Korea Institute of Machinery and Materials

Infrared (IR) polarizer is essential for thermal imaging applications such as mobility and military operations. High‐sulfur‐containing polymers have emerged as promising candidates for IR polarizers due to their mid‐wave IR (MWIR) transparency, addressing the limitations of inorganic materials, including their brittleness and high cost. However, poor thermal stability and limited IR range restrict their applicability. This study introduces a glassy IR polarizer based on poly(sulfur‐co‐hexavinyl disiloxane) (pSHVDS), a highly crosslinked sulfur‐rich polymer synthesized via sulfur chemical vapor deposition (sCVD). Self‐crosslinking of pSHVDS at high temperatures provided thermal stability during nanoimprint lithography, enabling the fabrication of high‐fidelity nano‐grating patterns (400 nm pitch, 150 nm width, 300 nm height). The broad transmittance and high aspect ratio of the nanopattern enabled the glassy‐pSHVDS MWIR polarizer to achieve over 50% transmittance of transverse magnetic field (TTM) and an extinction ratio (ER) exceeding 6000 across a broad IR range (3‐8 µm). An additional pSHVDS anti‐reflection coating further enhanced TTM to 84% and ER to 7200 at a wavelength of 4 µm, the highest ER reported for organic MWIR polarizers to date. The polarizer maintained its performance after 24 h at 100 °C, demonstrating exceptional thermal stability. These findings underscore the potential of glassy pSHVDS‐based polarizers for IR applications.

The demand for 5-axis machine tools is increasing because of their ability to efficiently machine materials into complex shapes compared to 3-axis machine tools in a single setup. However, various machining accuracy issues are encountered because of the kinematic error caused by ambient temperature changes. The ambient temperature change in the working environment results in the temperature variation of the machine components. This causes thermally induced kinematic error in the machine tool. This study proposed a method to obtain a thermally induced kinematic error compensation model considering various temperature states. The kinematic error was identified using various tool center point (TCP) positions and updated using a thermal error model. The compensation value at the TCP was calculated with the updated kinematic error. The volumetric error of the machine tool in the various temperature states was reduced by the proposed method.

This paper introduces a new development of a two-wheeled robotic wheelchair (TWW), designed to address the challenges of personal mobility for the elderly and individuals with lower limb disabilities. By incorporating a sliding seat mechanism and motorized support legs, the TWW enhances stability, comfort, and accessibility in narrow or uneven environments. Notably, the TWW has a minimum turning radius of 0.372[m], enhancing convenience in confined spaces. A dynamic inversion-based motion planner is developed to provide smooth and stable driving experiences by accurately converting user inputs into optimal trajectories. Furthermore, the system prioritizes safety through real-time fault detection, ensuring reliability in various scenarios. Experimental results validate the system’s enhanced posture stability and safety, highlighting its potential for a wide variety of applications in daily mobility and rehabilitation support.

Non-thermal plasma (NTP) has emerged as a promising therapeutic tool due to its anti-inflammatory properties; however, its molecular effects on vascular endothelial inflammation remain unclear. This study investigated the effects of NTP on tumor necrosis factor-alpha (TNF-α)-induced inflammation in human umbilical vein endothelial cells (HUVECs). NTP treatment significantly reduced intracellular reactive oxygen species (ROS) levels and downregulated the expression of adhesion molecules such as intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1), which are key markers of endothelial activation. In addition, NTP suppressed mRNA expression of pro-inflammatory cytokines, including TNF-α, interleukin-1 beta (IL-1β), and interleukin-6 (IL-6). Mechanistically, NTP inhibited the nuclear translocation of phosphorylated NF-κB p65, indicating attenuation of NF-κB signaling. These results demonstrate that NTP modulates inflammatory responses in endothelial cells by attenuating ROS generation and suppressing NF-κB-mediated signaling. Our findings suggest that NTP may serve as a potential therapeutic strategy for treating vascular inflammation and related pathologies.

This research presents a beam model-based fast optimization for the design of a lightweight car body frame at a concept design stage. Because the stiffness of a thin-walled beam frame is significantly affected by the stiffness of joints, the optimization is focused on the reinforcement of joint regions. To overcome the limitations of beam elements in accurately predicting the stiffness of a thin-walled beam frame, especially at joints, a higher-order beam theory (HOBT) is employed for beam modeling. The optimization problems are formulated as mean compliance minimization problems by employing two types of design components: joint springs and diaphragms. Instead of using the geometric parameters of joints as design variables, the directional stiffnesses of joint springs are employed as design variables. Joint reinforcement used for optimization is defined as a virtual part that has stiffness only against bending and torsional deformation, with zero stiffness against higher-order deformations such as warping or distortion. This approach facilitates the calculation of mass increase due to joint springs during optimization. The optimized results of joint springs are inversely designed to sectional shapes of reinforcement through topology optimization. The use of a diaphragm at a joint effectively suppresses the sectional distortion of thin-walled beams and significantly increases their stiffness. The locations of diaphragms are determined through optimization using a 0–1 formulation. The validity of the proposed optimization method is shown by solving subframe and car body frame problems. Joint reinforcement optimization of a car body frame with a 2.5% mass increase and the addition of 6 diaphragms reduced compliance by 13.88% and increased natural frequencies by up to 7.64%.

A multifunctional catalyst with enhanced polysulfide adsorption, rapid lithium diffusion, and exceptional catalytic activity is crucial for accelerating redox kinetics and effectively suppressing the shuttle effect in lithium–sulfur (Li–S) batteries. However, developing an efficient synthesis method for such catalysts remains challenging. Here, a sustainable, sulfur‐terminated MXene is introduced via a completely dry molten salt process, which avoids the need for harsh acid treatment, byproduct removal, and extensive rinsing, typical in MXene syntheses. Theoretical calculations and electrochemical data confirm that this sulfur‐terminated MXene serves as a powerful multifunctional catalyst, promoting rapid lithium diffusion, effective polysulfide adsorption, and superior catalytic performance, making it highly suitable for advanced separators in Li–S batteries. As a result, Li–S cells incorporating sulfur‐terminated MXene separators demonstrate a high capacity of 665 mAh g⁻¹ after 500 cycles at 1 C, with a remarkably low‐capacity decay rate of 0.05% per cycle. This study underscores the potential of precise surface termination control in MXenes to drive further advancements in Li–S battery technology.

The gauge factor (GF) is a critical parameter for strain sensors, but it faces limitations in achieving high GF values across a wide strain range. This work proposes a novel approach to enhance resistance changes within strains through synergistically combining controlled‐crack sizing and an ion‐bridging structure. This ion‐conductive bridge forms at the interface between graphene and polyvinyl chloride (PVC) gel. Precise management of the crack initiation and propagation on graphene is achieved by controlling adhesion force between graphene and PVC gel. The resulting PVC gel/graphene‐based strain sensor featuring this synergistic design exhibits exceptional sensitivity. It achieves GFs of 635 (ε < 40%), 1.5 × 10⁶ (40% < ε < 80%), and 7.8 × 10⁵ (80% < ε < 100%) over a 100% stretching range. This innovative ion‐bridging construction enables precise control over bridge connectivity at the interface, mitigating graphene's inherent stretchability limitations and enhancing the GF of PVC gel, thereby enhancing strain sensor performance. The sensor detects bending motions and monitors angles within higher strain ranges, making it suitable for wearable applications in human motion tracking. Furthermore, a PVC‐based posture correction system distinguishes various motions, including shoulder band stretching, armband stretching, and even full squats, showcasing its practicality and versatility.

Metasurfaces composed of subwavelength elements have garnered significant interest for their ability to manipulate electromagnetic waves in unique ways. However, their optical properties are often highly sensitive to incident angles and polarization states, which can limit practical applications. While introducing random perturbations helps alleviate this issue, it can complicate both the design and fabrication processes. Instead, a new class of metasurface designs based on aperiodic tiling is proposed, specifically ‘Einstein’ tiling, which systematically enhances angle and polarization tolerance. This method, retroactively applicable to many scatterer designs in previously reported metasurfaces as well, applies a single scatterer design across the entire surface, introducing aperiodicity and orientational diversity through predefined placement rules. This approach simplifies optimization and production. Our prototype, designed for structural color applications, shows tolerance to incident angles up to 45 ° in both reflectance spectra and structural colors and accommodates arbitrary polarization states. The proposed metasurface maintains many of the advantages of periodic metasurfaces, such as easily designable colors and facile color mixing. This work highlights the potential of aperiodic metasurfaces for applications requiring robust performance under diverse lighting conditions, offering an interesting new route toward practical optical metasurfaces.

This study introduces a straightforward method for fabricating CuO nanowires (CuONWs) decorated with gold nanoparticles (GNPs) to detect low-concentration urine glucose using an electrochemical process. The crystal structure and morphology of the electrode samples are analyzed using X-ray diffraction spectrometer and a field emission scanning electron microscope. The result reveals that CuO nanowires with a high density of 10 μm in length and approximately 50 nm in diameter were successfully decorated with tiny gold nanoparticles. The CuONWs/GNPs are utilized to develop a non-enzymatic glucose sensor demonstrating outstanding performance in glucose detection. The electrochemical sensor demonstrates a detection limit of 0.24 mM and a linear range from 0.6 to 8 mM. It is reusable and remains stable for at least 5 weeks. Furthermore, the proposed electrode exhibits excellent selectivity for glucose, even in the presence of common interfering substances in the urine, such as ascorbic acid, lactose, and uric acid. Its long-term stability and selectivity enable reliable glucose detection in intravenous sugar solution and human urine samples.

Interaction consists of verbal exchange as well as multimodal social signals. Multimodality is essential in communicating fruitfully by sending implicit and explicit information. When conversing, multimodal signals are transferred back and forth between interlocutors. Via this transfer, interlocutors constantly adapt their behaviors to those of their interlocutors. Virtual agents, which look and act like humans, should also consider the multimodal and adaptive aspect of the interaction they build with the users. In this chapter, we elaborate on adaptive behavior evaluation methods, multimodal adaptive behavior generation modeling, and the effect of a virtual agent providing real-time adaptive behaviors for cognitive behavioral therapy (CBT).

This paper proposes a generalized method for designing tendon-driven serial-chained manipulators with an arbitrary number of tendon redundancy. First, a special class of tendon-driven structures is defined by introducing the controllable block triangular form (CBTF) of a null space matrix and its complementary CBTF of a structure matrix, satisfying physical constraints related to the minimal connection of tendons and to the placement of actuators. Then it is shown that any general design of tendon-driven serial manipulators can be reduced to the design of such a special class of tendon-driven structures. Two associated design problems are derived and solved. The first design problem is about finding a complementary CBTF structure matrix for a given CBTF null space matrix using algebraic relations, whereas the second one seeks the both matrices that optimize the wanted structural characteristics based on the result of the first design problem. Numerical design examples are provided to show the validity of the proposed method.

2D transition‐metal dichalcogenides (TMDCs) have attracted attention as promising materials for next‐generation devices owing to their versatile electronic and optical properties. The phase variety of TMDCs provides strategic opportunities for performance enhancement. Herein, a novel method is proposed to synthesize wafer‐scale 1T phase MoS₂ and, simultaneously, induce a phase transition via a plasma‐assisted metal‐sulfidation process and spontaneous internal strain. With thicker MoS2 layers, the strong internal strain during synthesis suppresses the undesirable phase transition from the metastable 1T phase to the 2H phase, ensuring stabilization of the 1T phase. Furthermore, as‐synthesized 1T‐MoS₂ shows remarkable electrical properties owing to the narrow bandgap (0.4 eV) of its semi‐metallic state. As a result, the 1T‐phase MoS₂ floating gate (1T‐FG) flash memory demonstrates a wider memory window, a higher on/off ratio, and improved stability compared to the 2H‐phase MoS₂ floating gate (2H‐FG) flash memory. A 5 × 5 array structure is constructed to validate large‐scale integration. Notably, under light irradiation, a single 1T‐FG memory enables carrier trapping in the floating gate, even in the off state. This study introduces a facile phase control strategy and provides insights into advanced nonvolatile memory and optoelectronic synaptic functionalities.

Accurate parameter estimation of unknown objects is crucial for the precise and safe manipulation of robotic systems in applications such as object positioning, assembly, and collaborative manipulation tasks involving humans or multiple robot agents. However, measurement data obtained from sensors often contain uncertainties, making accurate parameter estimation challenging. In this paper, a systematic methodology for unmodeled dynamics identification that represents uncertainties in measured sensor data is proposed for accurate online parameter estimation of task objects. The sparse identification of nonlinear dynamics (SINDy) technique, a recent machine learning approach, is employed to identify unmodeled dynamics. First, in the learning process, an unmodeled dynamic equation can be obtained by establishing residual data, which are obtained by subtracting the dynamics of prior known objects from measured sensor data and by designing proper candidates that successfully capture uncertain behavior. Second, in the online parameter estimation process for an unknown object, estimation results that are not contaminated by uncertainties can be obtained by incorporating the identified unmodeled dynamic equation into the nominal object equation. To verify the robustness and estimation accuracy of the proposed methodology, experiments were conducted using various objects. The experiments demonstrate that the proposed method improves the estimation accuracy by reducing errors by 15.71 %, on average, compared to the estimation accuracy of conventional methods that only consider nominal object dynamics.

In this study, the optimal channel size of a printed circuit heat exchanger (PCHE) with a subcritical nitrogen gas cycle is determined through thermo-mechanical analyses. The objective is to maximize the structural integrity of the PCHE while maintaining its thermal performance. To achieve this, finite element analyses (FEAs) are conducted by varying the channel size of the PCHE, while constraining the thermal performance to a set value. The boundary conditions and transport properties required for the FEAs are derived through a two-step calculation process. First, the logarithmic mean temperature difference (LMTD) method, which assumes a simple one-dimensional shape and requires low computational cost, is applied. Second, thermal analyses through FEAs are performed to refine the LMTD results. Using these refined results, thermo-mechanical FEAs are carried out. Based on the stresses obtained from these analyses, the optimal channel size is obtained, ensuring both structural integrity and thermal performance.