Ambient Ionization Mass Spectrometry (AI-MS) techniques have revolutionized analytical chemistry by enabling rapid analysis of samples under atmospheric conditions with minimal to no preparation. In this study, the optimization of a cold atmospheric plasma for the analysis of food and pharmaceutical samples, liquid and solid, using a Heat-Assisted Dielectric Barrier Discharge Ionization (HA-DBDI) source is described. A significant enhancement in analyte signals was observed when a heating element was introduced into the design, potentially allowing for greater sensitivity. Furthermore, the synergy between the inlet temperature of the mass spectrometer and the heating element allows for precise control over the analytical process, leading to improved detection sensitivity and selectivity. Incorporating computational fluid dynamic (CFD) simulations into the study elucidated how heating modifications can influence gas transport properties, thereby facilitating enhanced analyte detection and increased signal intensity. These findings advance the understanding of HA-DBDI technology and provide valuable insights for optimizing AI-MS methodologies for a wide range of applications in food and pharmaceutical analysis.

中文翻译：

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用于食品和药物筛选中质谱分析的低成本热辅助环境电离源


环境电离质谱 (AI-MS) 技术能够在大气条件下快速分析样品，无需进行任何准备，从而彻底改变了分析化学。在本研究中，描述了使用热辅助介质阻挡放电电离 (HA-DBDI) 源对用于食品和药物样品（液体和固体）分析的冷大气等离子体进行优化。当设计中引入加热元件时，观察到分析物信号显着增强，从而可能实现更高的灵敏度。此外，质谱仪入口温度和加热元件之间的协同作用可以精确控制分析过程，从而提高检测灵敏度和选择性。将计算流体动力学 (CFD) 模拟纳入研究中，阐明了加热修改如何影响气体传输特性，从而促进增强分析物检测和增加信号强度。这些发现增进了对 HA-DBDI 技术的理解，并为优化 AI-MS 方法以适应食品和药物分析的广泛应用提供了宝贵的见解。