The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herei...The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herein,a fast,highly sensitive,and pollution-free approach is proposed,which combines ultraviolet(UV)absorption spectroscopy with Bayesian optimized least squares support vector machine(LSSVM)for detecting TN content in water.Water samples collected from sampling points near the Yangtze River basin in Chongqing of China were analyzed using national standard methods to measure TN content as reference values.The prediction of TN content in water was achieved by integrating the UV absorption spectra of water samples with LSSVM.To make the model quickly and accurately select the optimal parameters to improve the accuracy of the prediction model,the Bayesian optimization(BO)algorithm was used to optimize the parameters of the LSSVM.Results show that the prediction model performs well in predicting TN concentration,with a high coefficient of prediction determination(R^(2)=0.9413)and a low root mean square error of prediction(RMSE=0.0779 mg/L).Comparative analysis with previous studies indicates that the model used in this paper achieves lower prediction errors and superior predictive performance.展开更多
An experimental setup of laser-induced graphite plasma was built and the spectral characteristics and properties of graphite plasma were studied. From the temporal behavior of graphite plasma, the duration of CN parti...An experimental setup of laser-induced graphite plasma was built and the spectral characteristics and properties of graphite plasma were studied. From the temporal behavior of graphite plasma, the duration of CN partials （B2 ∑＋ →-X2 ∑＋） emission was two times longer than that of atomic carbon, and all intensities reached the maximum during the early stage from 0.2 μs to 0.8 μs. The electron temperature decreased from 11807 K to 8755 K, the vibration temperature decreased from 8973 K to 6472 K, and the rotational temperature decreased from 7288 K to 4491 K with the delay time, respectively. The effect of the laser energy was also studied, and it was found that the thresholds and spectral characteristics of CN molecular and C atomic spectroscopy presented great differences. At lower laser energies, the electron excited temperature, the electron density, the vibrational temperature and rotational temperature of CN partials increased rapidly. At higher laser energies, the increasing of electron excited temperature and electron density slow down, and the vibrational temperature and rotational temperature even trend to saturation due to plasma shielding and dissociation of CN molecules. The relationship among the three kinds of temperatures was Telec〉Tvib〉Trot at the same time. The electron density of the graphite plasma was in the order of 1017 cm-3 and 1018 cm-3.展开更多
为提高激光诱导击穿光谱(LIBS)技术在土壤重金属元素中的检测灵敏度和准确性,提出了基于螯合树脂富集结合空间约束的LIBS技术,实现了土壤中Cu元素的高灵敏度分析方法。实验优化了富集时间、激光能量和采样时间等参数对Cu I 324.75 nm和C...为提高激光诱导击穿光谱(LIBS)技术在土壤重金属元素中的检测灵敏度和准确性,提出了基于螯合树脂富集结合空间约束的LIBS技术,实现了土壤中Cu元素的高灵敏度分析方法。实验优化了富集时间、激光能量和采样时间等参数对Cu I 324.75 nm和Cu I 327.39 nm谱线的影响,建立了基于Cu元素特征谱线强度与含量线性关系的定标曲线。结果表明:树脂富集后的Cu元素谱线校准模型的决定系数(R^(2))为0.977和0.981,其检出限为1.368 mg/kg和1.062 mg/kg;增加空间约束后,Cu元素谱线校准模型的决定系数提升至0.986和0.986,其检出限降低为0.807 mg/kg和0.617 mg/kg,且其定标曲线对未知样品元素的预测含量(质量分数)达到93.40%到104.58%,验证了其在实际应用中的可靠性和准确性。展开更多
采用基底辅助激光诱导击穿光谱技术,以标准油中的Mg、Ti、Ni与Cr为目标元素进行定量分析。选定Mg II 279.55 nm、Ti I 334.94 nm、Ni I 352.45 nm与Cr I 425.44 nm为目标元素的定量分析谱线进行分析。考察样品预处理静置时间、样品油膜...采用基底辅助激光诱导击穿光谱技术,以标准油中的Mg、Ti、Ni与Cr为目标元素进行定量分析。选定Mg II 279.55 nm、Ti I 334.94 nm、Ni I 352.45 nm与Cr I 425.44 nm为目标元素的定量分析谱线进行分析。考察样品预处理静置时间、样品油膜平均厚度、探测延时和激光脉冲能量对Mg、Ti、Ni与Cr元素光谱信号强度与信背比的影响。在最优的实验条件下,利用6个标准油样品建立了标准曲线定标模型,得出Mg、Ti、Ni与Cr的检出限分别为3.10,8.17,18.79,6.10μg·g^-1。基于定标曲线,预测了另外5个标准油样品中Mg、Ti、Ni与Cr的质量比,相对误差分别为7.43%、8.91%、13.66%与10.40%。展开更多
基金supported by the National Natural Science Foundation of China(Nos.32171627 and 62105252)the Science and Technology Research Program of Chongqing Municipal Education Commission(No.KJZD-M202200602)the Hangzhou Science and Technology Development Project(No.202204T04).
文摘The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herein,a fast,highly sensitive,and pollution-free approach is proposed,which combines ultraviolet(UV)absorption spectroscopy with Bayesian optimized least squares support vector machine(LSSVM)for detecting TN content in water.Water samples collected from sampling points near the Yangtze River basin in Chongqing of China were analyzed using national standard methods to measure TN content as reference values.The prediction of TN content in water was achieved by integrating the UV absorption spectra of water samples with LSSVM.To make the model quickly and accurately select the optimal parameters to improve the accuracy of the prediction model,the Bayesian optimization(BO)algorithm was used to optimize the parameters of the LSSVM.Results show that the prediction model performs well in predicting TN concentration,with a high coefficient of prediction determination(R^(2)=0.9413)and a low root mean square error of prediction(RMSE=0.0779 mg/L).Comparative analysis with previous studies indicates that the model used in this paper achieves lower prediction errors and superior predictive performance.
基金supported by National Natural Science Foundation of China(No.61205149)Scientific Research Foundation for the Returned Overseas Chinese Scholars of State Education Ministry,Science Research Funds of Chongqing Municipal Education Commission(KJ1500436)+2 种基金Scientific and Technological Talents Training Project of Chongqing(CSTC2013kjrc-qnrc40002)Key Project of Foundation and Advanced Technology Research Project of Chongqing(CSTC2015jcyj B0358)Visiting Scholarship of State Key Laboratory of Power Transmission Equipment & System Security and New Technology(2007DA10512714409)
文摘An experimental setup of laser-induced graphite plasma was built and the spectral characteristics and properties of graphite plasma were studied. From the temporal behavior of graphite plasma, the duration of CN partials （B2 ∑＋ →-X2 ∑＋） emission was two times longer than that of atomic carbon, and all intensities reached the maximum during the early stage from 0.2 μs to 0.8 μs. The electron temperature decreased from 11807 K to 8755 K, the vibration temperature decreased from 8973 K to 6472 K, and the rotational temperature decreased from 7288 K to 4491 K with the delay time, respectively. The effect of the laser energy was also studied, and it was found that the thresholds and spectral characteristics of CN molecular and C atomic spectroscopy presented great differences. At lower laser energies, the electron excited temperature, the electron density, the vibrational temperature and rotational temperature of CN partials increased rapidly. At higher laser energies, the increasing of electron excited temperature and electron density slow down, and the vibrational temperature and rotational temperature even trend to saturation due to plasma shielding and dissociation of CN molecules. The relationship among the three kinds of temperatures was Telec〉Tvib〉Trot at the same time. The electron density of the graphite plasma was in the order of 1017 cm-3 and 1018 cm-3.
文摘采用基底辅助激光诱导击穿光谱技术,以标准油中的Mg、Ti、Ni与Cr为目标元素进行定量分析。选定Mg II 279.55 nm、Ti I 334.94 nm、Ni I 352.45 nm与Cr I 425.44 nm为目标元素的定量分析谱线进行分析。考察样品预处理静置时间、样品油膜平均厚度、探测延时和激光脉冲能量对Mg、Ti、Ni与Cr元素光谱信号强度与信背比的影响。在最优的实验条件下,利用6个标准油样品建立了标准曲线定标模型,得出Mg、Ti、Ni与Cr的检出限分别为3.10,8.17,18.79,6.10μg·g^-1。基于定标曲线,预测了另外5个标准油样品中Mg、Ti、Ni与Cr的质量比,相对误差分别为7.43%、8.91%、13.66%与10.40%。
