An Analytical Perspective on Determination of Free Base Nicotine in E-Liquids.

Journal of analytical methods in chemistry(2020)

引用 12|浏览4
暂无评分
摘要
In electronic cigarette users, nicotine delivery to lungs depends on various factors. One of the important factors is e-liquid nicotine concentration. Nicotine concentration in e-liquids ranges from 0 to >50 mg/mL. Furthermore, nicotine exists in protonated and unprotonated ("free base") forms. The two forms are believed to affect the nicotine absorption in body. Therefore, in addition to total nicotine concentration, e-liquids should be characterized for their free base nicotine yield. Two approaches are being used for the determination of free base nicotine in e-liquids. The first is applying a dilution to e-liquids followed by two methods: Henderson-Hasselbalch theory application or a Liquid-Liquid Extraction. The second is the without-dilution approach followed by 1H NMR method. Here, we carried out controlled experiments using five e-liquids of different flavors using these two approaches. In the dilution approach, the Henderson-Hasselbalch method was tested using potentiometric titration. The accuracy was found to be >98% for all five e-liquid samples (n = 3). A Liquid-Liquid Extraction was carried out using toluene or hexane as extraction solvent. The Liquid-Liquid Extraction technique was found to be limited by solvent interactions with flavors. Solvent extractions resulted in flavor dependent inaccuracies in free base nicotine determination (5 to 277% of calculated values). The without-dilution approach was carried out using 1H NMR as described by Duell et al. This approach is proposed to offer an independent and alternative scale. None of the methods have established a strong correlation between pre- and postvaporization free base nicotine yield. Here we present comparative results of two approaches using analytical techniques. Such a comparison would be helpful in establishing a standardized method for free base nicotine determination of e-liquids.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要