https://scholar.korea.ac.kr/handle/2021.sw.korea/855 Sun, 05 Apr 2026 17:21:21 GMT 2026-04-05T17:21:21Z https://scholar.korea.ac.kr/handle/2021.sw.korea/269532 Title: 3D flower-like Co3O4@ZnO nanostructures for trace-level acetone detection at low operating temperatures Authors: Hilal, Muhammad; Ali, Yasir; Cai, Zhicheng; Kim, Hyojung; Abdo, Hany S.; Alnaser, Ibrahim A.; Hwang, Yongha Abstract: Enhancing p-type metal oxide semiconductors (MOS) sensitivity at low temperatures is critical for detecting acetone, a toxic pollutant and diabetes biomarker. This study presents a 3D flower-like Co3O4@ZnO composite synthesized via additive-free hydrothermal method combined with inert gas calcination. The inert gas environment minimizes oxidation and oxygen interference, forming a robust nanoneedle-based hierarchical structure with high integrity, a large surface area (52.13 m(2)g(-1)), and uniform mesopores (similar to 10 nm) to facilitate efficient gas diffusion and reactions. The ZnO-Co3O4 heterojunction enhances band-bending modulation and refines carrier dynamics by synergizing ZnO's exceptional carrier mobility with Co3O4's robust redox catalytic activity, delivering markedly improved sensing performance. The optimized composite (CZ-3, Co3O4:ZnO = 0.5:0.5) demonstrated exceptional acetone sensing performance, achieving a 35.85% response to 100 ppm acetone at 150 degrees C, rapid response/recovery times of 40/28 s, a linear detection range of 1-150 ppm, and an ultra-low detection limit of 100 ppb. The sensor also exhibited a measurable response (0.35 %) to human exhaled breath, demonstrating its potential for non-invasive healthcare diagnostics. In contrast, the lower ZnO content in Co3O4 (CZ-1) sensor showed reduced performance, responding to 500 ppb acetone with a response of 29% to 100 ppm. These results emphasize the critical role of the heterojunction with an optimized balance of p- and n-MOS in enhancing sensing performance, highlighting a sustainable and scalable approach for advancing high-performance p-type MOS gas sensors. The proposed composite demonstrates significant potential for precise, low-temperature acetone detection in environmental monitoring and non-invasive healthcare diagnostics. Tue, 01 Jul 2025 00:00:00 GMT https://scholar.korea.ac.kr/handle/2021.sw.korea/269532 2025-07-01T00:00:00Z https://scholar.korea.ac.kr/handle/2021.sw.korea/270766 Title: Thermoforming 2D films into 3D electronics for high-performance, customizable tactile sensing Authors: Choi, Jungrak; Han, Chankyu; Lee, Donho; Kim, Hyunjin; Lee, Gihun; Ha, Ji-Hwan; Jeong, Yongrok; Ahn, Junseong; Park, Hyunkyu; Han, Hyeonseok; Cho, Seokjoo; Gu, Jimin; Park, Inkyu Abstract: The demand for tactile sensors in robotics, virtual reality, and health care highlights the need for high performance and customizability. Despite advances in vision-based technologies, tactile sensing remains crucial for precise interaction and subtle pressure detection. In this work, we present a design and fabrication method of customizable tactile sensors based on thermoformed three-dimensional electronics. This approach enables ultrawide modulus tunability (10 pascals to 1 megapascal) and superior mechanical properties, including negligible hysteresis and high creep resistance. These features allow the sensor to detect a broad spectrum of pressures, from acoustic waves to body weight, with high performance. The proposed sensors have high sensitivity (up to 5884 per kilopascal), high linearity (R2 = 0.999), low hysteresis (<0.5%), and fast response (0.1 milliseconds). We demonstrate applications in human-computer interaction and health care, showcasing their potential in various fields. This platform provides a scalable solution for fabricating versatile, high-performance tactile sensors. Wed, 14 May 2025 00:00:00 GMT https://scholar.korea.ac.kr/handle/2021.sw.korea/270766 2025-05-14T00:00:00Z https://scholar.korea.ac.kr/handle/2021.sw.korea/267939 Title: Synergistic MXene@NiO-ZnO heterostructures via dual-pressure hydrothermal synthesis for high-performance photoelectrochemical glucose sensing Authors: Hilal, Muhammad; Ali, Yasir; Cai, Zhicheng; Kim, Hyojung; Abdo, Hany S.; Alnaser, Ibrahim A.; Hwang, Yongha Abstract: The development of efficient, scalable, and highly sensitive photoelectrochemical (PEC) systems is critical for advancing non-enzymatic glucose sensing and other catalytic applications. In this study, a MXene@NiO-ZnO (NMZ) composite was synthesized via an advanced dual-pressure hydrothermal method, overcoming limitations of conventional synthesis techniques. This innovative approach combines dual-pressure regulation and active stirring to achieve homogeneous distribution of Ni2+ and Zn2+ cations with CO32− and OH⁻ anions onto MXene, resulting in a hierarchical structure with a high surface area (72.4 m2/g) and enhanced electrochemical active surface area (2 cm2). The composite demonstrates superior UV–visible absorption and efficient photogenerated charge separation, facilitated by a robust internal electric field at the ZnO/NiO (NZ) interface. MXene integration eliminates Schottky barriers, ensuring seamless electron flow and efficient charge transport to the electrode. NiO's valence band potential (2.45 eV vs. NHE) promotes OH⁻ radical formation and Ni(OH)2 generation, critical for glucose oxidation. As a PEC glucose sensor, the NMZ composite demonstrates outstanding performance, achieving high sensitivity (7651.2 μA•mM−1•cm−2), rapid response time (4 s), and excellent reproducibility, with a relative standard deviation (RSD) of 5.21 %, significantly outperforming conventional NZ composites. These findings demonstrate the potential of NMZ composites in advancing PEC-based sensing technologies and other catalytic applications. © 2025 Elsevier Ltd and Techna Group S.r.l. Thu, 01 May 2025 00:00:00 GMT https://scholar.korea.ac.kr/handle/2021.sw.korea/267939 2025-05-01T00:00:00Z https://scholar.korea.ac.kr/handle/2021.sw.korea/268514 Title: Touch Gesture Recognition-Based Physical HumanRobot Interaction for Collaborative Tasks Authors: Jung, Dawoon; Gu, Chengyan; Park, Junmin; Cheong, Joono Abstract: Human-robot collaboration (HRC) has recently attracted increasing attention as a vital component of next-generation automated manufacturing and assembly tasks, yet physical human-robot interaction (pHRI)-which is an inevitable component of collaboration-is often limited to rudimentary touches. This article therefore proposes a deep-learning-based pHRI method that utilizes predefined types of human touch gestures as intuitive communicative signs for collaborative tasks. To this end, a touch gesture network model is first designed upon the framework of the gated recurrent unit (GRU) network, which accepts a set of ground-truth dynamic responses (energy change, generalized momentum, and external joint torque) of robot manipulators under the action of known types of touch gestures and learns to predict the five representative touch gesture types and the corresponding link toward a random touch gesture input. After training the GRU-based touch gesture model using a collected dataset of dynamic responses of a robot manipulator, a total of 35 outputs (five gesture types with seven links each) is recognized with 96.94% accuracy. The experimental results of recognition accuracy correlated with the touch gesture types, and their strength results are shown to validate the performance and disclose the characteristics of the proposed touch gesture model. An example of an IKEA chair assembly task is also presented to demonstrate a collaborative task using the proposed touch gestures. By developing the proposed pHRI method and demonstrating its applicability, we expect that this method can help position physical interaction as one of the key modalities for communication in real-world HRC applications. Tue, 01 Apr 2025 00:00:00 GMT https://scholar.korea.ac.kr/handle/2021.sw.korea/268514 2025-04-01T00:00:00Z