Gilson Sara E, Andrews Hunter B, Sadergaski Luke R, Parkison Adam J
Radioisotope Science and Technology Division, Oak Ridge National Laboratory, 1 Bethel Valley Rd., Oak Ridge, Tennessee 37831, United States.
ACS Omega. 2024 Oct 16;9(43):43547-43556. doi: 10.1021/acsomega.4c05464. eCollection 2024 Oct 29.
In situ optical spectroscopy, spectropotentiometry, and multivariate analysis were applied to the Np(IV) nitrate system to better understand speciation and quantify HNO concentration. Thin-layer spectropotentiometry, or spectroelectrochemistry, was leveraged to isolate and stabilize Np(IV) without compromising the solution conditions and generate representative Vis-NIR absorption spectra from 0.5 to 10 M HNO and benchmark the corresponding Np(IV) molar absorptivity coefficients. Spectra were described with principal component analysis (PCA) to identify the purest Np(IV) absorbance spectra among other oxidation states [e.g., Np(V/VI)] at each acid concentration and then to identify the primary sources of variance within each Np(IV) spectrum with respect to Np(IV) nitrate complexes. Then, partial least-squares regression (PLSR) and support vector regression (SVR) models were built to predict HNO concentration from the Np(IV) spectral data. The nonlinear SVR model outperformed the linear PLSR model for the HNO concentration predictions. Finally, the inclusion of spectra collected in edge and center point HNO concentrations in the calibration set was determined to be crucial for producing models with strong predictive capabilities. The multivariate approach used in this study makes it possible to quantify HNO concentration solely based on Np(IV) absorption spectra, which is essential to quantifying processing streams in various online monitoring applications.
原位光谱学、光谱电位分析法和多变量分析被应用于硝酸钚（IV）体系，以更好地理解其形态并量化硝酸浓度。利用薄层光谱电位分析法，即光谱电化学，在不影响溶液条件的情况下分离并稳定钚（IV），并在0.5至10 M硝酸浓度范围内生成代表性的可见-近红外吸收光谱，同时对相应的钚（IV）摩尔吸光系数进行基准测试。通过主成分分析（PCA）对光谱进行描述，以识别在每种酸浓度下其他氧化态[如钚（V/VI）]中最纯净的钚（IV）吸收光谱，然后识别每个钚（IV）光谱中关于硝酸钚（IV）络合物的主要变异来源。然后，建立偏最小二乘回归（PLSR）和支持向量回归（SVR）模型，从钚（IV）光谱数据预测硝酸浓度。在硝酸浓度预测方面，非线性SVR模型优于线性PLSR模型。最后，确定在校准集中纳入在边缘和中心点硝酸浓度下收集的光谱对于生成具有强大预测能力的模型至关重要。本研究中使用的多变量方法使得仅基于钚（IV）吸收光谱来量化硝酸浓度成为可能，这对于在各种在线监测应用中量化工艺流至关重要。
