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The anhydrite and gypsum are the main sulfate minerals during evaporation of seawater or lake.They record the information about relative hydrogeology and the composition of mother liquor.Boron is diffluent element, and often occurs in all kinds of evaporites.Presently, the boron isotope has been applied widely in mineral deposits forming, geochemistry and palaeoenvironment.However, there is little research about characteristic of boron isotope in anhydrite and gypsum minerals, because of the low content of boron and micro-solubility in water and hydrochloric acid.This study developed a method of extracting and purifying boron in anhydrite and gypsum by phase transformation and ion-exchange.Firstly, the samples were mixed with ammonium hydrogen carbonate to transform the calcium sulfate to calcium carbonate.And diluted hydrochloric acid (1 mol/L) was added to resolve calcium carbonate.The percent conversion was about 85%in the first stage, and up to complete resolution by repeating this process.Secondly, boron specific ion-exchange resin ( Amberlite IRA 743 ) was used to gather the boron ions fully and further refined the samples with more than 1 μg of boron by anionic and cationic resin mixed by Ion Exchange Ⅱ and Dowex 50 W × 8.Finally, according to the modified method by He, the values of boron isotope were determined by TIMS.The boron content is analytically pure gypsum was 3.501 ± 0.128 μg/g ( n=12 , RSD=3.6%) and the average recovery was 100.47%.Besides, the δ11B value of analytically pure gypsum added with NIST SRM 951 was 17.98‰±0.21‰ (n=3, RSD=1.2%).This method has good repeatability and can meet the requirements of boron isotopic measurement of anhydrite and gypsum.
RESUMO
Objective To establish a computer-aided diagnosis (CAD) model for the classification and diagnosis of systemic lupus erythematosus (SLE) complicated with renal involvement,and to provide a new method for the timely detection and diagnosis of the disease.Methods Simulated annealing(SA) algorithm was used to optimize the penalty coefficient C and kernel function parameter g of the support vector machines(SVM) algorithm before an SA-SVM classifier model was established and was applied to the intelligent assistant diagnosis of SLE.Results Unlike the single SVM classifier,this method never fell into local optimum,and improved the classification accuracy of a classifier.The classification accuracy for SLE with renal involvement was as high as 98.72％.Conclusion The experimental results show that this classification model is well applicable to the intelligent diagnosis of SLE with renal involvement.