RESUMO
The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long short-term memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO2, NO2, and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO2 gas, which is a noteworthy achievement. The determination coefficient (R2) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.
RESUMO
Herein, ultraviolet B (UVB) persistent luminescence phosphors containing SrAl12O19: Ce3+, Sc3+ nanoparticles were reported. Thermoluminescence (TL) spectrum analysis reveals that the shallow trap induced by Sc3+ co-doping plays an important role in photoluminescence persistent luminescence (PersL) development, while the deep trap dominates the generation of optical stimulated luminescence (OSL). Owing the appearance of deep trap, the OSL is observed under light (700 nm - 900 nm) excitation. UVB luminescence exerts good bactericidal effects on pathogenic bacteria involved in the process of food spoilage. Thus, the smart window with SrAl12O19: Ce3+, Sc3+/PDMS produces UVB PersL to efficiently inactivate Escherichia coli and Staphylococcus aureus. In addition, the presence of the smart window delays the critical point of pork decay, and greatly reduces the time of pork spoilage. It maximizes the convenience of eradicating bacteria and preserving food, thus offering a fresh perspective on the use of UV light for food sterilization and preservation.
RESUMO
The fabrication of van der Waals (vdWs) heterostructures mainly extends to two-dimensional (2D) materials. Nevertheless, the current processes for obtaining high-quality 2D films are mainly exfoliated from their bulk counterparts or by high-temperature chemical vapor deposition (CVD), which limits industrial production and is often accompanied by defects. Herein, we first fabricated the type-II p-PdSe2/n-InSe vdWs heterostructure using the ultra-high vacuum laser molecular beam epitaxy (LMBE) technique combined with the vertical 2D stacking strategy, which is reproducible and suitable for high-volume manufacturing. This work found that the introduction of 365 nm UV light illumination can significantly improve the electrical transport properties and NO2 sensing performance of the PdSe2/InSe heterojunction-based device at room temperature (RT). The detailed studies confirm that the sensor based on the PdSe2/InSe heterojunction delivers the comparable sensitivity (Ra/Rg = â¼2.6 at 10 ppm), a low limit of detection of 52 ppb, and excellent selectivity for NO2 gas under UV light illumination, indicating great potential for NO2 detection. Notably, the sensor possesses fast response and full recovery properties (275/1078 s) compared to the results in the dark. Furthermore, the mechanism of enhanced gas sensitivity was proposed based on the energy band alignment of the PdSe2/InSe heterojunction with the assistance of investigating the surface potential variations. This work may pave the way for the development of high-performance, room-temperature gas sensors based on 2D vdWs heterostructures through the LMBE technique.
RESUMO
In this paper, we study time-varying graphical models based on data measured over a temporal grid. Such models are motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance the study of how stock prices interact with each other and how such interactions change over time. We propose a new model, LOcal Group Graphical Lasso Estimation (loggle), under the assumption that the graph topology changes gradually over time. Specifically, loggle uses a novel local group-lasso type penalty to efficiently incorporate information from neighboring time points and to impose structural smoothness of the graphs. We implement an ADMM based algorithm to fit the loggle model. This algorithm utilizes blockwise fast computation and pseudo-likelihood approximation to improve computational efficiency. An R package loggle has also been developed and is available on https://cran.r-project.org/. We evaluate the performance of loggle by simulation experiments. We also apply loggle to S&P 500 stock price data and demonstrate that loggle is able to reveal the interacting relationships among stock prices and among industrial sectors in a time period that covers the recent global financial crisis. The supplemental materials for this paper are also available online.
