Abstract

• Biochar nanospheres were grown in situ onto TiO 2 nanorod arrays to improve the extraction effect. • COF nanospheres were bonded to TiO 2 nanorod arrays to improve the extraction performance. • The structure-performance relationships and extraction mechanisms of extraction materials were summarized. • Three online IT-SPME-HPLC analytical methods were developed to detect organic pollutants. • These materials exhibited better extraction performance than some other materials. To improve the extraction efficiency of carbon fibers (CFs) toward organic pollutants, TiO 2 nanorod arrays (NARs) were grown in situ on CFs. Subsequently, biochar nanospheres and covalent organic framework nanospheres were separately introduced to functionalize the NARs. A sequence of materials was produced by regulating the reactant concentrations and characterized using a scanning electron microscope, an X-ray photoelectron spectrometer, an X-ray diffractometer, a Raman spectrometer, and a specific surface area analyzer. The materials were then deposited into separate poly(etheretherketone) tubes for in-tube solid-phase microextraction (IT-SPME). These tubes were evaluated with different types of organic pollutants (polycyclic aromatic hydrocarbons (PAHs), estrogens, bisphenols, and phthalate esters) using a combination of high-performance liquid chromatography and IT-SPME, and they exhibited diverse extraction performance. The extraction mechanism of each material is carefully discussed, and the structure–performance relationship is also summarized based on the chemical structures and extraction properties of the materials. The most efficient extraction materials for different analytes were discovered and used to develop analytical methods. Three online methods were used to sensitively detect PAHs, estrogens, and bisphenols in real water samples, respectively. Satisfactory results were obtained, including enrichment factors up to 6784, detection limits as low as 0.001 μg L −1 , linear ranges of 0.003–15.0 μg L −1 , and relative standard deviations ranging from 0.2% to 15.2%. The results indicate that these methods have some advantages over previous material-based methods.

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