Researchers at KAIST have developed a self-regenerating catalyst that restores its activity during operation, overcoming a major challenge in CO₂ conversion technology. The catalyst design strategy allows for continuous maintenance of active sites, enabling stable production of high-value chemicals.
A joint international research team has identified the core principle by which specific inhibitory neurons in the prefrontal cortex regulate cocaine-seeking behavior. The study revealed that addiction relapse is not due to a decline in brain function, but rather an imbalance in specific neural circuits.
Researchers have developed a groundbreaking technology that simultaneously analyzes gene expression, epigenome, and 3D genome structure within a single cell. This innovation enables precise identification of disease genes and their dynamics, laying the foundation for developing treatment strategies for complex diseases.
Researchers at KAIST have developed a technology that predicts atomic arrangement tendency of catalysts using artificial intelligence, leading to more efficient design of fuel cell catalysts. The zinc-platinum-cobalt catalyst secured both higher activity and superior long-term durability compared to commercial platinum catalysts.
A Korean research team developed a technology that resolves the most critical challenge of lithium-metal batteries, interfacial instability, by creating an intelligent protective layer. This allows lithium ions to move stably along the electrode surface, suppressing dendrite growth and extending battery lifespan.
Researchers at KAIST have developed a new image sensor technology that can accurately represent colors regardless of the angle at which light enters. The team achieved this by utilizing a 'metamaterial' that designs the movement of light through structures too small to be seen with the naked eye.
Researchers at KAIST create a new technique to process semiconductor surfaces uniformly down to the atomic level using carbon nanotubes as abrasive materials. This technology demonstrates potential to significantly improve surface quality and precision in advanced semiconductor processes like high-bandwidth memory.
Researchers at KAIST have developed an AI model that understands chemical principles to predict molecular structures, achieving up to 20 times higher accuracy than existing models. This technology can speed up research and development in various fields, including new drug development and material creation.
Researchers at KAIST have identified a new principle for catalyst design that allows ceria to selectively use different oxygen sources depending on the reaction environment. By adjusting the size of the catalyst, it can choose whether to use oxygen from the air or store it internally.
KAIST researchers propose a new approach to treating Alzheimer's disease by repositioning molecules without changing their chemical composition. The study demonstrates that subtle differences in molecular arrangement can regulate multiple disease-inducing factors, leading to significant improvements in memory deficits and cognitive imp...
Researchers at KAIST developed a new catalyst architecture that reduces precious-metal usage while enhancing hydrogen production and fuel-cell performance. The ultrathin nanosheet design increased the active surface area participating in reactions, enabling higher hydrogen production with less iridium.
Scientists directly observe charge density wave formation, fragmentation and persistence across a phase transition. Localized order is linked to strain, and isolated pockets persist above the transition temperature.
Researchers at KAIST have developed a new reference signal technology using laser light to precisely synchronize radio telescope observation timing and phase. This allows for clearer imaging of distant black holes and reduces phase delay errors between instruments.
Researchers at KAIST have developed a new surface-control technology to control, at the atomic level, the surface of indium phosphide magic-sized clusters, overcoming the limitation of luminescence efficiency below 1%. The breakthrough results in an 18-fold increase in brightness, opening up applications in next-generation displays and...
A KAIST research team developed a new analysis method to detect electronic traps in semiconductors with high sensitivity. The technique enables precise identification of defect sources, improving semiconductor performance and lifetime while reducing costs. The results demonstrate a 1,000x higher sensitivity than existing techniques.
Researchers at KAIST developed a new near-planar light outcoupling structure and OLED design method to significantly reduce light loss in OLED devices. This enables brighter displays with the same power consumption, extending battery life and reducing heat generation in mobile devices.
Researchers at KAIST have developed a new OLED technology that more than doubles screen brightness while maintaining the flat structure of OLED displays. The new technology uses a thin, near-planar light outcoupling structure and an OLED design method to significantly reduce light loss inside OLED devices.
A research team at KAIST has proposed an AI-driven approach to solve the long-standing mystery of gene function. The team suggests combining computational biology with experimental biology to discover gene functions, leveraging tools like AlphaFold and generative AI.
Researchers at KAIST and Yonsei University have identified Glial Progenitor Cells as the origin of IDH-mutant gliomas. The study reveals that these cells acquire the initial mutation long before a visible tumor mass forms, opening new paths for early diagnosis and treatment.
A Korean research team has developed a design method for core materials in all-solid-state batteries, improving performance while reducing costs. By introducing divalent anions like oxygen and sulfur, they enabled faster lithium-ion transport within solid electrolytes, lowering energy barriers.
Researchers have developed a new 3D printing method to create vertically stacked perovskite nanostructures, which can be used to make high-efficiency nanolasers. The technology achieves single-crystalline alignment and minimizes light loss, enabling commercialization in optical computing and quantum security applications.
KAIST researchers have developed a biomicrofluidic system that can recreate the process by which drug-induced muscle damage leads to kidney injury. The system allows for precise reproduction of inter-organ reactions, enabling the early prediction of drug side effects and identification of causes of acute kidney injury.
Researchers at KAIST have developed a new therapeutic approach that converts dormant immune cells inside tumors into potent anticancer agents. By reprogramming tumor-associated macrophages, they created CAR-macrophages that recognize and kill cancer cells while activating surrounding immune responses.
Researchers at KAIST developed 'SpecEdge,' a system that utilizes affordable, consumer-grade GPUs to provide AI services at lower costs. This technology significantly lowers LLM infrastructure costs and improves server throughput by up to 2.22 times.
Researchers have developed a xenogeneic-free culture platform that significantly enhances the migration and regenerative capacity of intestinal stem cells. The technology, called PLUS, maintains identical performance even after storage at room temperature for three years, securing industrial scalability.
Researchers at KAIST have developed generative AI technology that autonomously optimizes injection molding processes, achieving an error rate of just 1.63%. Additionally, they built an LLM-based knowledge transfer system, IM-Chat, to make on-site expertise accessible to anyone, regardless of language or cultural background.
The KAIST-UEL team has developed a transformative wheel capable of traversing the Moon's extreme terrains, including steep lunar pits and lava tubes. The 'origami' airless wheel expands from a compact size to overcome obstacles, while utilizing flexible materials and specialized design elements to withstand harsh lunar conditions.
Researchers developed a nasal antiviral platform using AI to stabilize interferon-lambda protein, ensuring effective diffusion and long-term retention in the nasal mucosa. The platform showed a massive improvement in stability, surviving for two weeks at 50℃ and inhibiting influenza virus levels by over 85% in animal models.
Researchers develop shape-morphing device to overcome pancreatic tumor microenvironment barriers, achieving remarkable therapeutic efficacy in mouse models. The device delivers low-intensity photostimulation that precisely targets cancer cells while preserving normal tissue.
A KAIST research team created a water-based air purification device that removes ultrafine dust with high efficiency, long-term stability, and low power consumption. The device operates without filters, ozone, or noise, making it an eco-friendly next-generation air purification platform.
High-nickel batteries suffer from rapid performance degradation due to an electrolyte additive called succinonitrile. Researchers found that this additive attaches strongly to nickel ions, destroying the protective electrical double layer and accelerating damage.
Researchers at KAIST have developed a technology to directly observe nano-sized water droplets using an Atomic Force Microscope, enabling precise analysis of wettability. This breakthrough is expected to improve hydrogen production, fuel cells, batteries, and semiconductor processes.
Researchers at KAIST have developed a new strategy to control the initiation timing and rate of mRNA protein production, enabling safer treatment. By using this method, proteins can be produced sequentially in a desired order, reducing the risk of side effects.
Researchers at KAIST have confirmed that red OLED light reduces amyloid-β plaques and improves long-term memory in an Alzheimer's disease model. The study shows that even short periods of light stimulation can lead to significant pathological improvements and cognitive enhancements.
Researchers at KAIST have found that depression is linked to an imbalance in the body's immune response, affecting brain function. The study identified novel biomarkers and a potential new approach for treating depression by integrating blood analysis, single-cell analysis, and patient-derived brain organoids.
The research team created a flexible hydrogel-based 'Latent-Radiative Thermostat' that autonomously switches between cooling and heating modes. The LRT can maintain temperatures up to 3.7 °C lower in summer and 3.5 °C higher in winter, reducing energy consumption by up to 153 MJ/m² compared to existing roof coatings.
A KAIST team has developed a modular co-culture platform to produce multicolored bacterial cellulose, overcoming the limitations of environmental challenges and complex processing requirements. The platform enables one-pot production of colored living materials without additional chemical processing.
A KAIST research team has developed a highly efficient technique to characterize complex multimode quantum operations, which is essential for scalable optical quantum computing and quantum communication technologies. The new 'Multimode Quantum Process Tomography' technique can analyze large-scale operations with less data, representing...
KAIST's research team presents a new direction for future software security research with their automatic C-to-Rust translation technology. The technology proves the mathematical correctness of the conversion, solving existing AI limitations and C language security issues.
A research team at KAIST has elucidated the principle by which cells decide their direction of movement without external stimuli. The study reveals that proteins interact within living cells to control cell movement, with specific combinations governing straight or changed direction.
Researchers at KAIST have developed a new technology called SHARE that can reconstruct high-quality 3D scenes using only ordinary images without precise camera pose information. This breakthrough enables rapid and precise reconstruction in real-world environments without additional training or complex calibration processes.
Researchers at KAIST have identified the molecular basis of nonspecific activation in killer T cells and proposed a new therapeutic strategy to control it. The study reveals that interleukin-15 can abnormally excite killer T cells, causing them to attack uninfected host cells.
Researchers at KAIST have created a new manufacturing method for high-performance green hydrogen electrolysis cells, reducing production time from six hours to just ten minutes. The technique uses microwaves to heat ceramic powders uniformly, achieving stable electrolyte formation at lower temperatures.
A comprehensive review paper analyzes the impact of AI on materials science and engineering, highlighting its role in Discovery, Development, and Optimization stages. The AI-based catalyst search platform demonstrates the potential for robots to autonomously design, execute, and optimize experiments.
A research team at KAIST developed a novel platform technology using a 0.02-second flash of light to generate an ultrahigh temperature of 3,000 °C. This process enables the highly efficient synthesis of catalysts and boosts hydrogen production efficiency by up to six times.
A KAIST research team found that in-home IoT data can accurately track mental health status using environmental data and smartphone-wearable data combined. Regular routines are essential for maintaining mental health, while variability in daily patterns is a key indicator of worsening mental health.
A joint research team found that countries with stricter environmental regulations see increased sales of green products like electric vehicles. This challenges the traditional 'pollution haven' hypothesis and suggests a link between strong regulations and eco-friendly product competitiveness.
Researchers at KAIST have developed a core AI semiconductor technology called PIMBA, which combines the Transformer's intelligence with the Mamba's efficiency. This technology results in a four-fold increase in inference speed and a 2.2-fold reduction in power consumption for Large Language Models.
Researchers at KAIST have developed a generative AI-based technology capable of identifying drugs and genetic targets that can guide cells toward a desired state. The model can predict the reactions of previously untested cell-drug combinations and reveal how drugs function inside cells.
A chemobiological platform converts glucose and glycerol into oxygenated precursors, which are then deoxygenated to produce benzene, toluene, ethylbenzene, and p-xylene. The integrated system uses a novel solvent system to streamline the conversion process.