Enhancing Sensitivity for High-Selectivity Gas Chromatography-Molecular Rotational Resonance Spectroscopy

ANALYTICAL CHEMISTRY(2021)

引用 5|浏览9
暂无评分
摘要
A next-generation gas chromatograph-molecular rotational resonance (MRR) spectrometer (GC-MRR) with instrumental improvements and higher sensitivity is described. MRR serves as a structural information-rich detector for GC with extremely narrow linewidths and capabilities surpassing H-1 nuclear magnetic resonance/Fourier transform infrared spectroscopy/mass spectrometry (MS) while offering unparalleled specificity in regard to a molecule's three-dimensional structure. With a Fabry-Perot cavity and a supersonic jet incorporated into a GC-MRR, dramatic improvements in sensitivity for molecules up to 244 Da were achieved in the microwave region compared to the only prior work, which demonstrated the GC-MRR idea for the first time with millimeter waves. The supersonic jet cools the analytes to similar to 2 K, resulting in a limited number of molecular rotational and vibrational levels and enabling us to obtain stronger GC-MRR signals. This has allowed the limits of detection of the GC-MRR to be comparable to a GC thermal conductivity detector with an optimized choice of gases. The performance of this GC-MRR system is reported for a range of molecules with permanent dipole moments, including alcohols, nitrogen heterocyclics, halogenated compounds, dioxins, and nitro compounds in the molecular mass range of 46-244 Da. The lowest amount of any substance yet detected by MRR in terms of mass is reported in this work. A theoretically unexpected finding is reported for the first time about the effect of the GC carrier gas (He, Ne, and N-2) on the sensitivity of the analysis in the presence of the gas driving the supersonic jet (He, Ne, and N-2) in the GC-MRR Finally, the idea of total molecule monitoring in the GC-MRR analogous to selected ion monitoring in GC-MS is illustrated. Structural isomers and isotopologues of bromobutanes and bromonitrobenzenes are used to demonstrate this concept.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要