Kaist

KAIST Identifies the Trigger of the Hyperactivatio..

(Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering) Scientists have been investigating the negative effects that the hyperactivation of fibrosis has on fibrotic diseases and cancer. A KAIST research team unveiled a positive feedback loop that bi-stably activates fibroblasts in collaboration with Samsung Medical Center. This finding will contribute to developing therapeutic targets for both fibrosis and cancer. Human fibroblasts are dormant in normal tissue, but show radical activation during wound healing. However, the principle that induces their explosive activation has not yet been identified. Here, Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering, in collaboration with Professor Seok-Hyung Kim from Samsung Medical Center, discovered the principle of a circuit that continuously activates fibroblasts. They constructed a positive feedback loops (PFLs) where Twist1, Prrx1, and Tenascin-C (TNC) molecules consecutively activate fibroblasts. They confirmed that these are the main inducers of fibroblast activation by conducting various experiments, including molecular biological tests, mathematical modeling, animal testing, and computer simulations to conclude that they are the main inducers of fibroblast activation. According to their research, Twist 1 is a key regulator of cancer-associated fibroblasts, which directly upregulates Prrx1 and then triggers TNC, which also increases Twist1 expression. This circuit consequently forms a Twist-Prrx1-TNC positive feedback loop. Activated fibroblasts need to be deactivated after wounds are healed. However, if the PFLs continue, the fibroblasts become the major cause of worsening fibrotic diseases and cancers. Therefore, the team expects that Twist1-Prrx1-TNC positive PFLs will be applied for novel and effective therapeutic targeting of fibrotic diseases and cancers. This research was published in Nature Communications on August 1, 2018. Figure 1. Twist1 increases tenascin-c expression in cancer-associated fibroblasts. Twist1 is a potent but indirect inducer of tenascin-c (TNC), which is essential for maintaining Twist1 expression in cancer-associated fibroblasts (CAFs). Figure 2. Summary of the study. The Twist1-Prrx1-TNC positive feedback regulation provides clues for understanding the activation of fibroblasts during wound healing under normal conditions, as well as abnormally activated fibroblasts in pathological conditions such as cancerous and fibrotic diseases. Under normal conditions, the PFL acts as a reversible bistable switch by which the activation of fibroblasts is “ON" above a sufficient level of stimulation and “OFF" for the withdrawal of the stimulus. However, this switch can be permanently turned on under pathologic conditions by continued activation of the PFL, resulting in sustained proliferation of fibroblasts.

Levitating 2D semiconductor for better performance

(from top: Professor Yeon Sik Jung and PhD candidate Soomin Yim) Atomically thin 2D semiconductors have been drawing attention for their superior physical properties over silicon semiconductors; nevertheless, they are not the most appealing materials due to their structural instability and costly manufacturing process. To shed some light on these limitations, a KAIST research team suspended a 2D semiconductor on a dome-shaped nanostructure to produce a highly efficient semiconductor at a low cost. 2D semiconducting materials have emerged as alternatives for silicon-based semiconductors because of their inherent flexibility, high transparency, and excellent carrier transport properties, which are the important characteristics for flexible electronics. Despite their outstanding physical and chemical properties, they are oversensitive to their environment due to their extremely thin nature. Hence, any irregularities in the supporting surface can affect the properties of 2D semiconductors and make it more difficult to produce reliable and well performing devices. In particular, it can result in serious degradation of charge-carrier mobility or light-emission yield. To solve this problem, there have been continued efforts to fundamentally block the substrate effects. One way is to suspend a 2D semiconductor; however, this method will degrade mechanical durability due to the absence of a supporter underneath the 2D semiconducting materials. Professor Yeon Sik Jung from the Department of Materials Science and Engineering and his team came up with a new strategy based on the insertion of high-density topographic patterns as a nanogap-containing supporter between 2D materials and the substrate in order to mitigate their contact and to block the substrate-induced unwanted effects. More than 90％ of the dome-shaped supporter is simply an empty space because of its nanometer scale size. Placing a 2D semiconductor on this structure creates a similar effect to levitating the layer. Hence, this method secures the mechanical durability of the device while minimizing the undesired effects from the substrate. By applying this method to the 2D semiconductor, the charge-carrier mobility was more than doubled, showing a significant improvement of the performance of the 2D semiconductor. Additionally, the team reduced the price of manufacturing the semiconductor. In general, constructing an ultra-fine dome structure on a surface generally involves costly equipment to create individual patterns on the surface. However, the team employed a method of self-assembling nanopatterns in which molecules assemble themselves to form a nanostructure. This method led to reducing production costs and showed good compatibility with conventional semiconductor manufacturing processes. Professor Jung said, “This research can be applied to improve devices using various 2D semiconducting materials as well as devices using graphene, a metallic 2D material. It will be useful in a broad range of applications, such as the material for the high speed transistor channels for next-generation flexible displays or for the active layer in light detectors.” This research, led by PhD candidate Soomin Yim, was published in Nano Letters in April. Figure 1. Image of a 2D semiconductor using dome structures

Improved Efficiency and Stability of CQD Solar Cel..

(from left: Professor Jung-Yong Lee and Dr. Se-Woong Baek) Recently, the power conversion efficiency (PCE) of colloidal quantum dot (CQD)-based solar cells has been enhanced, paving the way for their commercialization in various fields; nevertheless, they are still a long way from being commercialized due to their efficiency not matching their stability. In this research, a KAIST team achieved highly stable and efficient CQD-based solar cells by using an amorphous organic layer to block oxygen and water permeation. CQD-based solar cells are light-weight, flexible, and they boost light harvesting by absorbing near-infrared lights. Especially, they draw special attention for their optical properties controlled efficiently by changing the quantum dot sizes. However, they are still incompatible with existing solar cells in terms of efficiency, stability, and cost. Therefore, there is great demand for a novel technology that can simultaneously improve both PCE and stability while using an inexpensive electrode material. Responding to this demand, Professor Jung-Yong Lee from the Graduate School of Energy, Environment, Water and Sustainability and his team introduced a technology to improve the efficiency and stability of CQD-based solar cells. The team found that an amorphous organic thin film has a strong resistance to oxygen and water. Using these properties, they employed this doped organic layer as a top-hole selective layer (HSL) for the PbS CQD solar cells, and confirmed that the hydro/oxo-phobic properties of the layer efficiently protected the PbS layer. According to the molecular dynamics simulations, the layer significantly postponed the oxygen and water permeation into the PbS layer. Moreover, the efficient injection of the holes in the layer reduced interfacial resistance and improved performance. With this technology, the team finally developed CQD-based solar cells with excellent stability. The PCE of their device stood at 11.7％ and maintained over 90％ of its initial performance when stored for one year under ambient conditions. Professor Lee said, “This technology can be also applied to QD LEDs and Perovskite devices. I hope this technology can hasten the commercialization of CQD-based solar cells.” This research, led by Dr. Se-Woong Baek and a Ph.D. student, Sang-Hoon Lee, was published in Energy & Environmental Science on May 10. Figure 1. The schematic of the equilibrated structure of the amorphous organic film Figure 2. Schematic illustration of CQD-based solar cells and graphs showing their performance

Robotic Herding of a Flock of Birds Using Drones

A joint team from KAIST, Caltech, and Imperial College London, presents a drone with a new algorithm to shepherd birds safely away from airports R esearchers made a new algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated airspace. This novel approach allows a single autonomous quadrotor drone to herd an entire flock of birds away without breaking their formation. Professor David Hyunchul Shim at KAIST in collaboration with Professor Soon-Jo Chung of Caltech and Professor Aditya Paranjape of Imperial College London investigated the problem of diverting a flock of birds away from a prescribed area, such as an airport, using a robotic UVA. A novel boundary control strategy called the m-waypoint algorithm was introduced for enabling a single pursuer UAV to safely herd the flock without fragmenting it. The team developed the herding algorithm on the basis of macroscopic properties of the flocking model and the response of the flock. They tested their robotic autonomous drone by successfully shepherding an entire flock of birds out of a designated airspace near KAIST’s campus in Daejeon, South Korea. This study is published in IEEE Transactions on Robotics. “It is quite interesting, and even awe-inspiring, to monitor how birds react to threats and collectively behave against threatening objects through the flock. We made careful observations of flock dynamics and interactions between flocks and the pursuer. This allowed us to create a new herding algorithm for ideal flight paths for incoming drones to move the flock away from a protected airspace,” said Professor Shim, who leads the Unmanned Systems Research Group at KAIST. Bird strikes can threaten the safety of airplanes and their passengers. Korean civil aircraft suffered more than 1,000 bird strikes between 2011 and 2016. In the US, 142,000 bird strikes destroyed 62 civilian airplanes, injured 279 people, and killed 25 between 1990 and 2013. In the UK in 2016, there were 1,835 confirmed bird strikes, about eight for every 10,000 flights. Bird and other wildlife collisions with aircraft cause well over 1.2 billion USD in damages to the aviation industry worldwide annually. In the worst case, Canadian geese knocked out both engines of a US Airway jet in January 2009. The flight had to make an emergency landing on the Hudson River. Airports and researchers have continued to reduce the risk of bird strikes through a variety of methods. They scare birds away using predators such as falcons or loud noises from small cannons or guns. Some airports try to prevent birds from coming by ridding the surrounding areas of crops that birds eat and hide in. However, birds are smart. “I was amazed with the birds’ capability to interact with flying objects. We thought that only birds of prey have a strong sense of maneuvering with the prey. But our observation of hundreds of migratory birds such as egrets and loons led us to reach the hypothesis that they all have similar levels of maneuvering with the flying objects. It will be very interesting to collaborate with ornithologists to study further with birds’ behaviors with aerial objects,” said Professor Shim. “Airports are trying to transform into smart airports. This algorithm will help improve safety for the aviation industry. In addition, this will also help control avian influenza that plagues farms nationwide every year,” he stressed. For this study, two drones were deployed. One drone performed various types of maneuvers around the flocks as a pursuer of herding drone, while a surveillance drone hovered at a high altitude with a camera pointing down for recording the trajectories of the pursuer drone and the birds. During the experiments on egrets, the birds made frequent visits to a hunting area nearby and a large number of egrets were found to return to their nests at sunset. During the time, the team attempted to fly the herding drone in various directions with respect to the flock. The drone approached the flock from the side. When the birds noticed the drone, they diverted from their original paths and flew at a 45˚ angle to their right. When the birds noticed the drone while it was still far away, they adjusted their paths horizontally and made smaller changes in the vertical direction. In the second round of the experiment on loons, the drone flew almost parallel to the flight path of a flock of birds, starting from an initial position located just off the nominal flight path. The birds had a nominal flight speed that was considerably higher than that of the drone so the interaction took place over a relatively short period of time. Professor Shim said, “I think we just completed the first step of the research. For the next step, more systems will be developed and integrated for bird detection, ranging, and automatic deployment of drones.” “Professor Chung at Caltech is a KAIST graduate. And his first student was Professor Paranjape who now teaches at Imperial. It is pretty interesting that this research was made by a KAIST faculty member, an alumnus, and his student on three different continents,” he said. (Figure A. Case 1: drone approaches the herd with sufficient distance to induce horizontal deviation) (Figure B. Case 2: drone approaches the herd abruptly to cause vertical deviation)

It’s Time to 3D Sketch with Air Scaffolding

 People often use their hands when describing an object, while pens are great tools for describing objects in detail. Taking this idea, a KAIST team introduced a new 3D sketching workflow, combining the strengths of hand and pen input. This technique will ease the way for ideation in three dimensions, leading to efficient product design in terms of time and cost. For a designer's drawing to become a product in reality, one has to transform a designer's 2D drawing into a 3D shape; however, it is difficult to infer accurate 3D shapes that match the original intention from an inaccurate 2D drawing made by hand. When creating a 3D shape from a planar 2D drawing, unobtainable information is required. On the other hand, loss of depth information occurs when a 3D shape is expressed as a 2D drawing using perspective drawing techniques. To fill in these “missing links” during the conversion, "3D sketching" techniques have been actively studied. Their main purpose is to help designers naturally provide missing 3D shape information in a 2D drawing. For example, if a designer draws two symmetric curves from a single point of view or draws the same curves from different points of view, the geometric clues that are left in this process are collected and mathematically interpreted to define the proper 3D curve. As a result, designers can use 3D sketching to directly draw a 3D shape as if using pen and paper. Among 3D sketching tools, sketching with hand motions, in VR environments in particular, has drawn attention because it is easy and quick. But the biggest limitation is that they cannot articulate the design solely using rough hand motions, hence they are difficult to be applied to product designs. Moreover, users may feel tired after raising their hands in the air during the entire drawing process. Using hand motions but to elaborate designs, Professor Seok-Hyung Bae and his team from the Department of Industrial Design integrated hand motions and pen-based sketching, allocating roles according to their strengths. This new technique is called Agile 3D Sketching with Air Scaffolding. Designers use their hand motions in the air to create rough 3D shapes which will be used as scaffolds, and then they can add details with pen-based 3D sketching on a tablet (Figure 1). Figure 1. In the agile 3D sketching workflow with air scaffolding, the user (a) makes unconstrained hand movements in the air to quickly generate rough shapes to be used as scaffolds, (b) uses the scaffolds as references and draws finer details with them, (c) produces a high-fidelity 3D concept sketch of a steering wheel in an iterative and progressive manner. The team came up with an algorithm to identify descriptive hand motions from transitory hand motions and extract only the intended shapes from unconstrained hand motions, based on air scaffolds from the identified motions. Through user tests, the team identified that this technique is easy to learn and use, and demonstrates good applicability. Most importantly, the users can reduce time, yet enhance the accuracy of defining the proportion and scale of products. Eventually, this tool will be able to be applied to various fields including the automobile industry, home appliances, animations and the movie making industry, and robotics. It also can be linked to smart production technology, such as 3D printing, to make manufacturing process faster and more flexible. PhD candidate Yongkwan Kim, who led the research project, said, “I believe the system will enhance product quality and work efficiency because designers can express their 3D ideas quickly yet accurately without using complex 3D CAD modeling software. I will make it into a product that every designer wants to use in various fields.” “There have been many attempts to encourage creative activities in various fields by using advanced computer technology. Based on in-depth understanding of designers, we will take the lead in innovating the design process by applying cutting-edge technology,” Professor Bae added. Professor Bae and his team from the Department of Industrial Design has been delving into developing better 3D sketching tools. They started with a 3D curve sketching system for professional designers called ILoveSketch and moved on to SketchingWithHands for designing a handheld product with first-person hand postures captured by a hand-tracking sensor. They then took their project to the next level and introduced Agile 3D Sketching with Air Scaffolding, a new 3D sketching workflow combining hand motion and pen drawing which was chosen as one of the CHI (Conference on Human Factors in Computing Systems) 2018 Best Papers by the Association for Computing Machinery. - Click the link to watch video clip of SketchingWithHands

KAIST Reveals Mathematical Principle behind AI’s ‘..

(from left: Professor Jong Chul Ye, PhD candidates Yoseob Han and Eunju Cha) A KAIST research team identified the geometrical structure of artificial intelligence (AI) and discovered the mathematical principles of highly performing artificial neural networks, which can be applicable in fields such as medical imaging. Deep neural networks are an exemplary method of implementing deep learning, which is at the core of the AI technology, and have shown explosive growth in recent years. This technique has been used in various fields, such as image and speech recognition as well as image processing. Despite its excellent performance and usefulness, the exact working principles of deep neural networks has not been well understood, and they often suffer from unexpected results or errors. Hence, there is an increasing social and technical demand for interpretable deep neural network models. To address these issues, Professor Jong Chul Ye from the Department of Bio & Brain Engineering and his team attempted to find the geometric structure in a higher dimensional space where the structure of the deep neural network can be easily understood. They proposed a general deep learning framework, called deep convolutional framelets, to understand the mathematical principle of a deep neural network in terms of the mathematical tools in Harmonic analysis. As a result, it was found that deep neural networks’ structure appears during the process of decomposition of high dimensionally lifted signal via Hankel matrix, which is a high-dimensional structure formerly studied intensively in the field of signal processing. In the process of decomposing the lifted signal, two bases categorized as local and non-local basis emerge. The researchers found that non-local and local basis functions play a role in pooling and filtering operation in convolutional neural network, respectively. Previously, when implementing AI, deep neural networks were usually constructed through empirical trial and errors. The significance of the research lies in the fact that it provides a mathematical understanding on the neural network structure in high dimensional space, which guides users to design an optimized neural network. They demonstrated improved performance of the deep convolutional framelets’ neural networks in the applications of image denoising, image pixel in painting, and medical image restoration. Professor Ye said, “Unlike conventional neural networks designed through trial-and-error, our theory shows that neural network structure can be optimized to each desired application and are easily predictable in their effects by exploiting the high dimensional geometry. This technology can be applied to a variety of fields requiring interpretation of the architecture, such as medical imaging.” This research, led by PhD candidates Yoseob Han and Eunju Cha, was published in the April 26th issue of the SIAM Journal on Imaging Sciences. Figure 1. The design of deep neural network using mathematical principles Figure 2. The results of image noise cancelling Figure 3. The artificial neural network restoration results in the case where 80％ of the pixels are lost

Deep learning predicts drug-drug and drug-food int..

(Distinguished Professor Sang Yup Lee) A Korean research team from KAIST developed a computational framework, DeepDDI, that accurately predicts and generates 86 types of drug-drug and drug-food interactions as outputs of human-readable sentences, which allows in-depth understanding of the drug-drug and drug-food interactions. Drug interactions, including drug-drug interactions (DDIs) and drug-food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. However, current prediction methods do not provide sufficient details beyond the chance of DDI occurrence, or require detailed drug information often unavailable for DDI prediction. To tackle this problem, Dr. Jae Yong Ryu, Assistant Professor Hyun Uk Kim and Distinguished Professor Sang Yup Lee, all from the Department of Chemical and Biomolecular Engineering at Korea Advanced Institute of Science and Technology (KAIST), developed a computational framework, named DeepDDI, that accurately predicts 86 DDI types for a given drug pair. The research results were published online in Proceedings of the National Academy of Sciences of the United States of America (PNAS) on April 16, 2018, which is entitled “Deep learning improves prediction of drug-drug and drug-food interactions.” DeepDDI takes structural information and names of two drugs in pair as inputs, and predicts relevant DDI types for the input drug pair. DeepDDI uses deep neural network to predict 86 DDI types with a mean accuracy of 92.4％ using the DrugBank gold standard DDI dataset covering 192,284 DDIs contributed by 191,878 drug pairs. Very importantly, DDI types predicted by DeepDDI are generated in the form of human-readable sentences as outputs, which describe changes in pharmacological effects and/or the risk of ADEs as a result of the interaction between two drugs in pair. For example, DeepDDI output sentences describing potential interactions between oxycodone (opioid pain medication) and atazanavir (antiretroviral medication) were generated as follows: “The metabolism of Oxycodone can be decreased when combined with Atazanavir”; and “The risk or severity of adverse effects can be increased when Oxycodone is combined with Atazanavir”. By doing this, DeepDDI can provide more specific information on drug interactions beyond the occurrence chance of DDIs or ADEs typically reported to date. DeepDDI was first used to predict DDI types of 2,329,561 drug pairs from all possible combinations of 2,159 approved drugs, from which DDI types of 487,632 drug pairs were newly predicted. Also, DeepDDI can be used to suggest which drug or food to avoid during medication in order to minimize the chance of adverse drug events or optimize the drug efficacy. To this end, DeepDDI was used to suggest potential causal mechanisms for the reported ADEs of 9,284 drug pairs, and also predict alternative drug candidates for 62,707 drug pairs having negative health effects to keep only the beneficial effects. Furthermore, DeepDDI was applied to 3,288,157 drug-food constituent pairs (2,159 approved drugs and 1,523 well-characterized food constituents) to predict DFIs. The effects of 256 food constituents on pharmacological effects of interacting drugs and bioactivities of 149 food constituents were also finally predicted. All these prediction results can be useful if an individual is taking medications for a specific (chronic) disease such as hypertension or diabetes mellitus type 2. Distinguished Professor Sang Yup Lee said, “We have developed a platform technology DeepDDI that will allow precision medicine in the era of Fourth Industrial Revolution. DeepDDI can serve to provide important information on drug prescription and dietary suggestions while taking certain drugs to maximize health benefits and ultimately help maintain a healthy life in this aging society.” Figure 1. Overall scheme of Deep DDDI and prediction of food constituents that reduce the in vivo concentration of approved drugs

KAIST Develops Sodium Ion Batteries using Copper S..

(Dr. Sungjoo Kim, PhD candidates Hyeon Kook Seo, Jae Yeol Park and Joon Ha Chang, Emeritus Professor Jeong Yong Lee and Professor Jong Min Yuk) A KAIST research team recently developed sodium ion batteries using copper sulfide anode. This finding will contribute to advancing the commercialization of sodium ion batteries (SIBs) and reducing the production cost of any electronic products with batteries. Professor Jong Min Yuk and Emeritus Professor Jeong Yong Lee from Department of Materials Science and Engineering succeeded in developing a new anode material suitable for use in a SIB. Compared to the existing anode materials, the copper sulfide anode was measured to exhibit 1.5 times better cyclability with projected 40％ reduction in cost. Batteries used in various applications including mobile phones are lithium ion batteries, mostly referred as Li-ion batteries or LIBs. Though they are popularly used until now, large-scale energy storage systems require much inexpensive and abundant materials. Hence, a SIB has attracted enormous attention for their advantage over a lithium counterpart. However, one main obstacle to commercialization of SIB is the lack of suitable anodes that exhibit high capacity and the cycling stability of the battery. Hence, the research team recognized this need for a good anode material that could offer high electrical conductivity and theoretical capacity. The material was found to be copper sulfide, preferably in nanoplates, which “prefers to make an alloy with sodium and is thus promising for high capacity and long-term cyclability.” Further analysis presented in the study reveals that copper sulfide undergoes crystallographic tuning to make a room for sodium insertion. Results indicate that the sodium ion-insertion capacity of copper sulfide is as much as 1.5 times that of lithium ions for graphite. Furthermore, a battery with this new anode material retains 90％ of its original capacity for 250 charge-discharge cycles. With the natural abundance of sodium in seawater, this development may contribute to reduction in battery costs, which can be translated into up to 30％ cut in the price of various consumer electronics. Professor Lee expressed his hope for “the production of next-generation, high-performance sodium ion batteries”. Professor Yuk said, “These days, people are showing a great deal of interest in products related to renewable energy due to recent micro-dust issues ongoing in Korea. This study may help Korea get a head-start on renewable energy products”. This research, led by PhD candidate Jae Yeol Park and Dr. Sung Joo Kim, was published online in Nature Communications on March 2. Figure 1. The sodiation process of copper sulfide

KAIST Succeeds in Producing 50x More Stable Adsorb..

(From left: Professor Minkee Choi and PhD candidate Woosung Choi) A KAIST research team developed a technology to increase the stability of amine-containing adsorbents by fifty times, moving one step further toward commercializing stable adsorbents that last longer. Professor Minkee Choi from the Department of Chemical and Biomolecular Engineering and his team succeeded in developing amine-containing adsorbents that show high oxidative stability. The capture of the greenhouse gas carbon dioxide is an active ongoing research field, and some of the latest advancements point to amine-containing adsorbents as an efficient and environment-friendly way to capture carbon dioxide. However, existing amine-containing adsorbents are known to be unstable under oxidation, which chemically breaks down the adsorbent, thereby making it difficult to rely on amine-containing adsorbents for repeated and continued use. The researchers have discovered that the miniscule amount of iron and copper present in the amine accelerate the oxidative breakdown of the amine-containing adsorbent. Upon this discovery, they proposed the use of a chelator substance, which essentially suppresses the activation of the impurities. The team demonstrates that the proposed method renders the adsorbent up to 50 times slower in its deactivation rate due to oxidation, compared to conventional polyethyleneimine (PEI) / silica adsorbents. Figure 1 illustrates the superior performance of this oxidation-stable amine-containing adsorbent (shown in black squares), whose carbon dioxide-capturing capacity deteriorates by only a small amount (~8％). Meanwhile, the carbon dioxide-capturing capacity of the PEI/silica adsorbent (shown in red diamonds) degrades dramatically after being exposed to oxidative aging for 30 days. This stability under oxidation is expected to have brought amine-containing adsorbents one step closer to commercialization. As such, first author Woosung Choi describes the significance of this study as “having brought solid carbon dioxide adsorbents to commercializable standards”. In fact, Professor Choi explains that commercialization steps for his team’s carbon dioxide adsorbents are already underway. He further set forth his aim to “develop the world’s best carbon dioxide capture adsorbent”. This research, led by the PhD candidate Woosung Choi, was published online in Nature Communications on February 20. Figure 1. Carbon dioxide working capacity against oxidative aging time. Performance of the proposed method (black) degrades much more slowly (~50x) than that of existing methods. The novel adsorbent is thus shown to be more robust to oxidation.

KAIST Discloses the Formation of Burning Ice in Oc..

(from left: Professor Tae-Hyuk Kwon and PhD candidate Taehyung Park) ?A KAIST research team has identified the formation of natural gas hydrates, so-called flammable ice, formed in oceans. Professor Tae-Hyuk Kwon from the Department of Civil & Environmental Engineering and his team found that clay minerals in oceanic clay-rich sedimentary deposits promote formation of gas hydrates and proposed the principle of gas hydrate formation in the clayey sedimentary layers. Gas hydrates are ice-like crystalline structures composed of hydrogen-bonded water molecules encapsulating gas molecules. They are also known as burning ice. Their deposits are so huge that they gain attention for alternative energy. Conventionally, it was believed that formation of gas hydrates is limited in clay sedimentary deposits; however, unexpected abundance of natural gas hydrates in oceanic clay-rich sedimentary deposits raised the issue of how they formed. The surfaces of natural clay minerals are negatively charged and, thus, unavoidably generate physicochemical interactions between clay and water. Such clay-water interactions have a critical role in the occurrence of natural gas hydrates in clay-rich sedimentary formations. However, there has been experimental difficulty in analyzing hydrate formation because of the cations contained in clay particles, which balance the clay surface charges. Therefore, clay particles inevitably release the cations when mixed with water, which complicates the interpretation of experimental results. To overcome this limitation, the team polarized water molecules with an electric field and monitored the induction times of water molecules forming gas hydrates. They found that the 10 kV/m of electric field promoted gas hydrate nucleation under certain conditions rather than slowing it down, due to the partial breakage of the hydrogen bonded water clusters and the lowered thermal energy of water molecules. Professor Kwon said, “Through this research, we gained better insight into the origin of gas hydrates occurrence in clay-rich sedimentary deposits. In the near future, we will soon be able to commercially produce methane gas from natural gas hydrate deposits.” This research, led by PhD candidate Taehyung Park, was published online in Environmental Science and Technology on February 3. (doi: 10.1021/acs.est.7b05477) Figure 1. Formation of gas hydrates with water molecules Figure 2. Enhancement and inhibition of gas hydrates

Printed Thermo-Plasmonic Heat Patterns for Neurolo..

(Professor Nam and Dr. Kang, right) A KAIST team presented a highly customizable neural stimulation method. The research team developed a technology that can print the heat pattern on a micron scale to enable the control of biological activities remotely. The researchers integrated a precision inkjet printing technology with bio-functional thermo-plasmonic nanoparticles to achieve a ‘selective nano-photothermal neural stimulation method.’ The research team of Professor Yoonkey Nam at the Department of Bio and Brain Engineering expects this will serve as an enabling technology for personalized precision neuromodulation therapy for patients with neurological disorders. The nano-photothermal neural stimulation method uses the thermo-plasmonic effect of metal nanoparticles to modulate the activities of neuronal networks. With the thermo-plasmonic effect, metal nanoparticles can absorb specific wavelength of illuminated light to efficiently generate localized heat. The research team discovered the inhibitory behavior of spontaneous activities of neurons upon photothermal stimulation four years ago. Since then, they have developed this technology to control hyperactive behaviors of neurons and neural circuits, which is often found in neurological disorders such as epilepsy. In order to overcome the limitation on the spatial selectivity and resolution of the previously developed nano-photothermal method, the team adopted an inkjet printing technology to micro pattern the plasmonic nanoparticles (a few tens of microns), and successfully demonstrated that the nano-photothermal stimulation can be selectively applied according to the printed patterns. The researchers applied a polyelectrolyte layer-by-layer coating method to printing substrates in a way to improve the pattern fidelity and achieve the uniform assembly of nanoparticles. The electrostatic attraction between the printed nanoparticles and the coated printing substrate also helped the stability of the attached nanoparticles. Because the polyelectrolyte coating is biocompatible, biological experiments including cell culture are possible with the technology developed in this work. Using printed gold nanorod particles in a few tens of microns resolution over a several centimeters area, the researchers showed that highly complex heat patterns can be precisely formed upon light illumination according to the printing image. Lastly, the team confirmed that the printed heat patterns can selectively and instantaneously inhibit the activities of cultured hippocampal neurons upon near-infrared light illumination. Because the printing process is applicable to thin and flexible substrates, the technology can be easily applied to implantable neurological disorder treatment devices and wearable devices. By selectively applying the heat patterns to only the desired cellular areas, customized and personalized photothermal neuromodulation therapy can be applied to patients. “The fact that any desired heat patterns can be simply ‘printed’ anywhere broadens the applicability of this technology in many engineering fields. In bioengineering, it can be applied to neural interfaces using light and heat to modulate physiological functions. As another engineering application, for example, printed heat patterns can be used as a new concept of anti-counterfeit applications,” said the principal investigator, Yoonkey Nam at KAIST. This work, led mainly by Dr. Hongki Kang, was published in ACS Nano on February 5th 2018.

Recognizing Seven Different Face Emotions on a Mob..

(Professor Hoi-Jun Yoo) A KAIST research team succeeded in achieving face emotion recognition on a mobile platform by developing an AI semiconductor IC that processes two neural networks on a single chip. Professor Hoi-Jun Yoo and his team (Primary researcher: Jinmook Lee Ph. D. student) from the School of Electrical Engineering developed a unified deep neural network processing unit (UNPU). Deep learning is a technology for machine learning based on artificial neural networks, which allows a computer to learn by itself, just like a human. The developed chip adjusts the weight precision (from 1 bit to 16 bit) of a neural network inside of the semiconductor in order to optimize energy efficiency and accuracy. With a single chip, it can process a convolutional neural network (CNN) and recurrent neural network (RNN) simultaneously. CNN is used for categorizing and recognizing images while RNN is for action recognition and speech recognition, such as time-series information. Moreover, it enables an adjustment in energy efficiency and accuracy dynamically while recognizing objects. To realize mobile AI technology, it needs to process high-speed operations with low energy, otherwise the battery can run out quickly due to processing massive amounts of information at once. According to the team, this chip has better operation performance compared to world-class level mobile AI chips such as Google TPU. The energy efficiency of the new chip is 4 times higher than the TPU. In order to demonstrate its high performance, the team installed UNPU in a smartphone to facilitate automatic face emotion recognition on the smartphone. This system displays a user’s emotions in real time. The research results for this system were presented at the 2018 International Solid-State Circuits Conference (ISSCC) in San Francisco on February 13. Professor Yoo said, “We have developed a semiconductor that accelerates with low power requirements in order to realize AI on mobile platforms. We are hoping that this technology will be applied in various areas, such as object recognition, emotion recognition, action recognition, and automatic translation. Within one year, we will commercialize this technology.”