In this research, a self-reinforced composite material was manufactured using a single polyethylene material, and this self-reinforced composite material has excellent recyclability and is environmentally friendly compared to composite materials composed of other types of material, such as glass fiber reinforced composites (GFRP) and carbon fiber reinforced composites (CFRP). In this research, the manufactured self-reinforced composite material consists of an outer layer and an inner layer. To manufacture the outer layer, low density polyethylene (LDPE) films were laminated on high density polyethylene (HDPE) fabrics and knitted fabrics, and composite materials were prepared at various temperatures using hot stamping. A 3D printing process was utilized to manufacture the inner layer. After designing a structure with a cross-sectional shape of a triangle, circle, or hexagon, the inner layer structure was manufactured by 3D printing high-density polyethylene material. As an adhesive film for bonding the outer layer and the inner layer, a polyethylene-based self-reinforced composite material was prepared using a low-density polyethylene material. Input data for simulation of self-reinforced composite materials were obtained through tensile property analysis using a universal testing machine (UTM, Shimadzu, Kyoto, Japan), and the physical property values derived as output data and actual experimental values were obtained. As a result of the comparison, the error rate between simulation data and experimental data was 5.4% when the shape of the inner layer of self-reinforced composite material was a hexagon, 3.6% when it was a circle, and 7.8% when a triangular shape showed the highest value. Simulation in a virtual space can reduce the time and cost required for actual research and can be important data for producing high-quality products.

Carbon aerogel is a promising material in various applications, such as water treatment, insulators, catalysts, and sensors, due to its porosity, low density, conductivity, and good chemical stability. In this study, an inexpensive carbon aerogel was prepared through lyophilization and post-pyrolysis using waste paper. However, carbon aerogel, in the form of short belts, is randomly entangled without a crosslinking agent and has weak mechanical properties, thus limiting its applications, which would otherwise be various. In this paper, a novel strategy is proposed to fabricate a PDMS-coated carbon aerogel (Aerogel@PDMS). Benefiting from microwave heating, precise PDMS coating onto the carbon frame was able to be carried out in a short amount of time. PDMS coating firmly tied the carbon microstructure, maintaining a unique aerogel property without blocking its porous structure. FE-SEM, RAMAN, XPS, and FT-IR were all used to confirm the surface change in PDMS coating. Compressible stability and water contact angle measurement showed that Aerogel@PDMS is a perspective organic solvent absorbent due to its good resilience and its hydrophobicity, and, as a result, its organic solvent absorption capacity and repeated absorption were evaluated, ultimately suggesting a promising material in oil clean-up and pollution remediation in water. Based on our experimental results, we identified elastic carbon aerogels provided by a novel coating technology. In the future, then, the developed carbon/PDMS composite can be examined as a promising option for various applications, such as environmental sensors, virus sensors, and wearable sensors.

In this study, a transient viscosity adjustment method using a coaxial nozzle was explored to fabricate nanofibers from non-spinnable m-poly(hydroxyamide) (m-PHA). Unlike conventional electrospinning methods that often require additives to induce fiber formation, this approach relies on a sheath-core configuration, introducing tetrahydrofuran (THF) to the sheath to temporarily adjust solution viscosity. The diffusion of THF into the core m-PHA solution resulted in momentary solidification at the interface, promoting nanofiber formation without compromising polymer solubility. SEM and rheological analyses confirmed that optimized sheath-to-core flow ratios yielded nanofibers with significantly reduced particle formation. Notably, increasing the THF flow rate facilitated a faster solidification rate, enhancing jet elongation and resulting in uniform nanofibers with diameters of approximately 180–190 nm. Although complete nanofibers without beads were not achieved in this study, this coaxial electrospinning approach presents a possible pathway for fabricating nanofibers from polymers with limited spinnability, potentially expanding the application scope of electro-spun materials in high-performance fields.

Measuring torsional movement is crucial for tracking human kinematics in applications such as virtual reality, biomechanics, rehabilitation, and soft robotic joints. However, continuous, non-invasive torsion measurement remains challenging due to multi-axial deformations, where existing methods rely heavily on empirical data fitting or computationally expensive deep learning models requiring vast training datasets. To address these limitations, this paper proposes a training-free, highly precise torsion estimation framework that converts soft resistive strain sensor data into forearm rotation angles using closed-form kinematic-geometric modeling. Two geometric representations—the Cylinder Model and Truncated Cone Model—are established to analytically map non-linear tensile strain to torsion angles. Additionally, an online calibration algorithm is integrated to mitigate cumulative measurement drift caused by viscoelastic hysteresis. Under static conditions, the proposed models achieved a high coefficient of determination (R² = 0.9888) against reference IMU data. In dynamic trials, the hysteresis compensation algorithm maintained robust tracking performance (R² = 0.8033), achieving an angular root-mean-square error (RMSE) of 4.12° and a cycle peak prominence relative error of 0.42% for the Truncated Cone Model. In this single-subject proof-of-concept feasibility study (n = 1), the proposed framework demonstrated the feasibility of continuous torsional motion tracking through a training-free geometric approach, providing a preliminary foundation for future evaluation in wearable systems and soft robotics.