Although laser-induced breakdown spectroscopy(LIBS),as a fast on-line analysis technology,has great potential and competitiveness in the analysis of chemical composition and proximate analysis results of coal in therm...Although laser-induced breakdown spectroscopy(LIBS),as a fast on-line analysis technology,has great potential and competitiveness in the analysis of chemical composition and proximate analysis results of coal in thermal power plants,the measurement repeatability of LIBS needs to be further improved due to the difficulty in controlling the stability of the generated plasmas at present.In this paper,we propose a novel x-ray fluorescence(XRF) assisted LIBS method for high repeatability analysis of coal quality,which not only inherits the ability of LIBS to directly analyze organic elements such as C and H in coal,but also uses XRF to make up for the lack of stability of LIBS in determining other inorganic ash-forming elements.With the combination of elemental lines in LIBS and XRF spectra,the principal component analysis and the partial least squares are used to establish the prediction model and perform multi-elemental and proximate analysis of coal.Quantitative analysis results show that the relative standard deviation(RSD) of C is 0.15%,the RSDs of other elements are less than 4%,and the standard deviations of calorific value,ash content,sulfur content and volatile matter are 0.11 MJ kg,0.17%,0.79% and 0.41%respectively,indicating that the method has good repeatability in determination of coal quality.This work is helpful to accelerate the development of LIBS in the field of rapid measurement of coal entering the power plant and on-line monitoring of coal entering the furnace.展开更多
A new method is proposed to prospect copper deposits with portable XRF analyzer. The method is based on the close relation between Cu and the chalcophile elements or some other elements in the geochemical anomalies of...A new method is proposed to prospect copper deposits with portable XRF analyzer. The method is based on the close relation between Cu and the chalcophile elements or some other elements in the geochemical anomalies of a Cu deposit. Applications of the technique in Northeast China are presented.展开更多
To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters o...To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse sequences.The trained model was validated using simulated pulses.The relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM models.As a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping algorithm.For slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping algorithm.The model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,respectively.Ten measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.展开更多
We report an approach to the rapid, one-step, preparation of a variety of wide-bandgap silicon carbide/graphene nanosheet(Si C/GNSs) composites by using a high-density helicon wave plasma(HWP) source. The microstructu...We report an approach to the rapid, one-step, preparation of a variety of wide-bandgap silicon carbide/graphene nanosheet(Si C/GNSs) composites by using a high-density helicon wave plasma(HWP) source. The microstructure and morphology of the Si C/GNSs are characterized by using scanning electron microscopy(SEM), Raman spectroscopy, x-ray diffraction(XRD), x-ray photoelectron spectroscopy(XPS), and fluorescence(PL). The nucleation mechanism and the growth model are discussed. The existence of Si C and graphene structure are confirmed by XRD and Raman spectra.The electron excitation temperature is calculated by the intensity ratio method of optical emission spectroscopy. The main peak in the PL test is observed at 420 nm, with a corresponding bandgap of 2.95 e V that indicates the potential for broad application in blue light emission and ultraviolet light emission, field electron emission, and display devices.展开更多
基金supported by National Energy R&D Center of Petroleum Refining Technology of China(RIPP,SINOPEC)National Key Research and Development Program of China(No.2017YFA0304203)+5 种基金Changjiang Scholars and Innovative Research Team in University of Ministry of Education of China(No.IRT_17R70)National Natural Science Foundation of China(Nos.61975103,61875108,61775125 and 11434007)Industrial Application Innovation Project(No.627010407)Scientific and Technological Innovation Project of Shanxi Gemeng US-China Clean Energy R&D Center Co.,Ltd111 Project(D18001)Fund for Shanxi‘1331KSC’。
文摘Although laser-induced breakdown spectroscopy(LIBS),as a fast on-line analysis technology,has great potential and competitiveness in the analysis of chemical composition and proximate analysis results of coal in thermal power plants,the measurement repeatability of LIBS needs to be further improved due to the difficulty in controlling the stability of the generated plasmas at present.In this paper,we propose a novel x-ray fluorescence(XRF) assisted LIBS method for high repeatability analysis of coal quality,which not only inherits the ability of LIBS to directly analyze organic elements such as C and H in coal,but also uses XRF to make up for the lack of stability of LIBS in determining other inorganic ash-forming elements.With the combination of elemental lines in LIBS and XRF spectra,the principal component analysis and the partial least squares are used to establish the prediction model and perform multi-elemental and proximate analysis of coal.Quantitative analysis results show that the relative standard deviation(RSD) of C is 0.15%,the RSDs of other elements are less than 4%,and the standard deviations of calorific value,ash content,sulfur content and volatile matter are 0.11 MJ kg,0.17%,0.79% and 0.41%respectively,indicating that the method has good repeatability in determination of coal quality.This work is helpful to accelerate the development of LIBS in the field of rapid measurement of coal entering the power plant and on-line monitoring of coal entering the furnace.
文摘A new method is proposed to prospect copper deposits with portable XRF analyzer. The method is based on the close relation between Cu and the chalcophile elements or some other elements in the geochemical anomalies of a Cu deposit. Applications of the technique in Northeast China are presented.
基金This work was supported by the Open Project of the Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology(No.NLK2022-05)Central Government Guidance Funds for Local Scientific and Technological Development,China(No.Guike ZY22096024)+3 种基金Sichuan Natural Science Youth Fund Project(No.2023NSFSC1366)Open Research Fund of the National Engineering Research Center for Agro-Ecological Big Data Analysis&Application,Anhui University(No.AE202209)Research Fund of Guangxi Key Lab of Multi-source Information Mining&Security(MIMS22-04)National Natural Science Youth Foundation of China(No.12305214).
文摘To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse sequences.The trained model was validated using simulated pulses.The relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM models.As a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping algorithm.For slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping algorithm.The model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,respectively.Ten measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.
基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)and Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX202649)。
文摘We report an approach to the rapid, one-step, preparation of a variety of wide-bandgap silicon carbide/graphene nanosheet(Si C/GNSs) composites by using a high-density helicon wave plasma(HWP) source. The microstructure and morphology of the Si C/GNSs are characterized by using scanning electron microscopy(SEM), Raman spectroscopy, x-ray diffraction(XRD), x-ray photoelectron spectroscopy(XPS), and fluorescence(PL). The nucleation mechanism and the growth model are discussed. The existence of Si C and graphene structure are confirmed by XRD and Raman spectra.The electron excitation temperature is calculated by the intensity ratio method of optical emission spectroscopy. The main peak in the PL test is observed at 420 nm, with a corresponding bandgap of 2.95 e V that indicates the potential for broad application in blue light emission and ultraviolet light emission, field electron emission, and display devices.
