Researchers developed a novel liquid metal electronic ink that allows for micro-scale circuit printing at room temperature, with reversible stiffness switching depending on temperature. This advancement marks a significant leap toward next-generation wearable, implantable, and robotic devices with tunable mechanical properties.
A collaborative research team led by KAIST has developed a groundbreaking technology that uses advanced optical techniques combined with an AI-based deep learning algorithm to create realistic 3D images of cancer tissue. This breakthrough paves the way for next-generation non-invasive pathological diagnosis.
KAIST and Mainz researchers have predicted a 3D magnon Hall effect, demonstrating the ability of magnons to move freely and complexly in 3D space. This breakthrough could lead to novel functionalities in next-generation computing structures.
Researchers at KAIST have developed an AI-powered app called AACessTalk that enables meaningful communication between children with autism and their parents. The app uses personalized vocabulary cards and customized prompts to foster mutual engagement in dialogue.
A KAIST research team has identified a key neural pathway involved in forming fear memories triggered by psychological threats. The pIC-PBN circuit processes psychological distress and drives fear memory formation, offering a significant advance in understanding how psychological distress is processed.
Researchers at KAIST have identified a critical protein called SLIRP that acts as an 'immune switch' regulating immune responses to viruses and autoimmune diseases. Suppressing SLIRP can alleviate abnormal immune responses in patients with Sjögren's syndrome, coronavirus, and encephalomyocarditis virus infection.
Researchers developed T2IRay, a VR input method with precise object pointing, and ChoreoCraft, a virtual reality tool supporting choreographers' creativity. These technologies received Honorable Mention awards at CHI 2025 for their contributions to human-computer interaction.
KAIST researchers developed a highly sensitive mid-infrared photodetector that operates at room temperature, enabling low-cost mass production and real-time sensing of various molecular species. The technology has potential applications in environmental monitoring, medical diagnostics, and industrial process management.
Researchers developed an AI-based music creation support system called Amuse, which converts user inputs into harmonic structures to support composition. The system has high potential as a creative companion for musicians and is centered on the creator's initiative.
Researchers at KAIST have identified a key mechanism behind neuroinflammatory responses in Parkinson's disease, which is regulated by an RNA editing enzyme called ADAR1. This discovery suggests that targeting this enzyme could serve as a novel therapeutic strategy for treating the disease.
Researchers at KAIST discovered that DDX54 is the master regulator hindering immunotherapy's effectiveness in lung cancer. Supressing DDX54 enhances immune cell infiltration into tumors and improves immunotherapy efficacy.
Researchers at KAIST have successfully developed a treatment method that restores vision by inducing retinal regeneration and vision recovery in disease-model mice. The approach involves blocking the PROX1 protein, which suppresses retinal regeneration, using an antibody developed by Celliaz Inc.
Researchers at KAIST evaluated industrial microbial cell factories to identify suitable strains and optimal metabolic engineering strategies. Using genome-scale metabolic models, they calculated maximum theoretical yields and achievable yields under industrial conditions for 235 bio-based chemicals.
Researchers at KAIST have successfully developed an eco-friendly, bio-based plastic that combines the advantages of PET and nylon. The new material was produced through microbial fermentation and exhibited characteristics similar to high-density polyethylene, making it strong and durable enough for industrial use.
A new study proposes a theoretical framework for AI-based wearable blood pressure sensors, paving the way for non-invasive and continuous cardiovascular monitoring. The review highlights clinical aspects of implementation, real-time data transmission, and signal quality degradation, and presents strategies to address technical barriers.
Researchers at KAIST have discovered a molecular switch that can induce cancer reversal by capturing the moment of critical transition before normal cells become irreversibly cancerous. The technology uses single-cell RNA sequencing data and computer simulation analysis to identify the molecular switch.
The KAIST research team developed an AI-based technique to accurately predict Hall thruster performance, significantly reducing the time and cost associated with iterative design, fabrication, and testing. The trained neural network ensemble model offers detailed analyses of performance parameters, accounting for key design variables.
A KAIST research team identified core gene expression networks regulated by proteins that drive phenomena such as cancer development and tissue differentiation. The study revealed that IPMK acts as a critical transcriptional activator in these networks, enhancing SRF's protein activity.
Researchers at KAIST and U of Michigan developed a digital biomarker to predict symptoms of depression using smartwatch data. The technology estimates circadian rhythm disruption, which can lead to depression, anxiety, and other mental health issues.
The KAIST team developed a next-generation neuromorphic semiconductor-based integrated system that can learn and correct errors on its own. This technology will revolutionize AI use in everyday devices, making them faster, more private and energy-efficient.
Researchers at KAIST developed CamBio, a biotemplating method utilizing specific intracellular proteins to create functional nanostructures with high tunability. The method enables the selective synthesis of nanostructures from biological samples, showing improved performance in surface-enhanced Raman spectroscopy substrate detection.
A novel bio-inspired camera capable of ultra-high-speed imaging and high sensitivity has been developed by KAIST researchers. The camera mimics the visual structure of insect eyes and achieves frame rates thousands of times faster than conventional cameras, while providing clear images in low-light conditions.
The KAIST research team developed a highly stretchable microelectrode array to monitor organoids' functions, enabling real-time analysis of their states. The technology showed promise in high-throughput drug screening applications, revealing changes in signal characteristics according to size and identifying potential drug interactions.
Researchers at KAIST have developed a technology that can treat colon cancer by converting cancer cells into normal-like cells. The breakthrough involves creating a digital twin of the gene network associated with normal cell differentiation, leading to significant promise for reversible cancer therapies.
Researchers at KAIST developed a new method to learn without weight transport, enabling faster and more accurate learning. By pre-training with random noise, the team showed that neural networks can achieve high learning efficiency and solve the weight transport problem.
A KAIST research team has successfully produced a microbial-based plastic that is biodegradable and can replace existing PET bottles. The team used metabolic engineering to develop a microbial strain that efficiently produces pseudoaromatic dicarboxylic acids, which are better suited for producing polymers than traditional methods.
Researchers at KAIST introduced a new hybrid device structure with organic photo-semiconductors that expand the absorption range to near-infrared, improving power conversion efficiency. The device achieved a high internal quantum efficiency of 78% in the near-infrared region and improved stability for over 1,200 hours.
Researchers developed a novel AI approach to predict atomic-level chemical bonding information in 3D space, bypassing traditional supercomputer simulations. This methodology accelerates calculations by learning chemical bonding information using neural network algorithms from computer vision.
A KAIST team created a face-conforming LED mask with improved efficacy in skin rejuvenation. The FSLED mask demonstrated a 340% improvement in deep skin elasticity compared to conventional masks, showing significant benefits for anti-aging treatment.
Researchers from KAIST have developed a new hydrogen production system that overcomes current limitations of green hydrogen production. The system uses a water-splitting process with an aqueous electrolyte, achieving high energy density and long-term stability.
Researchers at KAIST have developed a thermoelectric material that can generate electricity from body temperature and maintain stable performance even in extreme environments. The material, made of bismuth telluride fibers, has higher bending strength and showed no change in electrical properties after repeated bending tests.
KAIST researchers developed a new electrochemical impedance spectroscopy (EIS) technology using small currents to diagnose electric vehicle batteries with high precision. This low-current EIS system minimizes thermal effects and safety issues during measurement, making it suitable for integration into vehicles.
Researchers at KAIST have developed a Janus metasurface capable of controlling asymmetric light transmission, enabling the creation of two independent optical systems with a single device. This technology also enables optical encryption by generating different images depending on the direction and polarization state of incoming light.
A Korean research team has successfully observed living organoids in real time at a high resolution using holotomography. The technology allows for long-term observation of dynamic changes and precise analysis of organoid responses to drug treatments.
Researchers at KAIST successfully developed single-atom editing technology that maximizes drug efficacy by converting oxygen atoms into nitrogen atoms in furan compounds. This breakthrough technology enables selective editing of complex natural products or pharmaceuticals, opening new doors for building libraries of drug candidates.
Researchers at KAIST have successfully developed a microbial strain that efficiently produces aromatic polyester using systems metabolic engineering. The team achieved the world's highest concentration (12.3±0.1 g/L) for efficient production of poly(PhLA), demonstrating the possibility of industrial-level production.
Holotomography offers a promising approach to biomedical research, providing high-resolution images of live cells and tissues at the organelle level. The KAIST research team has developed core technologies and demonstrated its applications in various fields, including regenerative medicine and cancer research.
A research team at KAIST has developed an AI-based methodology to predict the major elemental composition and charge-discharge state of NCM cathode materials with high accuracy using convolutional neural networks. The technology can analyze surface morphology images of batteries to determine their composition and lifespan.
Researchers at KAIST successfully clarified the three-dimensional, vortex-shaped polarization distribution inside ferroelectric nanoparticles using atomic electron tomography. This discovery has implications for ultra-high-density memory devices with capacities over 10,000 times greater than existing ones.
The KAIST-Yonsei University research team has developed a novel, high-performance paper coating material made from biodegradable plastic that improves the sustainability of paper packaging. The coating material was tested for its biodegradability and found to achieve 59-82% biodegradation in marine environments.
Researchers at KAIST have developed a hybrid sodium-ion battery with high energy and power density, enabling rapid charging in under a few seconds. The new battery technology has the potential to revolutionize energy storage for electric vehicles and other applications.
Researchers propose a direction of research on 'microbial food production from sustainable raw materials' to produce nutritious and eco-friendly foods. Microbial biomass is rich in protein, emits minimal carbon dioxide, and requires less water and space.
Researchers at KAIST have developed a novel ultra-low power memory device that can replace existing memory or be used in implementing neuromorphic computing. The new phase change memory device consumes 15 times less power than conventional devices, enabling the development of low-cost and energy-efficient artificial intelligence hardware.
A KAIST-Seoul National University Hospital research team has developed a computational workflow that predicts metabolites and metabolic pathways associated with somatic mutations in cancers. The workflow uses genome-scale metabolic models and mutation data to identify altered metabolic pathways that contribute to cancer progression.
A KAIST team developed an insect-mimicking semiconductor that mimics the optic nerve of insects to detect motion. The device operates at high efficiency and ultra-high speeds, and has been applied to a neuromorphic computing system for predicting vehicle paths. It achieved 92.9% less energy consumption compared to existing technology.
A recent study published in Cell Genomics has uncovered the quantitative and qualitative mutational impacts of ionizing radiation on normal cells. The research team found that exposure to low levels of radiation resulted in an average of 14 mutations per cell, primarily causing short base deletions and complex genomic rearrangements.
A KAIST research team has developed a biomimetic scaffold that generates electrical signals to promote bone tissue growth, providing a new method for utilizing the unique osteogenic abilities of hydroxyapatite. The flexible and free-standing scaffold demonstrated remarkable potential for promoting bone regeneration in rats.
A KAIST research team has developed a stretchable and adhesive microneedle sensor that can detect physiological signals without being affected by sweat and dead skin. The sensor allows for long-term stable control of wearable robots, enabling precise movement recognition for rehabilitation treatments.
A KAIST research team led by Professor Hawoong Jung identified the principle behind musical instincts emerging from the human brain without special learning using an artificial neural network model. The study found that cognitive functions for music forms spontaneously as a result of processing auditory information received from nature.
A KAIST research team has developed a technique called SynapShot, which allows for the real-time observation of synapse formation, extinction, and alterations. This breakthrough technique uses fluorescent proteins to track changes in synapses, offering new insights into brain function and potentially revolutionizing neurological research.