Researchers at KAIST developed technology to automatically identify and fabricate two-dimensional semiconductors, revealing the relationship between thickness and performance. The technology enables data-driven research and accelerates the commercialization of AI semiconductors and ultra-low-power semiconductors.
Researchers at KAIST have developed a core technology for stretchable displays that expands uniformly across the entire screen without distorting images. The platform uses auxetic structures to maintain isotropic expansion, allowing fine images on-screen to expand together while maintaining their original shapes.
A KAIST research team developed a foundational technology for 'temperature-based DNA synthesis,' synthesizing desired DNA using only temperature. The team also demonstrated a 'DNA temperature black box' that records temperature changes during shipping without electricity.
A KAIST research team analyzed the computational cost and energy consumption of AI agents, finding they consume up to 136.5 times more energy per query than conventional generative AI. This study highlights the importance of optimizing AI semiconductors, data centers, and power infrastructure for sustainable AI development.
Researchers at KAIST have developed a prodrug that selectively activates in the brain of patients with Alzheimer's disease, reducing reactive oxygen species and preventing amyloid beta aggregation. This approach shows promise for next-generation dementia treatment.
Researchers from KAIST have provided experimental evidence that electrons form a loop-like circulating order (loop-current order) before reaching the superconducting state. This discovery sheds light on the fundamental principles of superconductivity and offers a crucial clue for understanding unconventional superconductivity.
KAIST researchers have discovered a new way cancer 'hijacks' the blueprint for blood vessel development to fuel its growth. By reactivating a pre-existing gene regulatory program, tumors can drive angiogenesis without evolving entirely new mechanisms.
Researchers have developed a polymer membrane that separates crude oil at room temperature, significantly reducing the need for energy-intensive heating in conventional refining. The membrane uses self-assembled separation channels smaller than 2 nanometers to selectively separate lighter fractions from heavier components.
Researchers at KAIST developed DiSPo, a robot AI model that can flexibly improve task precision from coarse demonstrations, enabling robots to perform sophisticated tasks with high accuracy. The technology achieved significant improvements over existing models in simulation and real-world experiments.
A KAIST research team led by Professor Jee-Hwan Ryu has received the IEEE Robotics and Automation Letters (RA-L) Best Paper Award for the second consecutive year. The award-winning paper presents a novel assistive dressing technology that enables garments to autonomously unfold and move along the user's body using soft robotic principles.
Researchers at KAIST developed a next-generation database technology called AkasicDB, which integrates vector, graph, and relational databases into a single system. This technology reduces AI hallucinations and improves accuracy by up to 78%, addressing a key challenge in enterprise AI commercialization.
Researchers at KAIST have developed a highly stretchable piezoelectric fiber sensor that operates stably even under repeated deformation. The sensor can be stretched up to 668%, generating consistent electrical signals under various movements, and is expected to enable long-term monitoring of biosignals in wearable medical devices.
A joint research team from KAIST and international institutions developed 'Upsample Anything,' a universal technology that can enhance the visual performance of AI even with limited GPU memory. This achievement increases GPU memory efficiency by up to 16 times, allowing AI to perceive its surrounding environment more precisely.
Researchers create a highly efficient liquid-cooling system that cools semiconductor chips using room-temperature water. The system achieves a coefficient of performance (COP) of 106,000, approximately ten times higher than the previous world-leading result.
Researchers at KAIST have developed a new nano-printing technology that allows for the transfer of ultra-fine metal circuits onto plant leaves, fruits, curved automotive surfaces, and robot exteriors without causing damage. This technology has vast potential for applications in smart agriculture, wearable healthcare, and bioelectronics.
Researchers have developed a computational design technology that utilizes computer simulations to analyze and predict the scaling limits of transistors. The technology identifies the quantum tunneling limit, which varies depending on the type of metal and contact structure.
A new model developed by KAIST reveals that a shortage of agricultural workforce can limit farmland utilization in most regions, posing a significant risk to future food security. The study's findings suggest that sustainable development models and migration policies are crucial to addressing this issue.
A KAIST research team has synthesized a core raw material for fabricating asymmetric MXene, a so-called 'Janus-faced' nanomaterial with distinct functions on its two sides. This achievement establishes the foundation for implementing asymmetric MXene in various advanced technology fields.
Researchers at KAIST developed Video-Based Optimal Transport (VOTP), a technology that enables robots to understand human intentions and choose actions on their own with minimal data. This breakthrough accelerates the development of physical AI, which is expected to drastically shorten development time and costs.
The KAIST Mind Care & Growth Center consolidates psychological counseling services under one roof, providing systematic care to students. Researchers collaborate with AI experts to develop evidence-based interventions, addressing mental health challenges arising from AI dependency.
A KAIST research team has developed a technology that leverages natural ingredients to increase the strength of seaweed-based hydrogel by more than fivefold, while controlling its adhesiveness and degradation rate. The new material design strategy utilizes tannic acid to enhance mechanical strength and adhesiveness.
Researchers at KAIST have developed a novel 2D conductive Material that maintains single-layer electronic characteristics even when stacked in multiple layers. This breakthrough resolves the long-standing challenge of performance degradation in 2D materials, enabling high electrical conductivity and efficient electron transport.
A KAIST research team has developed a technology that controls the chemical environment around catalysts at the nanometer scale by designing DNA sequences. This allows for improved hydrogen production efficiency and increased yield of desired chemical products. The DNA layer acts like a traffic control center, guiding the movement of i...
A new analysis of 600,000 research papers reveals that dual-use research consistently has greater scientific impact than comparable research. Strengthening security oversight on dual-use research can impose disproportionate costs on domestic science, the study finds.
Researchers at KAIST discovered that a DNA repair enzyme uses a one-dimensional diffusion strategy to search for damaged sites. The team found that an intrinsically disordered region plays a key role in the DNA search process and stabilizes binding between APE1 and DNA with magnesium ions.
Researchers at KAIST have developed a new catalyst design technology that can improve the efficiency of key reactions in batteries and fuel cells. By adjusting the electrical environment around the catalyst, they were able to increase the desired reaction by 52%, leading to improved performance, lifespan, and stability.
Researchers developed a innovative technology that rapidly discharges bubbles and boosts hydrogen production efficiency by clearing pathway blockages in the catalyst layer. The new structure allows water and gas to pass through, improving stability and performance.
Researchers at KAIST have developed PAVAS, an AI technology that generates realistic sound effects in videos based on the physical properties of objects. The technology analyzes movement and collision characteristics to produce more immersive audio experiences.
Researchers at KAIST have developed an AI system that supports the initial psychiatric interview process, helping patients organize their symptoms and condition in advance. The system effectively obtained key clinical information within 30 minutes of conversation.
Researchers fabricated alloy catalysts made by mixing gold, silver, and palladium to analyze what substances these catalysts convert CO₂ into. Existing catalyst theories predicted that if the electronic reactivity of a catalyst is similar to that of copper, then it should produce multi-carbon compounds like ethylene and ethanol.
Researchers at KAIST have developed a new catalyst structure that improves the performance and durability of ammonia-based protonic ceramic fuel cells. The technology achieved world-class performance, recording a maximum power density of 2.04 W/cm², and demonstrated stable operation for over 255 hours.
Researchers at KAIST-Hanwha Solutions have established a new 'eco-friendly bio-platform' that can mass-produce sustainable raw materials for plastics and textiles using waste resources. The platform uses glycerol as a raw material to convert into 1,3-propanediol, a key material for plastics and cosmetics.
KAIST researchers have identified a neural circuit that switches between past and recent memories, allowing the brain to select necessary information. The study suggests that specific brain rhythms and states are crucial for effective memory retrieval, with longer online states leading to better recall of recent memories.
A real-time diagnostic smart dressing patch has been developed by KAIST to monitor diabetic foot wounds. The patch combines an optoelectronic sensor with a functional dressing to analyze glucose concentration, acidity, and temperature changes in real-time, enabling patients to check their condition using a smartphone.
Researchers at KAIST have developed a next-generation polarization sensor that can read the direction of light and change its own response, enabling high-accuracy object recognition in dark environments. This technology has the potential to improve autonomous driving and medical diagnostics with low energy consumption.
Scientists have identified the fundamental cause of performance decline in lithium metal batteries, which can lead to improved driving range and longer battery lifespan. The research team discovered that 'dead lithium' - electrically disconnected lithium - forms when irregular lithium deposition or stripping occurs, posing safety risks.
Researchers at KAIST demonstrated a chip-scale photonic approach for generating ultralow-noise and highly stable microwave and millimeter-wave signals based on optical frequency combs. The study enabled direct transfer of optical-reference stability to the microcomb, achieving record-breaking performance in low-offset frequencies.
Researchers at KAIST and Stanford University discovered that complex compositions of multimetallic nanoparticles lead to their growth into more uniform structures, contrary to conventional expectations. This phenomenon enables the creation of high-performance catalysts and eco-friendly energy materials.
A KAIST research team developed a new approach to solving combinatorial optimization problems, which can be implemented entirely using existing silicon processes. This enables faster and more accurate decision-making across various industries, including logistics, finance, and semiconductor design.
A new approach has been proposed to address the problem of overconfidence in AI, enabling it to recognize situations involving unfamiliar or unseen knowledge. By incorporating key principles of brain development, AI can develop the ability to distinguish 'what it knows' from 'what it does not know', a crucial step towards meta-cognition.
Researchers at KAIST have developed a novel diagnostic technology that can distinguish multiple viruses and variants by controlling the speed of CRISPR gene scissors. This method, called kinetic barcoding, interprets differences in reaction speeds as signal patterns to identify different viruses.
Researchers at KAIST developed an AI algorithm to correct image aberrations in microscopes, allowing for high-resolution imaging of deep biological tissues. The technology uses Neural Fields to track distortion and compensate for optical aberrations, overcoming the need for costly hardware.
A team of researchers from KAIST has developed a DNA-based molecular computer that can operate at a much smaller scale than conventional semiconductor devices. The system enables both computation and memory within the same device, opening up new possibilities for future computing technologies in bio and medical applications.
Researchers at KAIST developed a new technology using spin waves to process signals, reducing heat generation and power consumption. This breakthrough enables instantaneous frequency switching and is expected to pave the way for smart devices with less heat and longer battery life.
Researchers at KAIST InnoCORE successfully designed artificial proteins using AI to recognize specific compounds. The team developed an AI model that reflects protein-ligand interactions and designed binding proteins for six types of compounds, including metabolites and small-molecule drugs.
Researchers at KAIST have developed a new electrode design that blocks water while maintaining efficient electrical conduction and catalytic reactions. The technology achieves 86% efficiency in converting CO₂ into plastic precursors, outperforming previous systems.
A research team analyzed data from the Annals of the Joseon Dynasty and Mungwa Bangmok to characterize career patterns of over 14,600 Joseon officials. They found that functional recruitment systems led to stability, but concentrated power resulted in inequality and stratification, ultimately leading to a national decline.
A KAIST research team identified the mechanism by which Graphene Oxide exhibits powerful antibacterial effects against bacteria while remaining harmless to human cells. The technology has been applied to consumer products, including a graphene antibacterial toothbrush that has sold over 10 million units.
Professor Lee's doctoral thesis Empty Garden reinterprets uiwon through contemporary data and media language, exploring human sensation and existence in an AI-dominated environment. The work has been acquired by the Ashmolean Museum for its permanent collection, marking a rare distinction even within Oxford's 900-year history.
Researchers at KAIST discovered that skyrmions can spontaneously emerge from magnetism and lattice deformation in most magnetic materials. This finding could lead to the development of next-generation spintronics technology with higher data storage densities and lower power consumption.