In recent years, substantial efforts have been devoted to exploring reduced graphene oxide/TiO (RGO/TiO) composite materials; however, there is still a paucity of reports on the construction of reduced graphene oxide/TiO with oxygen vacancies (RGO/TiO-OV) via a facile two-step wet chemistry approach. In this work, we show a proof-of-concept study follow RGO introduced into TiO with oxygen vacancies, the role of oxygen vacancies as active sites in reduced graphene oxide-modified TiO. The photocatalytic performance and related properties of blank-TiO, blank-TiO with oxygen vacancies (blank-TiO-OV), RGO/TiO, and RGO/TiO-OV were comparatively studied. It was found that due to the incorporation of RGO, RGO/TiO and RGO/TiO-OV exhibit a higher photocatalytic performance under simulated solar light irradiation than their counterparts without rGO. More importantly, it was found that blank-TiO has a higher photocatalytic activity than blank-TiO-OV under simulated solar light irradiation. However, RGO/TiO shows a lower photocatalytic activity than rGO/TiO-OV. By a series of combined techniques, we found that the introduction of a component, such as RGO, with the matched energy band to TiO could lead to the formation of a long-lived electron-transfer state, thus prolonging the lifetime of the photogenerated charge carriers. Furthermore, during the photocatalytic process, RGO could tune the role of oxygen vacancies in TiO from recombination centers to active sites. These findings are of great significance for the design of effective photocatalytic materials in the field of solar energy conversion.

Accurate identification of coal and gangue is an important prerequisite for the effective separation of coal and gangue. The application of imaging technology combined with image processing steps (like enhancement, feature extraction, etc.) and classifier is used to identify coal and gangue, which effectively avoids the shortcomings of traditional methods (radiation, pollution, etc.). However, ordinary image detection is greatly influenced by environmental factors such as light, dust and so on. Multispectral imaging technology, as a new generation of optical non-destructive testing technology, is less affected by illumination, so we propose a new solution for the recognition of coal and gangue by using multispectral imaging. Firstly, we respectively tested the classification performance of different image feature extraction methods under GS-SVM, GA-SVM, and PSO-SVM classifiers, and selected the best feature extraction method is LBP. And then, we compared the classification effects under different wavelengths and found that the ninth wavelength works best. That is, the difference in imaging between coal and gangue at 773.776 nm is greatest. Finally, the performance of the proposed model for the identification of coal and gangue was carried out. And the highest classification accuracy can be obtained by using GS-SVM as the classifier, at which point, C = 8, g = 0.17678. The results show that multispectral imaging technology can be used for the identification of coal and gangue, and the prediction accuracy of the model combined with LBP feature extraction and GS-SVM can reach 96.25% (77/80). The conclusions could provide reference evidence for the intelligent dry selection in coal preparation plants and underground coal mine. INDEX TERMS Coal-gangue identification, multispectral imaging, feature extraction, support vector machine.

LIF spectroscopy combined with 1D CNN can identify mine water inrush quickly and accurately without complicated pretreatment.

Laser-induced fluorescence (LIF) technology is an advanced optical detection method, which has the advantages of fast, high precision and nondestructive testing, and is widely used in many fields. The general pattern recognition method for fluorescence spectral classification is highly dependent on pretreatment and dimension reduction. Specific pretreatment and dimension reduction methods are required for specific substances. Deep learning, especially the convolutional neural network (CNN), has the advantage of low dependence on data preprocessing and dimensionality reduction process, which makes it perform well in spectral classification. However, learning a useful CNN model for classification depends crucially on the expertise of parameter tuning and some ad hoc tricks, which is not suitable for chemometrics researchers. This paper presents a novel chemometrics tool for fluorescence spectra, principal component analysis network (PCANet), and more specifically a PCANet model with the optimized hyper-parameters (only optimized once). A two-stage cascaded PCANet model is constructed based on the liquor dataset, and the hyper-parameters are optimized and determined, which can make PCANet recognition model with the highest accuracy. Comparing the CNN model with two convolutional layers, the PCANet model is less affected by the size of the input image and the number of samples in the training set. At the same time, the performance of the two models on the mine water dataset is analyzed, and PCANet has higher recognition accuracy. That is to say, the PCANet is more accurate than CNN in fluorescence spectral classification, and its ability to expand application is stronger than that of the CNN model. The successful application of PCANet model with the optimized hyper-parameters (only optimized once) in the liquor dataset and the mine water dataset has important reference significance for the classification of fluorescence spectra of other substances in the future. INDEX TERMS Laser-induced fluorescence, spectral classification, principal component analysis network, hyper-parameter. I. INTRODUCTION Laser-induced fluorescence (LIF) [1], [2] is a spectroscopic method in which an atom or molecule is excited to a higher energy level by the absorption of laser light followed by spontaneous emission of light [3]. It was first reported by Zare [4], [5] and coworkers in 1968. The technology has many advantages, such as fast, high precision, high sensitivity, and nondestructive testing, so it is widely used in many fields like medicine, environment, biology, chemical industry, as well as food science. The associate editor coordinating the review of this manuscript and approving it for publication was Sungroh Yoon.

Accurate identification of coal and gangue is very important for realizing efficient separation of coal and gangue and clean utilization of coal. Therefore, a method for identifying coal and gangue by using multispectral spectral information and a convolutional neural network (CNN) model is proposed. First, 200 pieces of coal and 200 pieces of gangue in the Huainan mining area were collected as the experimental materials. The multispectral information of coal and gangue was collected, and the average value of each wavelength position was calculated to obtain the spectral information of the whole band. Then, based on the one-dimensional CNN (1D-CNN), namely, 1D-CNN-A and 1D-CNN-B, and with the help of stochastic gradient descent (SGD), Adam, Adamax, and Nadam optimizers, the rectified linear unit (ReLU) function and its improved function were used as the activation function to compare the identification ability of the identification models with different network structures and parameters. The best 1D-CNN model for identification of coal and gangue based on multispectral spectral information is obtained as follows: a network model containing three one-dimensional convolution units B, PReLU is used as the activation function, and Nadam is used as an optimizer to achieve the best identification effect. At this time, 40 coal samples in the test set can be accurately identified, and only one gangue sample in 40 gangue samples is wrongly predicted as coal. Finally, compared with the traditional recognition strategy (different combinations of principal component analysis and support vector machine), the excellent performance of this method is further proven. The results show that the combination of multispectral imaging and 1D-CNN can achieve accurate identification of coal and gangue without considering how to select appropriate preprocessing and feature extraction methods, which is of great significance in promoting the development of separation technology for coal and gangue.