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. 2024 Mar 6;9(11):12564–12574. doi: 10.1021/acsomega.3c05008

Blighia sapida Waste Biochar in Batch and Fixed-Bed Adsorption of Chloroquine Phosphate: Efficacy Validation Using Artificial Neural Networks

Adejumoke Abosede Inyinbor †,‡,*, Deborah Temitope Bankole †,‡,*, Abimbola Peter Oluyori †
PMCID: PMC10955583  PMID: 38524418

Abstract

graphic file with name ao3c05008_0008.jpg

The present study investigated the potency of biochar prepared from Blighia sapida seedpods (BSSPs) in the uptake of chloroquine phosphate (CQP) from single-component batch and multicomponent fixed-bed adsorption systems. BSSPs presented a highly porous structure with a BET surface area of 1122.05 m2/g, to which adsorption efficiency correlated. The Dubinin–Radushkevich isotherm energy was obtained as 129.09 kJ/mol, confirming the chemisorption nature of the BSSP-CQP adsorption system. The efficiency of the artificial neural network (ANN) was evaluated using the lowest mean square error (MSE = 7.27) and highest correlation coefficient (R2 = 0.9910). A good agreement between the experimental results and the ANN-predicted data indicated the efficiency of the model. The percentage removal of 95.78% obtained for the column adsorption studies indicated the effectiveness of BSSPs in a multicomponent system. The mechanism of the interaction proceeded via hydrogen bonding and electrostatic attraction. This was confirmed by the high desorption efficiency (69.11%) with a HCl eluent. The degree of reversibility was found to be 2.95, indicating the reusability potential of BSSPs. BSSPs are therefore considered multilayered adsorbents with potential applications in pharmaceutical wastewater treatment.

1. Introduction

Pharmaceuticals are complex molecules developed to preserve animal and human health.1,2 These molecules, on the other hand, are part of emerging contaminants discovered in the environment that have the potential to harm both humans and the environment. The extent of their toxicity has become a major concern as effects upon human and aquatic organism exposure are yet to be fully understood.3 Chloroquine phosphate (CQP) is a member of the quinoline group, which has bactericidal, antipyretic, and antiseptic properties.4 CQP has been primarily used in the treatment of malaria, and its enhanced antitumor activity has led to its use in cancer therapy. In addition, CQP has been used as an antifungal and for the treatment of rheumatic diseases and management of influenza A/H5N1, HIV, and SARS-CoV viruses.5−7

Regardless of the numerous benefits, CQP has been associated with serious side effects such as hypokalemia, blindness, cardiac arrest, arrhythmias, retinopathy, cerebral edema, renal failure, and, in the case of overdose, death of the embryo in treatments during pregnancy.4,8,9 Extensive use and overproduction of pharmaceuticals such as CQP can lead to their release into aquatic systems via sewage effluents, pharmaceutical industrial wastewater, and hospital effluents.10,11 Given their lipophilic nature and resistance to degradation, pharmaceuticals are persistent and hence a serious concern.11 In a bid to achieve a cleaner environment and sustainable community goals, a less expensive, adaptable, and effective wastewater treatment technique is required. Adsorption of various water pollutants using agricultural wastes has captivated the interest of environmental scientists.12−15 This is because agrowastes are readily available, amenable, effective, and low-cost materials suitable as adsorbent precursors.

The adsorption process is a complex process that is influenced by several operational parameters. Modeling such complex processes with simple linear multivariate correlations is difficult.16 The artificial neural network (ANN) is a model that can incorporate mathematical functions into nonlinear and linear systems. The ANN is superior to conventional models in terms of simplicity, capacity, and speed.16−18 Although the forecast and prediction usually made using the conventional adsorption data treatment techniques can be considered effective, a validation using artificial intelligence such as the ANN is superior. In addition, the use of the ANN to predict the adsorption efficiency of pharmaceuticals onto adsorbents from agrowastes is scarce, and therefore, it is one of the values of this study.

This novel study therefore surveys the following line of thoughts: baseline batch adsorption of CQP from aqueous media using acid-activated biochar prepared from Blighia sapida seedpods (BSSPs) was carried out. The equilibrium and kinetic adsorption data were fitted by using the isothermal and kinetic models. The adsorption data were further fitted and validated using the ANN, and desorption studies were used to assess the strength of the BSSP-CQP bonds. Furthermore, we investigated the antagonist and synergistic effects of a multicomponent system via fixed-bed adsorption for the feasibility of industrial applications. This was achieved by testing the efficacy of BSSPs on CQP removal in the presence of other contaminants via column adsorption. The effects of the column flow rate, BSSP bed height, and CQP initial concentration were all investigated to establish the conditions for effective removal. This study established the possibility of BSSP efficiency in real pharmaceutical industrial wastewater applications.

2. Materials and Methods

2.1. Collection, Pretreatment, and Acid Activation of B. sapida Wastes

B. sapida wastes were locally sourced from the Omu Aran community, in Kwara State, Nigeria (Latitude 8°8′24.82″ N, Longitude 5°5′46.73″E). Deionized water was utilized to thoroughly clean the obtained wastes, and they were then dried in an oven for 24 h at 105 °C. An Armfield hammermill FT2-A (serial number: 35404001) was used to reduce the particle size. 98% orthophosphoric acid was added to the sample in a ratio of 1:3. The mixture was heated until a paste was formed and charred at 350 °C for 90 min in a muffle furnace. The resulting biochar was washed with deionized water until a neutral pH was attained and dried in the oven at 105 °C for 24 h. A BSSP label was attached to the prepared biochar, which was then stored for later use.

2.2. Pharmaceutical Compounds and Reagents

The pharmaceutical company May & Baker located in Lagos, Nigeria, graciously gifted the chloroquine phosphate (batch no. 19098C3RT), ibuprofen (batch no. 1201/19SA-2993), ciprofloxacin (batch no. 105-200705-1), and lumefantrine (batch no. 21021LU3R11) used in this research. Additionally, only analytical-grade reagents and chemicals were used, while all solutions in this experiment were prepared with deionized water.

2.3. Characterization of BSSPs

The pH point of zero charge (pHpzc) was determined by adjusting 50 mL of 0.1 M NaCl between pH 2 and 10 with HCl and NaOH for acidic and basic medium, respectively. 0.1 g of BSSPs was introduced to each of the preadjusted NaCl solutions. The mixture was stirred for 24 h, and the final pH was recorded. The pHpzc value was determined as the intersection of the curve from the plot of (pHfinal – pHinitial) vs pHinitial.19,20 Other parameters such as percentage moisture content, volatile matter, ash content, and total fixed carbon were analyzed using the methods described by Ekpete et al. and Bello et al.7,21 The functional groups present on the surface of BSSPs were investigated using a Fourier transform infrared (FTIR) model (Nicolet iS10) in the range of 4000–400 cm–1. The BET surface area analysis was carried out using a micrometric porosity and surface area analyzer. The surface morphology and topography of BSSPs before and after CQP uptake were obtained by using scanning electron microscopy (SEM) (model: JEOL JSM-7600F, Japan) with an accelerating voltage ranging 10.0 kV, and an image at 500× magnification was selected for discussion.22

2.4. Adsorption Studies

2.4.1. Batch Adsorption Conditions for CQP Uptake onto BSSPs

Different adsorption experimental conditions were investigated for batch studies. Parameters such as pH (2, 4, 6, 8, and 10), initial CQP concentration (10, 20, 30, 40, and 50 mg/L), temperature (25, 35, 45, and 55 °C), and BSSP dosage (0.05, 0.10, 0.15, 0.20, and 0.25 g) were investigated. The adsorption processes were carried out in 250 mL conical flasks with 100 mL of CQP solution and 0.10 g of BSSP adsorbent, and a temperature-controlled water bath shaker was used for the entire time necessary to attain equilibrium. The supernatant was separated from the mixture, and the concentration of CQP after adsorption was estimated using a UV–visible spectrophotometer. A 0.1 M solution of hydrochloric acid and 0.1 M sodium hydroxide were employed to modify the pH of 100 mL of CQP solution for acidic and basic media, respectively. The optimal pH was then used throughout the experiment. Equation 1 was used to calculate the amount of CQP adsorbed onto BSSPs at different time intervals, and eq 2 was used to evaluate the removal efficiency, where Co and Ce are the initial and equilibrium concentrations of CQP, respectively, V is the volume of the adsorbate, qt is the quantity adsorbed at time t, and W is the mass of the BSSP adsorbent.15,23,24

2.4.1. 1
2.4.1. 2

2.4.2. Development of Optimal Conditions for Artificial Neural Network Prediction

The artificial neural networks (ANNs) are models made up of layers (input and output) that are linked by nodes. ANN topography is composed of layers, the number of neurons that act as processing units in each layer, and the transfer functions.16,17,24 This segment aimed to develop an ANN model that could successfully predict the efficiency of CQP adsorption with the best fit. In this work, the ANN model was developed in (MATLAB 2016a) mathematical software using a three-layered back-propagation neural network. The model was developed using the obtained datasets from the experimental procedure. The input variables include pH, initial CQP concentration, contact time, temperature, and BSSP dosage, while the adsorption efficiency (%) obtained from the input variables was used as the target. A total of 1,308 datasets were obtained from the effects of operational variables such as pH (2–10), initial CQP concentration (10–50 mg/L), contact time (5 min–equilibrium time), temperature (25–55 °C), and BSSP dosage (0.05–0.25 g) and their corresponding adsorption efficiencies. These datasets were divided into 70% (916 datasets), 15% (196 datasets), and 15% (196 datasets) for training, validation, and testing, respectively. The Levenberg–Marquardt algorithm was used to evaluate the best fit by varying the number of neurons in the hidden layer between 1 and 20. The hidden neuron with the lowest mean square error (7.27) and highest correlation coefficient (0.9910) was selected in varying the activation functions (Purelin, Tansig, and Logsig). Also, the activation function with the highest correlation coefficient (0.9855) was then used to predict the adsorption efficiency. In summary, the network with the optimal parameters (5-11-1) was then used to predict the adsorption efficiency.

2.4.3. Composition of the Simulated CQP Multicomponent Effluent

Batch adsorption studies of a single-component system involve the removal of one pollutant. However, industrial wastewater contains various pollutant molecules that can interact and compete with each other. In the case of pharmaceutical wastewater, these molecules are components of drug actives and other drug excipients used in the manufacturing process. However, there can be antagonistic, synergistic, and noninteraction between these molecules, which can make an adsorption process complicated.25,26 This segment was investigated to assess the efficiency of BSSPs for the uptake of CQP in the presence of other drug excipients. Two simulated multicomponent pharmaceutical effluents contain several drug excipients that are commonly found in untreated pharmaceutical effluents. The simulation was carried out following the idea of Lima et al. 2019 and Saucier et al. 2015.27,28 The component ratio was also developed according to the Handbook of Pharmaceutical Excipients.29 The simulated CQP multicomponent effluent’s composition is presented in Table 1.

Table 1. Components of Simulated Pharmaceutical Effluents.
  concentration (mg/L)
wastewater contents effluent A effluent B
phosphate 20 40
lumefantrine 5 10
ibuprofen 5 10
ciprofloxacin 5 10
sucrose 10 20
starch 5 10
sodium sulfate 5 10
aluminum oxide 5 10
calcium carbonate 10 20
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2.4.4. Fixed-Bed Adsorption Conditions and Operation Parameters for CQP Uptake onto BSSPs

We conducted the fixed-bed adsorption experiments on a pilot scale using a 35 cm length × 2.5 cm inner diameter column.1,30,31 The BSSP sample was packed between two layers of glass wool (2 cm each) at the top and bottom of the column to ensure an even flow of the influent and to prevent the adsorbent from flowing out. The CQP solution of known concentration was fed into the column, while the outlet solution was sampled by taking 10 cm3 at different time intervals. Fixed-bed parameters such as BSSP bed height (1 and 2 cm), column flow rate (6 and 12 mL/min), and initial CQP concentration (25 and 50 mg/L) were analyzed. Breakthrough curves, which express CQP concentration with respect to time, can explain the packed bed’s performance. The breakthrough time tb, time (ts), and exhaustion time (te) were calculated at Ce = 0.5 and 0.9. The values were extracted from the graph of the plot of Inline graphic. Equations 3a and 3b were used to calculate the maximum adsorption capacity of BSSPs (qs and qb in mg/g), where Q denotes the column’s flow rate, w is the adsorbent mass, Co is the initial CQP concentration, and Ct is the CQP concentration at time t. The obtained data from experiments were modeled using the Yoon–Nelson and Thomas models, to obtain additional information on the column’s performance.32,33

2.4.4. 3a
2.4.4. 3b

2.5. Desorption Studies of the BSSP-CQP System

After the adsorption of CQP onto BSSPs, the spent adsorbent was immediately rinsed with deionized water to eliminate the unadsorbed CQP layer on the BSSP’s surface. The spent BSSP was contacted with four eluents: 0.1 M hydrochloric acid (HCl), deionized water, 0.1 M sodium hydroxide (NaOH), and 0.1 M sodium chloride (NaCl). The desorption experiments were conditioned at 26 °C, 1 h, 130 rpm, 0.1 g of BSSP, and 40 mL for each of the eluents. The desorption efficiency was calculated using eq 4. The degree of reversibility was also investigated by calculating the desorption index (DI) using eq 5, where qde and qad are the quantity desorbed by each of the eluents and quantity adsorbed during the adsorption processes, respectively34

2.5. 4
2.5. 5

3. Results and Discussion

3.1. Adsorbent Characterization

The pH point of zero charge (pHpzc) is the point at which the material’s surface charge is zero. At pH values above pHpzc, the adsorbent’s surface is negatively charged, favoring the adsorption of positively charged molecules and vice versa.20,35 As indicated in Figure S1, pHpzc was determined to be 3.00. The moisture, ash, and volatile matter content values obtained were all significantly low, indicating that BSSPs are a suitable adsorbent for use in wastewater treatment.36 BSSPs demonstrated a highly porous structure with a BET surface area of 1122.05 m2/g, which was connected to the adsorption efficiency. Furthermore, the topography of the BSSP revealed unique interior pores, which are critical for efficient adsorption.

3.2. Batch Adsorption and Effects of Operational Parameters

3.2.1. Effects of pH

The pH of a solution significantly impacts interactive sorption.14,37 Pharmaceuticals exist in the anionic form at pH above pKa and the neutral form at pH below pKa. CQP has three pKa values of 4.0, 8.10, and 10.20.38 A maximum removal efficiency of 94.10% was obtained at pH 8, which was subsequently utilized for varying other parameters. High efficiencies of 76.08 and 96.10% were between pH 2 and 9. This could be explained as follows: the ionization constants of CQP are 8.10 and 10.20 corresponding to the nitrogen quinoline ring and diethylamino side chain nitrogen ring, respectively, while between pH 4 and 5, CQP is biprotonated.39 BSSP is, however, cationic at pH below pHpzc (3.0)20,22,40 and can form hydrogen bonding with the basic sites of CQP. BSSPs can also exist as anionic at pH above 3.0.41 The synergistic effect of various existences of CQP as well as multiple surface charges of BSSPs can explain the high efficiencies obtained between pH 2 and 9. Above pH 9, CQP is negatively charged, while the BSSP surface is negatively charged (pH above pHpzc), resulting in electrostatic repulsion. Hence, a sharp drop in the removal efficiency from 94.10 to 45.40% was observed.42 The comparison of experimental and ANN-predicted data for the influence of pH on CQP removal by BSSPs is shown in Figure 1. The comparison shows that the ANN model displayed an outstanding performance in predicting the data. Furthermore, the optimal ANN that gave the best prediction captured the system’s experimental variability, as the predicted data trend is similar to the experimental data trend. This indicates that the network has the potential to predict real data.43

Figure 1.

Figure 1

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Effect of pH on the uptake of CQP onto BSSPs.

3.2.2. Effects of the BSSP Dosage

The percentage removal increased marginally from 91.81 to 99.30% as the BSSP dosage increased to 0.10 from 0.05 g/100 mL (Figure 2). This may be attributed to the rise in the number of active sites that are currently available as the BSSP dosage increased, which in turn increased the surface area, resulting in a higher percentage removal.44 Also, the percentage removal decreased slightly as the dosage was further increased from 0.10 to 0.25 g (99.30–92.61%). This could be due to active site overlapping caused by excess BSSP particles.45 The ANN prediction and experimental results as a function of the BSSP dosage showed that they were in good agreement.

Figure 2.

Figure 2

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Effect of the BSSP dosage on the uptake of CQP removal.

3.2.3. Effects of the CQP Initial Concentration and Contact Time

Figure S2 presents the effect of contact time on the percentage removal of CQP at various concentrations (20, 30, 40, and 50 mg/L). For all of the investigated concentrations, CQP removal increased as the contact time increased. There was a rapid initial increase in the quantity adsorbed in the first 60 min, which slowed down until equilibrium was achieved. The rapid adsorption rate was ascribed to the fact that there are many existing adsorption sites on the BSSPs for CQP uptake at the beginning of adsorption. Adsorption rates slowed as adsorption sites became saturated.13,46 The evaluation of the experimental results with ANN predictions, as shown in Figure S1, was found to be in good agreement.

3.3. Kinetic Studies of CQP Uptake onto BSSPs

Five kinetic models were used to predict the mechanism and possible rate-controlling steps of the BSSP-CQP adsorption system. The models of pseudo-first-order (PFO), pseudo-second-order (PSO), Elovich, intraparticle diffusion (ID), and liquid film diffusion (LFD) were studied. The linear plots of these models and values derived for their parameters are depicted in Table 2. The estimated correlation coefficients (R2), Chi-square (χ2), and average relative error (ARE) established that the PSO model better described the experimental data. The estimated errors for the PSO model were very low (0.002–0.17 for χ2 and 0.29–1.04 for ARE). The R2 values (0.9980–0.9991) were obtained to be close to unity across all the investigated concentrations, indicating the suitability of the PSO model.32,47 The desorption coefficient (βEI) for the Elovich model was discovered to decrease as the starting CQP concentration increased, whereas the chemisorption rate (αEI) increased. Also, the values of αEI are greater than those of βEI, indicating that chemisorption governs the adsorption process.48 The intercept (C) values, which predicted the thickness of the boundary layer for the ID model, increased from 1.6256 to 3.1061 across the investigated concentrations (10–50 mg/g). This suggested the contribution of the boundary layer to the mechanism of adsorption.31 However, the R2 values of liquid film diffusion are greater than those of the ID model, which indicated that external diffusion and surface diffusion dominated the adsorption process.49,50 Similar findings from kinetic studies have been reported.32,51

Table 2. Kinetic Model Parameters of CQP Uptake onto BSSPsa.

 
 initial CQP concentration (mg/L)
  10 20 30 40 50
  qe,exp. 9.48 17.08 26.71 35.58 45.46
kinetics linear equation evaluated parameters  
pseudo-first-order log(qe – qt) = ln qe – K1t qe,cal. 4.37 12.91 15.18 26.51 31.51
K1 (min) 0.02 004 0.02 0.02 0.02
ARE 19.52 5.38 12.66 5.71 7.38
χ2 5.98 1.35 8.77 3.10 6.18
R2 0.9750 0.9931 0.9794 0.9930 0.9915
pseudo-second-order Inline graphic qe,cal. 9.64 17.79 27.85 37.73 48.31
K2 (min) 0.010 0.010 0.003 0.002 0.001
ARE 0.2895 0.6903 0.7094 1.0084 1.0445
χ2 0.0028 0.0281 0.0464 0.1228 0.1681
R2 0.9991 0.9988 0.9992 0.9980 0.9979
Elovich Inline graphic αEI (mg/(g min)) 20.67 36.65 41.88 53.99 61.51
βEI (g/mg) 0.8344 0.3361 0.2301 0.1549 0.1238
R2 0.9571 0.9429 0.9502 0.9443 0.9388
intraparticle diffusion qt = KDifft0.5 + C C1 1.6256 1.4822 1.3315 2.4518 3.1061
KDiff (mg/(g min1/2)) 1.2338 1.9805 3.1439 3.6592 4.7519
R2 0.9800 0.8581 0.9459 0.8855 0.8936
liquid film diffusion Inline graphic KLFD 0.02 0.03 0.02 0.03 0.03
R2 0.9783 0.9818 0.9788 0.9810 0.9638
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a

qe is the quantity of CQP adsorbed at equilibrium (mg/g), qt is the quantity of CQP adsorbed at time, t (mg/g), K1 is the rate constant of pseudo-first-order (maccount), t is the time (min), K2 is the rate constant of pseudo-second-order (g/(mg min)), α is the constant related to the rate of chemisorption, β is the constant that depicts the extent of the surface coverage, KDiff is the rate constant for intraparticle diffusion (mg/(g min1/2)), C extends insights into the boundary layer thickness, and KLFD is the coefficient rate for the process corresponding to the BSSP particle size.

3.4. Isothermal Studies on CQP Uptake onto BSSPs

Adsorption isotherms help describe the interactions of adsorbents and adsorbates at equilibrium. Each model reveals specific information, depending on individual underlying theories. The Hills, Langmuir, Hasley, Freundlich, and Dubinin–Radushkevich isotherms were selected to evaluate the BSSP-CQP adsorption system. The maximum monolayer adsorption capacity of the Langmuir isotherm was found to be 99.01 mg/g. The value of n for the Freundlich model was obtained to be greater than 1, which validated the favorability of the adsorption process.52 The correlation coefficient (R2 = 0.9729) of the Freundlich model was found to be larger than that of Langmuir (0.9358). This implied that multilayer adsorption occurred in the BSSP-CQP system. Also, the R2 values of the Hasley isotherm were obtained to be 0.9729, while that of the Hill isotherm was obtained to be 0.9546 (lower than that of Hasley). This confirmed the multilayered nature of the BSSP-CQP adsorption system.12 The Dubinin–Radushkevich isotherm adsorption energy (E) for the BSSP-CQP system was obtained to be 129.09 kJ/mol (higher than 8 kJ/mol), which indicated that the adsorption process is primarily through chemisorption.53 The result agrees with some reported studies using activated carbon prepared from agricultural wastes.54,55 Various evaluated isotherm parameters are listed in Table 3.

Table 3. Isotherm Model Parameters of CQP Uptake onto BSSPsa.

model linear equation evaluated parameters
Langmuir Inline graphic qmax (mg/g) 99.01
KL (L/mg) 0.3447
R2 0.9358
Freundlich Inline graphic KF (mg/g) 24.74
n 1.4112
R2 0.9729
Hasley Inline graphic 1/nH 0.7089
KH 0.0100
R2 0.9729
Hills Inline graphic nH 1.0173
KD 1.5900
qH 99.01
R2 0.9550
Dubinin–Radushkevich ln qe = ln qm – ADRKε2 qDR (mg/g) 51.99
Inline graphic E (kJ/mol) 129.09
Inline graphic R2 0.9988
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a

Co symbolizes the initial CQP concentration in mg/L, Ce represents the CQP concentration in mg/L, qe represents the amount of CQP adsorbed per mass of BSSP in mg/g, qm denotes the maximum monolayer adsorption capacity of BSSPs in mg/g, KL denotes the Langmuir constant in L/mg, KF denotes the Freundlich constant in mg/g, whose heterogeneity extent increases as the value approaches zero, nH and KD are Hill’s isotherm constants, KH is Hasley’s isotherm constant, B is the Redlich-Peterson constant, qDR is the theoretical adsorption isotherm saturation capacity in mg/g, and E denotes the mean free adsorption energy, which predicts the type of adsorption in kJ/mol.

3.5. Temperature Effects and Thermodynamic Studies

The role of temperature in controlling the strength of the adsorptive force of interaction between adsorbents and adsorbates is very crucial.47 The temperature effect on the removal of CQP onto BSSPs was investigated at 26, 35, 45, and 55 °C as shown in Figure 3. As the temperature increased, BSSP percentage removal also increased (92.76–98.19%); therefore, high temperature favored the adsorption system. This is because as the temperature of the adsorption system elevated, the mobility of the ions in solution increased, leading to increased interactions between BSSPs and CQP molecules.56

Figure 3.

Figure 3

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Effect of the temperature on the uptake of CQP onto BSSPs.

The ANOVA test and the posthoc test using the Duncan multiple range test (DMRT) were analyzed. The obtained P-value (0.072) was found to be greater than 0.05 (Table S6), which implied that there exists a significant difference among the investigated temperatures. Also, the DMRT analysis (Tables S7 and S8) showed that there is a slight difference between the percentages adsorbed at 26 and 35 °C, a slight difference between 35 and 45 °C, and a significant difference between 26 and 55 °C.

The entropy, Gibb’s free energy, and enthalpy are thermodynamic concepts evaluated alongside adsorption isotherms, which provide further insight into the nature of the adsorbate–adsorbent interaction. Table S1 displays the calculated thermodynamic parameters. The negative values of ΔG° (−6.34 to −10.89 kJ/mol) established the adsorption process’s spontaneity and feasibility. As the temperature increases, the process becomes more feasible. The positive value of ΔH° (+38.44 kJ/mol) established the BSP1-CQP system’s endothermic nature, whereas the positive value of ΔS° (+149.83 J/K/mol) reflected the adsorption system’s increased randomness.57

3.6. Postadsorption Analysis

Surface and morphological characterizations of BSSPs before and after CQP adsorption provided evidence of a successful adsorption process. The scanning electron microscopy revealed pores of different shapes and sizes before adsorption (Figure 4a). However, after the uptake of CQP (Figure 4b), these openings were observed to have been covered with what appeared like interwoven web-like matter. This is a result of the impregnation of the pores and attachment of CQP molecules, which is morphological evidence of the adsorption process.58

Figure 4.

Figure 4

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Surface morphology of the BSSP (a) before and (b) after the uptake of CQP.

With the aid of Fourier transform infrared spectroscopy, the surface chemistry of BSSPs was examined both before and after the adsorption process. Figure 5 displays the FTIR spectra of BSSPs before and after CQP uptake. The main characteristics of the FTIR spectrum include the band at 3410 cm–1 associated with −OH and N–H stretching, a low intensity band at 2880 cm–1 associated with C–H stretching, sharp peaks at 1620 cm–1 associated with C=O, bands at 1460 cm–1 associated with the aliphatic C–H stretching, a weak peak at 1240 cm–1 associated with the C–O stretching of aryl esters and phenols or O–H deformation of COOH, and the band at 1090 cm–1 assigned to the C–O stretch.59,60 The shifts in intensities observed confirmed the contribution of these functional groups in the adsorption system.61

Figure 5.

Figure 5

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FTIR spectra of BSSPs before and after the uptake of CQP.

3.7. Effects of Operational Parameters on Column Adsorption of CQP

3.7.1. Effects of the Flow Rate

While operational factors such as pH, bed height, and CQP initial concentration were kept steady at 8, 1 cm (equals 0.308 g of BSSP), and 50 mg/L, respectively, the flow rate was varied between 6 and 12 mL/min. At flow rates of 6 and 12 mL/min, as shown in Figure 7, when the flow rate was increased from 6 to 12 mL/min, the breakthrough time dropped from 790 to 240 min as illustrated in Figure S3. The amount of CQP adsorbed decreased as well, from 59142.86 to 14882.35 mg/g. This is because an elevation in the rate of flow decreased the time of physical contact between CQP and BSSPs. This causes the bulk of the CQP molecules to exit the column without proper contact with BSSP pores. As a result, the resistance mass transfer increases, creating an increased driving force in the column and hence resulting in lower service time of the bed and decreased quantity adsorbed.62

Figure 7.

Figure 7

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Desorption of CQP from the BSSP surface using different eluents.

3.7.2. Effects of the Initial Concentration

Figure S4 illustrates breakthrough curves for the effect of initial CQP concentration for 25 and 50 mg/L. pH, bed depth (0.308 g equiv of BSSP), and flow rate were all held at 8, 1, and 12 mL/min, respectively. The breakthrough and exhaustion times decreased from 15 to 12 min and 275 to 240 min, respectively, as the starting concentration increased from 25 to 50 mg/L. This could be because the higher initial CQP concentration raised the mass transfer driving force across the liquid film, resulting in earlier column exhaustion due to an accelerated adsorption rate.63 Furthermore, an increase in the initial CQP concentration causes BSSP pores to get saturated more quickly, thus reducing the column exhaustion time.64

3.7.3. Effects of the Bed Height

The breakthrough curve for the uptake of CQP onto BSSPs at bed heights of 1 cm (0.308 g equiv of BSSP) and 2 cm (0.612 g equiv of BSSP) is shown in Figure S5. At 8, 50 mg/L, 6 mL/min, and 8 mg/L, the operational parameters pH and initial CQP concentration were maintained. Tb and te increased from 15 to 45 min and 240 to 900 min, respectively, as the bed height increased from 1 to 2 cm (0.308–0.612 g). This can be ascribed to the fact that an increase in bed height increased the surface area of BSSPs, which aided the availability of more binding sites for CQP molecules, hence increasing the quantity of CQP adsorbed.65 The estimated quantity adsorbed for different column operational parameters is presented in Table S2. High removal efficiency has previously been reported on the adsorption of heavy metals using agrowastes.30

3.8. Breakthrough Curve Models

3.8.1. Thomas Model Evaluation for the Uptake of CQP onto BSSPs

The kinetic coefficients for the Thomas model and the maximum quantity adsorbed (mg/g) in the solid phase were calculated. These parameters were determined by calculating the slope and intercept of the Thomas model plot. The Thomas constant (KTH) decreased as the initial CQP concentration, column bed height, and flow rate increased. This is because the driving force between the CQP molecules and the adsorbent surface has increased.66Table S3 lists the evaluated parameters for the Thomas model.

3.8.2. Yoon–Nelson Model Evaluation for the Uptake of CQP onto BSSPs

The estimated parameters for the Yoon–Nelson model for CQP uptake onto BSSPs are presented in Table S4. As observed, as the flow rate increased, the Yoon–Nelson constant, KYN, increased and T decreased. On the other hand, as the initial concentration increased, the breakthrough time decreased as the column got saturated more rapidly. Also, the values of Texp and Tcal were found to be in good agreement with minimal Chi-square (χ2) error values. Similar trends have been reported.66,67

3.9. Treatment of the CQP Simulated Multicomponent Effluent

The simulation of a multicomponent effluent is essential to substantiate the efficiency of BSSPs in the treatment of real industrial effluents. Figure 6 presents the UV–visible spectra of the simulated multicomponent effluent before and after CQP removal onto BSSPs in the company of other contaminants. The percentage removal of the adsorption system was estimated to be 95.78% at the initial CQP concentration of 50 mg/L. The removal efficiencies for other components are presented in Table S5. Similar observations have been reported.27,28,68 With this estimated performance, BSSPs can serve as an active adsorbent in the treatment of multicomponent pharmaceutical industrial effluents.

Figure 6.

Figure 6

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Adsorption of CQP from the simulated multicomponent effluent onto BSSPs (50 mg/L).

3.10. Desorption Studies of the CQP-BSSP Adsorption System

Hydrochloric acid (HCl) had the highest desorption efficiency of 69.11%, while NaOH, NaCl, and deionized water have a desorption efficiency of 6.72, 5.85, and 4.61%, respectively (Figure 7). The BSSP (pHpzc = 3) has the highest adsorption efficiency at pH 8, suggesting that CQP has high activity in slightly basic medium. High desorption efficiency was obtained using a strong acid (HCl) as an eluent via ion exchange. Strong acids and bases have previously been reported to successfully desorb electrostatically attracted adsorbates/molecules.69 The HCl eluent has a desorption index of 2.95, which is greater than 1. This indicated a high degree of reversibility of the adsorption process.34 Hence, the feasibility of repeatedly using BSSPs in adsorption processes is a possibility.

4. Conclusions

Highly porous biochar (BSSPs) was successfully prepared from B. sapida wastes. The morphological appearance showed crevices and holes that can trap large molecules, while surface chemistry showed functional groups important in the adsorption process. Thus, BSSPs effectively scavenged CQP in both the batch and fixed-bed adsorption systems for simulated pharmaceutical wastewater. The mechanism of adsorption was found to be dominated by external diffusion. The involvement of the functional groups in the adsorption process was indicated by the variations in the intensity of the FTIR spectra produced before and after the adsorption of CQP. The ANN structure with 11 hidden neurons, Levenberg–Marquardt algorithm, and Logsig transfer function was found to be the best parameter combination for predicting the adsorption efficiency. The best operational parameters evaluated from the fixed-bed adsorption studies were 2 cm bed height and 6 mL/min flow rate. The findings of the desorption studies show that BSSPs have a reusability tendency.

Acknowledgments

The authors are grateful to May and Baker Pharmaceuticals, Lagos, Nigeria, for graciously gifting chloroquine phosphate active and other pharmaceutical actives. The authors also appreciate the proprietor and management of Landmark University for facility provision.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.3c05008.

  • pHpzc of BSSPs, effect of the initial concentration and contact time on CQP uptake onto BSSPs, breakthrough curves at different flow rates for CQP uptake onto BSSPs, effect of initial CQP concentration on the fixed-bed adsorption onto BSSPs, thermodynamic parameters of CQP uptake onto BSSPs, estimated quantity adsorbed for different column operational parameters, and removal efficiency of pharmaceuticals in the simulated mixture (PDF)

The authors declare no competing financial interest.

Supplementary Material

ao3c05008_si_001.pdf (129.3KB, pdf)

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