Evidence map›Paper›PMID 42198559›Full record

ArticleToxics2026

Effects of E-Liquid Formulations on Nicotine Vapor Pressure and Implications for Nicotine Delivery and Toxicity.

Kaiyuan Wang, Xue Gong

Abstract read
In one paragraph

Article in Toxics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Kaiyuan WangSchool of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China.ORCID 0000-0002-0976-2613
Xue GongVehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, School of Automobile and Transportation, Xihua University, Chengdu 610039, China.ORCID 0009-0001-0643-1915

Funding

National Natural Science Foundation of China 52406155Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province QCCK2025-003
6 · The paper itself

Abstract

Electronic cigarettes are widely used as alternatives to conventional cigarettes. However, the relationships among e-liquid formulation, nicotine volatility, and nicotine delivery remain insufficiently investigated due to limited data on nicotine vapor pressure in e-liquid systems. This study aimed to investigate the effects of e-liquid formulations on nicotine vapor pressure, explore the underlying mechanisms, and establish correlations with nicotine delivery and pharmacokinetics. A headspace method was applied to measure nicotine vapor pressure at 37 °C, with variables including nicotine concentration, PG/VG ratio, organic acid type and ratio, and water content. The results showed that nicotine vapor pressure increased linearly with free-base nicotine fraction and decreased monotonically with increasing PG/VG ratio, acid-to-nicotine molar ratio, and water content. In addition, nicotine vapor pressure followed the order: free-base e-liquids > nicotine salt e-liquids > conventional cigarettes. Then, a correlation analysis was conducted between nicotine vapor pressure and nicotine pharmacokinetics. Lower vapor pressure correlated with deeper lung deposition, higher plasma nicotine, and greater potential toxicity, while higher vapor pressure correlated with more deposition in the upper respiratory tract and potential local irritation. Overall, nicotine vapor pressure can serve as an indicator for predicting nicotine delivery, supporting the rational regulation of e-liquid formulations and health risk assessment.

Indexed as

e-cigarettese-liquidsnicotine deliverynicotine vapor pressurevolatility

Identifiers

PMID42198559
PMCPMC13211595

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.