The utilization of phosphorus-rich aquaculture wastewater (AQW) offers a promising avenue for the recovery of valuable compounds, particularly hydroxyapatite (HAp) materials. The aim of this study was to use Response Surface Methodology (RSM) to explore the potential of recycling AQW directly to produce HAp compounds due to its considerable commercial value. Using the Central Composite Design (CCD) within the RSM, about three parameters, such as pH (between 9 and 11), contact time (between 36 and 60 h), and stirring rate (between 120 and 240 rpm) were optimized. The response of the CCD, crystallinity, was evaluated by X-ray diffraction analysis. The optimized conditions, pH of 10, contact time of 48 h, and stirring rate of 180 rpm resulted in HAp crystals characterized according to the Joint Committee on Powder Diffraction Standard, JCPDS (9,003,549), with a crystallite size of 23.6 nm. Remarkably, about 89.97% of the phosphate was removed from the AQW under these conditions, emphasizing the potential of this approach to reduce waste while producing a commercially viable product. The study highlights the viability of developing a zero-waste industry, and the utilization of calcium apatite-based products derived from AQW offers a promising socio-economic strategy for the future.

  • Aquaculture wastewater acts as a resource for the recovery of valuable compounds.

  • Potential calcium apatite-based compounds were produced under different conditions.

  • Modeling from RSM was acheived to optimize the possibility of parameter efficiency to achieve the objective.

  • Optimum conditions for the highest recovery of phosphorus from aquaculture wastewater were studied.

  • The zero-waste approach in industry has long-term benefits in the future.

Aquaculture plays a significant role in the economies of countries, contributing significantly to gross domestic product (GDP) through local seafood supply and exporting valuable commodities (Kurniawan et al. 2021). By 2020, the cumulative aquaculture production reached 400,017 metric tons (mt), with a wholesale value of RM3,114,731. A freshwater aquaculture system is a technique for cultivating freshwater aquaculture in various environments such as fishponds, fish pens, reservoirs, former mining ponds, rice fields, or canvas tanks. Species such as tilapia, catfish, and carp are usually farmed in freshwater lakes, ponds, rivers, or reservoirs for commercial purposes (Mat Zain & Lee 2022). However, in the pre-harvest phase, wastewater produced is often contaminated with particles and nutrients from sources such as fertilizers, spilt feed, and reared animal waste (Nizam et al. 2020).

Phosphorus (P) is one of the most important elements and nutrients required for the growth of aquatic plants and also as food for other microorganisms living freely in water bodies and rivers (Anawar & Chowdhury 2020; Tiwari & Pal 2022). However, excessive phosphorus levels in water bodies can trigger excessive algae growth, indicating eutrophication. A low P concentration of 0.02 mg/L is sufficient to trigger eutrophication and disrupt aquatic life by reducing dissolved oxygen content and hindering sunlight, eventually leading to depopulation and development of underwater dead zones (Jin et al. 2019; Yang et al. 2022; Dadi et al. 2023). Although nitrogen is also a contributing element to eutrophication, the role of phosphorus plays a major role (Schindler et al. 2016).

The recovery of P is a major challenge. Since the demand for P in the industry itself is not too impressive, most of the P is preferably recovered as fertilizer and other valuable compounds. In particular, the recovery of phosphate from agricultural wastewater in the form of hydroxyapatite (HAp) by chemical precipitation is of utmost practical importance for completing the P cycle (Cichy et al. 2019). Former studies suggested that P recovery involves self-crystallization between calcium and phosphorus, toward the amorphous calcium phosphate precursor and subsequent growth of HAp. The pH will change during the calcium–phosphate crystallization interaction, indicating the involvement of proton release mechanism (Li et al. 2022). HAp [Ca10(PO4)6OH2] is a bioceramic material which has been used over the decades. It has durable thermal and chemical properties which have been used widely in medicine, wastewater treatment, and various industries (Akpan et al. 2021). HAp is synthesized by different methods depending on the structure and apparent properties (Sadat-Shojai et al. 2013). The simplest and cheapest method is the wet precipitation method. The wet precipitation method allows the size and morphology of nanoparticles to be regulated, leading to the formation of nanoparticles under certain conditions (Sajid & Płotka-Wasylka 2020; Shrestha et al. 2020).

In modern research practice, the integration of computer-aided methods has become a cornerstone for the further development of scientific investigations (Deng et al. 2023, 2024; Li et al. 2024). Through the use of computational modeling and analysis, researchers can explore complicated systems, fine-tune experimental parameters, and gain insights beyond the scope of conventional experimental methods (Singh 2021, 2023; Singh & Samsher 2021, 2022). The present study utilized RSM, a computational tool, to investigate the recovery of HAp from aquaculture wastewater (AQW) under specific parameter conditions. The materials obtained by RSM optimization were then analyzed and compared with commercial HAp to evaluate the physicochemical properties. This holistic approach not only increases the efficiency and precision of the present study but also creates a robust framework for addressing the challenges associated with sustainable resource management and waste reduction.

The development of aquaculture industries on a commercial scale shows that considerable amounts of phosphorus and nitrogen waste are produced. The abundance of these nutrients represents a promising opportunity for wastewater treatment aimed at zero waste, environmental friendliness and economic benefits. Proper management of this wastewater by converting pollutants into useful compounds for various applications supports a circular economy that reduces environmental impact while creating economic value and is a reliable new pathway to environmentally conscious and economically viable solutions. Thus, exploring the potential of using phosphorus-rich AQW for the recovery of valuable compounds, particularly HAp materials, opens up new avenues for resource recovery. Consequently, by understanding the potential impact of aquaculture on the aquatic environment and understanding the measures can be considered to minimize the environmental impact and it would be beneficial for the long-term sustainability of the sector. Phosphorus recovery and reuse are highly desirable. Therefore, this study focuses on the recovery of the element phosphorus from AQW to produce HAp, a valuable material. This approach can, therefore, reduce phosphorus pollution and increase the production of mineral phosphate in useful material.

Materials and sample collection

Chemicals used in this experiment included calcium oxide (CaO) purchased from Sigma Aldrich, and diammonium hydrogen phosphate [(NH4)2HPO4] and sodium hydroxide (NaOH) from Supelco. The freshwater AQW sample containing phosphate (P) was obtained from catfish farms at the Hatchery Centre, Universiti Malaysia Terengganu. For preservation of the P content, the wastewater sample was freshly collected and filtered to remove moss and algae, and then stored in the freezer for at least 2 days prior to use. The concentration of phosphate (P) in wastewater before and after the recovery was measured by Ion Chromatography and Anion instrumentation analysis. The final concentration of AQW was examined after the production of HAp to calculate the amount of P taken after the experiment. Figure 1 illustrates the framework of the experimental design used in this study.
Figure 1

The experimental framework used in this study.

Figure 1

The experimental framework used in this study.

Close modal

Experimental design

This study applied the RSM with Central Composite Design (CCD) to examine the effects of pH, contact time (hour), and stirring rate (rpm) as parameters to determine the optimum condition for the production of HAp. The reach parameter was analyzed at five specific levels (−ꭤ, −1, 0, +1, +ꭤ). The number of experiments generated for CCD was defined by Equation (1):
(1)
where Y is the number of experiments, x is the number of variables, and Cx is the number of center points repetitions. The parameters included a specific range of five levels (−ꭤ, −1, 0, +1, +ꭤ). There were three variables, six center points, and one repetition in this study. The relationship of independent and response variables is presented in the following second-degree polynomial Equation (2):
(2)
where b0 is a constant, bi are linear coefficients, and xi is the effect of each variable. The effect of the parameter is xixj. bij is the interaction coefficient and bij is the coefficient demonstrating the effect of variables xixi. Analysis of variance (ANOVA) was used to determine the adequacy of the constructed model to represent the observed data (Ebrahimi et al. 2014). The percentage of variability of the optimization parameters justified by the model is denoted by using the R2 statistic. The three-dimensional surface plots were portrayed to demonstrate the major and interactive effects of the independent and dependent variables. Table 1 displays the experimental parameters for RSM analysis. This analysis used DOE software (Design Expert13) and CCD.
Table 1

Parameter with the ranges and values for each

Parameter−ꭤ − 10+ 1+ ꭤ 
pH 10 11 12 
Contact time 28 36 48 60 68 
Stirring rate 80 120 180 240 280 
Parameter−ꭤ − 10+ 1+ ꭤ 
pH 10 11 12 
Contact time 28 36 48 60 68 
Stirring rate 80 120 180 240 280 

The CCD generated a total of 20 runs. The wet precipitate method was used to synthesize HAp and the AQW was the source of phosphate and at the same time can be a good medium for phosphate recovery. The pH, contact time, and stirring rate have ranges of 9–11, 36–60, and 120–240, respectively. In total, there were 20 conditions generated based on the CCD and the range of the parameter itself.

Analysis of physicochemical properties of calcium apatite-based compounds

The samples were characterized using a Rigaku MiniFlex Benchtop X-Ray Diffractometer (XRD) with Cu Kα radiation (λ¼1.5406 Å). It was carried out for 20 sets of samples that were generated by RSM to determine the crystallinity and recorded over the range 2θ = 10–80° by step counting at 0.03° with an interval of 0.6 s per data point. The samples were arranged in clumps at the center of a glass sample container and were examined at a high-tension generator set at 30 kV, 15 mA. To examine the crystallinity of materials, the XRD spectrum was analyzed using the computer program PDXL software. It was able to identify the crystallinity which can be measured via crystalline to the total area, including the amorphous phase. Measurement of the ratio between the total area covering both amorphous and crystalline phases enables the identification of their respective proportions. The crystalline area is determined by adding up the integrated intensities scattered over a suitable angular interval by the crystalline phase.

The Fourier Transform Infrared (FTIR) spectroscopy (Shimadzu IRTracer-100 model, Japan) was used to analyze the functional group of the sample product. The sample was powdered before analysis. The functional group of compounds was recorded in the region of 650–4,000 cm−1 of wavelength and resolution of 2 cm−1 as most of the molecules’ functional group side chains and crosslink absorb light in this infrared range of the electromagnetic spectrum.

Evaluation of RSM analysis

The determination of crystallinity of HAp under different conditions involving 20 runs include six central points with different combinations of parameters as presented in Table 2. The predicted values were obtained by a model fitting technique via software Design Expert ver. 13 and significantly correlated with the observed (actual) values. Table 2 presents the CCD analysis and Run 2 is considered as the most optimum condition.

Table 2

The predicted values generated using RSM and the actual crystallinity values calculated by the obtained Haps

RunA pHB Contact time (h)C Stirring rate (rpm)Crystallinity (%)
Compound identificationJoint Committee on Powder Diffraction Standard (JCPDS) No.
ActualPredicted
60 240 69.14 69.44 Carbonate hydroxyapatite 9,003,553 
10 48 180 69.40 68.83 Hydroxyapatite 9,011,093 
10 48 180 68.58 68.83 Hydroxyapatite 9,002,216 
36 120 63.17 63.52 Carbonate hydroxyapatite 9,003,552 
11 36 120 65.31 65.19 Hydroxyapatite 9,002,215 
36 240 69.06 69.21 Carbonate hydroxyapatite 9,003,551 
48 180 66.42 66.07 Hydroxylellestadite 9,009,375 
10 28 180 66.73 66.52 Chlorapatite 9,001,263 
60 120 64.79 64.86 Carbonate hydroxyapatite 9,003,555 
10 11 60 120 66.07 66.10 Hydroxyapatite-Dental 9,002,218 
11 11 60 240 70.02 69.85 Hydroxyapatite-Dental 9,002,224 
12 10 48 180 68.64 68.83 Hydroxyapatite 9,003,548 
13 10 48 280 71.63 71.48 Carbonate Hydroxyapatite 9,003,555 
14 10 48 180 68.53 68.83 Hydroxylellestadite 9,009,375 
15 10 68 180 67.52 67.47 Hydroxyapatite-Dental 9,002,226 
16 12 48 180 67.98 68.15 Apatite (CaOH) 9,010,050 
17 11 36 240 69.93 70.04 Hydroxyapatite-Dental 9,002,223 
18 10 48 80 63.71 63.61 Carbonate Hydroxyapatite 9,003,550 
19 10 48 180 70.01 68.83 Hydroxyapatite 9,002,214 
20 10 48 180 67.85 68.83 Hydroxyapatite 9,003,549 
RunA pHB Contact time (h)C Stirring rate (rpm)Crystallinity (%)
Compound identificationJoint Committee on Powder Diffraction Standard (JCPDS) No.
ActualPredicted
60 240 69.14 69.44 Carbonate hydroxyapatite 9,003,553 
10 48 180 69.40 68.83 Hydroxyapatite 9,011,093 
10 48 180 68.58 68.83 Hydroxyapatite 9,002,216 
36 120 63.17 63.52 Carbonate hydroxyapatite 9,003,552 
11 36 120 65.31 65.19 Hydroxyapatite 9,002,215 
36 240 69.06 69.21 Carbonate hydroxyapatite 9,003,551 
48 180 66.42 66.07 Hydroxylellestadite 9,009,375 
10 28 180 66.73 66.52 Chlorapatite 9,001,263 
60 120 64.79 64.86 Carbonate hydroxyapatite 9,003,555 
10 11 60 120 66.07 66.10 Hydroxyapatite-Dental 9,002,218 
11 11 60 240 70.02 69.85 Hydroxyapatite-Dental 9,002,224 
12 10 48 180 68.64 68.83 Hydroxyapatite 9,003,548 
13 10 48 280 71.63 71.48 Carbonate Hydroxyapatite 9,003,555 
14 10 48 180 68.53 68.83 Hydroxylellestadite 9,009,375 
15 10 68 180 67.52 67.47 Hydroxyapatite-Dental 9,002,226 
16 12 48 180 67.98 68.15 Apatite (CaOH) 9,010,050 
17 11 36 240 69.93 70.04 Hydroxyapatite-Dental 9,002,223 
18 10 48 80 63.71 63.61 Carbonate Hydroxyapatite 9,003,550 
19 10 48 180 70.01 68.83 Hydroxyapatite 9,002,214 
20 10 48 180 67.85 68.83 Hydroxyapatite 9,003,549 

The reliability of a model can be evaluated using ANOVA which considers uncertainties in the structural reliability of a model. The theoretical framework for calculating the probability of failure is known as the reliability index of structural systems (Chelladurai et al. 2020). Hence, the significance of the quadratic polynomial model was validated by performing the ANOVA test result as shown in Table 3.

Table 3

Summary analysis using ANOVA

SourceSum of squaresdfMean squareF-valuep-valueF-crit
Model 92.88 10.32 30.40 <0.0001 Significant 
A, pH 4.30 4.30 12.65 0.0052  
B, contact time 1.10 1.10 3.25 0.1016  
C, stirring rate 75.59 75.59 222.69 <0.0001  
AB 0.0903 0.0903 0.2661 0.6172  
AC 0.3486 0.3486 1.03 0.3347  
BC 0.6105 0.6105 1.80 0.2095  
A² 4.81 4.81 14.16 0.0037  
B² 6.02 6.02 17.74 0.0018  
C² 2.98 2.98 8.77 0.0143  
Residual 3.39 10 0.3394    
Lack of fit 0.5281 0.1056 0.1843 0.9565 not significant 
Pure error 2.87 0.5732    
Cor total 96.28 19     
SourceSum of squaresdfMean squareF-valuep-valueF-crit
Model 92.88 10.32 30.40 <0.0001 Significant 
A, pH 4.30 4.30 12.65 0.0052  
B, contact time 1.10 1.10 3.25 0.1016  
C, stirring rate 75.59 75.59 222.69 <0.0001  
AB 0.0903 0.0903 0.2661 0.6172  
AC 0.3486 0.3486 1.03 0.3347  
BC 0.6105 0.6105 1.80 0.2095  
A² 4.81 4.81 14.16 0.0037  
B² 6.02 6.02 17.74 0.0018  
C² 2.98 2.98 8.77 0.0143  
Residual 3.39 10 0.3394    
Lack of fit 0.5281 0.1056 0.1843 0.9565 not significant 
Pure error 2.87 0.5732    
Cor total 96.28 19     

The p-value represents the degree of significance of each factor. Factors that have a p-value (Prob. > F) of less than 0.0500 (p < 0.05) imply that the model terms are significant with a confidence level of 0.95. The F-value of the ‘model’ obtained in this study was 30.40 with a p-value of 0.0001. This result proved that the model generated was statistically significant with only a 0.1% chance of noise occurrence. The variables of A, C, A2, B2, and C2 had significant effects on the crystallinity since the p-values were less than 0.05. However, other variables were indicated as insignificant due to being higher than 0.05 of p-value. For single variables, variable B was identified as a limiting factor toward the optimization of HAp crystallinity. Since there was no influence of model reduction in insignificant variables, those insignificant variables were retained. The F-value with ‘lack of fit’ obtained was 0.1843 and the p-value was 0.9565 which implied that the lack of fit was not significantly relative to the pure error. Therefore, the relationship between crystallinity and the model was well within the chosen range.

This study also observed that the crystallinity, the pH, and the stirring rate were significant. These factors affected the crystallinity since the p-value was <0.05, implying the good factors. In acidic conditions, the phosphate ion, P, and Ca ion cannot combine to form the HAp crystal. However, HAp itself can self-integrate very well in alkaline conditions and forms a chemical bond between P and Ca (Goh et al. 2021). The maximum reaction of HAp produced in an alkaline condition compared to the acidic condition showed that the crystallinity is dependent on the pH of HAp.

For the stirring rate, the agglomeration between the particles will form a crystal and the speed of the stirring rate influences the size of crystallite and crystallinity since the particle agglomeration during the precipitation and maturation step controls the sizes of aggregates (Tourbin et al. 2020). Most particles are small and in micron size, and will become a greater proportion of larger agglomerates. As the stirring rates increase, the particle has a narrow size distribution, with most of the particles on a volume basic and having size between 1 and 10 μm (Baig et al. 2020; Molenaar et al. 2021). The fit statistic obtained from RSM showed the coefficient of determination R2 was 0.9647, implying that 96.47% of the variability in the response can be explained by the quadratic model. Also, ought to be close to the value of 1 and considered significantly very high indicating that this model is highly reliable.

The difference between the predicted R2 and the adjusted R2 should be less than 0.2. In this study, the adjusted R2 was 0.9330 and the predicted R2 was 0.9094, the difference between these two values was only 0.02 which was less than 0.2 and, therefore, is an acceptable agreement. Adequate precision is a measurement of the experimental design signal-to-noise ratio which compares the range of predicted values at design points to average prediction error and a ratio that is >4 is desirable. Statistically, the value of adequate precision in this case was 19.3134, indicating an adequate signal that can be used to navigate the design space.

The best-fitting model was established by a regression analysis equation for the quadratic regression model formed according to the variable parameters (Halis & Kocakulak 2024). Fitting the data into various models and their subsequent ANOVA showed that crystallinity toward the production of HAp was suitably described with a quadratic polynomial model as in Equation (3), in which the crystallinity units are in %, A is the pH, B is the contact time, and C is the stirring rate:
(3)
Evaluation of the selected models consists of residual analysis. Residual can be defined as the difference between the experimental and predicted values by the model (Sun et al. 2022). Figure 2 shows a normal probability indicating the close residuals (Figure 1). These figures show that the data are well correlated.
Figure 2

Normal plot of residual graph (a) and the predicted vs. actual value graph (b).

Figure 2

Normal plot of residual graph (a) and the predicted vs. actual value graph (b).

Close modal

The normal probability plot was closely accumulated at a certain point. These indicated an accurate model along with good agreement throughout the operating variables range as the residuals were distributed in a linear line (Regti et al. 2017). The normal distribution between the experimental and predicted values (Figure 1) indicated that the predicted result and the experimental result are highly similar and correlated strongly with each other. Although there were some points that showed slight deviation, most of the points were scattered in straight lines proving that there was great correspondence between the actual and predicted values (Bayuo et al. 2022). These plots (Figure 2(a) and 2(b)) were considered a good fit for the quadratic models.

Optimization of operating parameters was carried out to determine the best condition for the synthesis of HAp. Along with all parameters involved, the attempt to optimize the process parameter for HAp, for example optimum condition with optimum crystallinity and better purity. The optimum condition can be obtained by the response surface representing the contour plots with certain minimum and maximum response values obtained (Kavitha et al. 2015). The red dot on the top of the curve graph (Figure 3) represents the optimum condition for HAp production. The best condition was retrieved at a pH of 10, contact time of 48 h, and stirring rate of 180 rpm.
Figure 3

The contour plot (a) in three-dimensional version and (b) in two-dimensional version, represent between the parameters and response.

Figure 3

The contour plot (a) in three-dimensional version and (b) in two-dimensional version, represent between the parameters and response.

Close modal

Identification of the type of HAp compounds produced

Table 2 presents the identification of compounds produced by RSM and was evaluated by the XRD analysis using PDXL software to match the spectra of the Joint Committee on Powder Diffraction Standard, JCPDS. From the XRD pattern search analysis, six of 20 experiment runs showed the formation of significant chemical structure of HAp compounds, indicating the suitable condition for phosphorus recovery from freshwater AQW. About four runs produced carbonated hydroxyapatite which indicated that the lower pH (below 10) is not enough for Ca2+ and crystallization process.

According to Figure 4, HAp obtained from phosphate recovery (synthesis – Run 2) that was generated by RSM showed a different pattern compared to commercial HAp but the major peaks of HAp still appeared. Based on JCPDS 00-001-1008 (Joint Committee on Powder Diffraction Standard), the main intensity peaks of HAp appeared at level 20 and 2θ at 20–60°.
Figure 4

XRD graph between synthesized HAp selected by CCD analysis and commercial HAp.

Figure 4

XRD graph between synthesized HAp selected by CCD analysis and commercial HAp.

Close modal

In addition, the CCD of the synthesized HAp exhibited a low-intensity peak which was considered low crystallinity since the calcination temperature was only at 100 °C but was still amorphous. However, low crystalline HAp exhibited a high biodegradation rate and was more metabolically active compared to high crystalline HAp (Alparslan 2017). The HAp CCD crystallite size was calculated using the Scherrer equation and the size obtained was 23.6 nm (Mohd Pu'ad et al. 2019).

FTIR spectroscopy was used to determine the functional group of the selected compounds by RSM analysis; and the HAp compound is shown in Figure 5. The FTIR result showed that the bands at 551.64 and 606.37 cm−1 are attributed to the bending vibration phosphate and at this region become doublet peak. This splitting bending mode vibration of showed the transformation of HAp from amorphous to crystalline phase and this can be an index of crystallinity (Sroka-Bartnicka et al. 2017). The band at 1,020.75 cm−1 is the stretching vibration mode of . The peaks present at 1,403.86 and 1,466.41 cm−1 are attributed to the carbonate group (CO2−).
Figure 5

FTIR graph of the synthesized HAp.

Figure 5

FTIR graph of the synthesized HAp.

Close modal

The peak at 3,311.58 cm−1 was assigned to the stretching mode of the hydroxyl group (OH). The band is relatively sideways from the broad peak for the O–H stretch vibration of hydrogen-bonded OH group (Poralan et al. 2015).

Recovery of phosphate ion in AQW

The AQW originally contained phosphorus concentrations between 17 and 23 ppm, which were added to achieve an optimal Ca/P ratio of 1.67 to produce HAp. After the synthesis of HAp, the phosphate concentration was measured and an impressive removal rate of 89.97% was observed, indicating successful optimization using RSM and CCD. Despite this success, a major challenge remains in optimizing the HAp production process for practical applications in wastewater treatment. Factors such as cost and availability of materials, and variations in wastewater composition are obstacles to large-scale implementation. To overcome these limitations, further research is required to assess scalability, cost efficiency, and operational robustness to ensure the feasibility and sustainability of the technology for practical use.

The goal for the present study, i.e. the optimization and characterization of HAp via RSM, was successfully achieved. This finding also proved that freshwater AQW can be recycled, with phosphorus being the most significant element that can be recovered. By chemical treatment with calcium as an adsorbent for phosphorus, the phosphate content in wastewater can be effectively reduced and converted into a valuable form. By running 12 experiment sets, the optimized conditions to produce HAp selected by RSM analysis were as follows: pH of 10, 48 h, and stirring rate of 180 rpm through a wet precipitate method. Statistically, RSM through CCD for optimization HAp showed that the quadratic model is significant. The FTIR result showed all the functional groups consisted of HAp while XRD showed the intensity and peaks of the commercial Hap, proving that this study was able to get almost similar HAp to the standard HAp. At the same time, this study managed to recover the uptake phosphate, and the phosphate recovery was around 89.97% which is considered as a good performance. For future research, the method could be extended to a larger industrial scale. Investigating alternative methods of phosphate recovery from wastewater and conducting thorough long-term environmental impact assessments are essential next steps. These assessments will be critical to making informed decisions and developing policies for the widespread implementation of such wastewater treatment and resource recovery methods.

The authors thank Universiti Malaysia Terengganu and the Ministry of Higher Education (MOHE) for providing the laboratory facilities and financial support, respectively. This work was supported by the Fundamental Research Grant Scheme (FRGS) (FRGS/1/2020/TK0/UMT/01/1).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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