addition to Professor Il-Doo Kim, post-doctoral researcher Dr. Jaewan Ahn from the KAIST Applied Science Research Institute, Dean of the College of Engineering at KAIST Professor Choongsik Bae, and ACS Nano Editor-in-Chief Professor Paul S. Weiss from the University of California, Los Angeles also contributed to the publication of this ACS Nano special virtual issue.
The issue can be viewed and downloaded from the ACS Nano website at https://doi.org/10.1021/acsnano.1c01101.
Image credit: KAIST
Image usage restrictions: News organizations may use or redistribute this image,with proper attribution, as part of news coverage of this paper only.
Publication:
Ahn, J., et al. (2021) Celebrating 50 Years of KAIST: Collective Intelligence and Innovation for Confronting Contemporary Issues. ACS Nano 15(3): 1895-1907. Available online at https://doi.org/10.1021/acsnano.1c01101
Profile:
Il-Doo Kim, Ph.D
Chair Professor
idkim@kaist.ac.kr
http://advnano.kaist.ac.kr
Advanced Nanomaterials and Energy Lab.
Department of Materials Science and Engineering
Membrane Innovation Center for Anti-Virus and Air-Quality Control
https://kaist.ac.kr/
Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea
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2021.03.05 View 25164