Univ. Lille, CNRS, UMR 8523, PhLAM - Laboratoire de Physique des Lasers Atomes et Molécules, F-59000 Lille, France.
Faraday Discuss. 2019 Aug 15;218(0):115-137. doi: 10.1039/c8fd00238j.
The intricate chemistry of the carbonaceous particle surface layer (which drives their reactivity, environmental and health impacts) results in complex mass spectra. In this respect, detailed molecular-level analysis of combustion emissions may be challenging even with high-resolution mass spectrometry. Building on a recently proposed comprehensive methodology (encompassing all stages from sampling to data reduction), we propose herein a comparative analysis of soot particles produced by three different sources: a miniCAST standard generator, a laboratory diffusion flame and a single cylinder internal combustion engine. The surface composition is probed by either laser or secondary ion mass spectrometry. Two examples of multivariate analysis, Principal component analysis and hierarchical clustering analysis proved their efficiency in both identifying general trends and evidencing subtle differences that otherwise would remain unnoticed in the plethora of data generated during mass spectrometric analyses. Chemical information extracted from these multivariate statistical procedures contributes to a better understanding of fundamental combustion processes and also opens to practical applications such as the tracing of engine emissions.
碳质颗粒表面层的复杂化学性质（驱动其反应性、环境和健康影响）导致了复杂的质谱。在这方面，即使使用高分辨率质谱，对燃烧排放物进行详细的分子水平分析也可能具有挑战性。基于最近提出的一种综合方法（包括从采样到数据减少的所有阶段），我们在此提出了对三种不同来源的炭黑颗粒进行比较分析：微型 CAST 标准发生器、实验室扩散火焰和单缸内燃机。通过激光或二次离子质谱法探测表面成分。两种多元分析的例子，主成分分析和层次聚类分析，证明了它们在识别一般趋势和证明细微差异方面的效率，否则这些差异在质谱分析产生的大量数据中是无法察觉的。从这些多元统计过程中提取的化学信息有助于更好地理解基本的燃烧过程，也为实际应用（如发动机排放的追溯）开辟了道路。
