Target, suspect and non-target screening analysis from wastewater treatment plant effluents to drinking water using collision cross section values as additional identification criterion was written by Hinnenkamp, Vanessa;Balsaa, Peter;Schmidt, Torsten C.. And the article was included in Analytical and Bioanalytical Chemistry in 2022.Computed Properties of C32H39NO4 The following contents are mentioned in the article:
The anthropogenic entry of organic micropollutants into the aquatic environment leads to a potential risk for drinking water resources and the drinking water itself. Therefore, sensitive screening anal. methods are needed to monitor the raw and drinking water quality continuously. Non-target screening anal. has been shown to allow for a more comprehensive investigation of drinking water processes compared to target anal. alone. However, non-target screening is challenging due to the many features that can be detected. Thus, data processing techniques to reduce the high number of features are necessary, and prioritization techniques are important to find the features of interest for identification, as identification of unknown substances is challenging as well. In this study, a drinking water production process, where drinking water is supplied by a water reservoir, was investigated. Since the water reservoir provides surface water, which is anthropogenically influenced by wastewater treatment plant (WWTP) effluents, substances originating from WWTP effluents and reaching the drinking water were investigated, because this indicates that they cannot be removed by the drinking water production process. For this purpose, ultra-performance liquid chromatog. coupled with an ion-mobility high-resolution mass spectrometer (UPLC-IM-HRMS) was used in a combined approach including target, suspect and non-target screening anal. to identify known and unknown substances. Addnl., the role of ion-mobility-derived collision cross sections (CCS) in identification is discussed. To that end, six samples (two WWTP effluent samples, a surface water sample that received the effluents, a raw water sample from a downstream water reservoir, a process sample and the drinking water) were analyzed. Pos. findings for a total of 60 substances in at least one sample were obtained through quant. screening. Sixty-five percent (15 out of 23) of the identified substances in the drinking water sample were pharmaceuticals and transformation products of pharmaceuticals. Using suspect screening, further 33 substances were tentatively identified in one or more samples, where for 19 of these substances, CCS values could be compared with CCS values from the literature, which supported the tentative identification. Eight substances were identified by reference standards In the non-target screening, a total of ten features detected in all six samples were prioritized, whereby metoprolol acid/atenolol acid (a transformation product of the two β-blockers metoprolol and atenolol) and 1,3-benzothiazol-2-sulfonic acid (a transformation product of the vulcanization accelerator 2-mercaptobenzothiazole) were identified with reference standards Overall, this study demonstrates the added value of a comprehensive water monitoring approach based on UPLC-IM-HRMS anal. This study involved multiple reactions and reactants, such as 2-(4-(1-Hydroxy-4-(4-(hydroxydiphenylmethyl)piperidin-1-yl)butyl)phenyl)-2-methylpropanoic acid (cas: 83799-24-0Computed Properties of C32H39NO4).
2-(4-(1-Hydroxy-4-(4-(hydroxydiphenylmethyl)piperidin-1-yl)butyl)phenyl)-2-methylpropanoic acid (cas: 83799-24-0) belongs to piperidine derivatives. Piperidine has a role as a reagent, a protic solvent, a base, a catalyst, a plant metabolite, a human metabolite and a non-polar solvent. Industrially, piperidine is produced by the hydrogenation of pyridine, usually over a molybdenum disulfide catalyst. Pyridine can also be reduced to piperidine via a modified Birch reduction using sodium in ethanol.Computed Properties of C32H39NO4
Referemce:
Piperidine – Wikipedia,
Piperidine | C5H11N – PubChem