Teng Ma

Flame sensors are widely deployed in different fire monitoring scenarios. Identifying the origin of the flame, the fuel, often relies on the ultraviolet (UV) spectral signatures emitted by excited radicals during combustion. However, UV flame sensors capable of spectral discrimination typically require complex optical and material designs, resulting in high fabrication costs that hinder their application. To address this limitation, we proposed a multi-channel semiconductor UV flame sensor design based on an Al2O3-TiO2 double-layered nanostructure. This architecture was formed sequentially via anodization processes. By tuning the geometric parameters of the nanostructure, a seven-channel sensor array was designed to exhibit distinct wavelengthdependent responses within the spectral band of 280-320 nm, covering the • OH and NO• emissions. Simulations showed that the proposed structure achieved high responsivities and high internal quantum efficiencies in the target band. On this basis, a convolutional neural network (CNN) was trained to perform single-peak spectral discrimination, achieving a classification accuracy of 91% with a 0.1 nm label spacing, with a mean peak-position error of 0.32 nm. A U-Net-based network was further employed for dual-peak spectral reconstruction, yielding an average peak-intensity deviation ratio of 7.4% and high-fidelity recovery of the underlying spectra. These results demonstrate a compact UV flame sensing approach that combines Al2O3-TiO2 nanostructures with machine-learning-based spectral inference to enable high-resolution spectral discrimination for practical flame monitoring.

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.

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.