ArticleAnalytical and bioanalytical chemistry2024
Reliable biological indicator identification and evaluation of tobacco-derived nicotine using an ultra-sensitive gas chromatography-tandem mass spectrometric method.
Article in Analytical and bioanalytical chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Decoding Proton-Coupled Electron Transfer Mechanism of Nicotine for Multi-Scenario Portable Electrochemical Sensing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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Authors and funding
2 authors.
Funding
Abstract
Several countries have exempted synthetic nicotine from existing regulatory frameworks, resulting in the widespread substitution of synthetic nicotine (SN) in almost all e-cigarette products available. However, it remains uncertain whether the purported synthetic nicotine is indeed genuine SN. There is a need to develop biological indicators and an analytical method that more clearly distinguishes between the two sources. Impurities in neat tobacco-derived nicotine (TDN) were characterized and identified through non-targeted and targeted analysis. Gas chromatography-tandem mass spectrometry (GC-MS/MS) conditions were optimized for detecting biological indicators in e-cigarette products. Nine tobacco-related alkaloids were identified and selected as biological indicators for TDN. A liquid-liquid extraction and GC-MS/MS quantitative method were developed to detect nine biological indicators in e-cigarette products with the limit of quantification ranging from 0.2 to 4.2 µg L
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Registered trials
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.