{"title":"Online Measurement of Sodium Nitrite Based on Near-Infrared Spectroscopy","authors":"Xianzhe Xu, Yongshen Zhang, Mingmin Zhang, Dingming Li, Chen Zuo","doi":"10.3390/chemosensors12020022","DOIUrl":null,"url":null,"abstract":"In this study, a method was developed for the rapid online measurement of sodium nitrite solutions using near-infrared spectroscopy. A series of standard solutions of sodium nitrite at different concentrations were prepared, and the samples were measured in cuvettes and flow cells. Following the preprocessing of raw spectra and band selection, partial least squares were used to establish a prediction model, and the coefficient of determination (R2) of the validation set and the root mean square error of prediction (RMSEP) of the model were 0.9989 and 0.0338. The results demonstrate that the established model can meet the demands of online measurement and perform the rapid, nondestructive detection of sodium nitrite solutions, which provides some basis for the automated formulation of feedstock in spent fuel reprocessing.","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":"893 ","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/chemosensors12020022","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, a method was developed for the rapid online measurement of sodium nitrite solutions using near-infrared spectroscopy. A series of standard solutions of sodium nitrite at different concentrations were prepared, and the samples were measured in cuvettes and flow cells. Following the preprocessing of raw spectra and band selection, partial least squares were used to establish a prediction model, and the coefficient of determination (R2) of the validation set and the root mean square error of prediction (RMSEP) of the model were 0.9989 and 0.0338. The results demonstrate that the established model can meet the demands of online measurement and perform the rapid, nondestructive detection of sodium nitrite solutions, which provides some basis for the automated formulation of feedstock in spent fuel reprocessing.
期刊介绍：
ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric.
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