research
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Eco-Friendly Micro-Supercapacitors Using Fallen Leaves
Green micro-supercapacitors on a single leaf could easily be applied in wearable electronics, smart houses, and IoTs
A KAIST research team has developed graphene-inorganic-hybrid micro-supercapacitors made of fallen leaves using femtosecond laser direct writing. The rapid development of wearable electronics requires breakthrough innovations in flexible energy storage devices in which micro-supercapacitors have drawn a great deal of interest due to their high power density, long lifetimes, and short charging times. Recently, there has been an enormous increase in waste batteries owing to the growing demand and the shortened replacement cycle in consumer electronics. The safety and environmental issues involved in the collection, recycling, and processing of such waste batteries are creating a number of challenges.
Forests cover about 30 percent of the Earth’s surface and produce a huge amount of fallen leaves. This naturally occurring biomass comes in large quantities and is completely biodegradable, which makes it an attractive sustainable resource. Nevertheless, if the fallen leaves are left neglected instead of being used efficiently, they can contribute to fire hazards, air pollution, and global warming.
To solve both problems at once, a research team led by Professor Young-Jin Kim from the Department of Mechanical Engineering and Dr. Hana Yoon from the Korea Institute of Energy Research developed a novel technology that can create 3D porous graphene microelectrodes with high electrical conductivity by irradiating femtosecond laser pulses on the leaves in ambient air. This one-step fabrication does not require any additional materials or pre-treatment.
They showed that this technique could quickly and easily produce porous graphene electrodes at a low price, and demonstrated potential applications by fabricating graphene micro-supercapacitors to power an LED and an electronic watch. These results open up a new possibility for the mass production of flexible and green graphene-based electronic devices.
Professor Young-Jin Kim said, “Leaves create forest biomass that comes in unmanageable quantities, so using them for next-generation energy storage devices makes it possible for us to reuse waste resources, thereby establishing a virtuous cycle.”
This research was published in Advanced Functional Materials last month and was sponsored by the Ministry of Agriculture Food and Rural Affairs, the Korea Forest Service, and the Korea Institute of Energy Research.
-Publication
Truong-Son Dinh Le, Yeong A. Lee, Han Ku Nam, Kyu Yeon Jang, Dongwook Yang, Byunggi Kim, Kanghoon Yim, Seung Woo Kim, Hana Yoon, and Young-jin Kim, “Green Flexible Graphene-Inorganic-Hybrid Micro-Supercapacitors Made of Fallen Leaves Enabled by Ultrafast Laser Pulses," December 05, 2021, Advanced Functional Materials (doi.org/10.1002/adfm.202107768)
-ProfileProfessor Young-Jin KimUltra-Precision Metrology and Manufacturing (UPM2) LaboratoryDepartment of Mechanical EngineeringKAIST
2022.01.27 View 9606 -
AI Light-Field Camera Reads 3D Facial Expressions
Machine-learned, light-field camera reads facial expressions from high-contrast illumination invariant 3D facial images
A joint research team led by Professors Ki-Hun Jeong and Doheon Lee from the KAIST Department of Bio and Brain Engineering reported the development of a technique for facial expression detection by merging near-infrared light-field camera techniques with artificial intelligence (AI) technology.
Unlike a conventional camera, the light-field camera contains micro-lens arrays in front of the image sensor, which makes the camera small enough to fit into a smart phone, while allowing it to acquire the spatial and directional information of the light with a single shot. The technique has received attention as it can reconstruct images in a variety of ways including multi-views, refocusing, and 3D image acquisition, giving rise to many potential applications.
However, the optical crosstalk between shadows caused by external light sources in the environment and the micro-lens has limited existing light-field cameras from being able to provide accurate image contrast and 3D reconstruction.
The joint research team applied a vertical-cavity surface-emitting laser (VCSEL) in the near-IR range to stabilize the accuracy of 3D image reconstruction that previously depended on environmental light. When an external light source is shone on a face at 0-, 30-, and 60-degree angles, the light field camera reduces 54% of image reconstruction errors. Additionally, by inserting a light-absorbing layer for visible and near-IR wavelengths between the micro-lens arrays, the team could minimize optical crosstalk while increasing the image contrast by 2.1 times.
Through this technique, the team could overcome the limitations of existing light-field cameras and was able to develop their NIR-based light-field camera (NIR-LFC), optimized for the 3D image reconstruction of facial expressions. Using the NIR-LFC, the team acquired high-quality 3D reconstruction images of facial expressions expressing various emotions regardless of the lighting conditions of the surrounding environment.
The facial expressions in the acquired 3D images were distinguished through machine learning with an average of 85% accuracy – a statistically significant figure compared to when 2D images were used. Furthermore, by calculating the interdependency of distance information that varies with facial expression in 3D images, the team could identify the information a light-field camera utilizes to distinguish human expressions.
Professor Ki-Hun Jeong said, “The sub-miniature light-field camera developed by the research team has the potential to become the new platform to quantitatively analyze the facial expressions and emotions of humans.” To highlight the significance of this research, he added, “It could be applied in various fields including mobile healthcare, field diagnosis, social cognition, and human-machine interactions.”
This research was published in Advanced Intelligent Systems online on December 16, under the title, “Machine-Learned Light-field Camera that Reads Facial Expression from High-Contrast and Illumination Invariant 3D Facial Images.” This research was funded by the Ministry of Science and ICT and the Ministry of Trade, Industry and Energy.
-Publication“Machine-learned light-field camera that reads fascial expression from high-contrast and illumination invariant 3D facial images,” Sang-In Bae, Sangyeon Lee, Jae-Myeong Kwon, Hyun-Kyung Kim. Kyung-Won Jang, Doheon Lee, Ki-Hun Jeong, Advanced Intelligent Systems, December 16, 2021 (doi.org/10.1002/aisy.202100182)
ProfileProfessor Ki-Hun JeongBiophotonic LaboratoryDepartment of Bio and Brain EngineeringKAIST
Professor Doheon LeeDepartment of Bio and Brain EngineeringKAIST
2022.01.21 View 9719 -
Face Detection in Untrained Deep Neural Networks
A KAIST team shows that primitive visual selectivity of faces can arise spontaneously in completely untrained deep neural networks
Researchers have found that higher visual cognitive functions can arise spontaneously in untrained neural networks. A KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering has shown that visual selectivity of facial images can arise even in completely untrained deep neural networks.
This new finding has provided revelatory insights into mechanisms underlying the development of cognitive functions in both biological and artificial neural networks, also making a significant impact on our understanding of the origin of early brain functions before sensory experiences.
The study published in Nature Communications on December 16 demonstrates that neuronal activities selective to facial images are observed in randomly initialized deep neural networks in the complete absence of learning, and that they show the characteristics of those observed in biological brains.
The ability to identify and recognize faces is a crucial function for social behavior, and this ability is thought to originate from neuronal tuning at the single or multi-neuronal level. Neurons that selectively respond to faces are observed in young animals of various species, and this raises intense debate whether face-selective neurons can arise innately in the brain or if they require visual experience.
Using a model neural network that captures properties of the ventral stream of the visual cortex, the research team found that face-selectivity can emerge spontaneously from random feedforward wirings in untrained deep neural networks. The team showed that the character of this innate face-selectivity is comparable to that observed with face-selective neurons in the brain, and that this spontaneous neuronal tuning for faces enables the network to perform face detection tasks.
These results imply a possible scenario in which the random feedforward connections that develop in early, untrained networks may be sufficient for initializing primitive visual cognitive functions.
Professor Paik said, “Our findings suggest that innate cognitive functions can emerge spontaneously from the statistical complexity embedded in the hierarchical feedforward projection circuitry, even in the complete absence of learning”.
He continued, “Our results provide a broad conceptual advance as well as advanced insight into the mechanisms underlying the development of innate functions in both biological and artificial neural networks, which may unravel the mystery of the generation and evolution of intelligence.” This work was supported by the National Research Foundation of Korea (NRF) and by the KAIST singularity research project.
-PublicationSeungdae Baek, Min Song, Jaeson Jang, Gwangsu Kim, and Se-Bum Baik, “Face detection in untrained deep neural network,” Nature Communications 12, 7328 on Dec.16, 2021
(https://doi.org/10.1038/s41467-021-27606-9)
-ProfileProfessor Se-Bum PaikVisual System and Neural Network LaboratoryProgram of Brain and Cognitive EngineeringDepartment of Bio and Brain EngineeringCollege of EngineeringKAIST
2021.12.21 View 8413 -
A Study Shows Reactive Electrolyte Additives Improve Lithium Metal Battery Performance
Stable electrode-electrolyte interfaces constructed by fluorine- and nitrogen-donating ionic additives provide an opportunity to improve high-performance lithium metal batteries
A research team showed that electrolyte additives increase the lifetime of lithium metal batteries and remarkably improve the performance of fast charging and discharging. Professor Nam-Soon Choi’s team from the Department of Chemical and Biomolecular Engineering at KAIST hierarchized the solid electrolyte interphase to make a dual-layer structure and showed groundbreaking run times for lithium metal batteries.
The team applied two electrolyte additives that have different reduction and adsorption properties to improve the functionality of the dual-layer solid electrolyte interphase. In addition, the team has confirmed that the structural stability of the nickel-rich cathode was achieved through the formation of a thin protective layer on the cathode. This study was reported in Energy Storage Materials.
Securing high-energy-density lithium metal batteries with a long lifespan and fast charging performance is vital for realizing their ubiquitous use as superior power sources for electric vehicles. Lithium metal batteries comprise a lithium metal anode that delivers 10 times higher capacity than the graphite anodes in lithium-ion batteries. Therefore, lithium metal is an indispensable anode material for realizing high-energy rechargeable batteries. However, undesirable reactions among the electrolytes with lithium metal anodes can reduce the power and this remains an impediment to achieving a longer battery lifespan. Previous studies only focused on the formation of the solid electrolyte interphase on the surface of the lithium metal anode.
The team designed a way to create a dual-layer solid electrolyte interphase to resolve the instability of the lithium metal anode by using electrolyte additives, depending on their electron accepting ability and adsorption tendencies. This hierarchical structure of the solid electrolyte interphase on the lithium metal anode has the potential to be further applied to lithium-alloy anodes, lithium storage structures, and anode-free technology to meet market expectations for electrolyte technology.
The batteries with lithium metal anodes and nickel-rich cathodes represented 80.9% of the initial capacity after 600 cycles and achieved a high Coulombic efficiency of 99.94%. These remarkable results contributed to the development of protective dual-layer solid electrolyte interphase technology for lithium metal anodes.
Professor Choi said that the research suggests a new direction for the development of electrolyte additives to regulate the unstable lithium metal anode-electrolyte interface, the biggest hurdle in research on lithium metal batteries.
She added that anode-free secondary battery technology is expected to be a game changer in the secondary battery market and electrolyte additive technology will contribute to the enhancement of anode-free secondary batteries through the stabilization of lithium metal anodes.
This research was funded by the Technology Development Program to Solve Climate Change of the National Research Foundation in Korea funded by the Ministry of Science, ICT & Future Planning and the Technology Innovation Program funded by the Ministry of Trade, Industry & Energy, and Hyundai Motor Company.
- PublicationSaehun Kim, Sung O Park, Min-Young Lee, Jeong-A Lee, Imanuel Kristanto, Tae Kyung Lee, Daeyeon Hwang, Juyoung Kim, Tae-Ung Wi, Hyun-Wook Lee, Sang Kyu Kwak, and NamSoon Choi, “Stable electrode-electrolyte interfaces constructed by fluorine- and nitrogen-donating ionic additives for high-performance lithium metal batteries,” Energy Storage Materials,45, 1-13 (2022), (doi: https://doi.org/10.1016/j.ensm.2021.10.031)
- ProfileProfessor Nam-Soon ChoiEnergy Materials LaboratoryDepartment of Chemical and Biomolecular EngineeringKAIST
2021.12.16 View 7494 -
Connecting the Dots to Find New Treatments for Breast Cancer
Systems biologists uncovered new ways of cancer cell reprogramming to treat drug-resistant cancers
Scientists at KAIST believe they may have found a way to reverse an aggressive, treatment-resistant type of breast cancer into a less dangerous kind that responds well to treatment. The study involved the use of mathematical models to untangle the complex genetic and molecular interactions that occur in the two types of breast cancer, but could be extended to find ways for treating many others. The study’s findings were published in the journal Cancer Research.
Basal-like tumours are the most aggressive type of breast cancer, with the worst prognosis. Chemotherapy is the only available treatment option, but patients experience high recurrence rates. On the other hand, luminal-A breast cancer responds well to drugs that specifically target a receptor on their cell surfaces, called estrogen receptor alpha (ERα).
KAIST systems biologist Kwang-Hyun Cho and colleagues analyzed the complex molecular and genetic interactions of basal-like and luminal-A breast cancers to find out if there might be a way to switch the former to the latter and give patients a better chance to respond to treatment.
To do this, they accessed large amounts of cancer and patient data to understand which genes and molecules are involved in the two types. They then input this data into a mathematical model that represents genes, proteins and molecules as dots and the interactions between them as lines. The model can be used to conduct simulations and see how interactions change when certain genes are turned on or off.
“There have been a tremendous number of studies trying to find therapeutic targets for treating basal-like breast cancer patients,” says Cho. “But clinical trials have failed due to the complex and dynamic nature of cancer. To overcome this issue, we looked at breast cancer cells as a complex network system and implemented a systems biological approach to unravel the underlying mechanisms that would allow us to reprogram basal-like into luminal-A breast cancer cells.”
Using this approach, followed by experimental validation on real breast cancer cells, the team found that turning off two key gene regulators, called BCL11A and HDAC1/2, switched a basal-like cancer signalling pathway into a different one used by luminal-A cancer cells. The switch reprograms the cancer cells and makes them more responsive to drugs that target ERα receptors. However, further tests will be needed to confirm that this also works in animal models and eventually humans.
“Our study demonstrates that the systems biological approach can be useful for identifying novel therapeutic targets,” says Cho.
The researchers are now expanding its breast cancer network model to include all breast cancer subtypes. Their ultimate aim is to identify more drug targets and to understand the mechanisms that could drive drug-resistant cells to turn into drug-sensitive ones.
This work was supported by the National Research Foundation of Korea, the Ministry of Science and ICT, Electronics and Telecommunications Research Institute, and the KAIST Grand Challenge 30 Project.
-Publication Sea R. Choi, Chae Young Hwang, Jonghoon Lee, and Kwang-Hyun Cho, “Network Analysis Identifies Regulators of Basal-like Breast Cancer Reprogramming and Endocrine TherapyVulnerability,” Cancer Research, November 30. (doi:10.1158/0008-5472.CAN-21-0621)
-ProfileProfessor Kwang-Hyun ChoLaboratory for Systems Biology and Bio-Inspired EngineeringDepartment of Bio and Brain EngineeringKAIST
2021.12.07 View 7124 -
Scientists Develop Wireless Networks that Allow Brain Circuits to Be Controlled Remotely through the Internet
Wireless implantable devices and IoT could manipulate the brains of animals from anywhere around the world due to their minimalistic hardware, low setup cost, ease of use, and customizable versatility
A new study shows that researchers can remotely control the brain circuits of numerous animals simultaneously and independently through the internet. The scientists believe this newly developed technology can speed up brain research and various neuroscience studies to uncover basic brain functions as well as the underpinnings of various neuropsychiatric and neurological disorders.
A multidisciplinary team of researchers at KAIST, Washington University in St. Louis, and the University of Colorado, Boulder, created a wireless ecosystem with its own wireless implantable devices and Internet of Things (IoT) infrastructure to enable high-throughput neuroscience experiments over the internet. This innovative technology could enable scientists to manipulate the brains of animals from anywhere around the world. The study was published in the journal Nature Biomedical Engineering on November 25
“This novel technology is highly versatile and adaptive. It can remotely control numerous neural implants and laboratory tools in real-time or in a scheduled way without direct human interactions,” said Professor Jae-Woong Jeong of the School of Electrical Engineering at KAIST and a senior author of the study. “These wireless neural devices and equipment integrated with IoT technology have enormous potential for science and medicine.”
The wireless ecosystem only requires a mini-computer that can be purchased for under $45, which connects to the internet and communicates with wireless multifunctional brain probes or other types of conventional laboratory equipment using IoT control modules. By optimally integrating the versatility and modular construction of both unique IoT hardware and software within a single ecosystem, this wireless technology offers new applications that have not been demonstrated before by a single standalone technology. This includes, but is not limited to minimalistic hardware, global remote access, selective and scheduled experiments, customizable automation, and high-throughput scalability.
“As long as researchers have internet access, they are able to trigger, customize, stop, validate, and store the outcomes of large experiments at any time and from anywhere in the world. They can remotely perform large-scale neuroscience experiments in animals deployed in multiple countries,” said one of the lead authors, Dr. Raza Qazi, a researcher with KAIST and the University of Colorado, Boulder. “The low cost of this system allows it to be easily adopted and can further fuel innovation across many laboratories,” Dr. Qazi added.
One of the significant advantages of this IoT neurotechnology is its ability to be mass deployed across the globe due to its minimalistic hardware, low setup cost, ease of use, and customizable versatility. Scientists across the world can quickly implement this technology within their existing laboratories with minimal budget concerns to achieve globally remote access, scalable experimental automation, or both, thus potentially reducing the time needed to unravel various neuroscientific challenges such as those associated with intractable neurological conditions.
Another senior author on the study, Professor Jordan McCall from the Department of Anesthesiology and Center for Clinical Pharmacology at Washington University in St. Louis, said this technology has the potential to change how basic neuroscience studies are performed. “One of the biggest limitations when trying to understand how the mammalian brain works is that we have to study these functions in unnatural conditions. This technology brings us one step closer to performing important studies without direct human interaction with the study subjects.”
The ability to remotely schedule experiments moves toward automating these types of experiments. Dr. Kyle Parker, an instructor at Washington University in St. Louis and another lead author on the study added, “This experimental automation can potentially help us reduce the number of animals used in biomedical research by reducing the variability introduced by various experimenters. This is especially important given our moral imperative to seek research designs that enable this reduction.”
The researchers believe this wireless technology may open new opportunities for many applications including brain research, pharmaceuticals, and telemedicine to treat diseases in the brain and other organs remotely. This remote automation technology could become even more valuable when many labs need to shut down, such as during the height of the COVID-19 pandemic.
This work was supported by grants from the KAIST Global Singularity Research Program, the National Research Foundation of Korea, the United States National Institute of Health, and Oak Ridge Associated Universities.
-PublicationRaza Qazi, Kyle Parker, Choong Yeon Kim, Jordan McCall, Jae-Woong Jeong et al. “Scalable and modular wireless-network infrastructure for large-scale behavioral neuroscience,” Nature Biomedical Engineering, November 25 2021 (doi.org/10.1038/s41551-021-00814-w)
-ProfileProfessor Jae-Woong JeongBio-Integrated Electronics and Systems LabSchool of Electrical EngineeringKAIST
2021.11.29 View 11945 -
A Genetic Change for Achieving a Long and Healthy Life
Researchers identified a single amino acid change in the tumor suppressor protein in PTEN that extends healthy periods while maintaining longevity
Living a long, healthy life is everyone’s wish, but it is not an easy one to achieve. Many aging studies are developing strategies to increase health spans, the period of life spent with good health, without chronic diseases and disabilities. Researchers at KAIST presented new insights for improving the health span by just regulating the activity of a protein.
A research group under Professor Seung-Jae V. Lee from the Department of Biological Sciences identified a single amino acid change in the tumor suppressor protein phosphatase and tensin homolog (PTEN) that dramatically extends healthy periods while maintaining longevity. This study highlights the importance of the well-conserved tumor suppressor protein PTEN in health span regulation, which can be targeted to develop therapies for promoting healthy longevity in humans. The research was published in Nature Communications on September 24, 2021.
Insulin and insulin-like growth factor-1 (IGF-1) signaling (IIS) is one of the evolutionarily conserved aging-modulatory pathways present in life forms ranging from tiny roundworms to humans. The proper reduction of IIS leads to longevity in animals but often causes defects in multiple health parameters including impaired motility, reproduction, and growth.
The research team found that a specific amino acid change in the PTEN protein improves health status while retaining the longevity conferred by reduced IIS. They used the roundworm C. elegans, an excellent model animal that has been widely used for aging research, mainly because of its very short normal lifespan of about two to three weeks. The PTEN protein is a phosphatase that removes phosphate from lipids as well as proteins. Interestingly, the newly identified amino acid change delicately recalibrated the IIS by partially maintaining protein phosphatase activity while reducing lipid phosphatase activity.
As a result, the amino acid change in the PTEN protein maintained the activity of the longevity-promoting transcription factor Forkhead Box O (FOXO) protein while restricting the detrimental upregulation of another transcription factor, NRF2, leading to long and healthy life in animals with reduced IIS.
Professor Lee said, “Our study raises the exciting possibility of simultaneously promoting longevity and health in humans by slightly tweaking the activity of one protein, PTEN.”
This work was supported by the MInistry of Science and ICT through the National Research Foundation of Korea.
-Publication:Hae-Eun H. Park, Wooseon Hwang, Seokjin Ham, Eunah Kim, Ozlem Altintas, Sangsoon Park, Heehwa G. Son, Yujin Lee, Dongyeop Lee, Won Do Heo, and Seung-Jae V. Lee. 2021. “A PTEN variant uncouples longevity from impaired fitness in Caenorhabditis elegans with reduced insulin/IGF-1 signaling,” Nature Communications, 12(1), 5631. (https://doi.org/10.1038/s41467-021-25920-w)
-ProfileProfessor Seung-Jae V. LeeMolecular Genetics of Aging LaboratoryDepartment of Biological Sciences KAIST
2021.11.19 View 6958 -
Study Finds Player-Character Relationships Affected Game Satisfaction in the Last of Us Part II
Research analyzed player experiences with a polarizing game and found differences in how the players related to their characters
The action adventure game ‘The Last of Us’ was a big hit worldwide in 2014. However, its sequel, the Last of Us Part II divided opinions in the game community when it was released in 2020.
A research team from the Games and Life Lab in the Graduate School of Culture Technology at KAIST analyzed why the game players’ reviews were so polarized and found that player-character relationships influenced the game players’ satisfaction. This study, published in Frontiers in Psychology, will help developers of character-driven games foresee how different players will react to their games.
The team under Professor Young Yim Doh conducted in-depth interviews with 12 players from diverse nations, both those satisfied and dissatisfied with the game.
The team found that three elements affected the game players’ satisfaction. First, players’ satisfaction varied according to their tolerance of forced character switches. When a player is forced to switch their controlled figure in the game to another character that is introduced as the antagonist, most players initially had a negative reaction. The feeling of being forced to play in a way they didn’t want reduced their rights as a player. However, later on, some players viewed this character switch as an interesting transition and were more tolerant toward forced game play.
Second, the researchers found that the flexibility of character attachment is related to game satisfaction. Players who were unhappy about the game resisted building a relationship with the new antagonist character. Meanwhile, players who were happy about the game slowly formed an additional relationship with the new character. This led to the player feeling conflicting emotions, which satisfied players considered a meaningful experience of understanding a perspective of someone initially considered the enemy.
Lastly, the satisfaction of the play depended on how much the players could accept a changing character image in the game. Dissatisfied players found inconsistencies in the characters’ behavior and did not accept the new information about the characters. Meanwhile, satisfied players tried to understand and accept the new information and actions.
“Previous research on narrative games focused more on the game design than on the players’ experiences. To understand why reactions to the game were very different across players, we focused our research on differences in the players’ psychological experiences with the game.” said lead author and Master’s candidate Valérie Erb.
Co-author Dr. Seyeon Lee added, “This suggests that there is no one way to satisfy all players in a character-based narrative game. To satisfy a game’s players, it is important to understand the different players in the player base, target the right player group, and manage expectations accordingly.”
This research was supported by the Year 2020 Culture Technology R&D Program by the Ministry of Culture, Sports and Tourism and the Korea Creative Content Agency.
-PublicationErb V, Lee S, and Doh YY (2021) “Player-Character Relationship and Game Satisfaction in Narrative Game: Focus on Player Experience of Character Switch in The Last of Us Part II”
Frontiers in Psychology. 12:709926. (https://doi.org/10.3389/fpsyg.2021.709926)
-ProfileProfessor Young Yim DohGames and Life LabGraduate School of Culture TechnologyKAIST
2021.11.15 View 4752 -
Nanoscale Self-Assembling Salt-Crystal ‘Origami’ Balls Envelop Liquids
Mechanical engineers have devised a ‘crystal capillary origami’ technique where salt crystals spontaneously encapsulate liquid droplets
Researchers have developed a technique whereby they can spontaneously encapsulate microscopic droplets of water and oil emulsion in a tiny sphere made of salt crystals—sort of like a minute, self-constructing origami soccer ball filled with liquid. The process, which they are calling ‘crystal capillary origami,’ could be used in a range of fields from more precise drug delivery to nanoscale medical devices.The technique is described in a paper appearing in the journal Nanoscale on September 21.
Capillary action, or ‘capillarity,’ will be familiar to most people as the way that water or other liquids can move up narrow tubes or other porous materials seemingly in defiance of gravity (for example within the vascular systems of plants, or even more simply, the drawing up of paint between the hairs of a paintbrush). This effect is due to the forces of cohesion (the tendency of a liquid’s molecules to stick together), which results in surface tension, and adhesion (their tendency to stick to the surface of other substances). The strength of the capillarity depends on the chemistry of the liquid, the chemistry of the porous material, and on the other forces acting on them both. For example, a liquid with lower surface tension than water would not be able to hold up a water strider insect.
Less well known is a related phenomenon, elasto-capillarity, that takes advantage of the relationship between capillarity and the elasticity of a very tiny flat sheet of a solid material. In certain circumstances, the capillary forces can overcome the elastic bending resistance of the sheet.
This relationship can be exploited to create ‘capillary origami,’ or three-dimensional structures. When a liquid droplet is placed on the flat sheet, the latter can spontaneously encapsulate the former due to surface tension. Capillary origami can take on other forms including wrinkling, buckling, or self-folding into other shapes. The specific geometrical shape that the 3D capillary origami structure ends up taking is determined by both the chemistry of the flat sheet and that of the liquid, and by carefully designing the shape and size of the sheet.
There is one big problem with these small devices, however. “These conventional self-assembled origami structures cannot be completely spherical and will always have discontinuous boundaries, or what you might call ‘edges,’ as a result of the original two-dimensional shape of the sheet,” said Kwangseok Park, a lead researcher on the project. He added, “These edges could turn out to be future defects with the potential for failure in the face of increased stress.” Non-spherical particles are also known to be more disadvantageous than spherical particles in terms of cellular uptake.
Professor Hyoungsoo Kim from the Department of Mechanical Engineering explained, “This is why researchers have long been on the hunt for substances that could produce a fully spherical capillary origami structure.”
The authors of the study have demonstrated such an origami sphere for the first time. They showed how instead of a flat sheet, the growth of salt-crystals can perform capillary origami action in a similar manner. What they call ‘crystal capillary origami’ spontaneously constructs a smooth spherical shell capsule from these same surface tension effects, but now the spontaneous encapsulation of a liquid is determined by the elasto-capillary conditions of growing crystals.
Here, the term ‘salt’ refers to a compound of one positively charged ion and another negatively charged. Table salt, or sodium chloride, is just one example of a salt. The researchers used four other salts: calcium propionate, sodium salicylate, calcium nitrate tetrahydrate, and sodium bicarbonate to envelop a water-oil emulsion. Normally, a salt such as sodium chloride has a cubical crystal structure, but these four salts form plate-like structures as crystallites or ‘grains’ (the microscopic shape that forms when a crystal first starts to grow) instead. These plates then self-assemble into perfect spheres.
Using scanning electron microscopy and X-ray diffraction analysis, they investigated the mechanism of such formation and concluded that it was ‘Laplace pressure’ that drives the crystallite plates to cover the emulsion surface. Laplace pressure describes the pressure difference between the interior and exterior of a curved surface caused by the surface tension at the interface between the two substances, in this case between the salt water and the oil.
The researchers hope that these self-assembling nanostructures can be used for encapsulation applications in a range of sectors, from the food industry and cosmetics to drug delivery and even tiny medical devices.
-Publication
Kwangseok Park, Hyoungsoo Kim “Crystal capillary origami capsule with self-assembled nanostructure,” Nanoscale, 13(35), 14656-14665 (DOI: 10.1039/d1nr02456f)
-Profile
Professor Hyoungsoo Kim
Fluid and Interface Laboratory
http://fil.kaist.ac.kr
Department of Mechanical Engineering
KAIST
2021.11.04 View 7477 -
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 8080 -
Flexible Sensor-Integrated RFA Needle Leads to Smarter Medical Treatment
Clinical trial of flexible sensor-integrated radiofrequency ablation (RFA) needle tip monitors physical changes and steam pop
Researchers have designed a thin polymeric sensor platform on a radiofrequency ablation needle to monitor temperature and pressure in real time. The sensors integrated onto 1.5 mm diameter needle tip have proven their efficacy during clinical tests and expect to provide a new opportunity for safer and more effective medical practices. The research was reported in Advanced Science as the frontispiece on August 5.
Radiofrequency ablation (RFA) is a minimally invasive surgery technique for removing tumors and treating cardiovascular disease. During a procedure, an unintended audible explosion called ‘steam pop’ can occur due to the increased internal steam pressure in the ablation region. This phenomenon has been cited as a cause of various negative thermal and mechanical effects on neighboring tissue. Even more, the relationship between steam pop and cancer recurrence is still being investigated.
Professor Inkyu Park said that his team’s integrated sensors reliably detected the occurrence of steam pop. The sensors also monitor rapidly spreading hot steam in tissue. It is expected that the diverse properties of tissue undergoing RFA could be checked by utilizing the physical sensors integrated on the needle.
“We believe that the integrated sensors can provide useful information about a variety of medical procedures and accompanying environmental changes in the human body, and help develop more effective and safer surgical procedures,” said Professor Park.
Professor Park’s team built a thin film type pressure and temperature sensor stack with a thickness of less than 10 μm using a microfabrication process. For the pressure sensor, the team used contact resistance changes between metal electrodes and a carbon nanotube coated polymeric membrane. The entire sensor array was thoroughly insulated with medical tubes to minimize any exposure of the sensor materials to external tissue and maximize its biocompatibility.
During the clinical trial, the research team found that the accumulated hot steam is suddenly released during steam pops and this hot air spreads to neighboring tissue, which accelerates the ablation process. Furthermore, using in-situ ultrasound imaging and computational simulations, the research team could confirm the non-uniform temperature distribution around the RFA needle can be one of the primary reasons for the steam popping.
Professor Park explained that various physical and chemical sensors for different targets can be added to create other medical devices and industrial tools.
“This result will expand the usability and applicability of current flexible sensor technologies. We are also trying to integrate this sensor onto a 0.3mm diameter needle for in-vivo diagnosis applications and expect that this approach can be applied to other medical treatments as well as the industrial field,” added Professor Park. This study was supported by the National Research Foundation of Korea.
-PublicationJaeho Park, Jinwoo Lee, Hyo Keun Lim, Inkyu Park et al. “Real-Time Internal Steam Pop Detection during Radiofrequency Ablation with a Radiofrequency Ablation Needle Integrated with a Temperature and Pressure Sensor: Preclinical and clinical pilot tests," Advanced Science (https://doi/org/10.1002/advs.202100725) on August 5, 2021
-ProfileProfessor Inkyu ParkMicro & Nano Tranducers Laboratory http://mintlab1.kaist.ac.kr/
Department of Mechanical EngineeringCollege of EngineeringKAIST
2021.10.20 View 6825 -
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 9468