A mathematical model to predict electronic cigarette nicotine yield -

Abstract

Electronic cigarettes (ECIGs) are devices designed to deliver nicotine and some sensory features of cigarette smoking without combusting tobacco. They are marketed as a reduced harm smoking alternative to conventional cigarette. While they have become increasingly popular in recent years, little is known about their safety and efficacy. With continuously and rapidly evolving product design features and use behaviors, public health officials face the task of developing regulations for an ever moving target. Though design features and user behavior highly influence ECIG nicotine emission and understanding these factors is relevant to regulation, evaluating these factors in the human or analytical lab is a time consuming and costly process. In this study, a mathematical model based on principles of heat and mass transfer was developed to help regulators rapidly screen proposed product designs based on nicotine emissions. The model predicts potential nicotine and particulate matter yield from ECIG devices as a function of design features and user puffing behavior. The predicted variables from the model were tested against experimental measurements conducted over a range of ECIG design features and puff variables. The results show that the predicted and measured values are strongly correlated, with high coefficients of determination. The results also revealed that the different factors affecting nicotine emissions were well captured in the mathematical model. Thus, this model can be used to identify products that are likely to be ineffective or to pose increased risk of abuse potential, and to help guide selection of use conditions and product designs for subsequent human laboratory investigations.