Chemiresistive gas sensors have gained significant attention and have been widely applied in various fields. However, the gap between experimental observations and fundamental sensing mechanisms hinders systematic optimization. Despite the critical role of modeling in explaining atomic-scale interactions and offering predictive insights beyond experiments, existing reviews on chemiresistive gas sensors remain predominantly experimental-centric, with a limited systematic exploration of the modeling approaches. Herein, we present a comprehensive overview of the modeling approaches for chemiresistive gas sensors, focusing on two critical processes: the reception and transduction stages. For the reception process, density functional theory (DFT), molecular dynamics (MD), ab initio molecular dynamics (AIMD), and Monte Carlo (MC) methods were analyzed. DFT quantifies atomic-scale charge transfer, and orbital hybridization, MD/AIMD captures dynamic adsorption kinetics, and MC simulates equilibrium/non-equilibrium behaviors based on statistical mechanics principles. For the transduction process, band-bending-based theoretical models and power-law models elucidate the resistance modulation mechanisms, although their generalizability remains limited. Notably, the finite element method (FEM) has emerged as a powerful tool for full-process modeling by integrating gas diffusion, adsorption, and electronic responses into a unified framework. Future directions highlight the use of multiscale models to bridge microscopic interactions with macroscopic behaviors and the integration of machine learning, accelerating the iterative design of next-generation sensors with superior performance.

Drones equipped with visible and infrared sensors play a vital role in urban road supervision. However, conventional methods using RGB-IR image pairs often struggle to extract effective features. These methods treat these spectra independently, missing the potential benefits of their interaction and complementary information. To address these challenges, we designed the Multispectral Feature Mutual Guidance Network (MFMG-Net). To prevent learning bias between spectra, we have developed a Data Augmentation (DA) technique based on the mask strategy. The MFMG module is embedded between two backbone networks, promoting the exchange of feature information between spectra to enhance extraction. We also designed a Dual-Branch Feature Fusion (DBFF) module based on attention mechanisms, enabling deep feature fusion by emphasizing correlations between the two spectra in both the feature channel and space dimensions. Finally, the fused features feed into the neck network and detection head, yielding ultimate inference results. Our experiments, conducted on the Aerial Imagery (VEDAI) dataset and two other public datasets (M3FD and LLVIP), showcase the superior performance of our method and the effectiveness of MFMG in enhancing multispectral feature extraction for drone ground detection.

To examine the impact of the strain rate on the anisotropic mechanical characteristics of single-crystal silicon, nanoindentation and micro-tensile–compression tests were performed. This study analyzed the effects of varying crystal orientations at different strain rates on load–displacement behavior, elastic modulus, hardness, fracture toughness, and true stress–strain responses. The nanoindentation results showed that at room temperature, single-crystal silicon exhibited an elastic recovery rate of approximately 42%. Notably, the elastic modulus remained unaffected by strain rate variations, whereas hardness increased with higher strain rates. Fracture toughness at room temperature displayed marked anisotropy, with the orientation exhibiting the lowest value at 0.691 MPa·m^1/2 and the orientation showing the highest one at 0.797 MPa·m^1/2. Additionally, tensile and compression experiments revealed that the fracture strength of -oriented silicon increased from 117 MPa at a strain rate of 0.001 s⁻¹ to 550 MPa at a strain rate of 0.01 s⁻¹.

Metal oxide semiconductor (MOS) gas sensors have garnered significant attention and have been widely utilized across various fields. However, due to their complex sensing processes and nonlinear responses, the modeling, optimization and design of MOS sensors still remain challenging. Herein, we present a finite-element-method approach to model the behavior of MOS gas sensors. It was confirmed that the proposed model precisely reproduced the responses of different sensors over a wide concentration range. Based on the model, we systematically investigated how the sensor response was influenced by multiple factors such as adsorption isotherms, geometric parameters, material parameters, and electrical parameters. The simulation results not only closely align with existing experimental data and theoretical analysis, but also provide key insights for the optimization of MOS gas sensors. This work provides new methodologies for MOS sensor design and paves the way for high-performance MOS gas sensors.

To enhance the long-term storage reliability and safety of micro-miniature fuze, meet the technical requirements for rapid wartime deployment, this study conducts simulation research on the failure mechanisms of micro-miniature fuze in hygrothermal storage environments. Based on Fick’s second law, heat transfer theory, and the invariant failure mechanism of vulnerable materials in the micro-delay initiation circuit module, the process of external moisture diffusion into the circuit is analysed. A moisture diffusion simulation model analogous to heat conduction is established, revealing distinct hygroscopic kinetics: ABS plastic exhibits the highest moisture absorption rate, followed by potting compound and PCB. The durations required for each material to reach 90% of equilibrium moisture content under ambient humidity are quantified as 3 days, 7 days, and 70 days, respectively. Furthermore, by integrating thermal expansion and hygroscopic swelling strain models, a coupled hygro-thermal-stress simulation model is constructed based on the superposition principle. This establishes a hygrothermal-stress coupling simulation methodology, identifies plastic housing, potting compound, and semiconductor chip wire bonding as critical failure locations where induced hygrothermal stresses exceed material ultimate strengths. These components are determined to be pivotal in fuze failure during prolonged hygrothermal storage. Through coupled hygro-thermal-stress simulation analysis, this research resolves the issues of unclear hygrothermal stress mechanisms, unquantifiable failure risks, and undetermined failure modes. It provides an effective methodology for analysing long-term storage failures in fuze and enhancing their reliability and safety.