A KAIST research team has identified the cause of measurement artifacts in nanoscale battery analysis, which can lead to misinterpretation of ion movement. The team developed a method to reduce these artifacts by smoothing battery material surfaces.
Researchers at KAIST discovered that weak hydrogen bonds can block copper's tendency to reshape its bonding structure, making it less stable. This finding opens new avenues for selective metal separation and recognition, as well as catalyst design.
KAIST researchers develop SafeQL, a technology that identifies and selectively corrects errors in AI-generated SQL queries, reducing the need to regenerate entire queries. The technology improves data retrieval accuracy and speed, accelerating the adoption of AI work assistants in enterprise environments.
KAIST researchers have developed a new catalyst that can remove tetrafluoromethane (CF₄), a greenhouse gas 6,000 times more potent than CO₂, with high efficiency. The catalyst, called entropy-stabilized aluminate (ESA), harnesses the power of disorder to stabilize its structure and maintain performance over extended periods.
A KAIST-developed micro-LED mask boosts skin rejuvenation and brightening through close skin contact, while PN injections provide synergistic benefits for skin regeneration and recovery. The mask achieves 340% greater improvement in deep skin elasticity than conventional LED masks.
A KAIST team uses AI to identify optimal material recipe for 3D-printable, highly stretchable material. The material printed reliably on a DLP 3D printer and showed high stretchability, extending to over six times its original length.
KAIST researchers analyzed key challenges in microbial food industry and proposed growth strategies for next-generation protein market. The study emphasizes the importance of manufacturing readiness, market entry strategies, and regulatory compliance in commercializing microbial foods.
Researchers at KAIST have developed a low-cost smartphone-based technology to detect hidden cameras by analyzing reflections from objects. The technology, called SweepLED, uses deep learning-based analysis to distinguish camera lenses from ordinary objects, achieving 94% detection accuracy. The technology has potential to be developed ...
A team of Korean researchers has created a miniaturized wireless brain implant that can deliver drugs and light to precisely modulate targeted neurons remotely. The device overcomes distance and location constraints, enabling long-term studies of brain disorders and therapeutic devices.
Researchers create unique 'artificial fingerprints' using nanoparticles that can be authenticated with smartphone flashlight and laser pointer. The technology has potential applications in anti-counterfeiting and electronic device authentication.
A KAIST research team developed a 3D digital twin of a commercial graphite anode to analyze localized degradation mechanisms during fast charging. They found that binder and pore space distribution significantly impacted battery performance and lifespan.
Researchers created a technology that controls the thickness and structure of a polymer coating, allowing more water droplets to form and detach quickly. This enhances heat transfer performance during condensation, improving energy efficiency in power plants and industrial applications.
A KAIST research team has identified the molecular lock that keeps cells trapped in an altered state, opening a new path toward releasing that lock and reversing a cell's fate. The team developed a fundamental control technology called ROOT that can regulate these circuits and restore biological states to their original condition.
KAIST researchers develop a new 'oxygen tunnel' structure to stabilize oxygen vacancies in oxide semiconductors, achieving world-class current density and data retention time. The technology is expected to improve next-generation compute-in-memory systems and accelerate AI era advancements.
A KAIST research team has developed a new electrode material that sequentially stores zinc ions and protons, enabling high storage capacity and fast charging/discharging performance. The material achieved 368.7 mAh g⁻¹ storage capacity and retained 46.9% of its capacity even at 16-fold increased charging/discharging rates.
A Korean research team developed a patient-specific blood-brain tumor barrier-on-a-chip model to predict individual responses to glioblastoma therapies. The chip recreates a patient's own tumor cells together with the surrounding peritumoral vascular environment, allowing for precise predictions of treatment responses.
Researchers develop a novel semiconductor technology that tunes noise to process different types of signals, enabling selective encoding of time-series signals across different frequency bands. The technology achieved accuracies of 94.8% in human activity recognition and 95.0% in speech recognition.
Researchers at KAIST create microscale chiral pinwheel arrays through self-assembly of achiral liquid-crystal molecules, enabling circularly polarized light with a desired rotation direction. This breakthrough simplifies the production of chiral optical materials, paving the way for next-generation displays and optical communications.
The KAIST research team has successfully developed a new, eco-friendly hydrogen separation membrane that filters hydrogen through a molecular network. The membrane achieved a high bridge connectivity degree of 73% and showed significant improvements in hydrogen permeability and selectivity.
The KAIST research team developed an explainable AI technology that detects patterns of foreign-linked influence operations in online news comments. The model identifies 23,998 accounts exhibiting patterns consistent with public-opinion manipulation, targeting division and confrontation within Korean society.
A KAIST research team has demonstrated for the first time that a porous material can arrange disordered gas molecules into a crystal-like structure. Using xenon as a model system, they identified a specific cobalt-based material that stabilizes xenon in a regular lattice, showcasing a breakthrough in gas crystallization.
Researchers developed a programmable dynamic memtransistor that can process data at different speeds, reducing prediction errors by up to 40-fold. The technology enables accurate information processing even when input speeds vary.
A KAIST research team has developed a technology that reconstructs the shape, optical thickness, and position of a transparent object from a single shot. The technique uses an optical model and AI framework to analyze light intensity and overcome challenges in conventional phase imaging.
A KAIST research team has developed a process that cuts the production time for vanadium redox flow batteries' core material by 67%, overcoming a critical bottleneck to commercialization. This breakthrough could significantly accelerate the development of large-capacity energy storage technology.
Researchers at KAIST have developed a technology converting CO2 dissolved in seawater into calcium carbonate, enabling permanent storage and helping the ocean absorb more carbon dioxide. The system reduced electricity consumption by up to 54% and produced high-purity hydrogen and magnesium hydroxide.
A research team at KAIST has developed RL-SPH, a reinforcement-learning-based method that can independently generate feasible solutions satisfying all constraints. The technique achieved a 100% feasibility rate across five benchmarks, reducing the primal gap by an average of 28.6 times and improving search efficiency.
A KAIST research team has developed an RNA-based therapeutic strategy that blocks a brain signal to prevent muscle loss and extend survival in cancer patients. The treatment showed significant improvements in both muscle loss and metabolic dysfunction, with a notable increase in survival rates among treated mice.
A KAIST research team developed two core technologies to correct AI hallucinations caused by sensory misinterpretation. The first technology uses the Diverse Negative Attributes method to accurately understand special camera sensors, while the second technology Modality-Adaptive Decoding blocks cross-modal hallucinations at the source.
Researchers at KAIST have developed Stable-GFlowNet, a new AI safety verification framework that uncovers seven times more hidden vulnerabilities in AI than existing methods. The technology is expected to serve as a foundation for developing safer and more trustworthy generative AI models.
A KAIST research team found that algal blooms accelerate the early-stage weathering of plastics, leading to microplastic formation. The study suggests a new direction for managing water pollution and plastic waste together.
A KAIST research team developed a next-generation world model that learns executable theories from observation alone. The Neural Theorizer (NEO) model discovers reusable primitives and composes them into executable programs to explain new situations.
A research team at KAIST has uncovered the molecular mechanism by which amino acid signals activate cell growth signaling. The findings suggest a potential basis for next-generation anticancer therapies that target abnormal growth signaling in tumor cells.
Researchers developed a self-aligned, thin-film growth technology to grow tellurium film in a uniform crystal orientation at a low temperature of 150°C. This approach enables the precise fabrication of high-quality semiconductor films for next-generation semiconductors and optoelectronic devices.
Researchers at KAIST have created an antibody that precisely targets intracellular cancer mutations using computational methods. The antibody selectively recognizes only cancer cells carrying the KRAS(G12D) mutation, demonstrating its potential for precision antibody therapeutics.
A KAIST research team has developed a non-contact, non-destructive method to measure minute variations in battery electrode thickness, improving battery safety and quality by identifying invisible defects during manufacturing. The technology combines terahertz waves with an optical frequency comb for ultra-precise measurements.
A joint KAIST research team developed a new framework for analyzing networks of circulating amino acids, reflecting the body's metabolic state. They showed that this approach can predict recurrence or metastasis in patients with colorectal cancer more accurately than current methods.
A KAIST research team has developed a new molecular system that can selectively switch the pathway through which electrons are transferred during oxygen activation. The findings provide a fundamental design principle for next-generation catalysts and energy-conversion technologies.
Researchers at KAIST have developed a groundbreaking oocyte and embryo analysis technology using holotomography to predict developmental potential in IVF. This non-invasive method could revolutionize the selection of embryos for transfer, offering a more objective and quantitative approach.
A KAIST research team developed an XR comics platform, ComiXR, that enables users to read and create comics in immersive environments. The platform demonstrated increased immersion when comic elements were positioned at different depths and incorporated sensory experiences such as facial expression tracking.
Researchers at KAIST create a metal structure that changes shape using light, without any light-absorbing coating. The technology could enable new possibilities for tactile interfaces and wearable devices.
A KAIST research team identified a previously unrecognized immune mechanism through which anti-CTLA-4 promotes B-cell responses in tumor-draining lymph nodes. This response is critical for the antitumor effects of anti-CTLA-4 therapy, opening a new avenue for treating intractable brain tumors like glioblastoma.
KAIST develops robot that can autonomously choose gait strategy for its surroundings, switching between walking, running, jumping, and other locomotion skills. The team generated 15.5 hours of training data using computer simulations alone, enabling the robot to move quickly and stably in real outdoor environments.
Researchers developed Buffer-and-Reinforce framework to preserve AI safety during personalized fine-tuning, maintaining high safety even in extreme settings. The framework achieved strong customized performance and state-of-the-art safety without additional safety data or increased computational cost.
A joint KAIST–University of Tokyo research team found that temperature-related suicide mortality is expected to rise globally due to climate change. Regional differences in future rises are driven by varying temperature-suicide relationships, with East Asia and South America projected to see relatively small increases.
Researchers from KAIST and MIT create a transmissive mid-infrared spatial light modulator that can perform various sensor functions using electrical signals alone. The device has demonstrated stable performance after over 16,700 switching cycles and is expected to enable software-defined sensors with reconfigurable optical hardware.
A KAIST research team analyzed key challenges in commercializing biomanufacturing and proposed an AI-based strategy to address them. The study suggests a phased approach to simplify production processes and expand into high-value markets first.
A KAIST-Sungkyunkwan University joint research team has developed a new two-dimensional semiconductor structure where electricity flows without obstruction. The team demonstrated that current can flow across the boundary between semi-metallic and semiconducting regions without being blocked.
A KAIST-led research team developed an AI framework that analyzes real-life daily activity and environmental data from older adults to identify digital behavioral markers of cerebrovascular disease risk. The study found that irregular daily rhythms, low indoor humidity, and changes in sleep patterns can serve as important clues for det...
A KAIST study found that immigration-related legislation can lead to increased toxic chemical releases from manufacturing facilities in the US. The research team analyzed data from 14,390 facilities and found a significant increase in pollution when political attention shifted to immigration.
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.