Tracking Dynamic Chemical Reactivity Networks with High-Resolution Mass Spectrometry: A Case of Microplastic-Derived Dissolved Organic Carbon

溶解有机碳 质谱法 反应性(心理学) 环境化学 化学转化 化学 乙醚 有机化学 色谱法 医学 替代医学 病理
作者
Vittorio Albergamo,Wendel Wohlleben,Desirée L. Plata
出处
期刊:Environmental Science & Technology [American Chemical Society]
被引量:1
标识
DOI:10.1021/acs.est.3c08134
摘要

Chemical degradation testing often involves monitoring the loss of a chemical or the evolution of a single diagnostic product through time. Here, we demonstrate a novel approach to tracing complex degradation networks using mass-spectrometry-based methods and open cheminformatics tools. Ester- and ether-based thermoplastic polyurethane (TPU_Ester and TPU_Ether) microplastics (350 μm) and microplastics-derived dissolved organic carbon (MP-DOC) were photoweathered in a simulated marine environment and subsequently analyzed by liquid chromatography coupled to high-resolution mass spectrometry. We formula-annotated 1342 and 2344 unique features in the MP-DOC of TPU_Ester and TPU_Ether, respectively. From these, we extracted 199 and 568 plausible parent–transformation product pairs via matching of features (a) with complementary increasing and decreasing trends (Spearman's correlation coefficient between normalized intensity and time), (b) spectral similarities of at least three accurate mass MS2 fragments, and (c) at least 3 ppm agreement between the theoretical and measured change in m/z between the parent–transformation product formula. Molecular network analysis revealed that both chain scission and cross-linking reactions occur dynamically rather than degradation proceeding in a monotonic progression to smaller or more oxygenated structures. Network nodes with the highest degree of centrality were tentatively identified using in silico fragmentation and can be prioritized for toxicity screening or other physicochemical properties of interest. This work has important implications for chemical transformation tracking in complex mixtures and may someday enable improved elucidation of environmental transformation rules (i.e., structure–reactivity relationships) and fate modeling.
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