The Effect of Flow Rate on a Third-Generation Sub-Ohm Tank Electronic Nicotine Delivery System—Comparison of CORESTA Flow Rates to More Realistic Flow Rates

Many types of electronic cigarettes (ECs) are currently in use, but the default flow rate used to simulate puffing is centered on tobacco cigarette flow rates. CORESTA offers several methods and technical guides for evaluation of ECs but there are few puffing topography studies focusing on sub-ohm ECs; differences between real-world usage and that found in the literature appear large. This study focuses on how power and flow rate affect the nicotine yield of a sub-ohm EC. A puffing system (Puff3rd) has been designed and used to produce and collect EC aerosol. Nicotine yield was measured by GC–MS at three power levels and four flow rates. Data analysis was conducted in SAS using the MIXED procedure. Power, flow rate, and their interaction were all significant predictors of nicotine yield. Nicotine yield increased with both the vaping power and the puff flow rate with significant interaction of the two. Findings indicate that using the current CORESTA flow rate (1100 mL/min) to evaluate third-generation ECs underestimates nicotine yield and likely overestimates pyrolysis products. Real users are expected to have 2–3× the nicotine dose measured at 1100 mL/min, which could confound epidemiological studies seeking to link nicotine delivery to product satisfaction and acceptability.

Effect of free-base and protonated nicotine on nicotine yield from electronic cigarettes with varying power and liquid vehicle

Nicotine in electronic cigarette (ECIG) liquids can exist in a free-base or protonated (or “salt”) form. Protonated nicotine is less aversive upon inhalation than free-base nicotine, and many ECIG manufacturers have begun marketing protonated nicotine products, often with high nicotine concentrations. Regulations intended to control ECIG nicotine delivery limit nicotine concentration but do not consider nicotine form. In this study, we systematically examined the effect of nicotine form on nicotine yield for varying powers and liquid vehicles. A Kanger Subox Mini-C tank ECIG (0.5 Ω) was used to generate aerosols at varying powers (5–45 W) from liquid solutions that contained either free-base or protonated nicotine at 15 mg/g concentration, with a liquid vehicle consisting of either propylene glycol (PG) or vegetable glycerin (VG), resulting in four different solutions (free-base/PG, free-base/VG, protonated/PG, and protonated/VG). Nicotine yield was quantified using gas chromatography-mass spectrometry. Nicotine yields were not influenced by nicotine form under any condition investigated. At each power level, PG-based liquids resulted in approximately double the nicotine yield of VG-based liquids. Nicotine concentrations in the aerosols matched those of the parent liquids for both the PG and VG conditions. Increasing power led to greater nicotine yield across all conditions. The amount of nicotine emitted by an ECIG is independent of whether the nicotine is free-base or protonated, however the liquid vehicle has a strong effect on yield. Regulations intended to limit nicotine emissions must consider not only nicotine concentration, but also liquid vehicle and device power.

Might limiting liquid nicotine concentration result in more toxic electronic cigarette aerosols?

Some jurisdictions have instituted limits on electronic cigarette (ECIG) liquid nicotine concentration, in an effort to control ECIG nicotine yield, and others are considering following suit. Because ECIG nicotine yield is proportional to the product of liquid nicotine concentration (milligram per millilitre) and device power (watts) regulations that limit liquid nicotine concentration may drive users to adopt higher wattage devices to obtain a desired nicotine yield. In this study we investigated, under various hypothetical regulatory limits on ECIG liquid nicotine concentration, a scenario in which a user of a common ECIG device (SMOK TF-N2) seeks to obtain in 15 puffs the nicotine emissions equivalent to one combustible cigarette (ie, 1.8 mg). We measured total aerosol and carbonyl compound (CC) yields in 15 puffs as a function of power (15–80 W) while all else was held constant. The estimated nicotine concentration needed to achieve combustible cigarette-like nicotine yield at each power level was then computed based on the measured liquid consumption. We found that for a constant nicotine yield of 1.8 mg, reducing the liquid nicotine concentration resulted in greater amount of liquid aerosolised (p<0.01) and greater CC emissions (p<0.05). Thus, if users seek a given nicotine yield, regulatory limits on nicotine concentration may have the unintended consequence of increasing exposure to aerosol and respiratory toxicants. This outcome demonstrates that attempting to control ECIG nicotine yield by regulating one factor at a time may have unintended health effects and highlights the need to consider multiple factors and outcomes simultaneously when designing regulations.

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

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.

An Electronic Aerosol Delivery System for Functional Magnetic Resonance Imaging

Background: Public health concerns over the addictive potential of electronic cigarettes (e-cigs) have heightened in recent years. Brain function during e-cig use could provide an objective measure of the addictive potential of new vaping products to facilitate research; however, there are limited methods for delivering e-cig aerosols during functional magnetic resonance imaging (fMRI). The current study describes the development and feasibility testing of a prototype to deliver up to four different e-cig aerosols during fMRI. Methods: Standardized methods were used to test the devices’ air flow variability, nicotine yield, and free radical production. MRI scans were run with and without the device present to assess its safety and effects on MRI data quality. Five daily smokers were recruited to assess plasma nicotine absorption from e-liquids containing nicotine concentrations of 8, 11, 16, 24, and 36 mg/ml. Feedback was collected from participants through a semi-structured interview and computerized questionnaire to assess comfort and subjective experiences of inhaling aerosol from the device. Results: Nicotine yield captured from the aerosol produced by the device was highly correlated with the nicotine concentration of the e-liquids used (R2 = 0.965). Nicotine yield was reduced by a mean of 48% and free radical production by 17% after traveling through the device. The e-liquid containing the highest nicotine concentration tested (36 mg/ml) resulted in the highest plasma nicotine boost (6.6 ng/ml). Overall, participants reported that the device was comfortable to use and inhaling the e-cig aerosols was tolerable. The device was determined to be safe for use during fMRI and had insignificant effects on scan quality. Conclusions: With the current project, we were able to design a working prototype that safely and effectively delivers e-cig aerosols during fMRI. The device has the potential to be used to assess brain activation during e-cig use and to compare brain reactivity to varying flavors, nicotine concentrations, and other e-cig characteristics.

Assessment of Nicotine Exposure From Active Human Cigarette Smoking Time

Summary The burning of a cigarette is a series of consecutive sequences of both passive and active burnings when a smoking cycle is applied to the cigarette. A previous study, using a smoking machine, showed that cigarette nicotine yields are dependent linearly on the difference between the time of smouldering (passive burning) and the time of smoking (active burning). It is predicted that the smoker’s nicotine yield increases when the intensity of smoking increases, i.e., when the time to smoke a cigarette (smoking time) decreases. Note that observations made on machines might not be comparable to human behaviours. The aim of this study was to determine whether nicotine mouth-level exposure could be predicted through measurement of human smoking time. A smoking behaviour study was conducted to compare human smoking nicotine yields obtained from both filter tip analysis and the cigarette burning time model. Results showed that smokers’ exposure to the smoke depends essentially on the speed at which the cigarette is smoked. An increase in human smoking intensity, resulting in a decrease in smoking time, generates an increase in smoke exposure, whatever the puff number, puff duration, puff volume and filter ventilation (open or blocked). The association of a machine smoking yield with a corresponding smoking time, and the time taken by a consumer to smoke the cigarette would provide information on the exposure to smoke constituents in a simple and effective manner.

Relationship Between Mainstream Cigarette Smoke “Tar” and Nicotine Yields

The purpose of this research was to examine the relationship between mainstream “tar” and nicotine yields and refine the commonly used linear model with a positive intercept to incorporate non-linearity and product-toproduct differences in filler nicotine content. “Tar” and nicotine yields are examined for a wide range of cigarettes (U.S. and international) using the Cambridge filter (CF), ISO, and the more intense MDPH, and HC smoking methods. Particularly at very low machine yields, a nonlinearity is observed that can be more accurately modeled by a power law relationship, and can be further improved by incorporating the concentration of nicotine in the cigarette filler into the equation. The resultant power law relationship is the better statistical fit to the available data, avoids the physical implausibility of positive nicotine yield at zero “tar” yield and lack of dependence on filler nicotine that are inherent in the simple linear model relating nicotine yield to “tar” yield alone, and explains the nonconstancy of the “tar”-to-nicotine ratio. The relationship between “tar” and nicotine can be affected by the use of very long or very short puff intervals, and, with the same tobacco blend and the same “tar” yield, longer cigarettes tend to have a slightly higher nicotine yield than shorter cigarettes.