Few-layered graphene oxides as superior adsorbents for the removal of Pb(II) ions from aqueous solutions

Wenbao Jia,Songsheng Lu 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.7

Few-layered graphene oxides (GOs) were successfully synthesized from graphite using Hummers’ method. The synthesized GOs were characterized in detail by SEM, AFM, XRD, and FTIR spectroscopy. The prepared GOswere used as adsorbents to preconcentrate Pb(II) ions from large volumes of aqueous solutions. The effects of pH, ionicstrength and temperature on the removal of Pb(II) ions from solution to GOs were investigated. The sorption of Pb(II)on GOs was dependent on pH values and independent of ionic strength, which suggested that Pb(II) sorption on GOswas mainly dominated by strong inner-sphere surface complexation. The maximum adsorption capacities (Csmax) ofPb(II) on GOs were calculated to be 344 mg/g at 293 K, 487 mg/g at 308 K, and 758mg/g at 333 K, respectively. TheCsmax values are the highest sorption capacities of today’s materials for the sorption of Pb(II) ions from aqueous solutions. The thermodynamic parameters were calculated from the temperature-dependent sorption isotherms, and the resultsindicated that Pb(II) sorption on GOs was spontaneous and endothermic. The results suggested that the GOs were promisingmaterials for the preconcentration of Pb(II) and other kinds of heavy metal ions from aqueous solutions in environmentalpollution cleanup in real work.

Research on the optimization method for PGNAA system design based on Signal-to-Noise Ratio evaluation

JiaTong Li,WenBao Jia,DaQian Hei,Zeen Yao,Can Cheng 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.6

In this research, for improving the measurement performance of Prompt Gamma-ray Neutron ActivationAnalysis (PGNAA) set-up, a new optimization method for set-up design was proposed and investigated. At first, the calculation method for Signal-to-Noise Ratio (SNR) was proposed. Since the SNR could becalculated and quantified accurately, the SNR was chosen as the evaluation parameter in the newoptimization method. For discussing the feasibility of the SNR optimization method, two kinds of PGNAAset-ups were designed in the MCNP code, based on the SNR optimization method and the previous signaloptimization method, respectively. Meanwhile, the single element spectra analysis method was proposed, and the analysis effect of single element spectra as well as element sensitivity were used forcomparing the measurement performance. Since the simulation results showed the better measurementperformance of set-up designed by SNR optimization method, the experimental set-ups were built forthe further testing, finally demonstrating the feasibility of the SNR optimization method for PGNAA setup design

A design of transmission-type multi-target X-ray tube based on electric field modulation

Zhao, Lei,Jia, Wenbao,Jin, Limin,Shan, Qing,Cheng, Can,Zhu, Hongkui,Hei, Daqian Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.9

Multi-target X-ray tube is a new type X-ray source, and can be applied in many fields such as sensitive X-ray fluorescence analysis and medical imaging. In this work, we report an electric field modulation multi-target X-ray tube, which contains four targets (Cr, Ni, Au, Mo) coated on a Beryllium (Be) window. A four-valve electric field deflector was developed to deflect the electron beam to bombard the corresponding targets. Particle dynamics analysis software was employed to simulate the particle tracking of electron beam. The results show that the 30 keV electron beam could get a 6.7 mm displacement on the target plane by 10⁵ V/m electric field. The focus areas are about 2 mm × 5 mm and 4 mm × 2.5 mm after deflection in two directions. Thermal behavior calculated by ANSYS shows that the designed target assembly could withstand a 10 W continuous power. The optimum target thicknesses and emission spectra were obtained by Geant4 when the thickness of Be window was 300 mm and the electron beam incident angle was 0.141 rad. The results indicate that this multi-target X-ray tube could provide different X-ray sources effectively.

Optimization of image reconstruction method for dual-particle time-encode imager through adaptive response correction

Zhao Dong,Jia Wenbao,Hei Daqian,Cheng Can,Cheng Wei,Liang Xuwen,Li Ji 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.5

Time-encoded imagers (TEI) are important class of instruments to search for potential radioactive sources to prevent illicit transportation and trafficking of nuclear materials and other radioactive sources. The energy of the radiation cannot be known in advance due to the type and shielding of source is unknown in practice. However, the response function of the time-encoded imagers is related to the energy of neutrons or gamma-rays. An improved image reconstruction method based on MLEM was proposed to correct for the energy induced response difference. In this method, the count vector versus time was first smoothed. Then, the preset response function was adaptively corrected according to the measured counts. Finally, the smoothed count vector and corrected response were used in MLEM to reconstruct the source distribution. A one-dimensional dual-particle time-encode imager was developed and used to verify the improved method through imaging an AmeBe neutron source. The improvement of this method was demonstrated by the image reconstruction results. For gamma-ray and neutron images, the angular resolution improved by 17.2% and 7.0%; the contrast-to-noise ratio improved by 58.7% and 14.9%; the signal-to-noise ratio improved by 36.3% and 11.7%, respectively.

Identification of Pb–Zn ore under the condition of low count rate detection of slim hole based on PGNAA technology

Huang Haolong,Cai Pingkun,Jia Wenbao,Zhang Yan 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.5

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F1 score for both types of ores is greater than 0.9.

Online analysis of iron ore slurry using PGNAA technology with artificial neural network

Huang Haolong,Cai Pingkun,Liang Xuwen,Jia Wenbao 한국원자력학회 2024 Nuclear Engineering and Technology Vol.56 No.7

Real-time analysis of metallic mineral grade and slurry concentration is significant for improving flotation efficiency and product quality. This study proposes an online detection method of ore slurry combining the Prompt Gamma Neutron Activation Analysis (PGNAA) technology and artificial neural network (ANN), which can provide mineral information rapidly and accurately. Firstly, a PGNAA analyzer based on a D-T neutron generator and a BGO detector was used to obtain a gamma-ray spectrum dataset of ore slurry samples, which was used to construct and optimize the ANN model for adaptive analysis. The evaluation metrics calculated by leave-one-out cross-validation indicated that, compared with the weighted library least squares (WLLS) approach, ANN obtained more precise and stable results, with mean absolute percentage errors of 4.66% and 2.80% for Fe grade and slurry concentration, respectively, and the highest average standard deviation of only 0.0119. Meanwhile, the analytical errors of the samples most affected by matrix effects was reduced to 0.61 times and 0.56 times of the WLLS method, respectively

Prevalence and Molecular Characterization of Echinococcus granulosus sensu stricto in Northern Xinjiang, China

Baoping Guo,Zhuangzhi Zhang,Xueting Zheng,Yongzhong Guo,Li Zhao,Ren Cai,Bingjie Wang,Mei Yang,Xi Shou,Wenbao Zhang,Bin Jia 대한기생충학ㆍ열대의학회 2019 The Korean Journal of Parasitology Vol.57 No.2

Echinococcus granulosus is an important zoonotic parasite globally causing cystic echinococcosis (CE) in hu- mans and animals. In this study, prevalence of CE and variation of cox1 gene sequence were analyzed with isolates E. granulosus collected from different areas in northern Xinjiang, China. The survey showed that 3.5% of sheep and 4.1% of cattle were infected with CE. Fragment of cox1 was amplified from all the positive sheep and cattle samples by PCR. In addition, 26 positive samples across the 4 areas were included. The isolates were all E. granulosus sensu stricto (s.s.) containing 15 haplotypes (Hap1-15), and clustered into 2 genotypes, G1 (90.1%, 91/101) and G3 (9.9%, 10/101). Hap1 was the most common haplotype (48.5%, 49/101). Hap9 were found in humans samples, indicating that sheep and cattle reservoir human CE. It is indicate that E. granulosus may impact on control of CE in livestock and humans in the region.