Magnetic Mesoporous Carbon/β-Cyclodextrin-Chitosan Nanocomposite for Extraction and Preconcentration of Multi-Class Emerging Contaminant Residues in Environmental Samples.
Geaneth Pertunia MashileAnele MpupaPhiliswa Nosizo NomngongoPublished in: Nanomaterials (Basel, Switzerland) (2021)
This study reports the development of magnetic solid-phase extraction combined with high-performance liquid chromatography for the determination of ten trace amounts of emerging contaminants (fluoroquinolone antibiotics, parabens, anticonvulsants and β-blockers) in water systems. Magnetic mesoporous carbon/β-cyclodextrin-chitosan (MMPC/Cyc-Chit) was used as an adsorbent in dispersive magnetic solid-phase extraction (DMSPE). The magnetic solid-phase extraction method was optimized using central composite design. Under the optimum conditions, the limits of detection (LODs) ranged from 0.1 to 0.7 ng L-1, 0.5 to 1.1 ng L-1 and 0.2 to 0.8 ng L-1 for anticonvulsants and β-blockers, fluoroquinolone and parabens, respectively. Relatively good dynamic linear ranges were obtained for all the investigated analytes. The repeatability (n = 7) and reproducibility (n = 5) were less than 5%, while the enrichment factors ranged between 90 and 150. The feasibility of the method in real samples was assessed by analysis of river water, tap water and wastewater samples. The recoveries for the investigated analytes in the real samples ranged from 93.5 to 98.8%, with %RSDs under 4%.
Keyphrases
- solid phase extraction
- molecularly imprinted
- high performance liquid chromatography
- liquid chromatography tandem mass spectrometry
- tandem mass spectrometry
- gas chromatography mass spectrometry
- simultaneous determination
- liquid chromatography
- ultra high performance liquid chromatography
- gas chromatography
- drug delivery
- heavy metals
- mass spectrometry
- wastewater treatment
- atomic force microscopy
- highly efficient
- quantum dots
- ionic liquid
- climate change
- human health
- electronic health record
- angiotensin ii
- adverse drug
- neural network