Manipulation of the photoluminescence of lead halide perovskite quantum dots with mechanically reconfigurable 3D photonic crystals.

Reconfigurable 3D photonic crystals (3DPCs) are promising for dynamic emission devices, owing to their unique properties. Here, we integrated the perovskite quantum dot film together with 3D reconfigurable photonic crystals (PCs) to form quantum dot/photonic crystal heterostructures and investigated their interactions at their interfaces. The photonic bandgaps of the presented 3DPCs can be dynamically tuned by heating and applying external mechanical forces, and they can be stably fixed in the intermediate states. By tuning the photonic bandgaps of the 3DPCs, a maximum photoluminescence (PL) enhancement of 11 times that of CsPb(I/Br)3 quantum dots has been achieved. It has been revealed that the combined effects of increased density of photon states and the greatly confined and enhanced electric field on the upper surface of 3DPCs contribute to the enhanced Purcell effect, which in turn leads to the enhanced photoluminescence.

Quantitative analysis of blended corn-olive oil based on Raman spectroscopy and one-dimensional convolutional neural network.

Blended vegetable oil is a vital product in the vegetable oil market, and quantifying high-value vegetable oil is of great significance to protect the rights and interests of consumers. In this study, we established a one-dimensional convolutional neural network (1D CNN) quantitative identification model based on Raman spectra to identify the amount of olive oil in a corn-olive oil blend. The results show that the 1D CNN model based on 315 extended average Raman spectra can quantitatively identify the content of olive oil, with R2p and RMSEP values of 0.9908 and 0.7183 respectively. Compared with partial least squares regression (PLSR) and support vector regression (SVR), although the index is not optimal, it provides a new analytical method for the quantitative identification of vegetable oil.

Identification and quantification of adulterated honey by Raman spectroscopy combined with convolutional neural network and chemometrics.

In this study, Raman spectroscopy combined with convolutional neural network (CNN) and chemometrics was used to achieve the identification and quantification of honey samples adulterated with high fructose corn syrup, rice syrup, maltose syrup and blended syrup, respectively. The shallow CNNs utilized to analyze honey mixed with single-variety syrup classified samples into four categories by the adulteration concentration with more than 97% accuracy, and the general CNN model for simultaneously detecting honey adulterated with any type of syrup obtained an accuracy of 94.79%. The established CNNs had the best performance compared with several chemometric classification algorithms. In addition, partial least square regression (PLS) successfully predicted the purity of honey mixed with single syrup, while coefficients of determination and root mean square errors of prediction were greater than 0.98 and less than 3.50, respectively. Therefore, the proposed methods based on Raman spectra have important practical significance for food safety and quality control of honey products.