SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3553-7
Gesture interaction has emerged as a highly effective interface for intelligent human-computer interaction, attributed to its intuitive interaction modality and multi-dimensional control capabilities. However, traditional gesture interaction devices often depend on predefined encoding rules, which substantially limit interaction efficiency and degrade user experience. This study introduces an innovative intelligent finger ring interaction system based on a triboelectric nanogenerator utilizing PDMS/SrTiO3 composite thin film (PS-TENG). The system maps freehand writing gestures directly to textual information input, thereby eliminating the need for complex gesture encoding schemes and offering a user-friendly, low-learning-curve input method. By integrating a deep learning model, the system achieves recognition accuracies of 98.21% for English letters, 96.87% for Arabic numerals, and 96.44% for Chinese characters. Furthermore, it supports secure and encrypted data transmission and enables wireless interaction for gaming control. These findings indicate that the intelligent finger ring interaction system possesses significant potential for practical applications in information input and wireless control.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3686-6
Fused silica (SiO2) exhibits exceptional thermal stability and dielectric properties, making it an attractive material for aerospace and military applications. However, its relatively poor mechanical performance has limited its widespread practical utilization. This study proposed an innovative approach to fabricate SiO2-hexagonal boron nitride (hBN) composite ceramics via spark plasma sintering (SPS), leveraging the high-temperature phase transformation of cubic boron nitride (cBN) to introduce randomly oriented hBN as a reinforcing phase within the SiO2 matrix. The randomly oriented hBN nanoplates allow cracks to propagate along stronger grain boundaries, rather than along weaker interlayers of hBN, significantly improving the overall strength and fracture toughness of the composite. The maximum flexural strength and fracture toughness achieved are 183.4 MPa and 2.06 MPa m1/2 respectively, which are 3.6 times and 4 times that of fused SiO2. Concurrently, the composites exhibit low dielectric constants (ε = 3.58–3.69) and dielectric losses (tan δ < 0.0087) at 1 MHz. This work successfully enhanced the mechanical performance of fused SiO2 while preserving its excellent dielectric characteristics, opening new possibilities for its potential applications in advanced structural and functional fields.
Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202509072
Accurate identification of pollutant emission source parameters is critical for effective pollution response. This study evaluates the performance of genetic algorithm (GA), Nelder-Mead simplex (NM), particle swarm optimization (PSO), and their coupled variants on multi-dimensional, multi-extremum benchmark functions, and develops a source parameter inversion technique integrating PSO-NM with a Gaussian dispersion model. Validation via sulfur hexafluoride (SF6) single-point and multi-point release experiments demonstrates that PSO-NM achieves mean values closest to theoretical optima on Shubert, Hartmann, and Shekel functions, with superior stability and precision. In single-point source experiments, the relative deviation of source strength (Q) inversion ranges from -27.1% to 38.5%, with positional errors below 10 m, indicating robust convergence and repeatability. Multi-point source inversion exhibits stability across two scenarios but with reduced accuracy compared to single-point cases. When source strength is unknown, inversion accuracy for low-release sources (relative deviation 37.3%-70.4%) surpasses that for high-release sources; when position is unknown, positional deviations generally remain below 50 m, with low-release sources yielding better x0 deviations (-1.6 to 8.2 m) but slightly worse y0, z0, and distance parameters. Inversion errors primarily stem from meteorological non-stationarity, inter-source interference, algorithmic local optima, low-concentration measurement noise, and model assumptions. Future improvements may incorporate real-time meteorological correction and source-specific constraints to enhance accuracy and robustness in complex scenarios. The findings provide technical support for precise source tracing, monitoring, and refined management of pollutant emissions at microscale in industrial parks and enterprises.