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Attachable Skin Monitors that Wick the Sweat Away
- A silicone membrane for wearable devices is more comfortable and breathable thanks to better-sized pores made with the help of citric acid crystals. -
A new preparation technique fabricates thin, silicone-based patches that rapidly wick water away from the skin. The technique could reduce the redness and itching caused by wearable biosensors that trap sweat beneath them. The technique was developed by bioengineer and professor Young-Ho Cho and his colleagues at KAIST and reported in the journal Scientific Reports last month.
“Wearable bioelectronics are becoming more attractive for the day-to-day monitoring of biological compounds found in sweat, like hormones or glucose, as well as body temperature, heart rate, and energy expenditure,” Professor Cho explained. “But currently available materials can cause skin irritation, so scientists are looking for ways to improve them,” he added.
Attachable biosensors often use a silicone-based compound called polydimethylsiloxane (PDMS), as it has a relatively high water vapour transmission rate compared to other materials. Still, this rate is only two-thirds that of skin’s water evaporation rate, meaning sweat still gets trapped underneath it.
Current fabrication approaches mix PDMS with beads or solutes, such as sugars or salts, and then remove them to leave pores in their place. Another technique uses gas to form pores in the material. Each technique has its disadvantages, from being expensive and complex to leaving pores of different sizes.
A team of researchers led by Professor Cho from the KAIST Department of Bio and Brain Engineering was able to form small, uniform pores by crystallizing citric acid in PDMS and then removing the crystals using ethanol. The approach is significantly cheaper than using beads, and leads to 93.2% smaller and 425% more uniformly-sized pores compared to using sugar. Importantly, the membrane transmits water vapour 2.2 times faster than human skin.
The team tested their membrane on human skin for seven days and found that it caused only minor redness and no itching, whereas a non-porous PDMS membrane did.
Professor Cho said, “Our method could be used to fabricate porous PDMS membranes for skin-attachable devices used for daily monitoring of physiological signals.”
“We next plan to modify our membrane so it can be more readily attached to and removed from skin,” he added.
This work was supported by the Ministry of Trade, Industry and Energy (MOTIE) of Korea under the Alchemist Project.
Image description:
Smaller, more uniformly-sized pores are made in the PDMS membrane by mixing PDMS, toluene, citric acid, and ethanol. Toluene dilutes PDMS so it can easily mix with the other two constituents. Toluene and ethanol are then evaporated, which causes the citric acid to crystallize within the PDMS material. The mixture is placed in a mould where it solidifies into a thin film. The crystals are then removed using ethanol, leaving pores in their place.
Image credit:
Professor Young-Ho Cho, KAIST
Image usage restrictions:
News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only.
Publication:
Yoon, S, et al. (2021) Wearable porous PDMS layer of high moisture permeability for skin trouble reduction. Scientific Reports 11, Article No. 938. Available online at https://doi.org/10.1038/s41598-020-78580-z
Profile:
Young-Ho Cho, Ph.D
Professor
mems@kaist.ac.kr
https://mems.kaist.ac.kr
NanoSentuating Systems Laboratory
Department of Bio and Brain Engineering
https://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
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2021.02.22 View 13584 -
Highly Deformable Piezoelectric Nanotruss for Tactile Electronics
With the importance of non-contact environments growing due to COVID-19, tactile electronic devices using haptic technology are gaining traction as new mediums of communication.
Haptic technology is being applied in a wide array of fields such as robotics or interactive displays. haptic gloves are being used for augmented information communication technology. Efficient piezoelectric materials that can convert various mechanical stimuli into electrical signals and vice versa are a prerequisite for advancing high-performing haptic technology.
A research team led by Professor Seungbum Hong confirmed the potential of tactile devices by developing ceramic piezoelectric materials that are three times more deformable. For the fabrication of highly deformable nanomaterials, the research team built a zinc oxide hollow nanostructure using proximity field nanopatterning and atomic layered deposition. The piezoelectric coefficient was measured to be approximately 9.2 pm/V and the nanopillar compression test showed an elastic strain limit of approximately 10%, which is more than three times greater than that of the bulk zinc oxide one.
Piezoelectric ceramics have a high piezoelectric coefficient with a low elastic strain limit, whereas the opposite is true for piezoelectric polymers. Therefore, it has been very challenging to obtain good performance in both high piezoelectric coefficients as well as high elastic strain limits. To break the elastic limit of piezoelectric ceramics, the research team introduced a 3D truss-like hollow nanostructure with nanometer-scale thin walls.
According to the Griffith criterion, the fracture strength of a material is inversely proportional to the square root of the preexisting flaw size. However, a large flaw is less likely to occur in a small structure, which, in turn, enhances the strength of the material. Therefore, implementing the form of a 3D truss-like hollow nanostructure with nanometer-scale thin walls can extend the elastic limit of the material. Furthermore, a monolithic 3D structure can withstand large strains in all directions while simultaneously preventing the loss from the bottleneck. Previously, the fracture property of piezoelectric ceramic materials was difficult to control, owing to the large variance in crack sizes. However, the research team structurally limited the crack sizes to manage the fracture properties.
Professor Hong’s results demonstrate the potential for the development of highly deformable ceramic piezoelectric materials by improving the elastic limit using a 3D hollow nanostructure. Since zinc oxide has a relatively low piezoelectric coefficient compared to other piezoelectric ceramic materials, applying the proposed structure to such components promised better results in terms of the piezoelectric activity.
“With the advent of the non-contact era, the importance of emotional communication is increasing. Through the development of novel tactile interaction technologies, in addition to the current visual and auditory communication, mankind will enter a new era where they can communicate with anyone using all five senses regardless of location as if they are with them in person,” Professor Hong said.
“While additional research must be conducted to realize the application of the proposed designs for haptic enhancement devices, this study holds high value in that it resolves one of the most challenging issues in the use of piezoelectric ceramics, specifically opening new possibilities for their application by overcoming their mechanical constraints.
The research was reported in Nano Energy and supported by the Ministry of Science and ICT, the Korea Research Foundation, and the KAIST Global Singularity Research Project.
-Profile: Professor Seungbum Hong
seungbum@kaist.ac.kr
http://mii.kaist.ac.kr/
Department of Materials Science and Engineering
KAIST
2021.02.02 View 11149 -
Wirelessly Rechargeable Soft Brain Implant Controls Brain Cells
Researchers have invented a smartphone-controlled soft brain implant that can be recharged wirelessly from outside the body. It enables long-term neural circuit manipulation without the need for periodic disruptive surgeries to replace the battery of the implant. Scientists believe this technology can help uncover and treat psychiatric disorders and neurodegenerative diseases such as addiction, depression, and Parkinson’s.
A group of KAIST researchers and collaborators have engineered a tiny brain implant that can be wirelessly recharged from outside the body to control brain circuits for long periods of time without battery replacement. The device is constructed of ultra-soft and bio-compliant polymers to help provide long-term compatibility with tissue. Geared with micrometer-sized LEDs (equivalent to the size of a grain of salt) mounted on ultrathin probes (the thickness of a human hair), it can wirelessly manipulate target neurons in the deep brain using light.
This study, led by Professor Jae-Woong Jeong, is a step forward from the wireless head-mounted implant neural device he developed in 2019. That previous version could indefinitely deliver multiple drugs and light stimulation treatment wirelessly by using a smartphone. For more, Manipulating Brain Cells by Smartphone.
For the new upgraded version, the research team came up with a fully implantable, soft optoelectronic system that can be remotely and selectively controlled by a smartphone. This research was published on January 22, 2021 in Nature Communications.
The new wireless charging technology addresses the limitations of current brain implants. Wireless implantable device technologies have recently become popular as alternatives to conventional tethered implants, because they help minimize stress and inflammation in freely-moving animals during brain studies, which in turn enhance the lifetime of the devices. However, such devices require either intermittent surgeries to replace discharged batteries, or special and bulky wireless power setups, which limit experimental options as well as the scalability of animal experiments.
“This powerful device eliminates the need for additional painful surgeries to replace an exhausted battery in the implant, allowing seamless chronic neuromodulation,” said Professor Jeong. “We believe that the same basic technology can be applied to various types of implants, including deep brain stimulators, and cardiac and gastric pacemakers, to reduce the burden on patients for long-term use within the body.”
To enable wireless battery charging and controls, researchers developed a tiny circuit that integrates a wireless energy harvester with a coil antenna and a Bluetooth low-energy chip. An alternating magnetic field can harmlessly penetrate through tissue, and generate electricity inside the device to charge the battery. Then the battery-powered Bluetooth implant delivers programmable patterns of light to brain cells using an “easy-to-use” smartphone app for real-time brain control.
“This device can be operated anywhere and anytime to manipulate neural circuits, which makes it a highly versatile tool for investigating brain functions,” said lead author Choong Yeon Kim, a researcher at KAIST.
Neuroscientists successfully tested these implants in rats and demonstrated their ability to suppress cocaine-induced behaviour after the rats were injected with cocaine. This was achieved by precise light stimulation of relevant target neurons in their brains using the smartphone-controlled LEDs. Furthermore, the battery in the implants could be repeatedly recharged while the rats were behaving freely, thus minimizing any physical interruption to the experiments.
“Wireless battery re-charging makes experimental procedures much less complicated,” said the co-lead author Min Jeong Ku, a researcher at Yonsei University’s College of Medicine.
“The fact that we can control a specific behaviour of animals, by delivering light stimulation into the brain just with a simple manipulation of smartphone app, watching freely moving animals nearby, is very interesting and stimulates a lot of imagination,” said Jeong-Hoon Kim, a professor of physiology at Yonsei University’s College of Medicine. “This technology will facilitate various avenues of brain research.”
The researchers believe this brain implant technology may lead to new opportunities for brain research and therapeutic intervention to treat diseases in the brain and other organs.
This work was supported by grants from the National Research Foundation of Korea and the KAIST Global Singularity Research Program.
-Profile
Professor Jae-Woong Jeong
https://www.jeongresearch.org/
School of Electrical Engineering
KAIST
2021.01.26 View 26213 -
Professor Bumjoon Kim Named Scientist of the Month
Professor Bumjoon Kim from the Department of Chemical and Biomolecular Engineering won January’s Scientist of the Month Award presented by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF) on January 6. Professor Kim also received 10 million won in prize money.
Professor Kim was recognized for his research in the field of fuel cells. Since the first paper on fuel cells was published in 1839 by the German chemist Friedrich Schonbein, there has been an increase in the number of fields in which fuel cells are used, including national defense, aerospace engineering, and autonomous vehicles.
Professor Kim developed carbonized block copolymer particles with high durability and a high-performance fuel cell. Block copolymers are two different polymers cross-linked into a chain structure. Various nanostructures can be made effectively by using the attractive and repulsive forces between the chains.
Professor Kim used the membrane emulsification technique, employing a high-performance separation membrane to develop a platform that makes the mass production of highly durable carbonized particles possible, which he then used to develop high-performance energy devices like fuel cells.
The carbonized particles designed by Professor Kim and his research team were used to create the world’s more durable fuel cells that boast outstanding performance while using only five percent of the costly platinum needed for existing commercialized products.
The team’s research results were published in the Journal of the American Chemical Society and Energy Environmental Science in May and July of last year.
“We have developed a fuel cell that ticks all the boxes including performance, durability, and cost,” said Professor Kim. “Related techniques will not be limited to fuel cells, but could also be applied to the development of various energy devices like solar cells and secondary cells,” he added.
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2021.01.22 View 12242 -
DeepTFactor Predicts Transcription Factors
A deep learning-based tool predicts transcription factors using protein sequences as inputs
A joint research team from KAIST and UCSD has developed a deep neural network named DeepTFactor that predicts transcription factors from protein sequences. DeepTFactor will serve as a useful tool for understanding the regulatory systems of organisms, accelerating the use of deep learning for solving biological problems.
A transcription factor is a protein that specifically binds to DNA sequences to control the transcription initiation. Analyzing transcriptional regulation enables the understanding of how organisms control gene expression in response to genetic or environmental changes. In this regard, finding the transcription factor of an organism is the first step in the analysis of the transcriptional regulatory system of an organism.
Previously, transcription factors have been predicted by analyzing sequence homology with already characterized transcription factors or by data-driven approaches such as machine learning. Conventional machine learning models require a rigorous feature selection process that relies on domain expertise such as calculating the physicochemical properties of molecules or analyzing the homology of biological sequences. Meanwhile, deep learning can inherently learn latent features for the specific task.
A joint research team comprised of Ph.D. candidate Gi Bae Kim and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering at KAIST, and Ye Gao and Professor Bernhard O. Palsson of the Department of Biochemical Engineering at UCSD reported a deep learning-based tool for the prediction of transcription factors. Their research paper “DeepTFactor: A deep learning-based tool for the prediction of transcription factors” was published online in PNAS.
Their article reports the development of DeepTFactor, a deep learning-based tool that predicts whether a given protein sequence is a transcription factor using three parallel convolutional neural networks. The joint research team predicted 332 transcription factors of Escherichia coli K-12 MG1655 using DeepTFactor and the performance of DeepTFactor by experimentally confirming the genome-wide binding sites of three predicted transcription factors (YqhC, YiaU, and YahB).
The joint research team further used a saliency method to understand the reasoning process of DeepTFactor. The researchers confirmed that even though information on the DNA binding domains of the transcription factor was not explicitly given the training process, DeepTFactor implicitly learned and used them for prediction. Unlike previous transcription factor prediction tools that were developed only for protein sequences of specific organisms, DeepTFactor is expected to be used in the analysis of the transcription systems of all organisms at a high level of performance.
Distinguished Professor Sang Yup Lee said, “DeepTFactor can be used to discover unknown transcription factors from numerous protein sequences that have not yet been characterized. It is expected that DeepTFactor will serve as an important tool for analyzing the regulatory systems of organisms of interest.”
This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries from the Ministry of Science and ICT through the National Research Foundation of Korea.
-Publication
Gi Bae Kim, Ye Gao, Bernhard O. Palsson, and Sang Yup Lee. DeepTFactor: A deep learning-based tool for the prediction of transcription factors. (https://doi.org/10.1073/pnas202117118)
-Profile
Distinguished Professor Sang Yup Lee
leesy@kaist.ac.kr
Metabolic &Biomolecular Engineering National Research Laboratory
http://mbel.kaist.ac.kr
Department of Chemical and Biomolecular Engineering
KAIST
2021.01.05 View 9872 -
Extremely Stable Perovskite Nanoparticles Films for Next-Generation Displays
Researchers have reported an extremely stable cross-linked perovskite nanoparticle that maintains a high photoluminescence quantum yield (PLQY) for 1.5 years in air and harsh liquid environments. This stable material’s design strategies, which addressed one of the most critical problems limiting their practical application, provide a breakthrough for the commercialization of perovskite nanoparticles in next-generation displays and bio-related applications.
According to the research team led by Professor Byeong-Soo Bae, their development can survive in severe environments such as water, various polar solvents, and high temperature with high humidity without additional encapsulation. This development is expected to enable perovskite nanoparticles to be applied to high color purity display applications as a practical color converting material. This result was published as the inside front cover article in Advanced Materials.
Perovskites, which consist of organics, metals, and halogen elements, have emerged as key elements in various optoelectronic applications. The power conversion efficiency of photovoltaic cells based on perovskites light absorbers has been rapidly increased. Perovskites are also great promise as a light emitter in display applications because of their low material cost, facile wavelength tunability, high (PLQY), very narrow emission band width, and wider color gamut than inorganic semiconducting nanocrystals and organic emitters. Thanks to these advantages, perovskites have been identified as a key color-converting material for next-generation high color-purity displays. In particular, perovskites are the only luminescence material that meets Rec. 2020 which is a new color standard in display industry.
However, perovskites are very unstable against heat, moisture, and light, which makes them almost impossible to use in practical applications. To solve these problems, many researchers have attempted to physically prevent perovskites from coming into contact with water molecules by passivating the perovskite grain and nanoparticle surfaces with organic ligands or inorganic shell materials, or by fabricating perovskite-polymer nanocomposites. These methods require complex processes and have limited stability in ambient air and water. Furthermore, stable perovskite nanoparticles in the various chemical environments and high temperatures with high humidity have not been reported yet.
The research team in collaboration with Seoul National University develops siloxane-encapsulated perovskite nanoparticle composite films. Here, perovskite nanoparticles are chemically crosslinked with thermally stable siloxane molecules, thereby significantly improving the stability of the perovskite nanoparticles without the need for any additional protecting layer.
Siloxane-encapsulated perovskite nanoparticle composite films exhibited a high PLQY (> 70%) value, which can be maintained over 600 days in water, various chemicals (alcohol, strong acidic and basic solutions), and high temperatures with high humidity (85℃/85%). The research team investigated the mechanisms impacting the chemical crosslinking and water molecule-induced stabilization of perovskite nanoparticles through various photo-physical analysis and density-functional theory calculation.
The research team confirmed that displays based on their siloxane-perovskite nanoparticle composite films exhibited higher PLQY and a wider color gamut than those of Cd-based quantum dots and demonstrated perfect color converting properties on commercial mobile phone screens. Unlike what was commonly believed in the halide perovskite field, the composite films showed excellent bio-compatibility because the siloxane matrix prevents the toxicity of Pb in perovskite nanoparticle.
By using this technology, the instability of perovskite materials, which is the biggest challenge for practical applications, is greatly improved through simple encapsulation method.
“Perovskite nanoparticle is the only photoluminescent material that can meet the next generation display color standard. Nevertheless, there has been reluctant to commercialize it due to its moisture vulnerability. The newly developed siloxane encapsulation technology will trigger more research on perovskite nanoparticles as color conversion materials and will accelerate early commercialization,” Professor Bae said.
This work was supported by the Wearable Platform Materials Technology Center (WMC) of the Engineering Research Center (ERC) Project, and the Leadership Research Program funded by the National Research Foundation of Korea.
-Publication:
Junho Jang, Young-Hoon Kim, Sunjoon Park, Dongsuk Yoo, Hyunjin Cho, Jinhyeong Jang, Han Beom Jeong, Hyunhwan Lee, Jong Min Yuk, Chan Beum Park, Duk Young Jeon, Yong-Hyun Kim, Byeong-Soo Bae, and Tae-Woo Lee. “Extremely Stable Luminescent Crosslinked Perovskite Nanoparticles under Harsh Environments over 1.5 Years” Advanced Materials, 2020, 2005255. https://doi.org/10.1002/adma.202005255.
Link to download the full-text paper:
https://onlinelibrary.wiley.com/doi/10.1002/adma.202005255
-Profile: Prof. Byeong-Soo Bae (Corresponding author)
bsbae@kaist.ac.kr
Lab. of Optical Materials & Coating
Department of Materials Science and Engineering
Korea Advanced Institute of Science and Technology (KAIST)
2020.12.29 View 13695 -
Electrosprayed Micro Droplets Help Kill Bacteria and Viruses
With COVID-19 raging around the globe, researchers are doubling down on methods for developing diverse antimicrobial technologies that could be effective in killing a virus, but harmless to humans and the environment.
A recent study by a KAIST research team will be one of the responses to such efforts. Professor Seung Seob Lee and Dr. Ji-hun Jeong from the Department of Mechanical Engineering developed a harmless air sterilization prototype featuring electrosprayed water from a polymer micro-nozzle array. This study is one of the projects being supported by the KAIST New Deal R&D Initiative in response to COVID-19. Their study was reported in Polymer.
The electrosprayed microdroplets encapsulate reactive oxygen species such as hydroxyl radicals, superoxides that are known to have an antimicrobial function. The encapsulation prolongs the life of reactive oxygen species, which enable the droplets to perform their antimicrobial function effectively. Prior research has already proven the antimicrobial and encapsulation effects of electrosprayed droplets.
Despite its potential for antimicrobial applications, electrosprayed water generally operates under an electrical discharge condition, which can generate ozone. The inhalation of ozone is known to cause damage to the respiratory system of humans. Another technical barrier for electrospraying is the low flow rate problem. Since electrospraying exhibits the dependence of droplet size on the flow rate, there is a limit for the amount of water microdroplets a single nozzle can produce.
With this in mind, the research team developed a dielectric polymer micro-nozzle array to perform the multiplexed electrospraying of water without electrical discharge. The polymer micro-nozzle array was fabricated using the MEMS (Micro Electro-Mechanical System) process. According to the research team, the nozzle can carry five to 19 micro-nozzles depending on the required application.
The high aspect ratio of the micro-nozzle and an in-plane extractor were proposed to concentrate the electric field at the tip of the micro-nozzle, which prevents the electrical discharge caused by the high surface tension of water. A micro-pillar array with a hydrophobic coating around the micro-nozzle was also proposed to prevent the wetting of the micro-nozzle array.
The polymer micro-nozzle array performed in steady cone jet mode without electrical discharge as confirmed by high-speed imaging and nanosecond pulsed imaging. The water microdroplets were measured to be in the range of six to 10 μm and displayed an antimicrobial effect on Escherichia coli and Staphylococcus aureus.
Professor Lee said, “We believe that this research can be applied to air conditioning products in areas that require antimicrobial and humidifying functions.”
Publication:
Jeong, J. H., et al. (2020) Polymer micro-atomizer for water electrospray in the cone jet mode. Polymer. Vol. No. 194, 122405. Available online at https://doi.org/10.1016/j.polymer.2020.122405
Profile: Seung Seob Lee, Ph.D.
sslee97@kaist.ac.kr
http://mmst.kaist.ac.kr/
Professor
Department of Mechanical Engineering (ME)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr
Daejeon 34141, Korea
Profile: Ji-hun Jeong, Ph.D.
jiuni6022@kaist.ac.kr
Postdoctoral researcher
Department of Mechanical Engineering (ME)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr
Daejeon 34141, Korea
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2020.12.21 View 13778 -
Emeritus Professor Jae-Kyu Lee Wins the AIS LEO Award
Emeritus Professor Jae-Kyu Lee has won the Association for Information Systems LEO Award 2020. Professor Lee, the first Korean to receive the LEO Award, was recognized for his research and development in preventative cyber security, which is a major part of the efforts he leads to realize what Professor Lee has named "Bright Internet."
Established in 1999, this award was named after the world’s first business application of computing, the Lyons Electronic Office and recognizes outstanding individuals in the field of information systems. The LEO Award recognized four winners including Professor Lee this year.
He has been professor and HHI Chair Professor at KAIST from 1985 to 2016 since he has received his Ph.D. in information and operations management from the Wharton School, University of Pennsylvania. He served as the Dean of College of Business and supervised around 30 doctoral students. He is currently the Distinguished Professor of School of Management at Xi’an Jiaotong University.
His research mainly focused on the creation of Bright Internet for preventive cybersecurity, improving relevance of research from Axiomatic Theories, and development of AI for electronic commerce and managerial decision support.
He is a fellow and was the president of the Association for Information Systems, and co-chaired the International Conference on Information Systems in 2017. He was the founder of Principles for the Bright Internet and established the Bright Internet Research Center at KAIST and Xi’an Jiatong University. He also established the Bright Internet Global Summit since ICIS 2017 in Seoul, and organized the Bright Internet Project Consortium in 2019 as a combined effort of academia-industry partnership. (www.brightinternet.org.)
He was a charter member of the Pacific Asia Conference in Information Systems, and served as conference chair. He was the founder editor-in-chief of the journal, Electronic Commerce Research and Applications (Elsevier), and was the founding chair of the International Conference on Electronic Commerce. In Korea, her served as president of Korea Society of Management Information Systems and Korea Society of Intelligent Information Systems.
"I am honored to be designated the first Korean winner of the honorable LEO Award," Lee said. "Based on my life-long efforts for developments in the field, I will continue to contribute to the research and development of information media systems."
2020.12.16 View 6951 -
A Comprehensive Review of Biosynthesis of Inorganic Nanomaterials Using Microorganisms and Bacteriophages
There are diverse methods for producing numerous inorganic nanomaterials involving many experimental variables. Among the numerous possible matches, finding the best pair for synthesizing in an environmentally friendly way has been a longstanding challenge for researchers and industries.
A KAIST bioprocess engineering research team led by Distinguished Professor Sang Yup Lee conducted a summary of 146 biosynthesized single and multi-element inorganic nanomaterials covering 55 elements in the periodic table synthesized using wild-type and genetically engineered microorganisms. Their research highlights the diverse applications of biogenic nanomaterials and gives strategies for improving the biosynthesis of nanomaterials in terms of their producibility, crystallinity, size, and shape.
The research team described a 10-step flow chart for developing the biosynthesis of inorganic nanomaterials using microorganisms and bacteriophages. The research was published at Nature Review Chemistry as a cover and hero paper on December 3.
“We suggest general strategies for microbial nanomaterial biosynthesis via a step-by-step flow chart and give our perspectives on the future of nanomaterial biosynthesis and applications. This flow chart will serve as a general guide for those wishing to prepare biosynthetic inorganic nanomaterials using microbial cells,” explained Dr.Yoojin Choi, a co-author of this research.
Most inorganic nanomaterials are produced using physical and chemical methods and biological synthesis has been gaining more and more attention. However, conventional synthesis processes have drawbacks in terms of high energy consumption and non-environmentally friendly processes. Meanwhile, microorganisms such as microalgae, yeasts, fungi, bacteria, and even viruses can be utilized as biofactories to produce single and multi-element inorganic nanomaterials under mild conditions.
After conducting a massive survey, the research team summed up that the development of genetically engineered microorganisms with increased inorganic-ion-binding affinity, inorganic-ion-reduction ability, and nanomaterial biosynthetic efficiency has enabled the synthesis of many inorganic nanomaterials.
Among the strategies, the team introduced their analysis of a Pourbaix diagram for controlling the size and morphology of a product. The research team said this Pourbaix diagram analysis can be widely employed for biosynthesizing new nanomaterials with industrial applications.Professor Sang Yup Lee added, “This research provides extensive information and perspectives on the biosynthesis of diverse inorganic nanomaterials using microorganisms and bacteriophages and their applications. We expect that biosynthetic inorganic nanomaterials will find more diverse and innovative applications across diverse fields of science and technology.”
Dr. Choi started this research in 2018 and her interview about completing this extensive research was featured in an article at Nature Career article on December 4.
-ProfileDistinguished Professor Sang Yup Lee leesy@kaist.ac.krMetabolic &Biomolecular Engineering National Research Laboratoryhttp://mbel.kaist.ac.krDepartment of Chemical and Biomolecular EngineeringKAIST
2020.12.07 View 11162 -
Scientist of October: Professor Jungwon Kim
Professor Jungwon Kim from the Department of Mechanical Engineering was selected as the ‘Scientist of the Month’ for October 2020 by the Ministry of Science and ICT and the National Research Foundation of Korea. Professor Kim was recognized for his contributions to expanding the horizons of the basics of precision engineering through his research on multifunctional ultrahigh-speed, high-resolution sensors. He received 10 million KRW in prize money.
Professor Kim was selected as the recipient of this award in celebration of “Measurement Day”, which commemorates October 26 as the day in which King Sejong the Great established a volume measurement system.
Professor Kim discovered that the time difference between the pulse of light created by a laser and the pulse of the current produced by a light-emitting diode was as small as 100 attoseconds (10-16 seconds). He then developed a unique multifunctional ultrahigh-speed, high-resolution Time-of-Flight (TOF) sensor that could take measurements of multiple points at the same time by sampling electric light. The sensor, with a measurement speed of 100 megahertz (100 million vibrations per second), a resolution of 180 picometers (1/5.5 billion meters), and a dynamic range of 150 decibels, overcame the limitations of both existing TOF techniques and laser interferometric techniques at the same time. The results of this research were published in Nature Photonics on February 10, 2020.
Professor Kim said, “I’d like to thank the graduate students who worked passionately with me, and KAIST for providing an environment in which I could fully focus on research. I am looking forward to the new and diverse applications in the field of machine manufacturing, such as studying the dynamic phenomena in microdevices, or taking ultraprecision measurement of shapes for advanced manufacturing.”
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2020.10.15 View 12113 -
Slippery When Wet: Fish and Seaweed Inspire Ships to Reduce Fluid Friction
Faster ships could be on the horizon after KAIST scientists develop a slippery surface inspired by fish and seaweed to reduce the hull's drag through the water.
Long-distance cargo ships lose a significant amount of energy due to fluid friction. Looking to the drag reduction mechanisms employed by aquatic life can provide inspiration on how to improve efficiency.
Fish and seaweed secrete a layer of mucus to create a slippery surface, reducing their friction as they travel through water. A potential way to mimic this is by creating lubricant-infused surfaces covered with cavities. As the cavities are continuously filled with the lubricant, a layer is formed over the surface.
Though this method has previously been shown to work, reducing drag by up to 18%, the underlying physics is not fully understood. KAIST researchers in collaboration with a team of researchers from POSTECH conducted simulations of this process to help explain the effects, and their findings were published in the journal Physics of Fluids on September 15.
The group looked at the average speed of a cargo ship with realistic material properties and simulated how it behaves under various lubrication setups. Specifically, they monitored the effects of the open area of the lubricant-filled cavities, as well as the thickness of the cavity lids.
They found that for larger open areas, the lubricant spreads more than it does with smaller open areas, leading to a slipperier surface. On the other hand, the lid thickness does not have much of an effect on the slip, though a thicker lid does create a thicker lubricant buildup layer.
Professor Emeritus Hyung Jin Sung from the KAIST Department of Mechanical Engineering who led this study said, “Our investigation of the hydrodynamics of a lubricant layer and how it results in drag reduction with a slippery surface in a basic configuration has provided significant insight into the benefits of a lubricant-infused surface.”
Now that they have worked on optimizing the lubricant secretion design, the authors hope it can be implemented in real-life marine vehicles.
“If the present design parameters are adopted, the drag reduction rate will increase significantly,” Professor Sung added.
This work was supported by the National Research Foundation (NRF) of Korea.
Source:
Materials provided by American Institute of Physics.
Publication:
Kim, Seung Joong, et al. (2020). A lubricant-infused slip surface for drag reduction. Physics of Fluids. Available online at https://doi.org/10.1063/5.0018460
Profile:
Hyung Jin Sung
Professor Emeritus
hyungjin@kaist.ac.kr
http://flow.kaist.ac.kr/index.php
Flow Control Lab. (FCL)
Department of Mechanical Engineering
http://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
(END)
2020.10.12 View 7706 -
Deep Learning Helps Explore the Structural and Strategic Bases of Autism
Psychiatrists typically diagnose autism spectrum disorders (ASD) by observing a person’s behavior and by leaning on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), widely considered the “bible” of mental health diagnosis.
However, there are substantial differences amongst individuals on the spectrum and a great deal remains unknown by science about the causes of autism, or even what autism is. As a result, an accurate diagnosis of ASD and a prognosis prediction for patients can be extremely difficult.
But what if artificial intelligence (AI) could help? Deep learning, a type of AI, deploys artificial neural networks based on the human brain to recognize patterns in a way that is akin to, and in some cases can surpass, human ability. The technique, or rather suite of techniques, has enjoyed remarkable success in recent years in fields as diverse as voice recognition, translation, autonomous vehicles, and drug discovery.
A group of researchers from KAIST in collaboration with the Yonsei University College of Medicine has applied these deep learning techniques to autism diagnosis. Their findings were published on August 14 in the journal IEEE Access.
Magnetic resonance imaging (MRI) scans of brains of people known to have autism have been used by researchers and clinicians to try to identify structures of the brain they believed were associated with ASD. These researchers have achieved considerable success in identifying abnormal grey and white matter volume and irregularities in cerebral cortex activation and connections as being associated with the condition.
These findings have subsequently been deployed in studies attempting more consistent diagnoses of patients than has been achieved via psychiatrist observations during counseling sessions. While such studies have reported high levels of diagnostic accuracy, the number of participants in these studies has been small, often under 50, and diagnostic performance drops markedly when applied to large sample sizes or on datasets that include people from a wide variety of populations and locations.
“There was something as to what defines autism that human researchers and clinicians must have been overlooking,” said Keun-Ah Cheon, one of the two corresponding authors and a professor in Department of Child and Adolescent Psychiatry at Severance Hospital of the Yonsei University College of Medicine.
“And humans poring over thousands of MRI scans won’t be able to pick up on what we’ve been missing,” she continued. “But we thought AI might be able to.”
So the team applied five different categories of deep learning models to an open-source dataset of more than 1,000 MRI scans from the Autism Brain Imaging Data Exchange (ABIDE) initiative, which has collected brain imaging data from laboratories around the world, and to a smaller, but higher-resolution MRI image dataset (84 images) taken from the Child Psychiatric Clinic at Severance Hospital, Yonsei University College of Medicine. In both cases, the researchers used both structural MRIs (examining the anatomy of the brain) and functional MRIs (examining brain activity in different regions).
The models allowed the team to explore the structural bases of ASD brain region by brain region, focusing in particular on many structures below the cerebral cortex, including the basal ganglia, which are involved in motor function (movement) as well as learning and memory.
Crucially, these specific types of deep learning models also offered up possible explanations of how the AI had come up with its rationale for these findings.
“Understanding the way that the AI has classified these brain structures and dynamics is extremely important,” said Sang Wan Lee, the other corresponding author and an associate professor at KAIST. “It’s no good if a doctor can tell a patient that the computer says they have autism, but not be able to say why the computer knows that.”
The deep learning models were also able to describe how much a particular aspect contributed to ASD, an analysis tool that can assist psychiatric physicians during the diagnosis process to identify the severity of the autism.
“Doctors should be able to use this to offer a personalized diagnosis for patients, including a prognosis of how the condition could develop,” Lee said.
“Artificial intelligence is not going to put psychiatrists out of a job,” he explained. “But using AI as a tool should enable doctors to better understand and diagnose complex disorders than they could do on their own.”
-ProfileProfessor Sang Wan LeeDepartment of Bio and Brain EngineeringLaboratory for Brain and Machine Intelligence https://aibrain.kaist.ac.kr/
KAIST
2020.09.23 View 11566