化学
微塑料
指纹(计算)
热解
环境化学
人工智能
有机化学
计算机科学
作者
Sirui Pu,Chudong Wei,Lichun Zhang,Rui Liu,Yi Lv
标识
DOI:10.1021/acs.analchem.5c00653
摘要
As emerging pollutants, microplastics are pervasive in marine, terrestrial, and atmospheric environments as well as human tissues due to their small size, large specific surface area, and strong adsorption capacity. Consequently, developing efficient and rapid methods for identifying and differentiating microplastics is critical. Herein, a novel pyrolysis-assisted cataluminescence (CTL) sensor array was constructed, capable of efficiently distinguishing seven distinct types of microplastic samples. By pyrolyzing microplastics at high temperatures into small molecular compounds and introducing them into the CTL sensor array, which was based on 0–9% Eu3+-doped LaAlO3 as sensing materials, the system achieved a response time of no more than 2 s and an average recovery time of approximately 7 s. Through the integration of thermodynamic (signal-to-noise ratio) and kinetic (response time) factors from the CTL response curves, unique fingerprint patterns were generated, enabling high-throughput and rapid differentiation of multiple microplastics. Mechanistic studies revealed that the doping with Eu3+ ions played a dominant role in regulating the CTL signal. Different microplastic samples generated distinct types and concentrations of reaction intermediates during the pyrolysis-assisted CTL process, leading to variations in luminescence efficiency and thus forming unique fingerprint patterns. The proposed front-end pyrolysis combined with the back-end CTL method provided valuable insights for the rapid differentiation of structurally complex and low-reactive emerging pollutants, particularly nonvolatile solid samples, thereby expanding the application scope of CTL detection.
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