Toward Better Characterization of a Free-Base Nicotine Fraction in e-Liquids and Aerosols.

Given that nicotine salts are a growing market, methods are needed to characterize nicotine forms in e-cigarette vaping products. By lowering the free-base nicotine fraction (α fb ) in favor of protonated forms, the addition of organic acids to the e-liquid mix greatly modulates nicotine pharmacokinetics and improves vapers' craving. This research investigated (1) the performance of pH measurement, liquid-liquid extraction (LLE), and acid/nicotine molar ratio calculation methods for α fb estimation in 6 nicotine benzoate and nicotine salicylate e-liquids and (2) nicotine protonation in the aerosol post vaporization. Aerosols were generated with a JUUL device and another mod-pod on a vaping machine to assess device effects. E-liquid and aerosol samples were then analyzed after further analytical optimization of previous methods and careful consideration of biases. Globally, performances were comparable between methods. α fb accounted for less than 5% of nicotine content regardless of experimental conditions. α fb were consistent between e-liquids and aerosols irrespective of e-cigarette devices. Hence, e-liquids are adequate surrogates for aerosols, facilitating the establishment of regulations. pH measurement is one of the most used methods and enables the establishment of relative scales for e-liquid classification but lacks automation possibility. Until now, the extent of sample dilution remained arbitrary. The dilution factor was fixed at 10, as usually achieved, since no effect of dilution was noted. pH values ranged from 5.3 to 6.3 in accordance with the literature. By contrast, LLE relies on the specificity of organic solvent for free-base nicotine extraction, causing discrepancies in previous studies. Here, the results were similar to α fb values from pH determination. Yet, LLE presented the highest variability and was the most time-consuming protocol. Finally, α fb calculation from molar ratio was the most robust and versatile method. Estimations can be made in silico from reported composition data and/or after liquid chromatography routine analysis.