Gas sensors are vital tools in areas such as environmental monitoring, industrial safety, and personal healthcare. Among various sensing materials, semiconductor metal oxides (SMOs) are widely studied owing to their high sensitivity, good stability, and notable catalytic activity. To overcome inherent drawbacks of pure SMOs—such as high operating temperatures, limited selectivity, sluggish response/recovery behavior, and inadequate long-term stability—functionalization with noble metals has emerged as a powerful modification strategy. This review systematically outlines the primary mechanisms through which noble metals enhance gas sensing performance and analyzes the key factors influencing sensor behavior. Finally, we discuss the current challenges and future directions in the development of noble metal-modified SMO gas sensors.

In this review, mass-sensing humidity sensors are comprehensively summarized from the aspects of humidity sensing mechanisms, humidity sensing materials, and emerging applications. Images reproduced with permission, see Figure 1 for details.

In recent years, rapid industrial development has led to the emission of diverse gaseous pollutants into the atmosphere. To detect and monitor these pollutants, gas sensors have become a critical technology. Researchers have developed numerous gas-sensitive materials, among which MXenes—a novel class of two-dimensional materials—have garnered significant attention. Owing to their excellent electron transport properties, abundant surface functional groups, and large specific surface area, MXenes find wide applications in catalysis, sensing, electromagnetic shielding, water treatment, and beyond. However, despite these outstanding properties, MXenes’ susceptibility to environmental degradation has hindered their broader development and application as long-term stable gas-sensitive materials. While recent studies have investigated degradation mechanisms and explored various stability enhancement strategies, comprehensive reviews specifically focusing on stability improvements for gas-sensing applications remain scarce. This review first examines the current research on MXene oxidation processes in different environments. Subsequently, it systematically summarizes existing strategies to enhance MXene’s long-term stability and its implementation in gas sensing, including optimization of preparation methods, surface protection and modification, composite construction, and other approaches. Finally, the review concludes by summarizing current progress and outlining future perspectives.

Owing to high electrical conductivity, layered structure, and abundant surface functional groups, transition metal carbides/nitrides (MXenes) have received enormous interest in the field of gas sensors at room temperature. In this work, we synthesize a heterostructured nanocomposite with indium oxide (In₂O₃) decorated on titanium carbide (Ti₃C₂T_x) nanosheets by electrostatic self-assembly and develop it for high-selectivity NO₂ gas sensing at room temperature. Self-assembly formation of multiple heterojunctions in the In₂O₃/Ti₃C₂T_x composite provide abundant NO₂ gas adsorption sites and high electron transfer activity, which is conducive to improving the gas-sensing response of the In₂O₃/Ti₃C₂T_x gas sensor. Assisted by rich adsorption sites and hetero interface, the as-fabricated In₂O₃/Ti₃C₂T_x gas sensor exhibits the highest response to NO₂ among various interference gases. Meanwhile, a detection limit of 0.3 ppm, and response/recovery time (197.62/93.84 s) is displayed at room temperature. Finally, a NO₂ sensing mechanism of In₂O₃/Ti₃C₂T_x gas sensor is constructed based on PN heterojunction enhancement and molecular adsorption. This work not only expands the gas-sensing application of MXenes, but also demonstrates an avenue for the rational design and construction of NO₂-sensing materials.

During electron beam cold hearth melting (EBCHM) of Ti-6wt%Al-4wt%V titanium alloy, aluminum volatilization causes compositional segregation in the ingot, significantly degrading material performance. Traditional methods (e.g., the Langmuir equation) struggle to accurately predict aluminum diffusion and compensation behaviors, while computational fluid dynamics (CFD), although capable of resolving multiphysics fields in the molten pool, suffer from high computational costs and insufficient research on segregation control. To address these issues, this study proposes a CFD-machine learning (backpropagation neural network, CFD-ML(BP)) approach to achieve precise prediction and optimization of aluminum segregation. First, CFD simulations are performed to obtain the molten pool’s temperature field, flow field, and aluminum concentration distribution, with model reliability validated experimentally. Subsequently, a BP neural network is trained using large-scale CFD datasets to establish an aluminum concentration prediction model, capturing the nonlinear relationships between process parameters (e.g., casting speed, temperature) and compositional segregation. Finally, optimization algorithms are applied to determine optimal process parameters, which are validated via CFD multiphysics coupling simulations. The results demonstrate that this method predicts the average aluminum concentration in the ingot with an error of ≤3%, significantly reducing computational costs. It also elucidates the kinetic mechanisms of aluminum volatilization and diffusion, revealing that non-monotonic segregation trends arise from the dynamic balance of volatilization, diffusion, convection, and solidification. Moreover, the most uniform aluminum distribution (average 6.8 wt.%, R² = 0.002) is achieved in a double-overflow mold at a casting speed of 18 mm/min and a temperature of 2168 K.