Hydrogen sulfide (H 2 S) gas is one of the highly toxic, lethal, and corrosive gases in the environment that can cause a series of inimical effects after human exposure. Therefore, developing gas-sensitive nanomaterials that could effectively detect the ultralow concentration of H 2 S is of utmost significance. In this study, a perovskite quantum dot (QD) structure is synthesized through a simple solution method in which CsPbBr 3 QDs are surface capped with tributyltin oxide (TBTO). It was employed as a chemiresistor to H 2 S, exhibiting exceptional repeatability, superior selectivity, and an ultralow limit of detection of 250 ppb with a response sensitivity of 0.58 at room temperature, which is the highest value for H 2 S sensors based on metal-halide perovskite structure. The response/recovery time is 278/730 s at a 100 ppm concentration of H 2 S. Furthermore, we proposed the sensing mechanism of dispersion-sensing effect of the metallo-organic ligand by theory calculation to explain the excellent response performance. The chemical adsorption sensitivity of the related chemical groups is amended when the metallo-organic molecular is engrossed on the surface of CsPbBr 3 QDs and becomes an isolated ligand. Thus, the H 2 S molecule is first absorbed by the isolated TBTO molecule, which prevents H 2 S from penetrating the perovskite CsPbBr 3 , forming a Pb−S bond, and damaging the sensing performance. Meanwhile, the H 2 S adsorption also affects the charge distribution of the internal CsPbBr 3 QDs through a metalloorganic TBTO molecule, enhancing the sensitivity and response to H 2 S. Overall, the nanomaterial's structure and gas response mechanism could be extended to detecting other gases.
Objectives: This study aims to develop an engineering solution to breath tests using an electronic nose (e-nose), and evaluate its diagnosis accuracy for silicosis. Influencing factors of this technique were explored. Methods: 398 non-silicosis miners and 221 silicosis miners were enrolled in this cross-sectional study. Exhaled breath was analyzed by an array of 16 organic nanofiber sensors along with a customized sample processing system. Principal Component Analysis was used to visualize the breath data, and classifiers were trained by two improved cost-sensitive ensemble algorithms (RF and XGBoost) and two classical algorithms (KNN and SVM). All subjects were included to train the screening model, and an early detection model was run with silicosis cases in stage I. Both 5-fold cross-validation and external validation were adopted. Difference in classifiers caused by algorithms and subjects was quantified using a two-factor analysis of variance. The association between personal smoking habits and classification was investigated by the chi-square test. Results: Classifiers of ensemble learning performed well in both screening and early detection model, with an accuracy range of 0.817 to 0.987. Classical classifiers showed relatively worse performance. Besides, the ensemble algorithm type and silicosis cases inclusion had no significant effect on classification (p>0.05). There was no connection between personal smoking habits and classification accuracy. Conclusion: Breath tests based on an e-nose consisted of 16x sensor array performed well in silicosis screening and early detection. Raw data input showed a more significant effect on classification compared with the algorithm. Personal smoking habits had little impact on models, supporting the applicability of models in large-scale silicosis screening. The e-nose technique and the breath analysis methods reported are expected to provide a quick and accurate screening for silicosis, and extensible for other diseases.
The humidity sensors based on metal halide perovskite have achieved outstanding improvement. However, there are few reports of perovskite nanocrystals for humidity sensors. Herein, CsPdBr3 nanocrystals with high surface‐to‐volume ratio are synthesized by the in situ spin‐coating method. Subsequently, the humidity sensor based on CsPdBr3 nanocrystals is prepared and the response mechanism is demonstrated to originate from the formation of the Pd––O bond on the surface by performing density functional theory calculation. The humidity sensor shows a fast response/recovery time of 0.68/2.43 s in a relative humidity range from 88.9%RH to 4.7%RH; the fast response/recovery properties are used to monitor human respiratory and noncontact switch. Moreover, the hydromechanical characteristics of unsaturated soil are obtained by noncontact testing with the humidity sensor for the first time in geological engineering, and the strong correlation between relative humidity and hydromechanical characteristics is revealed by mathematical derivation, which lays the theoretical foundation for the noncontact real‐time sensing of hydromechanical characteristics of unsaturated soil. These findings not only open the door to the development of perovskite nanocrystals materials for humidity measurement, but also offer a new method for nondestructive and real‐time test of the mechanical properties of soil in geological engineering.
Methanol is a respiratory biomarker for pulmonary diseases, including COVID-19, and is a common chemical that may harm people if they are accidentally exposed to it. It is significant to effectively identify methanol in complex environments, yet few sensors can do so. In this work, the strategy of coating perovskites with metal oxides is proposed to synthesize core–shell CsPbBr3@ZnO nanocrystals. The CsPbBr3@ZnO sensor displays a response/recovery time of 3.27/3.11 s to 10 ppm methanol at room temperature, with a detection limit of 1 ppm. Using machine learning algorithms, the sensor can effectively identify methanol from an unknown gas mixture with 94% accuracy. Meanwhile, density functional theory is used to reveal the formation process of the core–shell structure and the target gas identification mechanism. The strong adsorption between CsPbBr3 and the ligand zinc acetylacetonate lays the foundation for the formation of the core–shell structure. The crystal structure, density of states, and band structure were influenced by different gases, which results in different response/recovery behaviors and makes it possible to identify methanol from mixed environments. Furthermore, due to the formation of type II band alignment, the gas response performance of the sensor is further improved under UV light irradiation.
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