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Article

Magnetic Mesoporous Carbon/β-Cyclodextrin–Chitosan Nanocomposite for Extraction and Preconcentration of Multi-Class Emerging Contaminant Residues in Environmental Samples

by
Geaneth Pertunia Mashile
1,2,
Anele Mpupa
1,2 and
Philiswa Nosizo Nomngongo
1,2,3,*
1
Department of Chemical Sciences, University of Johannesburg, Doornfontein Campus, P.O. Box 17011, Doornfontein 2028, South Africa
2
Department of Science and Innovation (DSI)/National Research Foundation (NRF) South African Research Chair (SARChI): Nanotechnology for Water, University of Johannesburg, Doornfontein 2028, South Africa
3
Department of Science and Innovation (DSI)/Mintek Nanotechnology Innovation Centre, University of Johannesburg, Doornfontein 2028, South Africa
*
Author to whom correspondence should be addressed.
Nanomaterials 2021, 11(2), 540; https://doi.org/10.3390/nano11020540
Submission received: 26 January 2021 / Revised: 12 February 2021 / Accepted: 17 February 2021 / Published: 20 February 2021
(This article belongs to the Special Issue Development of Nanomaterials for Applications in Trace Analysis)
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Abstract

:
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%.

1. Introduction

The use of chemicals such as preservatives, pharmaceuticals, plasticizers, perfumes, UV filters and microplastics, among others, is universal [1]. Among these chemicals, pharmaceutical and personal care products (PPCPs) have attracted a lot of interest due to their extensive use in the prevention or treatment of human and animal diseases as we are improving the quality of life [2]. Evidence indicates that conventional water treatment processes for the removal of PPCPs such as coagulation, filtration, disinfection and flocculation, amongst others, are ineffective in the complete removal of PPCPs. These chemicals have been detected in different environmental compartments at concentration levels ranging from ng L−1 to μg L−1 [3,4,5]. In addition, the detection of PPCPs in South African surface waters has been poorly evaluated and has only increased in recent years [6,7,8,9,10,11,12,13,14,15]. The presence of PPCPs in the environment is of global concern, as they are said to be biologically active [2]. As a results of their biological activity and endocrine disruptive effects, they can pose serious health effects to living organisms and the environment [2]. Therefore, it is important to investigate the occurrence of PPCPs in different environmental compartments.
Thus far, several analytical techniques, such as capillary electrophoresis (CE) [2,16,17], high-performance liquid chromatography (HPLC) [18,19,20], liquid chromatography–mass spectrometry (LC-MS) [21], ultra-performance liquid chromatography coupled with tandem mass spectrometry [1,2,3] and gas chromatography–mass spectrometry (GC-MS) [22,23,24,25], have been employed for quantification of PPCPs in numerous matrices. Owing to matrix effects of complex environmental samples and trace levels of PPCPs, sample cleanup prior to analytical detection and quantification is required. For this reason, different preconcentration and extraction procedures such as liquid-phase microextraction techniques (LPME) [26], traditional solid-phase extraction (SPE) [2,16], dispersive solid-phase extraction (DSPE) [19,27,28], solid-phase microextraction (SPME) [29], stir bar sorptive extraction (SBSE) [21,30] and supramolecular solvent-based LPME [20,21,30,31,32], among others, have been developed for PPCPs analysis.
Among the abovementioned techniques, adsorbent-based extraction methods have received significant attention in recent years. This is due to the use of different solid-phase materials that can be tuned based on target analytes. Until now, different kinds of adsorbents have been used for extraction and preconcentration of PPCPs. These include multi-walled carbon nanotubes [33], nanofibers [27,28,34], graphene oxide nanocomposites [19,35], biopolymer-based composites [36,37], activated carbon, metal–organic frameworks [2,38] and metal oxide nanocomposites [28]. Recently, our previous research prepared a biodegradable superabsorbent based on a magnetic mesoporous carbon/β-cyclodextrin–chitosan (MMPC/CycChit) nanocomposite for removal of fluoroquinolones (FQs) from environmental samples [39].
In addition to sample pretreatment, complete chromatographic separation is necessary for the selective and sensitive detection of target analytes, especially with a UV detector [40]. The separation of pharmaceuticals in HPLC-DAD is mostly achieved using stationary phases containing n-octylsilyl- (C8) and n-octadecylsilyl- (C18) functional groups bound to the silica surface through reverse-phase liquid chromatographic (RPLC) separation [41]. However, the choice of a suitable mobile phase could allow the achievement of good separation. Acetonitrile and methanol are widely used organic mobile phases for the HLPC separation of pharmaceuticals [40,41]. Researchers have reported that in order to achieve a better resolution, shorter retention times and reproducible results, a mixture of acetonitrile and methanol together with the use of additives (such as formic acid, acetate buffer and phosphoric acid) can be used [42,43].
The objective of this work was to develop a rapid, robust and simple method for extraction and preconcentration of anticonvulsants, beta-blockers, parabens and fluoroquinolones (FQs) in environmental samples. The method was based on dispersive magnetic solid-phase extraction (DMSPE) based on the previously reported MMPC/CycChit adsorbent combined with high-performance liquid chromatography with diode array detection (HPLC-DAD). The choice of analytes to be investigated was based on the previous studies which revealed that anticonvulsants, beta-blockers, parabens and fluoroquinolones (FQs) are frequently detected in South African water systems [7,31,36,44,45]. The experimental factors (mass of adsorbent, eluent type, eluent volume, extraction time, desorption time and sample pH) affecting the extraction and preconcentration procedure were optimized using univariate and multivariate approaches. According to our knowledge, no studies have been performed on the analysis of emerging multi-class pollutants using dispersive magnetic solid-phase extraction with an MMPC/CycChit nanocomposite as the adsorbent.

2. Materials and Methods

2.1. Materials

Ethanol, methanol (MeOH) (HPLC grade), acetonitrile (ACN) (HPLC grade) and ortho-phosphoric acid were acquired from Sigma-Aldrich (St. Louis, MO, USA). Standards of the target analytes were obtained from Sigma and their corresponding information is illustrated in Table 1. The stock solution containing fluoroquinolones (FQs), parabens, β-blockers and anticonvulsants was prepared by dissolving appropriate amounts of analytes of interest in methanol. These analytes included danofloxacin (DANO), enrofloxacin (ENRO), levofloxacin (LEVO), atenolol (ANL), propranolol hydrochloride (PPNL), carbamazepine (CBZ), methylparaben (MP), ethylparaben (EP), propylparaben (PP) and butylparaben (BP). The simulated sample solution was prepared by diluting appropriate volumes of stock solution with tap water free from the target analytes. A set of calibration standards was prepared from stock solution and diluted with ultrapure water. The stock solution was stored in the refrigerator at 4–8 °C, and the simulated sample solutions were prepared daily. The simulated solutions were used throughout the method development stages. Details of chromatographic conditions and other instrumentations used are presented in Section S1 in the Supplementary Information.

2.2. Samples and Sample Collection

Both wastewater samples, raw (influent) and treated (effluent), used in this study were collected at different points in the Daspoort Wastewater Treatment Plant (Pretoria, Gauteng, South Africa), while river and tap water samples were collected from the Apies River and the laboratory. The samples were collected using pre-cleaned 500 mL glass bottles. After sampling, the water samples were stored at 4 °C for a maximum of 1 week until being analyzed.

2.3. UA-MSPDE Preconcentration Procedure

The extraction procedure was conducted based on a literature report by [9]. Briefly, 20–60 mg of an adsorbent (MMPC/Cyc-Chit) was added in sample glass bottles. Simulated sample solution (20 mL) at a concentration level of 100 µg L−1 for each analyte was placed in the sample bottles containing respective masses of MMPC/Cyc-Chit adsorbent. The extraction and preconcentration steps were assisted by ultrasonication for 10–30 min. The analyte-loaded adsorbent was separated from the sample via an external magnet, and the liquid phase was discarded. Subsequently, the analytes were eluted from the adsorbent using 2 mL of eluent solvent. The capabilities of different eluent solvents were in investigated. These include acetonitrile (ACN), ultrapure deionized water, methanol (MeOH), mixture of ACN/MeOH (50:50), mixture of ACN/H2O (50:50), mixture of MeOH/H2O (50:50) and 0.01 mol L−1 H3PO4/CAN. The elution process was achieved by ultrasonic dispersion for 10 min. Similarly, the eluent solvent was separated from the adsorbent by magnetic decantation, and the analytes in the eluent solvent were analyzed using HPLC-DAD. The effect of independent variables, that is, extraction time (10–30 min), mass of adsorbent (20–60 mg) and sample pH (4–9), were evaluated using central composite design (CCD) at 5 levels. The percentage recoveries of each investigated analyte were used as the dependent variable (analytical response). Nineteen randomized experiments were performed, eight at the factorial points, six at the axial points and five at the central point.

2.4. Quality Assurance/Quality Control (QA/QC)

The quality assurance/quality control (QA/QC) of the DMSPE-HPLC-DAD method was performed according to our previous study [46]. Firstly, blank samples were injected to the HPLC system and none of the target analytes were detected. These results provided assurance that blank correction from all investigated samples was not necessary. During the analysis of the samples, standard solutions of each analyte at 10 and 100 ng L−1 were used as QA/QC samples. Blank samples processed in a similar manner to real samples and above-mentioned QA/QC standard solutions were analyzed after every tenth sample. However, when samples were less than ten, the QA/QC procedure was followed after every three samples.

3. Results and Discussion

3.1. Optimization of Desorption Conditions

The elution process of the adsorbates from the adsorbent was investigated in order to attain the highest percentage recoveries of the analytes. The selection of a suitable eluent is important because of the differences in physicochemical properties of organic solvents used as eluents and the analytes to be desorbed. In this study, acetonitrile (ACN), ultrapure deionized water, methanol (MeOH), a mixture of ACN/MeOH (50:50), a mixture of ACN/H2O (50:50) and a mixture of MeOH/H2O (50:50) were used for the elution of propranolol, atenolol, carbamazepine, fluoroquinolones and parabens. The desorption process was carried out via ultrasonication, and preliminary experiments showed that five minutes was long enough to attain quantitative recoveries. As seen, methanol was found to the best solvent (Figure 1A,B) for the desorption of β-blockers, CBZ and parabens. This suggested that β-blockers, CBZ and parabens were highly soluble in methanol. However, all the investigated desorption solvents were not suitable for elution of fluoroquinolones (Figure 1C). This might be because of the strong π–π or electrostatic interactions between the FQs and the nanocomposite [47,48,49]. As seen in Figure 1C, the mixture of acetonitrile and water had recoveries greater than 50%. Therefore, the desorption capabilities of different mixtures of 0.01 mol L−1 H3PO4 and ACN were investigated. The result obtained revealed that the quantitative recoveries were obtained with when 55:45 (v/v) of the 0.01 mol L−1 H3PO4/ACN mixture was used (Figure 1D). This suggested that the acidified desorption solution led to the cationic forms of FQs. Moreover, the surface of the adsorbent at lower pH values is positive. This phenomenon resulted in electrostatic repulsion between the analytes and the adsorbent, thus promoting the desorption of FQs from the surface of the nanocomposite. For further studies, methanol and mixtures of 0.01 mol L−1 H3PO4/ACN (45:55 v/v) were selected as suitable eluents. These findings are in line with previous studies [31].

3.2. Optimization of the Preconcentration Procedure

To obtain the most satisfactory extraction and preconcentration conditions, the effect of various parameters (sample pH, mass of adsorbent and extraction time) was investigated. The optimization of these parameters was achieved using central composite design (CCD), and the design matrix together with the respective responses is reported in Supplementary Tables S1–S3. The experimental data were analyzed using analysis of variance (ANOVA) (Figure 2). Pareto charts for each analyte revealed that the sample pH and mass of adsorbent were significant at the 95% confidence level for all the analytes. In contrast, Figure 2C shows that the sample pH and mass of adsorbent and their interactions were statistically significant for the preconcentration of parabens.
The interactive effects of the investigated variables were examined using 3D response surface plots (Figures S1–S3). As observed from the ANOVA results, the sample pH proved to be one of the critical factors on the extraction and preconcentration of parabens. This was due to the fact that pH is known to have the ability to affect the charges of both the adsorbent and the analytes depending on the analyte pKa and pH at the point of zero charge (pHPZC) of the adsorbent [50]. When the sample pH is higher than the analyte pKa, the analyte remains in its neutral form. In contrast, pH ≤ pKa results in the protonation of the analyte, which influences the adsorbent–analyte interaction. In the case of fluoroquinolones, the analytical response increased with increasing sample pH, and the %R was attained between pH 6 and 8. This is because fluoroquinolones can exist in three forms in aqueous systems, that is, cationic (pH > pKa2), zwitterionic (pKa1 ≤ pH ≤ pKa2) and anionic (pH < pKa1), and these forms are pH-dependent [39]. Consequently, the adsorption mechanism is also dependent on the adsorbent surface charge. Moreover, the extraction analytes depend on the adsorbent surface charge, and the pHPZC of the nanocomposite used in this study was 8.0, implying that it bears a negative surface charge at pH values higher than 8 [39]. As seen in Figures S1–S3, percentage recoveries increased up to pH 8; after that, a significant decrease was observed. This might be due to the electrostatic repulsion between the analytes and adsorbent [33].
Figure 3 presents the desirability profiles and summary of the optimum conditions desired to obtain maximum recoveries of (A) β-blockers and anticonvulsants, (B) fluoroquinolones and (C) parabens. Figure 3 presents the individual desirability scores for the preconcentration of target analytes (left-hand side, bottom). The %R obtained from the plots for each parameter in the model is presented at the top left-hand side. According to Mashile et al. [36], the plots on the top left-hand side present the changes in the level of each individual variable and its analytical response as well as its overall desirability. According to Figure 3, the minimum, central and maximum %R values were 21–33.3%, 60.5–66.3% and 99.3–100%, respectively. These %R values correspond to desirability values of 0.0, 0.5 and 1.0. To obtain maximum recoveries of the target analytes, the desirability score of 1.0 was chosen as the target value for the optimization of the individual variables [36]. As seen from Figure 3A–C, the desirable recoveries were obtained at pH 6.5, ET 23 min and MA 57 mg for β-blockers, anticonvulsants and fluoroquinolones (Figure 3A,B), while a desirable recovery was obtained at pH 7, ET 23 min and MA 50 mg for parabens (Figure 3C). To validate the optimum conditions, preconcentration of target analytes was carried out using the optimal conditions, and the %R values ranged from 97.9 ± 2.1 to 98.7 ± 2.5%. The experimental values were in agreement with the predicted data obtained from the desirability function profile. Suggesting that a response surface methodology model based on central composite design was valid and appropriate for optimization of the DMSPE method.

3.3. Validation of the Preconcentration Method

The analytical performance of the DMSPE/HPLC-DAD method was assessed using limits of detection (LODs), limits of quantification (LOQs), the dynamic linear range, precision (repeatability and reproducibility), the enhancement factor and spike recovery tests. The LODs and LOQs were calculated from seven measurements of the lowest standard of the calibration at a signal-to-noise ratio (S/N) of 3 and 10. The linearity of the method was investigated using a series of standard solutions containing a mixture of target analytes at a concentration range of 0–500, 0–1500 and 0–300 µg L−1 for β-blockers and anticonvulsants, fluoroquinolones and parabens, respectively. Wide linearity with correlation coefficients (R2) up to 0.9993 was obtained. The repeatability (expressed as relative standard deviation (%RSD) was investigated by same-day analysis of 10 consecutive replicates of 100 ng L−1, while reproducibility (interday) %RSD experiments were conducted over a 5-day period. The analytical figures of merit results are summarized in Table 2 (detailed individual results are presented in Supplementary Data, Tables S4–S6). The analytical performance of the developed method was compared with other sorbent-based sample preparation techniques reported in the literature [1,22,23,51,52,53] (Table 3).
The applicability of the proposed method was assessed by analyzing β-blockers and anticonvulsants, fluoroquinolones and parabens in wastewater, river water and tap water samples. The samples were spiked at two levels with 50 and 100 ng L−1 for β-blockers, anticonvulsants and parabens and 5 and 20 ng L−1 for fluoroquinolones. The results obtained were used to evaluate the accuracy of the method. The spike recovery experiments were carried out in triplicates and the %RSD was estimated (Table 4, detailed individual results and typical chromatograms are presented in Supplementary Data, Tables S7–S9 and Figures S3–S6).
Pharmaceutical and personal care products have been identified and detected in almost all ecological compartments across the world. As seen, carbamazepine, levofloxacin and butylparaben were not detected in wastewater, river water and tap water samples (Tables S7–S9, Supplementary Data). Among the detected PPCPs, parabens were found at the highest concentration in influent wastewater, i.e., 937 ± 10 ng L−1 for methylparaben and 781 ± 11 ng L−1 for propylparaben (Table S9). In addition, most studied analytes were not detected in tap water samples, except methylparaben (3.89 ± 0.09 ng L−1) and propylparaben (4.81 ± 0.05 ng L−1). The concentrations of anticonvulsants, beta-blockers, parabens and fluoroquinolones were compared with those reported in other countries. As seen, the levels for beta-blockers, anticonvulsants and fluoroquinolones obtained in this study were within the lower end of the ranges reported in the literature (Table 5). Paraben levels were lower than those reported in some parts of South Africa (up to 1988 ng L−1), Egypt (up to 6380 ng L−1), Kenya (30–1160 ng L−1), China (up to 5960 ng L−1), Turkey (17,000–33,000 ng L−1) and the United Kingdom (2642–11,601 ng L−1). Furthermore, paraben concentrations were found to be higher than those reported in Pakistan, Portugal, Spain, Brazil and Poland (Table 5).

4. Conclusions

This study described the broad use of a sample pretreatment procedure with the application of a versatile biodegradable supersorbent MMPC/Cyt-Chit for the extraction and preconcentration of multi-class PPCPs from environmental water samples, where preconcentration of multi-class PPCPs was carried out by the simple dispersive magnetic solid-phase extraction technique prior to chromatographic detection. Evaluations for the suitable elution solvent and sorbent pH were performed in order to select the best extraction conditions. Based on the results obtained, methanol was the best desorption solvent, while the zeta potential of the nanocomposite indicated that it was positively charged at pH below 8 and negatively charged at pH above 8.0, making it a suitable material for the preconcentration of analytes with a high or low pKa value. Moreover, the combination of a nanocomposite with properties such as a large specific surface area and predominantly porous structure with the DMSPE technique yielded high recoveries up to 99% for all analytes. The DMSPE-HPLC-DAD technique was also tested in spiked water samples where it demonstrated that the linearity of the method ranged from 0.05 to 400, 0.05 to 300, 0.10 to 350 and 0.2 to 1000 µg L−1 for beta-blockers, parabens, anticonvulsants and fluoroquinolones, respectively. In addition, their LODs ranged from 0.0045 to 0.07 µg L−1 with a correlation coefficient (R2) of up to 0.9991 for all PPCPs analyzed. Therefore, this indicated that the optimized DMSPE-HPLC-DAD technique was suitable for the simultaneous preconcentration of multi-class PPCPs from different aquatic matrices. The method also proved to be sensitive and cost-effective as less time and sorbent mass were used to simultaneously extract and quantify all analytes before HPLC determination.

Supplementary Materials

The following are available online at https://www.mdpi.com/2079-4991/11/2/540/s1, Table S1: CCD matrix and analytical response: preconcentration of beta-blockers and CBZ, Table S2: CCD matrix and analytical response: preconcentration of fluroquinolones, Table S3: CCD matrix and analytical response: preconcentration of parabens, Table S4: Analytical characteristics of the DMSPE-HPLC-DAD method for determination of atenolol, propranolol and carbamazepine, Table S5: Analytical characteristics of the DMSPE-HPLC-DAD method for determination of DANO, ENRO and LEVO, Table S6: Analytical characteristics of the DMSPE-HPLC-DAD method for determination of four parabens, Table S7: Determination of atenolol, propranolol and carbamazepine in real water samples (ng L−1, n = 3), Table S8: Determination of DANO, ENRO and LEVO in real water samples (ng L−1, n = 3), Table S9: Determination of MP, EP, PP and BP in real water samples (ng L−1, n = 3), Figure S1: 3D plots for the interaction of optimum parameters for preconcentration of beta-blockers and anticonvulsants, Figure S2: 3D plots for the interaction of optimum parameters for preconcentration of three fluoroquinolones, Figure S3: 3D plots for the interaction of optimum parameters for preconcentration of four parabens, Figure S4: Typical chromatograms for preconcentration of beta-blockers and anticonvulsants in influent samples, Figure S5: Typical chromatograms for preconcentration of fluoroquinolones in effluent samples, Figure S6: Typical chromatograms for preconcentration of parabens in influent samples.

Author Contributions

Conceptualization, G.P.M. and P.N.N.; methodology, G.P.M., A.M. and P.N.N.; software, P.N.N.; validation, G.P.M.; formal analysis, G.P.M., A.M. and P.N.N.; investigation, G.P.M.; resources, P.N.N.; data curation, G.P.M. and A.M.; writing—original draft preparation, G.P.M. and A.M.; writing—review and editing, G.P.M. and A.M.; visualization, G.P.M.; supervision, P.N.N.; funding acquisition, G.P.M. and P.N.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the University of Johannesburg, South Africa (Department of Chemical Sciences) and National Research Foundation (grant nos. 91230 and 118991, South Africa).

Acknowledgments

The authors wish to thank the University of Johannesburg, Department of Chemical Sciences for providing the laboratory space.

Conflicts of Interest

The authors declare no conflict of interest.

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Nanomaterials 11 00540 g001 550
Figure 1. Recoveries of different elution volumes using different solvents for residual analytes. (A) Anticonvulsants and β-blockers (atenolol (ANL), propranolol hydrochloride (PPNL) and carbamazepine (CBZ)), (B) parabens (methylparaben (MP), ethylparaben (EP), propylparaben (PP) and butylparaben (BP)), (C,D) fluoroquinolones (danofloxacin (DANO), enrofloxacin (ENRO), levofloxacin (LEVO)).
Figure 1. Recoveries of different elution volumes using different solvents for residual analytes. (A) Anticonvulsants and β-blockers (atenolol (ANL), propranolol hydrochloride (PPNL) and carbamazepine (CBZ)), (B) parabens (methylparaben (MP), ethylparaben (EP), propylparaben (PP) and butylparaben (BP)), (C,D) fluoroquinolones (danofloxacin (DANO), enrofloxacin (ENRO), levofloxacin (LEVO)).
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Figure 2. Pareto chart for standardized effects on variables for preconcentration of (A) β-blockers and anticonvulsants, (B) fluoroquinolones and (C) parabens.
Figure 2. Pareto chart for standardized effects on variables for preconcentration of (A) β-blockers and anticonvulsants, (B) fluoroquinolones and (C) parabens.
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Figure 3. Desirability function for optimization of independent variables for (A) β-blockers and anticonvulsants, (B) fluoroquinolones and (C) parabens.
Figure 3. Desirability function for optimization of independent variables for (A) β-blockers and anticonvulsants, (B) fluoroquinolones and (C) parabens.
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Table
Table 1. List of pharmaceutical and personal care product (PPCP) compounds, chemical structures, molecular masses and pKa values.
Table 1. List of pharmaceutical and personal care product (PPCP) compounds, chemical structures, molecular masses and pKa values.
Analytes ClassChemical Structures Molecular Mass (g mol−1) pKa Values
DanofloxacinFluoroquinolones Nanomaterials 11 00540 i001 357.37 6.22 and 9.43
Enrofloxacin Fluoroquinolones Nanomaterials 11 00540 i002 359.4 6.19 and 7.59
Levofloxacin Fluoroquinolones Nanomaterials 11 00540 i003 361.368 6.02 and 8.15
Atenolol β-blockers Nanomaterials 11 00540 i004 266.336 9.6
Propranolol hydrochlorideβ-blockers Nanomaterials 11 00540 i005 295.80 9.4
Carbamazepine Anticonvulsant Nanomaterials 11 00540 i006 236.27 13.9
Methyl paraben Preservatives Nanomaterials 11 00540 i007 152.15 8.3
Ethyl paraben Preservatives Nanomaterials 11 00540 i008 166.17 8.3
Propyl paraben Preservatives Nanomaterials 11 00540 i009 180.20 8.2
Butyl paraben Preservatives Nanomaterials 11 00540 i010 194.23 8.2
Table
Table 2. Summary of analytical characteristics of the DMSPE-HPLC-DAD method for determination of β-blockers and anticonvulsants, fluoroquinolones and parabens.
Table 2. Summary of analytical characteristics of the DMSPE-HPLC-DAD method for determination of β-blockers and anticonvulsants, fluoroquinolones and parabens.
Analytical Performancesβ-Blockers and AnticonvulsantsFluoroquinolones Parabens
LODs (ng L−1)0.1–0.70.45–1.10.2–0.8
LOQs (ng L−1)0.33–2.31.5–3.70.67–2.7
Linearity (µg L−1)LOQ-400LOQ-1000LOQ-300
R20.9987–0.99910.9979–0.99900.9987–0.9993
Repeatability (%RSD)1.9–2.51.8–3.41.5–2.7
Reproducibility (%RSD)3.1–4.32.8–4.42.9–4.4
Table
Table 3. Comparison of the present study with other solid-phase extraction methods for analysis of multi-class pollutants.
Table 3. Comparison of the present study with other solid-phase extraction methods for analysis of multi-class pollutants.
Adsorbent/MethodMass of Adsorbent (mg)AnalytesLOD (µg L−1)Linearity (µg L−1)Correlation Coefficient (R2)Refs.
MM-CMC/IT-SPME-HPLC-FLDN/ADANO, ENRO0.14–0.610.001–5.00.9980[51]
Oasis HLB-SPE-LC/MS/MS60Atenolol, carbamazepine1.01–69.301.87–138.60.9669–0.9999[52]
Carbowax 20M/FPSE/GC-MSNot indicatedEP, BP0.009–0.0210,05–5000.9992–0.9997[22]
OasisHLB/ RDSE/GC-MS40MP, EP, PP, BP0.02–0.150.06–0.440.9904–0.9989[23]
Mixed mode cationic exchange cartidges (MXC)60MP0.010.06–11220.9999[1]
C18,Floracil, QuEChERS/UPLC-QqQ-MS50 mgBeta-blockers: Atenolol, propranolol; Preservatives: BP, MP, PP; Anticonvulsant: carbamazepine0.093–0.121.0–200.0>0.95[53]
MMPC/Cyc-Chit/HPLC-DAD50 mgBeta-blockers: atenolol, propranolol0.0001–0.0007LOQ-4000.9987–0.9991This work
Parabens: MP, EP, PP, BP0.0001–0.0007LOQ-3000.9987–0.9993
Anticonvulsant: carbamazepine0.0003LOQ-3500.9989
Quinolones: DANO, LEVO, ENRO0.00045–0.0011LOQ-10000.9987–0.9990
Table
Table 4. Determination of β-blockers and anticonvulsants, fluoroquinolones and parabens in real water samples (n = 3).
Table 4. Determination of β-blockers and anticonvulsants, fluoroquinolones and parabens in real water samples (n = 3).
SamplesAnalytesInitial (ng L−1)Found (ng L−1) a%R%RSDFound (ng L−1) b%R%RSD
Influent β-blockers, anticonvulsantsND-28.942.7–72.186.3–94.42.8–4.593.3–11887.8–93.31.6–3.4
FluoroquinolonesND-7.334.87–12.193.8–97.33.9–4.219.1–33.992.8–1003.7–4.3
Parabens1.37–93746.2–98489.7–94.32.3–3.192.7–107891.3–96.12.6–3.5
Effluent Β-blockers, anticonvulsantsND-7.6548.4–55.495.4–98.73.1–4.395.5–10596.8–97.11.7–4.9
Fluoroquinolones ND-2.074.92–6.8896.2–98.32.4–3.219.3–21.394.4–98.12.1–3.1
ParabensND-43.148.7–92.797.3–99.11.9–2.496.5–14196.5–97.91.8–2.8
Tap waterβ-blockers, anticonvulsantsND48.9–49.697.7–99.12.6–3.598.9–99.598.0–99.52.0–4.0
Fluoroquinolones ND4.91–4.9898.1–99.61.4–1.819.8–19.998.9–99.51.7–1.8
ParabensND-4.8149.0–53.897.9–99.31.9–2.497.8–10497.8–99.31.3–1.6
River waterβ-blockers, anticonvulsantsND-4.9249.5–53.397.0–98.92.2–4.299.1–10297.0–99.11.7–2.9
FluoroquinolonesND-3.124.94–8.0294.8–98.71.9–2.519.4–22.495.6–96.31.0–1.4
ParabensND-40.148.5–88.896.6–98.81.7–2.497.1–13796.6–98.21.3–1.9
a Samples spiked with 5 ng L−1 for fluoroquinolones and 50 ng L−1 for β-blockers, anticonvulsants and parabens. b Samples spiked with 20 ng L−1 for fluoroquinolones and 100 ng L−1 for β-blockers, anticonvulsants and parabens.
Table
Table 5. Global concentrations (ng L−1) of β-blockers and anticonvulsants, fluoroquinolones and parabens in environmental samples.
Table 5. Global concentrations (ng L−1) of β-blockers and anticonvulsants, fluoroquinolones and parabens in environmental samples.
Country β-BlockersAnticonvulsants Fluoroquinolones Parabens References
South Africa0.96–39,0004.0–94110–22570–1988[31,36,44,45,54]
Latvia0–15018–50250–400-[55]
Egypt0–1870–342-0–6380[56,57,58]
Kenya-0–430-30–1160[59,60]
Spain10–606628–2830–215314–720[21,61,62,63]
Italy0–570–137--[64]
Pakistan0.99–45211–152–37,000110–228[65,66]
China0–99523–1150–20320–5960[65,66,67,68,69,70,71]
Brazil0.02–1.8969 90–788[72,73,74,75]
Canada114 2034 -[76,77,78]
Poland69–205 2.0248.70.01–5.03[79,80,81,82]
Portugal220–6900.32–1.60 2.1–51[83,84]
Turkey 0.92–24.25 17,000–33,000[85]
United Kingdom93–38813–561802642–11,601[86,87]
South Africa0–28-0–430–937This study
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Mashile, G.P.; Mpupa, A.; Nomngongo, P.N. Magnetic Mesoporous Carbon/β-Cyclodextrin–Chitosan Nanocomposite for Extraction and Preconcentration of Multi-Class Emerging Contaminant Residues in Environmental Samples. Nanomaterials 2021, 11, 540. https://doi.org/10.3390/nano11020540

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Mashile GP, Mpupa A, Nomngongo PN. Magnetic Mesoporous Carbon/β-Cyclodextrin–Chitosan Nanocomposite for Extraction and Preconcentration of Multi-Class Emerging Contaminant Residues in Environmental Samples. Nanomaterials. 2021; 11(2):540. https://doi.org/10.3390/nano11020540

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Mashile, Geaneth Pertunia, Anele Mpupa, and Philiswa Nosizo Nomngongo. 2021. "Magnetic Mesoporous Carbon/β-Cyclodextrin–Chitosan Nanocomposite for Extraction and Preconcentration of Multi-Class Emerging Contaminant Residues in Environmental Samples" Nanomaterials 11, no. 2: 540. https://doi.org/10.3390/nano11020540

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