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Exposure to mustard gas can cause damage or death to human beings, depending on the concentration and duration. Thus, developing high-performance mustard-gas sensors is highly needed for early warning. Herein, ultrathin WO 3 nanosheet-supported Pd nanoparticles hybrids (WO 3 NSs/Pd) are prepared as chemiresistive sulfur mustard simulant (e.g., 2-chloroethyl ethyl sulfide, 2-CEES) gas sensors. As a result, the optimal WO 3 NSs/Pd-2 (2 wt % of Pd)-based sensor exhibits a high response of 8.5 and a rapid response/recovery time of 9/92 s toward 700 ppb 2-CEES at 260 °C. The detection limit could be as low as 15 ppb with a response of 1.4. Moreover, WO 3 NSs/Pd-2 shows good repeatability, 30-day operating stability, and good selectivity. In WO 3 NSs/Pd-2, ultrathin WO 3 NSs are rich in oxygen vacancies, offer more sites to adsorb oxygen species, and make their size close to or even within the thickness of the so-called electron depletion layer, thus inducing a large resistance change (response). Moreover, strong metal-support interactions (SMSIs) between WO 3 NSs and Pd nanoparticles enhance the catalytic redox reaction performance, thereby achieving a superior sensing performance toward 2-CEES. These findings in this work provide a new approach to optimize the sensing performance of a chemiresistive sensor by constructing SMSIs in ultrathin metal oxides.

The utilization of steel slag for CO 2 sequestration is an effective way to reduce carbon emissions. The reactivity of steel slag in CO 2 sequestration depends mainly on material and process parameters. However, there are many puzzles in regard to practical applications due to the different evaluations of process parameters and the lack of investigation of material parameters. In this study, 318 samples were collected to investigate the interactive influence of 12 factors on the carbonation reactivity of steel slag by machine learning with SHapley Additive exPlanations (SHAP). Multilayer perceptron (MLP), random forest, and support vector regression models were built to predict the slurry-phase CO 2 sequestration of steel slag. The MLP model performed well in terms of prediction ability and generalization with comprehensive interpretability. The SHAP results showed that the impact of the process parameters was greater than that of the material parameters. Interestingly, the iron ore phase of steel slag was revealed to have a positive effect on steel slag carbonation by SHAP analysis. Combined with previous literature, the carbonation mechanism of steel slag was proposed. Quantitative analysis based on SHAP indicated that steel slag had good carbonation reactivity when the mass fractions of "CaO + MgO", "SiO 2 + Al 2 O 3 ", "Fe 2 O 3 ", and "MnO" varied from 50-55%, 10-15%, 30-35%, and <5%, respectively.