research
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Deep Learning Predicts Drug-Drug and Drug-Food Interactions
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
2018.04.18 View 10311 -
KAIST Develops Sodium Ion Batteries using Copper Sulfide
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
2018.04.17 View 6116 -
Producing 50x More Stable Adsorbent
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.
2018.04.16 View 5467 -
Formation of Burning Ice in Oceanic Clay Rich Sediment Disclosed
(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
2018.04.09 View 5943 -
Printed Thermo-Plasmonic Heat Patterns for Neurological Disorder Treatment
(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.
2018.04.06 View 6432 -
A New Theory Improves Button Designs
Pressing a button appears effortless. People easily dismisses how challenging it is. Researchers at KAIST and Aalto University in Finland, created detailed simulations of button-pressing with the goal of producing human-like presses.
The researchers argue that the key capability of the brain is a probabilistic model. The brain learns a model that allows it to predict a suitable motor command for a button. If a press fails, it can pick a very good alternative and try it out. "Without this ability, we would have to learn to use every button like it was new," tells Professor Byungjoo Lee from the Graduate School of Culture Technology at KAIST.
After successfully activating the button, the brain can tune the motor command to be more precise, use less energy and to avoid stress or pain. "These factors together, with practice, produce the fast, minimum-effort, elegant touch people are able to perform."
The brain uses probabilistic models also to extract information optimally from the sensations that arise when the finger moves and its tip touches the button. It "enriches" the ephemeral sensations optimally based on prior experience to estimate the time the button was impacted. For example, tactile sensation from the tip of the finger a better predictor for button activation than proprioception (angle position) and visual feedback.
Best performance is achieved when all sensations are considered together. To adapt, the brain must fuse their information using prior experiences. Professor Lee explains, "We believe that the brain picks up these skills over repeated button pressings that start already as a child. What appears easy for us now has been acquired over years."
The research was triggered by admiration of our remarkable capability to adapt button-pressing. Professor Antti Oulasvirta at Aalto University said, "We push a button on a remote controller differently than a piano key. The press of a skilled user is surprisingly elegant when looked at terms of timing, reliability, and energy use. We successfully press buttons without ever knowing the inner workings of a button. It is essentially a black box to our motor system. On the other hand, we also fail to activate buttons, and some buttons are known to be worse than others."
Previous research has shown that touch buttons are worse than push-buttons, but there has not been adequate theoretical explanation.
"In the past, there has been very little attention to buttons, although we use them all the time" says Dr. Sunjun Kim from Aalto University. The new theory and simulations can be used to design better buttons.
"One exciting implication of the theory is that activating the button at the moment when the sensation is strongest will help users better rhythm their keypresses."
To test this hypothesis, the researchers created a new method for changing the way buttons are activated. The technique is called Impact Activation. Instead of activating the button at first contact, it activates it when the button cap or finger hits the floor with maximum impact.
The technique was 94% better in rapid tapping than the regular activation method for a push-button (Cherry MX switch) and 37% than a regular touchscreen button using a capacitive touch sensor. The technique can be easily deployed in touchscreens. However, regular physical keyboards do not offer the required sensing capability, although special products exist (e.g., the Wooting keyboard) on which it can be implemented.
The simulations shed new light on what happens during a button press. One problem the brain must overcome is that muscles do not activate as perfectly as we will, but every press is slightly different. Moreover, a button press is very fast, occurring within 100 milliseconds, and is too fast for correcting movement. The key to understanding button-pressing is therefore to understand how the brain adapts based on the limited sensations that are the residue of the brief press event.
The researchers also used the simulation to explain differences among physical and touchscreen-based button types. Both physical and touch buttons provide clear tactile signals from the impact of the tip with the button floor. However, with the physical button this signal is more pronounced and longer.
"Where the two button types also differ is the starting height of the finger, and this makes a difference," explains Professor Lee. "When we pull up the finger from the touchscreen, it will end up at different height every time. Its down-press cannot be as accurately controlled in time as with a push-button where the finger can rest on top of the key cap."
Three scientific articles, "Neuromechanics of a Button Press", "Impact activation improves rapid button pressing", and "Moving target selection: A cue integration model", will be presented at the CHI Conference on Human Factors in Computing Systems in Montréal, Canada, in April 2018.
2018.03.22 View 6874 -
Recognizing Seven Different Face Emotions on a Mobile Platform
(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.”
2018.03.09 View 6722 -
Activation of Bystander Immune Cells during Acute Hepatitis A.
A KAIST research team has identified a process of tissue damage caused by bystander immune cells in acute viral infections. This research will pave the way for research to understand the principles of tissue damage in viral infections and immune diseases, and can point toward a possible therapeutic target for the treatment.
Upon viral infection, viral replication itself destroys human cells, but in some cases, viral replication is not the direct cause of the tissue damage. In particular, the destruction of infected cells is the primary cause of tissue damage during non-cytopathic viral infections such as hepatitis A virus, hepatitis B virus and hepatitis C virus. However, the underlying pathological mechanisms involved in the tissue damage during viral infections have not been fully elucidated.
Specificity is one of the most important characteristics of the immune system. In general, infection from a certain virus specifically activates immune cells targeting the virus, while other immune cells specific to different viruses remain inactive.
An immune cell not specific to an infected virus is called a bystander immune cell. A phenomenon that activates irrelevant immune cells not originally targeting the infecting virus, called the activation of bystander immune cells, is already known to the world; however, its clinical significance has not been investigated thoroughly.
Professor Eui-Cheol Shin and Professor Su-Hyung Park from the Graduate School of Medical Science and Engineering analyzed patients with acute hepatitis A, in collaboration with Chung-Ang University Hospital.
The team found not only immune cells specifically targeting the hepatitis A virus were activated, but also bystander immune cells were activated and involved in the damaging of liver tissues during acute hepatitis A.
According to the research, when a person is infected with hepatitis A virus, hepatitis A virus-infected cells produce IL-15, which induces the activation of bystander immune cells. Activated bystander immune cells exert innate-like cytotoxicity, triggered by activating receptors NKG2D and NKp30 and this can lead to liver injury.
Through describing the cause of excessive tissue damage during acute viral hepatitis, the research outcome is expected to provide critical contributions for the development of potential therapeutic intervention that can minimize tissue damage caused by viral hepatitis and immune disorders.
Professor Shin said, “This is a novel research case that discovered the clinical significance of bystander immune cell activation, which was previously unknown. We will continue to work on establishing treatments which could prevent tissue damage in viral and immune diseases in the future.”
This research was published in Immunity on January 2.
Figure 1. Graphical abstract
2018.03.06 View 6320 -
KAIST Finds the Principle of Electric Wind in Plasma
(From left: Professor Wonho Choe and PhD Sanghoo Park)
A KAIST team identified the basic principle of electric wind in plasma. This finding will contribute to developing technology in various applications of plasma, including fluid control technology.
Professor Wonho Choe from the Department of Physics and his team identified the main principle of neutral gas flow in plasma, known as ‘electric wind’, in collaboration with Professor Se Youn Moon’s team at Chonbuk National University.
Electric wind in plasma is a well-known consequence of interactions arising from collisions between charged particles (electrons or ions) and neutral particles. It refers to the flow of neutral gas that occurs when charged particles accelerate and collide with a neutral gas.
This is a way to create air movement without mechanical movement, such as fan wings, and it is gaining interest as a next-generation technology to replace existing fans. However, there was no experimental evidence of the cause.
To identify the cause, the team used atmospheric pressure plasma. As a result, the team succeeded in identifying streamer propagation and space charge drift from electrohydrodynamic (EHD) force in a qualitative manner.
According to the team, streamer propagation has very little effect on electric wind, but space charge drift that follows streamer propagation and collapse was the main cause of electric wind.
The team also identified that electrons, instead of negatively charged ions, were key components of electric wind generation in certain plasmas.
Furthermore, electric wind with the highest speed of 4 m/s was created in a helium jet plasma, which is one fourth the speed of a typhoon. These results indicate that the study could provide basic principles to effectively control the speed of electric wind.
Professor Choe said, “These findings set a significant foundation to understand the interactions between electrons or ions and neutral particles that occur in weakly ionized plasmas, such as atmospheric pressure plasmas. This can play an important role in expanding the field of fluid-control applications using plasmas which becomes economically and commercially interest.”
This research, led by PhD Sanghoo Park, was published online in Nature Communications on January 25.
Figure 1. Plasma jet image
Figure 2. The differences in electric wind speeds and voltage pulse
2018.03.02 View 6929 -
Successful Synthesis of Gamma-Lanctam Rings from Hydrocarbons
(The team of Professor Chang, far right, at the Department of Chemistry)
KAIST chemists have designed a novel strategy to synthesize ring-shaped cyclic molecules, highly sought-after by pharmaceutical and chemical industries, and known as gamma-lactams. This study describes how these five-membered rings can be prepared from inexpensive and readily available feedstock hydrocarbons, as well as from complex organic molecules, such as amino acids and steroids.
Gamma-lactams find several applications in medicinal, synthetic, and material chemistry. For example, they are included in a large number of pharmaceutically active compounds with antibiotic, anti-inflammatory, and anti-tumoral functions. This research was published in Science on March 2.
Conversion of hydrocarbons into nitrogen-containing compounds is an important area of research, where the challenge lies in breaking strong carbon-hydrogen (C−H) bonds, and converting them into carbon-nitrogen (C–N) bonds in a controlled fashion. For this reason, hydrocarbons are difficult to use as starting materials, albeit the fact that they exist in large quantities in nature.
Over the last 35 years, chemists have found ways of converting simple hydrocarbons into nitrogen-containing rings, such as indoles or pyrrolidines, but gamma-lactams proved impossible to prepare using the same approaches. Researchers hypothesized that such failure was due to alternative chemical pathways that steer the reaction away from the wanted rings: The reaction intermediate (carbonylnitrene) quickly breaks down into unsought products. Using computer models of the desired and undesired reaction pathways, the team found a strategy to completely shut down the latter in order to obtain the longed-for gamma-lactams. For the first time, these four carbons and one nitrogen cyclic molecules were obtained directly from simple feedstock chemicals.
Led by Professor Chang Sukbok at the Department of Chemistry, the team designed the winning reaction with the help of computer simulations that analyze the reaction mechanisms and calculate the energy required for the reaction to take place. According to such computer predictions, the reaction could follow three pathways, leading to the formation of either the desired gamma-lactam, an unwanted product (isocyanate), or the degradation of the catalyst caused by the substrate reacting with the catalyst backbone. Combining experimental observations and detailed computer simulations, the team designed an iridium-based catalyst, highly selective for the gamma-lactam formation. In this way, the two undesired pathways were systematically shut down, leaving the formation of the nitrogen-containing ring as the only possible outcome. Professor Chang is also in charge of the Center for Catalytic Hydrocarbon Functionalizations at the Institute for Basic Science (IBS).
“With this work we offer a brand new solution to a long-standing challenge and demonstrate the power of what we call mechanism-based reaction development,” explains Professor Baik Mu-Hyun, a corresponding author of the study.
Beyond using cheap feedstock hydrocarbons as substrates, the team was also successful in converting amino acids, steroids, and other bio-relevant molecules into gamma-lactams, which might find a variety of applications as plant insecticide, drugs against parasitic worms, or anti-aging agents. This new synthetic technology gives much easier access to these complicated molecules and will enable the development of potential drugs in a much shorter amount of time at a lower cost.
Figure 1: Selective amidation reaction using newly designed iridium (Ir) catalysts. Abundant in nature Hydrocarbons are used as substrates to synthesize nitrogen-containing ring, called gamma-lactams.
Figure 2: Three possible reaction pathways and energy barriers predicted by computational chemistry. The scientists developed new iridium-based catalysts that are highly selective for the C–H insertion pathway which leads to the desired gamma-lactam molecules.
Figure 3: Interesting gamma-lactams derived from natural and unnatural amino acids, steroids, etc., which may be used to protect plants against insects, fight parasitic worms, or as anti-aging agents.
2018.03.02 View 7749 -
Aqueous Storage Device Needs Only 20 Seconds to Go
(from left: PhD candidate Il Woo Ock and Professor Jeung Ku Kang)
A KAIST research team developed a new hybrid energy storage device that can be charged in less than half a minute. It employs aqueous electrolytes instead of flammable organic solvents, so it is both environmentally friendly and safe. It also facilitates a boosting charge with high energy density, which makes it suitable for portable electronic devices.
Professor Jeung Ku Kang and his team from the Graduate School of Energy, Environment, Water, and Sustainability developed this hybrid energy storage with high energy and power densities along over a long cycle life by assembling fibre-like polymer chain anodes and sub-nanoscale metal oxide cathodes on graphene.
Conventional aqueous electrolyte-based energy storage devices have a limitation for boosting charges and high energy density due to low driving voltage and a shortage of anode materials.
Energy storage device capacity is determined by the two electrodes, and the balance between cathode and anode leads to high stability. In general, two electrodes show differences in electrical properties and differ in ion storage mechanism processes, resulting in poor storage and stability from the imbalance.
The research team came up with new structures and materials to facilitate rapid speed in energy exchange on the surfaces of the electrodes and minimize the energy loss between the two electrodes.
The team made anodes with graphene-based polymer chain materials. The web-like structure of graphene leads to a high surface area, thereby allowing higher capacitance.
For cathode materials, the team used metal oxide in sub-nanoscale structures to elevate atom-by-ion redox reactions. This method realized higher energy density and faster energy exchange while minimizing energy loss.
The developed device can be charged within 20 to 30 seconds using a low-power charging system, such as a USB switching charger or a flexible photovoltaic cell. The developed aqueous hybrid energy device shows more than 100-fold higher power density compared to conventional aqueous batteries and can be rapidly recharged. Further, the device showed high stability with its capacity maintained at 100% at a high charge/discharge current.
Professor Kang said, “This eco-friendly technology can be easily manufactured and is highly applicable. In particular, its high capacity and high stability, compared to existing technologies, could contribute to the commercialization of aqueous capacitors. The device can be rapidly charged using a low-power charging system, and thus can be applied to portable electronic device.”
This research, led by a PhD candidate Il Woo Ock, was published in Advanced Energy Materials on January 15.
Figure 1. Switching wearable LED kit with two AHCs in series charged by a flexible photovoltaic cell
Figure 2. Schematic diagram for aqueous hybrid capacitors
Figure 3. TEM images of anode and cathode
2018.02.28 View 12151 -
Low-power, Flexible Memristor Circuit for Mobile and Wearable Devices
(from left: Yunyong Nam, Professor Sung-Yool Choi and Byung Chul Jang)
A KAIST research team succeeded in developing an energy efficient, nonvolatile logic-in-memory circuit by using a memristor. This novel technology can be used as an energy efficient computing architecture for battery-powered flexible electronic systems, such as mobile and wearable devices.
Professor Sung-Yool Choi from the School of Electrical Engineering and Professor Sang-Hee Ko Park from the Department of Materials Science and Engineering developed a memristive nonvolatile logic-in-memory circuit.
Transistor-based conventional electronic systems have issues with battery supply and a long standby period due to their volatile computing architecture. The standby power consumption caused by subthreshold leakage current limits their potential applications for mobile electronic devices. Also, their physical separation of memory and processor causes power consumption and time delay during data transfer.
In order to solve this problem, the team developed a logic-in-memory circuit that enables data storage as well as logic operation simultaneously. It can minimize energy consumption and time delay because it does not require data transfer between memory and processor.
The team employed nonvolatile, polymer-based memristors and flexible back-to-back Schottky diode selector devices on plastic substrates. Unlike the conventional architecture, this memristive nonvolatile logic-in-memory is a novel computing architecture that consumes a minimal amount of standby power. This one-selector-one memristor (1S-1M) solved the issue of undesirable leakage currents, known as ‘sneak currents’.
They also implemented single-instruction multiple-data (SIMD) to calculate multiple values at once.
The proposed parallel computing method using a memristive nonvolatile logic-in-memory circuit can provide a low-power circuit platform for battery-powered flexible electronic systems with a variety of potential applications.
Professor Choi said, “Flexible logic-in-memory circuits integrating memristor and selector device can provide flexibility, low power, memory with logic functions. This will be a core technology that will bring innovation to mobile and wearable electronic systems.”
This research, collaborated with Ph.D. candidates Byung Chul Jang and Yunyong Nam, was published and chosen as the cover of Advanced Functional Materials on January 10.
Figure 1. Cover of the Advanced Functional Materials
Figure 2. Schematic illustration and cross-sectional TEM image of flexible memristive nonvolatile logic-in-memory circuit
Figure 3. Test performance
Figure 4. Parallel logic operation within 1S-1M memristor array
2018.02.21 View 6318