A KAIST research team developed an anti-icing film coating technology using gold nanoparticles and cellulose nanocrystals. The film can uniformly pattern gold nanorods in quadrants through simple evaporation, achieving enhanced plasmonic photothermal properties.
Researchers at KAIST develop a fluid switch using ionic polymer artificial muscles that operates at ultra-low power and produces a force 34 times greater than its weight. This technology has the potential to be immediately applied in various industrial settings.
A KAIST research team developed a new conductive polymer material that achieved both high electrical performance and elasticity, introducing the world’s highest-performing stretchable organic solar cell. The team built a device that can be stretched up to 40% during operation, demonstrating its applicability for wearable devices.
Researchers at KAIST have developed high-performance strains producing a variety of compounds, including succinic acid, biodegradable plastics, and biofuels. They provide insights into advancements in polyamide monomer production and synthesizing bio-based polyamides through chemical conversion.
Researchers at KAIST have developed a micro-vacuum assisted selective transfer printing (µVAST) technology to improve the transfer of microLED chips. The technology uses laser-induced etching to create micro-hole arrays on glass substrates, allowing for precise alignment and higher adhesion switchability.
Researchers at KAIST have developed eco-friendly technologies for producing plastics and processing waste plastics using microorganisms. The team presented the latest microorganism-based technologies that can produce plastics from renewable biomass resources and decompose waste plastics, contributing to a circular economy.
A new AI-powered satellite analysis technique reveals the economic conditions of regions with limited data, such as North Korea. The approach combines human input with machine learning to provide detailed economic maps and monitor progress towards Sustainable Development Goals.
Researchers used AI to discover 464 types of enzymes in E. coli and verified their predictions through in vitro enzyme assay. The developed AI can predict a total of 5360 enzyme EC numbers, enabling accurate analysis of metabolic processes and development of eco-friendly microbial factories.
Researchers developed a new intravenous needle that softens via body temperature on insertion, reducing tissue damage and blood-borne disease risks. The P-CARE needle's variable stiffness characteristics make it flexible upon insertion, allowing for more comfortable injections.
A novel computer simulation program 'iBridge' was developed at KAIST to predict gene targets for efficient production of valuable compounds in microbial cell factories. The system successfully established E. coli strains capable of producing three high-demand compounds, including panthenol and nylon components.
A KAIST research team has identified excessive astrocyte-mediated synapse removal as the cause of mental diseases induced by childhood abuse trauma. This mechanism is linked to stress hormones and can lead to abnormal neural networks and complex behavioral abnormalities.
Researchers at KAIST have developed microbial cell factories that can produce a variety of food and cosmetic compounds, including natural pigments, flavors, and functional compounds. These eco-friendly alternatives can help address global food shortages and environmental concerns.
A KAIST research team created a water-resistant, transparent, and flexible OLED using MXene nanotechnology. The material can emit and transmit light even when exposed to water. The study focused on producing an adequate encapsulation structure and suitable process design to improve the reliability of MXene OLED.
Researchers found that sleep patterns vary significantly across cultures and geographical locations. The study used commercially available smartwatches to collect data from over 52 million individuals in 11 countries, revealing discrepancies between self-reported data and digital logs.
A novel coupling mechanism involving leaky mode has been uncovered, enabling zero crosstalk between closely spaced waveguides. This discovery drastically increases the coupling length of transverse-magnetic (TM) mode, expanding the potential for dense photonic integration.
Researchers discovered a way to dissipate heat near hot spots in semiconductors by utilizing surface plasmon polaritons. The new method increased thermal conductivity by 25% and has implications for high-performance semiconductor device development.
Researchers found that L1 jumping genes can be widely activated in normal cells, leading to the accumulation of genomic mutations over time. The study highlights the critical role of epigenetic changes in regulating L1 jumping gene activity.
Researchers at KAIST have developed a new sRNA tool that can effectively inhibit target genes in various bacteria, including both Gram-negative and Gram-positive bacteria. The BHR-sRNA system was shown to suppress pathogenicity in antibiotic-resistant pathogens and improve industrial strains for high-value-added chemical production.
A KAIST research team has developed a highly sensitive, wearable piezoelectric blood pressure sensor for continuous health monitoring. The sensor's accuracy meets international standards, with errors within ±5 mmHg and a standard deviation under 8 mmHg for both systolic and diastolic blood pressure.
Researchers at KAIST have successfully developed a new X-ray microscope technology that can overcome the resolution limitations of existing microscopes. This breakthrough enables high-resolution imaging of nanoscale structures, with a resolution of 14 nm, which is comparable to that of electron microscopes. The technology uses random d...
Researchers at KAIST have developed a hybrid system that combines electrochemical CO2 conversion with microbial bioconversion to produce bioplastics. The system resulted in the world's highest productivity, producing up to 83% of cell dry weight as bioplastic from CO2.
A KAIST research team has developed an advanced AI-based drug interaction prediction technology that analyzed the interaction between Paxlovid ingredients and other prescription drugs. The study identified potential drug-drug interactions and alternative drugs with low adverse effects, which can aid in developing new treatments.
A research team led by Professor Kwang-Hyun Cho at KAIST has developed a fundamental technology to revert metastatic lung cancer cells to a non-metastatic state. This breakthrough could lead to the elimination of drug resistance and metastatic potential in cancer cells, while increasing their responsiveness to chemotherapy.
Researchers found that female lymphoma patients who received afternoon chemotherapy had a 12.5 times reduced mortality rate and a 2.8 times decreased cancer recurrence rate compared to morning treatment. This is due to the body's natural circadian rhythm, which affects white blood cell counts and bone marrow proliferation.
Researchers at KAIST developed a quadrupedal robot control technology that enables robots to walk robustly on deformable terrain like sandy beaches. The technology uses artificial neural networks to simulate ground characteristics and adapt to changing environments, allowing the robot to maintain balance and perform high-speed walking.
The 30-year history of metabolic engineering has progressed significantly, enabling microorganisms to efficiently produce chemicals and degrade recalcitrant contaminants. Recent breakthroughs in systems metabolic engineering and data science have driven advancements in sustainability and health.
Researchers from KAIST and Chungnam National University have developed a new equation to predict drug interactions, improving accuracy by 80% compared to the existing FDA formula. The new equation takes into account factors such as gut bioavailability and enzyme concentration.
M.A.R.V.E.L.'s magnetic soles made of Electro-Permanent Magnet (EPM) and Magneto-Rheological Elastomer (MRE) enable fast movement on uneven surfaces. The robot can climb at speeds of up to 70 cm/s on walls and 50 cm/s on ceilings, making it the world's fastest walking climbing robot.
A KAIST research team has developed a surface-lighting microLED patch for UV-induced melanogenesis inhibition. The patch demonstrated significant suppression of melanin synthesis and minimized epidermal photo-toxicity, making it a promising treatment option for skin diseases such as spots and freckles.
Researchers established a novel strategy to treat Huntington's disease by converting the disease-causing form of the huntingtin protein into its disease-free form. This process maintains the original function of the protein, offering a new approach to tackle the neurodegenerative disorder.
Researchers have developed a systematic strategy for creating phage-resistant E. coli strains, solving a major problem in industrial fermentation. The approach integrates a defense system and mutations to restrict phage life cycle, maintaining bacterial functionality and productivity.
Researchers have developed an interactive metabolic map of bio-based chemicals, providing a versatile tool for easy assessment and optimization of synthetic pathways. The map enables exploration and analysis of complex networks of biological and/or chemical reactions, facilitating the design and production of desired chemicals.
Researchers at KAIST developed a novel fusion protein drug, αAβ-Gas6, which efficiently eliminates amyloid beta (Aβ) without causing inflammatory side effects. The study showed that αAβ-Gas6 promoted the robust uptake of Aβ without inflammation and neurotoxicity.
Researchers at KAIST have developed a method to produce lutein in E. coli bacteria using glycerol as a cheap carbon source. The production process involves systems metabolic engineering and substrate channeling to overcome bottleneck enzymes that inhibit lutein biosynthesis.
A KAIST research team has discovered a new role for somatostatin, a protein-based neurotransmitter, in reducing the toxicity caused by Alzheimers disease. When somatostatin is met with copper and Aβ proteins, it attenuates the toxicity and agglomeration of metal-Aβ complexes.
A team led by Professor Song Min Kim developed a system that can support concurrent communications for tens of millions of IoT devices using backscattering millimeter-level waves. The system offers internet connectivity on a mass scale to IoT devices at a low installation cost.
Researchers demonstrate a new platform for guiding compressed mid-infrared light waves in ultra-thin van der Waals crystals, enabling strong light-matter interactions and improved detection limits. The use of atomically-smooth gold crystals provides a low-loss environment for the propagation of phonon-polaritons.
Researchers developed a new imaging technique called PICASSO that allows for the use of more than 15 colors to image and parse overlapping proteins. This approach, which employs artificial intelligence, enables accurate information unmixing without reference spectra measurements, making it suitable for complex specimens like the brain.
Scientists at KAIST have discovered a new polymer mesophase structure that forms through a random copolymer sequence. This unique structure is characterized by a bilayer-folded lamellar mesophase, which exhibits properties such as birefringence and viscoelasticity.
A new neuromorphic memory device simulates both neurons and synapses in a single unit cell, enabling the development of brain-like artificial intelligence. This breakthrough achieves synergistic interactions between neurons and synapses, overcoming current limitations in neuromorphic computing.
Scientists at KAIST have proposed a novel 'stashing system' inspired by the human brain's neural activity, which efficiently handles mathematical operations for artificial intelligence. This technology reduces power consumption by 37% while maintaining accuracy, paving the way for next-generation semiconductor chips.
A new methodology has been demonstrated to achieve full 360° active phase modulation for metasurfaces while maintaining uniform levels of optical amplitude. The strategy involves using two optical resonances with specific properties to overcome the trade-offs between dynamic phase and amplitude control.
The KAIST research team has developed LightPC, a lightweight persistence centric system that ensures both data and execution persistence using only non-volatile memory. This technology reduces power consumption and increases performance by minimizing internal volatile memory components and increasing parallelism.
A team of scientists from KAIST has developed a method to directly measure the frequency of floral scent emissions in lilies using optical interferometry. This technology reveals the temporal pattern of scent release and provides new insights into the ecological evolution of plant-pollinator interactions.
Cell-to-cell variability in antibiotic stress response is found to increase as the number of rate-limiting steps in signaling pathways increases. This discovery could lead to more effective chemotherapies for cancer treatment.
A research team at KAIST developed a method for direct measurement of dielectric tensors in 3D anisotropic structures, overcoming previous limitations. This breakthrough enables the exploration of inaccessible nematic structures and interactions in non-equilibrium dynamics.
A team of researchers from KAIST has developed a mind-reading system that can interpret arm movement directions from neural signals in the brain. The new system, based on a machine-learning algorithm and mathematical probability model, successfully classified arm movements in 24 directions in three-dimensional space.
Researchers developed a machine learning technique that can identify different bacteria in arbitrary media with accuracies of up to 98% using surface-enhanced Raman spectroscopy and deep learning. The technique, called DualWKNet, enables rapid detection without the need for bacterial separation steps.
Researchers at KAIST developed an AI-powered microscope that can image live cells in 3D without exogenous labeling agents, achieving high accuracy and speed. The 'AI microscope' uses deep learning to predict fluorescence images from holographic images, confirming its potential for various biological research.
A KAIST research team has developed graphene-inorganic-hybrid micro-supercapacitors made of leaves using femtosecond direct laser writing lithography. The innovation enables mass production of flexible and green graphene-based electronic devices, reducing waste and environmental issues associated with traditional batteries.