Sol-gel based biogenic silica composite as green nanosorbent for chemometric optimization of micro-solid-phase extraction of beta blockers

化学 色谱法 萃取(化学) 固相萃取 BETA(编程语言) 相(物质) 复合数 硅胶 有机化学 复合材料 材料科学 计算机科学 程序设计语言
作者
Hakimu Nsubuga,Chanbasha Basheer,Muhammad B. Haider,Rasheed Bakdash
出处
期刊:Journal of Chromatography A [Elsevier]
卷期号:1554: 16-27 被引量:19
标识
DOI:10.1016/j.chroma.2018.04.044
摘要

An environmentally friendly micro-solid phase extraction (μ-SPE) method utilizing a plant based nanocomposite as a sorbent for determination of trace level beta blockers (ß-blockers) in hospital wastewater prior to Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. μ-SPE extraction conditions were evaluated using a multivariate chemometric approach. Rice husk silica-carbon nanocomposite (5-20Wt.% Fe) in glycerol were synthesized via hydrolytic sol-gel method. The nanosorbent were fully characterized and then evaluated for μ-SPE of trace level ß-blockers in hospital wastewater. To establish the best extraction conditions at minimal experimental cost, multivariate techniques based on fractional factorial (FFD) and central composite designs (CCD) with desirability function (DF) were used to optimize the extraction conditions. Experimental results showed good agreement with predicted values and logical DF was realized at relatively low extraction time. Under optimized conditions, good linearity ranges (0.02-5.0μgL-1) with correlation of determinations higher than 0.9954 were obtained. The limits of detection and quantitation for the five ß-blockers (atenolol, alprenolol, pindolol, acebutolol and propranolol) ranged from 4.0-6.4 and 13.0-19.0ngL-1, respectively. Inter-day and intra-day precision (percent relative standard deviations, n=5) were lower than 8.3% while relative recoveries for hospital wastewater samples (80.6-105.1%) were in satisfactory ranges. This experimental approach therefore, demonstrated simplicity, reduction in the experimental runs, effectively increased sensitivity of LC-MS/MS and was hence suitable for complex matrix sample analysis.
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