Ultra-Sensitive Wearable MXene Sensor for Real-Time Human-Machine Interfaces

Abstract In the era of autonomous systems and multifunctional devices, sensors serve as vital sensory components in our Internet of Things and technologically advanced society. At the end of the synthetic 2D nanomaterials research, MXenes are not just chemicals but materials, depending on how they are synthesized for targeted applications, such as dual-functional temperature and pressure-sensitive wearable sensing. The current findings introduce the potential strategic role of nitrogen atoms to the Ti-Carbonitride (Ti3CNTz) structure in a controlled compositional stoichiometry of Ti3C1.8N0.2Tz, Ti3C1.5N0.5Tz, Ti3CNTz, Ti3C2Tx to deliver an ultrahigh sensitivity (300%–400% temperature & pressure sensitivity enhancement) and durability in real-time human-machine sensing interface applications. These recorded outstanding dual-sensing performance outplays many other MXene stoichiometries, graphene-related 2D nanomaterials, and their associated composites. Synchrotron radiation-based X-ray absorption fine structure and density functional theory analysis reveal that incorporating low N content (e.g., Ti3C1.8N0.2Tz) enhances temperature sensitivity by boosting electrical conductivity, and an upshift in the vibrational spectrum with increased lattice deformability significantly improves pressure sensitivity. We provide valuable insights for developing advanced sensing materials, emphasizing the need to investigate the fundamental mechanisms that control the interactions among layered 2D MXene materials and the sensing device functions that bridge human and machine interfaces. A research team, affiliated with UNIST has unveiled a groundbreaking wearable sensor material, capable of detecting body temperature, coughing, swallowing, and other subtle physiological signals when applied to the skin. Led by Professors Soo-Hyun Kim and Soon-Yong Kwon from the Graduate School of Semiconductor Materials and Devices Engineering, the team developed a novel titanium carbonitride-based MXene (Ti3CNTz) with unparalleled sensitivity to both temperature and pressure. This innovation achieves over three times the temperature sensitivity and more than four times the pressure sensitivity of conventional MXenes, enabling precise detection of minute biological cues. This advanced material, Ti3CNTz, benefits from carefully optimized nitrogen content, which enhances electrical conductivity and lattice vibrational responses. Its unique structure not only boosts sensitivity but also enhances mechanical durability, as validated through both theoretical and experimental analysis. In practical applications, sensors made from this MXene accurately distinguished subtle vocal cord vibrations, blinking, pulse waves, and gait patterns—all without direct contact. Remarkably, they can even detect temperature changes from a short distance, such as infrared heat emitted by a smartphone flash. Professor Kim highlights that this multifunctional sensor represents a transformative development in next-generation human-machine interfaces and electronic skin. Its versatility paves the way for numerous applications in healthcare, energy storage, catalysis, and electromagnetic shielding. Published in Advanced Functional Materials, this research was supported by the National Research Foundation of Korea (NRF) and the InnoCORE program of the Ministry of Science and ICT. Journal Reference Debananda Mohapatra, Ju-Hyoung Han, Hyun Jin Kang, et al., "Anomalous Pressure-Temperature Ultrahigh Sensitivities in Atomically Engineered Carbonitride MXenes for Multifunctional Wearable Human–Machine Interfaces: Joint Computational–Experimental Elucidations," Adv. Funct. Mater., (2026).

2026.05.18

New Study Unveils High-Performance Dye-Sensitized Electrode for Artificial Photosynthesis

Abstract Dye-sensitized photoelectrochemical cells hold promise for artificial photosynthesis but face challenges such as low photocurrents and limited stability. To address these limitations, a cascade-type dye-sensitized photoelectrode is developed by encapsulating a dye-sensitized TiO2 layer and redox mediator within platinum-sputtered nickel foil. This buried-junction design enables spatially controlled cascade charge transfer, featuring effective photoconversion and Ni-catalyzed water oxidation, while suppressing undesirable recombination current leakage. Through a comprehensive study involving the selection of redox mediators and water oxidation catalysts, the best-performing photoelectrode for water splitting achieves a photocurrent of 14.0 mA cm−2 at 0.72 V vs. reversible hydrogen electrode (RHE), a Faradaic efficiency of 98%, and photostability of 30 hours. Moreover, the versatility of our design extends to bias-free H2O2 production, achieving a photocurrent density of 7.83 mA cm−2, a Faradaic efficiency of 92.2%, and a record-high solar-to-fuel efficiency of 4.15% (2.13 µmol min−1 cm−2), with photostability of 150 hours. A research team at UNIST has introduced an innovative dye-sensitized electrode that marks a significant leap in artificial photosynthesis technology. Demonstrating exceptional efficiency and durability, the system can produce hydrogen peroxide solely using sunlight—bringing us closer to sustainable chemical manufacturing. Led by Professor Tae-Hyuk Kwon from the Department of Chemistry and Professor Ji-Wook Jang from the School of Energy and Chemical Engineering, the team developed an electrode that mimics natural electron-transfer processes in plants. The device features an organic dye layer combined with a redox mediator, encapsulated within a nickel foil structure. This configuration facilitates a cascade-like, stepwise electron transfer—from the dye to the mediator, then to the nickel substrate, and ultimately to the catalyst—minimizing charge loss and enhancing stability. Unlike conventional designs with dyes exposed directly to electrolytes, this architecture prevents degradation and significantly prolongs operational lifespan. The electrode achieved a Faradaic efficiency of 98% in water splitting and demonstrated stable performance over 150 hours. When used for sunlight-driven hydrogen peroxide production, it attained a solar-to-fuel efficiency of 4.15%, setting a new global record without requiring external voltage. “By optimizing the electrode interface, we were able to enhance efficiency and durability simultaneously,” explained Professor Kwon. “This environmentally friendly system paves the way for sustainable production of valuable chemicals using solar energy.” According to the research team, this breakthrough addresses core challenges in artificial photosynthesis—namely, efficiency, stability, and environmental safety—paving the path toward scalable, renewable fuel and chemical production. Its simplicity and eco-friendly design hold great promise for future applications in green chemistry and renewable energy. This research was participated by researchers Jun-Hyeok Park, Kyounglim Kim, and Jinyoung Lee as the first co-authors. The findings of this research have been published in Advanced Functional Materials on April 13, 2026. Journal Reference Jun-Hyeok Park, Kyounglim Kim, Jinyoung Lee, et al., "Bias-Free Highly Efficient and Stable Dye-Sensitized Photoelectrochemical Cells via Cascade Charge Transfer," Adv. Funct. Mater., (2026).

2026.05.15

New Electrode Converts CO2 into Chemicals While Generating Electricity

Abstract Heterogeneous catalysts comprising metal nanoparticles (NPs) on oxide supports are widely employed in high-temperature electrochemical devices such as solid oxide cells (SOCs). Unfortunately, these catalysts frequently exhibit structural instability at metal-oxide interfaces due to lattice mismatch, resulting in diminished catalytic activity and overall performance degradation over time. This work introduces an unprecedented approach of synthesizing intermetallic supports with metal-metal junctions by utilizing layered double hydroxide (LDH) structures. The LDH-derived framework undergoes controlled phase transitions, yielding an intermetallic structure decorated with exsolved Co─Fe alloy nanoparticles under reducing conditions, which would be the key for effectively mitigating the interfacial strain. This engineered electrode demonstrates exceptional electrocatalytic activity toward fuel oxidation reaction at high temperature regimes above 700°C. Furthermore, composite formation with oxygen ion conductive Gd0.1Ce0.9O2-δ (GDC) simultaneously augments electrochemical performance and structural stability, achieving a peak power density of 1.57 W cm−2 at 800°C under H2 fuel, while maintaining stable operation under SOC operations. This work hence presents an innovative strategy for designing structurally robust, efficient, and durable metal-metal junctions, thereby advancing the fields of high-temperature electrochemistry and catalysis. A research team affiliated with UNIST has unveiled a novel electrode material that significantly enhances the performance and stability of high-temperature electrochemical devices capable of converting carbon dioxide (CO2) into valuable chemicals while simultaneously generating electricity. Led by Professor Seungho Cho of the Department of Materials Science and Engineering at UNIST, the team collaborated with researchers from POSTECH, Seoul National University, and Nanjing University of Information Science and Technology (NUIST) to develop a metal-supported solid oxide cell (SOC) electrode utilizing layered double hydroxides (LDHs). This innovative design sustains high efficiency at temperatures exceeding 700°C, effectively addressing longstanding stability challenges associated with conventional ceramic-supported electrodes. The new electrode achieved a maximum power density of 1.57 W/cm² at 800°C—approximately 50% higher than existing solutions—and demonstrated stable operation over 200 hours during CO2 reduction to carbon monoxide. Its all-metal construction ensures resilience against thermal degradation, a common obstacle in high-temperature applications. This breakthrough leverages the distinctive properties of LDHs, layered materials containing uniformly dispersed cobalt and iron ions. Through controlled thermal processing, the researchers induced phase transformations that facilitate the formation of metal-metal junctions and promote the exsolution of catalytic nanoparticles. These processes effectively alleviate interfacial strain and significantly enhance durability. In addition, the incorporation of gadolinium-doped ceria (GDC) further optimized oxygen ion transport, thereby improving overall electrochemical performance. The research team noted, “Reducing electrode replacement lowers operational costs and accelerates the deployment of clean energy technologies. Our approach offers a versatile pathway toward sustainable fuel and chemical production from CO2.” They further stated, "This study pioneers the high-temperature application of layered double hydroxides in solid oxide electrodes—an advancement with far-reaching implications for energy efficiency and environmental sustainability." Contributing researchers include Hyunmin Kim (currently at Stanford University), Yoon Seo Kim (currently at Korea Institute of Science and Technology), and Hwakyoung Seo from Seoul National University, as first authors. The findings were published in Advanced Functional Materials on April 16, 2026. This research was supported by the InnoCORE program of UNIST and the National Research Foundation of Korea (NRF). Journal Reference Hyunmin Kim, Zhengyu Wu, Yoon Seo Kim, et al., "Engineering Metal-Metal Junctions from Layered Double Hydroxide Frameworks for High-Rate Solid Oxide Cells," Adv. Funct. Mater., (2026).

2026.05.15

Revolutionizing Semiconductor Design: AI Cuts Development Time from Months to Just One Day

Abstract Artificial Intelligence (AI) has been increasingly utilized across various fields, including communications, healthcare, and Computer-Aided Design (CAD). However, AI has shown relatively limited advances in analog and RF circuit design, which are critical for modern communication systems such as 5G due to their higher complexity and nonlinear characteristics. For example, the Inductor-Capacitor Voltage-Controlled Oscillator (LC-VCO) is a crucial component in frequency synthesizers, determining the performance of RF systems, including high data transmission rates and wide bandwidth. In fact, LC-VCO design is challenging due to the high parameter variability and complex interactions between design variables, making it challenging to optimize parameters to meet target specifications. Thus, this study proposes a comprehensive LC-VCO design methodology compatible across multiple process nodes and supports optimization down to the layout level. We use Reinforcement Learning (RL) to navigate the nonlinear design space efficiently for schematic optimization and apply an algorithm from Gradient Descent to optimize the design at the physical layout level. We highlight that the versatility of our methodology is demonstrated by producing optimized Figures of Merit (FoM) across various technology nodes and frequency ranges, showcasing its potential as a universal design tool accessible to all users. A team of researchers from UNIST and Kyungpook National University (KNU) has unveiled a pioneering AI system capable of completing the complex design of high-performance semiconductor circuits in just one day—an achievement that drastically reduces the traditional development timeline, which can take several months. Led by Professor Heein Yoon of UNIST’s Department of Electrical Engineering and Professor Taigon Song of KNU, the team developed an integrated AI platform that automates the entire process—from schematic design to physical chip layout. This breakthrough technology streamlines a typically multi-stage, resource-intensive task, offering faster, more efficient, and highly optimized circuit design solutions. The initial focus was on the design of the Inductor-Capacitor Voltage-Controlled Oscillator (LC-VCO), a critical component in 5G and emerging 6G communication systems. LC-VCOs generate the carrier signals essential for high-speed data transmission. Designing these circuits involves balancing numerous variables—such as inductor and transistor sizes—to minimize noise and power consumption. However, translating schematic designs into physical layouts often introduces parasitic effects that can impair performance. The new model integrates circuit schematic optimization with physical layout design, addressing these challenges holistically. During the schematic phase, reinforcement learning algorithms explore various parameter configurations to meet specific frequency and performance targets. Subsequently, in the layout phase, the system employs gradient descent methods to iteratively refine physical design parameters—such as wire widths and spacing—to enhance overall circuit performance. Gradient descent, a well-established optimization technique, incrementally adjusts design variables by following the gradient of the objective function toward an optimal solution. This integrated approach drastically accelerates the design process. Tasks that previously required up to 119 hours can now be completed in approximately 28.5 hours—reducing the overall time by over 76%. Moreover, the system’s adaptability across different semiconductor process nodes is enabled by transfer learning. Models trained on one technology node (e.g., 65nm) can be efficiently adapted to others (such as 40nm or 28nm), using just around 10% additional data. This AI-driven methodology not only shortens development cycles and reduces costs but also addresses the industry’s growing need for efficient design automation amid a global talent shortage. By ensuring high performance while minimizing manual effort, this technology is poised to significantly impact the production of next-generation communication and AI chips. The researchers anticipate expanding this framework beyond LC-VCOs to automate various analog and RF circuit designs, further accelerating innovation in semiconductor technology. The research was led by Sungjin Kim of UNIST and Hyunsoo Lee of KNU, who served as co-first authors, with additional contributions from Hong Seong-min of KNU. The findings of this research have been published online in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) on April 3, 2026. The study has been supported by the Ministry of Science and ICT (MSIT), the National Research Foundation of Korea (NRF), the Ministry of Education (BK21 Four), the Ministry of Trade, Industry and Energy (MOTIE), the Semiconductor Design Education Center (IDEC), Samsung Electronics, Axion Co., Ltd., and the Institute for Information & Communications Technology Planning & Evaluation (IITP). Journal Reference Sungjin Kim, Hyunsoo Lee, Seongmin Hong, et al., "A Framework of Automated LC-VCO Design with Physical Layout Based on Reinforcement Learning, IEEE TCAD, (2026).

2026.05.12

Ultrafast 3D Microfabrication Technology Produces Complex Structures in Just 60 Seconds

Abstract Despite the advantages of additive manufacturing (AM) in creating customized 3D shapes, conventional layer-by-layer approaches are limited by low production rates, restricting their broader applications. Volumetric additive manufacturing (VAM) has emerged as a promising technique, enabling the simultaneous photopolymerization of entire volumes, which significantly reduces fabrication time. However, current computed axial lithography requires manual operations per print cycle, such as loading resin into a vial, physically placing and aligning the vial (with or without an index-matching medium), and removing the printed object, limiting continuous, high-throughput production of multiple parts. Here, we propose a dispensing volumetric additive manufacturing (DVAM) method that prints and dispenses each part within a droplet in less than a minute. The printing process occurs within a single droplet dispensed from a glass pipette, enabling simultaneous printed object removal and resin replenishment in a second. Light pattern distortion caused by the absence of the index-matching fluid was corrected through real-time droplet profile estimation and inverse ray-tracing within the optical system. We demonstrate rapid serial VAM of 10 different objects within 10 min. This approach establishes a practical pathway toward scalable, high-throughput volumetric manufacturing, enabling rapid production of complex 3D structures without the operational bottlenecks of conventional VAM workflows. A research team affiliated with UNIST has achieved a major breakthrough in 3D microfabrication, enabling the rapid production of intricate 3D structures within a single minute. The new technology promises to significantly accelerate manufacturing processes in fields, ranging from biomedical devices to microelectronics. Led by Professor Im Doo Jung from the Department of Mechanical Engineering at UNIST, the research team developed a cutting-edge dispensing volumetric additive manufacturing (DVAM) system.The new approach moves beyond traditional layer-by-layer methods, allowing for the swift, continuous fabrication of diverse three-dimensional shapes within a matter of minutes by employing a volumetric printing process. DVAM utilizes a fine resin droplet dispensed from a glass pipette as the entire build volume. Light is projected onto the droplet to cure the desired shape in real-time. Once a structure is solidified, compressed air expels the finished object, and a new droplet is immediately dispensed for the next cycle. This seamless process enables high-speed, continuous production without the need for post-processing or layer-by-layer assembly. Unlike conventional volumetric printing methods—such as Computed Axial Lithography (CAL)—which require filling a resin container, matching refractive indices with immersion fluids, and manually removing finished parts, the UNIST team’s approach eliminates these constraints. By performing printing within a single resin droplet that acts as the entire volume, they achieve rapid, uninterrupted manufacturing. One of the main challenges was optical distortion caused by the droplet’s curved surface. To overcome this, the researchers integrated artificial intelligence with inverse ray-tracing optical calculations. A deep learning-based AI accurately detects the droplet’s profile in real-time, enabling the system to mathematically correct for distortions and project precise light patterns for uniform curing. This advanced system successfully produced intricate structures, such as the Eiffel Tower and The Thinker, within just 10 minutes—demonstrating the ability to fabricate ten different objects with an average time of around 60 seconds each. Hongryung Jeon, the first author of the study, explained, “Unlike traditional 3D printing that builds objects layer by layer, our method cures the entire volume at once and continuously dispenses resin without additional post-processing, increasing speed by over 100 times. This opens up exciting possibilities for large-scale, rapid production of micro-scale components.” Professor Jung, the corresponding author, emphasized, “Speed has long been a limiting factor in 3D printing, especially for customized manufacturing. By moving away from conventional photopolymerization and employing artificial intelligence to compensate for optical distortions, we have significantly advanced the potential of ultrafast volumetric fabrication. Now, complex shapes can be produced in seconds—eliminating long wait times.” The findings of this research have been published online in Advanced Functional Materials (Impact Factor: 19.0, top 5% in JCR) on March 21, 2026. The project was supported by the National Research Foundation of Korea (NRF), the Ministry of Science and ICT (MSIT), the Institute for Information & communications Technology Planning & Evaluation (IITP), and the Ministry of Trade, Industry & Energy (MOTIE). Journal Reference Hongryung Jeon, Yunsoo Lee, Seobin Park, et al., "Dispensing Volumetric Additive Manufacturing," Adv. Funct. Mater., (2026).

2026.05.12

New AI Algorithm to Enhance Accuracy of Thermal and Stress Predictions in Semiconductors

Abstract PDE surrogate models such as FNO and PINN struggle to predict solutions across inputs with diverse physical units and scales, limiting their out-of-distribution (OOD) generalization. We propose a π-invariant test-time projection that aligns test inputs with the training distribution by solving a log-space least squares problem that preserves Buckingham π-invariants. For PDEs with multidimensional spatial fields, we use geometric representative π-values to compute distances and project inputs, overcoming degeneracy and singular points that limit prior π methods. To accelerate projection, we cluster the training set into K clusters, reducing the complexity from O(MN) to O(KN) for the M training and N test samples. Across wide input scale ranges, tests on 2D thermal conduction and linear elasticity achieve MAE reduction of up to ≈91% with minimal overhead. This training-free, model-agnostic method is expected to apply to more diverse PDE-based simulations. A research team affiliated with UNIST has introduced a novel AI-based algorithm that enhances the accuracy of thermal and mechanical predictions across various scales, from microchips to large pipelines. Led by Professor Changwook Jeong from the Graduate School of Semiconductor Materials and Devices Engineering, their π-invariant test-time projection method realigns input data to conform with physical laws, addressing a crucial challenge in AI modeling—accurate predictions when faced with unfamiliar or out-of-distribution data. The algorithm identifies the most physically similar data within existing training sets based on a dimensionless ratio derived from Buckingham's π theorem. It then transforms new inputs into familiar, physically consistent forms without retraining the model, operating in log space to preserve physical ratios. This approach is computationally efficient, reducing processing costs by approximately 99% compared to traditional methods. Applied to 2D thermal conduction and linear elasticity problems, the technique achieved up to a 91% reduction in prediction error, even under conditions outside the original training range. It also demonstrated promising results in fluid dynamics, improving the accuracy of Navier–Stokes equation predictions in complex scenarios. This advancement is expected to accelerate and economize simulations in semiconductor design, packaging reliability, battery management, and structural safety analysis—fields where varying sizes and conditions demand both precision and efficiency. The study has been supported by the National Research Foundation of Korea (NRF) and the Institute of Information & Communications Technology Planning & Evaluation (IITP). Journal Reference Seokki Lee, Min-Chul Park, Giyong Hong, and Changwook Jeong, "Buckingham π-Invariant Test‑Time Projection for Robust PDE Surrogate Modeling," ICLR 2026 .

2026.05.11