Adsorption and photo catalysis are the most popular methods applied for the reduction of amount of pollutants that enter water bodies. The main challenge in the process of adsorption is the demonstration of the experimental data obtained from sorption processes. For many decades most of the researchers used adsorption and kinetic of adsorption as a repetitive work to describe the adsorption data by using common models such as, Langmuir and Freundlich for adsorption isotherms; PFO and PSO models for kinetics. This has been done without careful evaluation of the characteristics of adsorption process. It has been well understood that adsorption does not degrade the pollutant to eco-friendly products and photo catalysis will not degrade without adsorption of the pollutant on the catalyst. Therefore, understanding the detailed mechanism of adsorption, as well as, photo catalysis has been presented in this paper. During photo catalysis: modification towards suppression of electron-hole recombination, improving visible light response, preventing agglomeration, controlling the shape, size, morphology, etc. are the most important steps. This mini review also widely discusses the key points behind adsorption and photo catalysis.
Organic compound and heavy metal pollution presents an important global environmental problem due to its toxic effects that may accumulate in the food chain [
Several physical and chemical methods were used to remove dyes and heavy metals from polluted water such as: photo catalysis, coagulation flocculation, adsorption, etc. Among all these methods, adsorption and photo catalysis have been suggested as cheaper and more effective than chemical or physical techniques. These are preferred over other methods because of their relatively simple design, operation, cost effectiveness and energy efficiency [
Several materials were successfully used in processes of discoloration of water pollutants such as clays, composites clay-alginate, polydopamine microspheres, chitosan, activated carbon, metal oxides, etc. [
In order to reduce the drawbacks behind bare metal oxides during photocatalytic process (such as: fast electron-hole recombination, agglomeration and lack of visible light absorption) [
Among different synthesis methods used to synthesize heterojunction metal oxides, the solution-based approach is the simplest, least energy consuming and easy to control the morphology and sizes of the nanomaterials by controlling the different experimental factors [
This review also wisely discuss and summarizes the detailed mechanism of adsorption and photocatalysis, adsorption isotherms and kinetics, the linear and nonlinear fitness of adsorption, error function, modification of bare metal oxides (minimizing e−/h+ recombination and tuning to the visible light response) towards efficient photacatalysis and conditions/parameters optimization and different analytical techniques.
Adsorption study comprises of two main aspects; Equilibrium and Kinetic studies. An adsorption isotherm assists to know the adsorption mechanism pathways and the rate of adsorptive uptake which is dependent on the adsorption mechanism. The basis for kinetics study is the kinetic isotherm, which is obtained experimentally by following the adsorbed amount against time. Kinetic investigations develop a model to describe the adsorption rate. Ideally, the model should, with minimal complexity, 1) reveal the rate limiting mechanism and 2) Extrapolate to operating conditions of interest. Accomplishing these two targets should enable one to identify operating conditions with minimal mass transfer resistance and predict adsorbent performance [
Many models of varied complexity have been developed to predict the uptake rate and mechanism of adsorption. Among those PFO and PSO models, most commonly used two models in liquid adsorption for kinetic studies. Langmuir Isotherm and Freundlich isotherms were used for controlling the sorption mechanisms for adsorption isotherms [
Adsorption is a surface phenomenon in which adsorptive (gas or liquid) molecules bind to a solid surface. However, in practice, adsorption is performed as an operation, either in batch or continuous mode, in a column packed with porous sorbents. Under such circumstances, mass transfer effects are inevitable. The three common steps includes: Film diffusion (external diffusion), Pore diffusion [intraparticle diffusion (IPD)] and Surface reaction), but, the seven classical steps involved are: 1) Diffusion of the reactants from the bulk phase (boundary layer) to the external surface of the catalyst pellet (film diffusion or interphase diffusion), 2) Diffusion of the reactant from the pore mouth through the catalyst pores to the immediate vicinity of the internal catalytic surface; the point where the chemical transformation occurs, (pore diffusion or intraparticle diffusion), 3) Adsorption of reactants on the inner catalytic surface, 4) Reaction at specific active sites on the catalyst surface, 5) desorption of the products from the inner surface, 6) Diffusion of the products from the interior of the pellet to the pore mouth at the external surface, and 7) Diffusion of the products from the external pellet surface to the bulk fluid (interphase diffusion), are generally used as the key for explanation (
film surrounding the adsorbent particles takes place through the proper concentration gradient [
Where, F Represents the fraction of solute adsorbed at any time, t(h), as calculated by, F = qt/qo. When Bt vs. t(h) plotted and if the linear lines pass through the origin the rate limiting step become particle diffusion if not it governed by external mass transport mechanism [
During all the above mentioned processes, two main types of interaction, namely, physical and chemical, are possible between adsorbent and adsorbate [
There are six different types of adsorption isotherms as shown in
When the monolayer formation of the adsorbed molecules is complete, multilayer formation starts to take place corresponding to the “sharp knee” of the isotherms. As the relative pressure approaches unity a sudden rise shows the bulk condensation of adsorbate gas to liquid. Finally, type three (III) and type four (V) isotherms do not have the “sharp knee” shape indicating that a stronger adsorbate-adsorbate interactions than adsorbate-adsorbent interaction.
Adsorption equilibrium information is the most important piece of information needed for a proper understanding of an adsorption process. A proper understanding and interpretation of adsorption isotherms is critical for the overall improvement of adsorption mechanism pathways and effective design of adsorption system. But most of the studies apply only Langmuir and Freundlich
models and depending on coefficient of determination (R2) values the mechanism of adsorption judged by declaring the process as physical, chemical or both. Since most those models fits well with different experimental data, confirming with other models to know whether those models are perfect or not become the most important requirement. The selective mini review is presented in the
As an example [
Kinetics of adsorption using the statistical least squares method, several adsorption studies fit the PFO and PSO models. Therefore, without applying any work used to reduce/eliminate the diffusion-adsorption mechanism (transport influences; like intra particle diffusion), deciding the mechanism of adsorption only depending on adsorption-reaction models become wrong interpretation of mechanism of kinetics of adsorption. Generally suggested by earlier research group, [
Adsorbate | Adsorbent | Fitted Isotherm | Fitted Kinetics | Ref. |
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Pb(II) | Illite/Smectite Clay | Langmuir | - | [ |
Hg(II) | activated Carbon from Rosmarinusofficinalis leaves | Langmuir | PSO | [ |
Phosphate anion | Membranes | Flory-Huggins | - | [ |
As | Fe -Ti bimetal oxides | Freundlich & Langmuir | PFO | [ |
Atrazine | Eucalyptus tereticornis L. (EB) | Freundlich, Koble?Corrigan, Toth and Fritz-Schluender | PSO | [ |
Imidacloprid | PFO | |||
Reactive Black 5 | Bentonite clay | Harkin-Jura and Freundlich | PSO | [ |
Cu(II) | Peanut hulls | Langmuir | PFO, PSO & Elovich | [ |
Rhodamine B (RhB) | Raphiahookerie fruit epicarp | Freundlich | PSO & Elovich | [ |
2,4,6-trichlorophenol | oil palm empty fruit bunch-based activated carbon | Freundlich & RP | PSO & Boyd-IPD | [ |
Maxilon blue GRL, and direct yellow DY 12 | Activated carbon from coconut husk | Fritz?Schlunder (heterogeneous with multi-layers) | PSO | [ |
Zn2+ | Phosphoric Acid Modified Rice Husk | Langmuir 25 J/mol (Temkin) 0.7 kJ/mol (DRK) | - | [ |
Cd (II) | Chitosan and methyl orange onto bentonite | R2 insufficient to decide: ∆q (%) & other should be used | Nonlinear than linear | [ |
Isotherms | Isotherm Equation | Uses and notes | Ref. |
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One-Parameter Isotherm | |||
Henry’s | q e = K H E C e | Simplest adsorption isotherm | |
Two-Parameter Isotherm | |||
Hill-Deboer | ln [ C e ( 1 − θ ) θ ] − θ 1 − θ = − ln k 1 − k 2 θ R T Math_9# | o Defines the case where there is mobile adsorption and lateral interaction and K2 is the energetic constant of the interaction between adsorbed molecules (kJ∙mol−1) | [ |
Fowler- Guggenheim | ln [ C e ( 1 − θ ) θ ] = − ln k F G + 2 w θ R T Math_11# | o Side contact between adsorbed molecules and the heat of adsorption (w) varies linearly with loading. If attractive, w = positive, repulsive w = negative and w = 0 no interaction between absorbate. But applicable only if θ < 0.6 | [ |
Langmuir | q e = q m k L C e 1 + k L C e C e q e = 1 q m k L + C e q m R L = 1 1 + k L C o | o Homogeneous binding sites (same affinities), alike sorption energies, and no interactions between adsorbed species. RL is separation aspect which decides whether the adsorption is un-favorable when its value is >1, linear (=1), favourable (0 < RL < 1), and irreversible (RL = 0) | [ |
Freundlich | q e = k f c e 1 / n log q e = log k F + 1 n log c e | o Heterogonous surfaces(varied affinities), 1/n (adsorption intensity) that specifies the energy and the heterogeneity of the adsorbent sites (1/n < 1= Langmuir, 1/n > 1cooperative adsorption and generally, n in b/n 2 - 10 indicates good favorability of sorption | [ |
Dubinin- Radushkevich | q e = q s exp ( − β ε 2 ) ln q e = ln q s − β ε 2 ε = R T ln ( 1 + 1 C e ) & E = 1 2 β | o Fit for intermediate range of adsorbate concentrations. Used to calculate energy (E) and physical, if the value is less than 8 and chemical if the E values are in between 8 and 16 kJ・mol−1 Kβ = Dubinin-Radushkevich isotherm constant (mol2/kJ2), ε = Dubinin?Radushkevich isotherm constant and qs is saturation capacity (mg/g) | [ |
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Temkin | q e = R T b ( k T c e ) q e = R T b ln k T + R T b ln c e | o Effective only for an intermediate range of adsorbate concentrations and gives information for adsorbate/adsorbate interactions, where b (J/mol) is Temkin isotherm constant kT (L/g)―Temkin isotherm equilibrium binding constant | [ |
Flory-Huggins | ln ( θ C o ) = n ln ( 1 − θ ) + ln k F H Δ G o = − R T ln ( k F H ) ln ( 1000 ∗ q e C e ) = Δ s o R − Δ H o R T | o Account for the characteristic surface coverage of the adsorbed adsorbate on the adsorbent and the spontaneity of the process using Δ G o value obtained from KFH, where, n is number of adsorbates occupying adsorption sites, and KFH is Flory-Huggins equilibrium constant (L・mol−1) | [ |
Hill-de Boer | C e = θ ( 1 − θ ) exp ( θ ( 1 − θ ) − k 2 θ R T ) ln [ C e ( 1 − θ ) ( 1 − θ ) ] − θ ( 1 − θ ) = − ln k 1 − ( k 2 θ R T ) | o Defines the case a mobile adsorption and later interaction among adsorbed molecules, where K1 is constant (L・mg−1) and K2 is the energetic constant of the interaction between adsorbed molecules (kJ・mol−1), A positive K2 means attraction and negative value means repulsion between adsorbed species | [ |
Halsey | q e = 1 n H I n k H − 1 n H ln c q e | o Multilayer adsorption at a relatively large distance from the surface, where KH and n are constants | [ |
Harkin-Jura | 1 q e 2 = B A − ( 1 A ) log c e | o Multilayer adsorption having heterogeneous pore distribution, where B and A are constants | [ |
Jovanovic | q e = ln q max − K J C e | o Assumes Langmuir plus mechanical contacts b/n adsorbate and adsorbent | [ |
Elovich | q e q m = K E C e exp ( − q e q m ) Math_32# | o Define the kinetics of chemisorption and Multilayer adsorption, where KE is equilibrium constant (L・mg−1) and qm is maximum adsorption capacity (mg・g−1) | [ |
Kiselev | K 1 C e = θ ( 1 − θ ) ( 1 + k n θ ) 1 C e ( 1 − θ ) = k 1 θ + k i k n | o Localized monomolecular layer, valid only when θ > 0.68 and Kn―constant for formation of complex between adsorbed molecules | [ |
Three-Parameter Isotherms | |||
Redlich-Peterson | q e = A C e 1 + B c e β ln c e q e = β ln C e − ln A | o A mixture of the Langmuir and Freundlich isotherms (the mechanism of adsorption is a blend of the two) and applicable both for homogeneous or heterogeneous systems. A & B are RP isotherm constants (L・g−1) and β is exponent lies b/n 0 and 1 for heterogeneous adsorption system | [ |
q e = A B c e 1 − β | A / B = K F and ( 1 − β ) = 1 / n | ||
Sips | q e = K s c e β s 1 − a s c e β s β s ln c e = − ln ( k s q e ) + ln ( a s ) | o A combination of the Langmuir and Freundlich isotherms, where as & Ks are isotherm constants and βs is isotherm exponent. At low adsorbate concentration reduces to the Freundlich model and at high concentration it predicts the Langmuir model | [ |
Toth | q e = q m k L c e [ 1 + ( k L c e ) n ] 1 / n q e q m = θ = k L c e [ 1 + ( k L c e ) n ] 1 / n ln q e n q m n − q e n = n ln k L + n ln c e | o Modification of the Langmuir equation and describes heterogeneous systems which satisfy both low and high end boundary of adsorbate concentration, both KL & n is isotherm constant and when n = 1 reduces to Langmuir & n far from 1 shows heterogeneity, this where evaluated by nonlinear curve fitting method using sigma plot software | [ | |
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Koble-Carrigan | q e = A k C e p 1 + C e p B k 1 q e = ( 1 A k C e p ) + B k A k | o A combination of both Langmuir and Freundlich isotherms and all Ak, Bk and p are isotherm constant. Solver add-in function of the Microsoft Excel and valid only when p ≥ 1 | [ | |
Kahn | q e = q max b k C e ( 1 + b k c e ) a k | o Used for adsorption of bi-solute sorption in dilute solution, where, 𝑎𝑘 is isotherm exponent and bk is isotherm constant | [ | |
Radke-Prausniiz | q e = q M R P K R P C e ( 1 + K R P C e ) M R P | o Chose at low concentrations, where qMRP = qmax, KRP is equilibrium and constant and MRP is exponent o Reduces to linear at low concentration, at high [ ] reduces to Freundlich and When MRP = 0 becomes Langmuir isotherm model | [ | |
Langmuir- Freundlich | q e = q M L F ( K L F C e ) n 1 + ( K L F C e ) n | o At low concentration becomes Freundlich and at high becomes the Langmuir isotherm model, where, qMLF = qmax, KLF is constant for heterogeneous solid; n is heterogeneity index lies b/n 0 and 1. | ||
Jossens | C e = q e H exp ( F q e p ) ln ( C e q e ) = − ln ( H ) + F q e p | o Work based on energy distribution of adsorbate-adsorbent interactions at heterogeneous adsorption sites, where H (Henry’s), p & F are constants o p is characteristic of the adsorbent regardless of temperature and the nature. | [ | |
Four-Parameter Isotherms | ||||
Fritz-Schlunder | q e = q m F S K F S C e 1 + q m C e M F S | o Due to large number of coefficients, makes it to fit a wide range of experimental results, where, qmFS = qmax, KFS is equilibrium constant and MFS is exponent. And if MFS = 1 it becomes the Langmuir and high concentrations reduces to Freundlich, The constants are evaluated by nonlinear regression analysis. | [ | |
Bauder | q e = q m b o C e 1 + x + y 1 + b o C e 1 + x | o Used to estimate the Langmuir coefficients (b and qml) by measurement of tangents at different equilibrium concentrations shows that they are not constants in a broad range, where bo is equilibrium constant and x is & Y is parameters | [ | |
Weber-Van Vliet | c e = p 1 q e ( p 2 q e p 3 + p 4 ) | o Describe wide range of adsorption systems, where p1, p2, p3, & p4 are isotherm parameters which defined by multiple nonlinear curve fitting method. | [ | |
Marczewski-Jaroniec | q e = q M M J [ ( k M J C e ) n M J 1 + ( k M J C E ) n M J ] M M J n M J | o General Langmuir equation, where nMJ and MMJ are parameters characterize the Tells the Heterogeneity of the surface, where MMJ describes the spreading of distribution in the path of higher adsorption energy, and 𝑛𝑀𝐽 lesser adsorption energies | ||
Five-Parameter Isotherms | ||||
Fritz and Schlunder | q e = q m F S s K 1 C e α F S 1 + k 2 C e β F S | o Define more wide range of adsorption systems, where qmFS = qmax and K1, K2, αFS & βFS are parameter constants. And this model approaches Langmuir model when the value of αFS and βFS equals 1; for higher concentrations it reduces to Freundlich model. | [ | |
highly flexible formula that combines many different models with different controlling mechanisms. The rate constants obtained was the result of complex interaction between different controlling mechanisms which are simply empirical
Name | Isotherm Equation | Uses and notes | Ref. |
---|---|---|---|
Adsorption reaction kinetics | |||
PFO | d q d t = K 1 ( q e − q ) log ( q e − q t ) = log q e − K 1 2.303 t Modified d q d t K 1 = q e q ( q e − q ) q q e + ln ( q e − q ) = ln ( q e ) − K 1 t ; K 1 = D π 2 r 2 | o Valid only at the initial stage of adsorption, where, k1 decrease with increasing Co, time & particle size Its qe is often much farther from the experimental value Affected by reaction conditions (pH, Concentration) | [ |
PSO | d q d t = K 1 ( q e − q ) 2 t q t = 1 k 2 q e 2 + 1 q e t | o qe is often less than, but close to, the experimental value and K2 decrease with increasing initial concentrations, time and particle size | [ |
Elovich | d q d t = α exp ( − β q ) q = 1 β ln ( α β ) + 1 β ln t | o Suitable for kinetics far from equilibrium where desorption does not occur, where α is the initial sorption rate (mg/g・min), β is a desorption constant related to the extent of θ & Ea for chemisorption, mostly both increases with increasing Co | [ |
First-order reversible | d C B d t = − d C A d t = k a C A − k d C B & K a K d − C B e C A e ln ( 1 − C A O − C A C A O − C e ) = − ( k a + k d ) t | o Limiting form for Langmuir kinetics model when adsorption is in the Henry regime, where ka is adsorption rate constant & kd is desorption rate constant and CA0, CA and Ce are initial bulk, at time t and equilibrium concentrations (mg/L) | [ |
Avrami | d q d t = k ⋅ n ⋅ t n − 1 ( q e − q ) * q = q e − q e exp ( − k t n ) ln ( ln q e q e − q ) = n ⋅ ln t + n ⋅ ln k | o Kinetic system that describes a time-dependent rate coefficient (fractal-like kinetics), where n is a model constant related to the adsorption mechanism and its value can be integer or fraction. | [ |
General | d q d t = k n ( q e − q ) n ** q = q e − q e ( k n q e n − 1 t ( n − 1 ) + 1 ) 1 n − 1 ; n ≠ 1 | o Developed to compensate for the deficiencies of PFO and PSO, n can be an integer or non-integer rational number, and must be determined by an experiment. | [ |
Combined (Avrami* and General**) d q d t = k n t m − 1 ( q e − q ) n q = q e − 1 ( k n ( n − 1 ) t m m + 1 q e n − 1 ) 1 n − 1 ; n ≠ 1 | |||
Adsorption?diffusion model | |||
Crank | q q e = 1 + 2 R π r ∑ n − 1 ∞ ( − 1 ) n n sin n π r R exp ( − D n 2 π 2 t R 2 ) * q ¯ = 3 R 2 ∫ 0 R q r 2 d r ** inserting* into ** becomes q ¯ q e = 1 − 6 π 2 ∑ n − 1 ∞ 1 n 2 exp ( − D n 2 π 2 t R 2 ) | o D and r, respectively denote the intraparticle diffusivity (cm2/min) and the radial distance (cm) from the center of the spherical particles. q ¯ is average value of q in the spherical particle of radius R at a time, t. External diffusion and surface reaction are assumed to be more rapid than IPD | [ |
Vermeule | q ¯ q e = 1 − exp ( − D π 2 t R 2 ) ln ( 1 − ( q ¯ q e ) 2 ) = − D π 2 t R 2 * | o Used to check whether IPD is the sole rate-limiting step, a straight line on a plot of 1 − ( q ¯ / q e ) 2 vs. t passing through the origin indicates IPD is the sole rate-controlling step. | [ |
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Weber- Morris | q = K i d t + B | o The third most common candidate model after PFO and PSO models, used for modeling adsorption kinetics limited by IPD, where B is the initial adsorption and kid increases with increasing Co. To say the kinetics controlled by IPD model the line should pass through origin. | [ |
Bangham | log ( log C e C o − q ⋅ m ) = log ( K o m 2.303 V ) + α log t | o Assumes IPD to be the only rate-controlling step and used to check whether pore diffusion is the sole rate-controlling mechanism, where ko and α are constants. It also checks whether pore diffusion is the sole rate-controlling mechanism | [ |
Linear film diffusion | d c d t = − k f ( C − C s ) C C o = exp ( − k f t ) | o Cs = adsorbate concentration at the liquid?solid interface (mg/L). At short times, Cs is negligibly small (Cs ≈ 0) | [ |
MSRDCK | d q d t = k ( 1 + τ 1 / 2 t 1 / 2 ) ( C o − γ q ) ( q e − q ) Where, γ = C o − C q e , k = 4 π r o D γ & τ = r o 2 D π q = q e exp ( a t + b t 1 / 2 ) − 1 u e q exp ( a t + b t 1 / 2 ) − 1 where u e q = 1 − C e C o , a = k C o ( u e q − 1 ) & b = 2 k C o τ 1 / 2 ( u e q − 1 ) | o A kinetic model that includes the surface reaction and film diffusion, where control the constant a accounts surface reaction & b surface diffusion and film diffusion, ro = particle radius (cm); D = film diffusivity (cm2/min) | [ |
Multi- exponential | q = q e [ 1 − ∑ i = 1 N a i exp ( − k i t ) ∑ i = 1 N a i ] | o Has multiple parallel routes that contribute to the total adsorbate uptake by different small and large sites, where ki is the rate coefficient for route i, ai is the weight coefficient that reflects the share of route i (N) and for N = 1, this model is reduced to the PFO model | [ |
constants without physicochemical meaning and provide little insights into adsorption mechanisms and no meaningful mechanism can be confidently postulated from these models. In addition to the mechanism of reaction, it is necessary to calculate important sorption parameters like adsorption activation energy with the help of Arrhenius equation D s = D s o exp ( − E a / R T ) [
The nonlinear and linear models were used to define the kinetics curves. Their validities can be determined using coefficient of determination (R2) and standard deviation (SD) Δq (%) [
Most linear regression creates discrimination on PFO than PSO based on R2 values decision. Some studies qualified to PSO by linear regression, surprisingly fits PFO by nonlinear regression [
Linear regression is a method used to model the association among a scalar dependent variable and one independent variable. The well-fitting line is the line that reduces the sum of the squared errors (SSE) of prediction. The strength of the linear association between two variables is quantified by the square of the correlation coefficient (R) which is known as the determination of the coefficient (R2). The higher R2 value (lower values of error functions) indicates a stronger linear relationship.
Nonlinear regression can be a dominant substitute to linear regression because it suggests the most flexible curve fitting functionality. For nonlinear model, sum of square must be minimized by an iterative method. The nonlinear regression line is the line that minimizes the sum of squared deviations of prediction (also called the sum of squares error). The standard error of the estimate (S) is the square root of the average squared deviation. This parameter measures the accuracy of predictions. The smaller the standard error of the estimate indicates the more accurate prediction. In nonlinear regression, good of fitness method is used after error analysis.
Error functions (
In the literature, these error statistics were calculated in addition to the correlation of determination (R2) to confirm and support the model already discriminated by R2. High R2 values correspond to low error statistics in most cases. When R2 values are too near to discriminate the PFO and PSO models, RMSE comes in convenient [
Name | Error equation | Note | Ref. |
---|---|---|---|
SSE/ERRSQ | ∑ i = 1 n ( q e , c a l − q e , e x p ) 2 | It is indicator for accuracy, in which the best fit of the data can be assessed from the sum-of-squares value. The smallest value for SSE indicates the best fit data for the model. | [ |
HYBRID | ∑ i = 1 n 100 n − p [ ( q e , m e a s − q e , c a l ) q e , m e a s ] | The error function was developed to improve ERRSQ fit at low concentrations. | [ |
ARE | 100 n ∑ i = 1 n | ( q e , m e a s − q e , c a l ) q e , m e a s | | which indicates a tendency to under or overestimate the experimental data, attempts to minimize the fractional error distribution across the entire studied concentration range | [ |
χ2 | ∑ i = 1 n ( q e , c a l − q e , exp ) 2 q e , c a l | χ2 is also similar to SSE. Smaller values of χ2 also indicate a better fit of the model. | [ |
SE | 1 n ∑ i = 1 n ( q e , c a l − q e , e x p ) 2 | It is also used to judge the equilibrium model. A smaller value for SE indicates a better fit of the model | [ |
∆q (%) | 100 1 n − 1 ∑ i = 1 n ( q e , m e a s − q e , c a l q e , m e a s ) 2 | According to the number of degrees of freedom in the system, it is similar to some respects of a modified geometric mean error distribution | |
R2 | ∑ i = 1 n ( q c a l − q e x p ¯ ) 2 ∑ i = 1 n ( q e , c a l − q e , e x p ¯ ) 2 + ∑ i = 1 n ( q e , c a l − q e , e x p ) 2 | The correlation coefficient (R2) is the common measure of analytical accuracy. Its value is within the range of 0 < R2 ≤ 1, where a high value reflects an accurate analysis. | [ |
SAE | ∑ i = 1 n | q e , m a s s − q e , c a l | | with an increase in the errors will provide a better fit, leading to the bias towards the high concentration data | [ |
SRE | [ ∑ i = 1 n ( q e , m a s s − q e , c a l ) − ARE ) 2 ] n − 1 | [ |
The past study [
Numerous water treatment techniques such as, adsorption, membrane separation, coagulation, etc., have been engaged using nano materials (having unique electronic, optical, magnetic and mechanical properties) to remove pollutants. Nevertheless, these techniques only focus on changing the pollutants from aqueous solution to solid phase [
From promising photocatalyst or adsorbent, nowadays, metal oxide nanomaterials, such as zinc oxide (ZnO), titanium dioxide (TiO2), aluminium oxide (Al2O3), iron (III) oxide (Fe2O3), copper oxide (CuO), zirconia (ZrO2), vanadium(V) oxide (V2O5), niobium pentoxide (Nb2O5) and tungsten trioxide (WO3) have been actively useful in environmental wastewater management scheme [
Similar to TiO2, ZnO is an n-type metal oxide but has not been well studied in earlier studies. It has been proposed as another photocatalyst similar to TiO2 as it possess same band gap energy (3.2 eV) but gives higher absorption efficacy across a large fraction of the solar spectrum (large free-exciton binding energy) [
In the past substantial efforts such as: doping with impurities/dopant [
In order to decrease the recombination of photogenerated electron-hole pairs (charge carrier separation enhancement), structural stability and for isolation of the redox sites at various modification methods (such as: integrating with equivalent bandgap materials, designing with exposed reactive facets, modification with carbon nanostructures and hierarchical morphologies coupling two semiconductors) have been through. Among those methods researchers choose coupling of two/more semiconductors metal oxides having related or different band gaps (TiO2/ZnO, SnO2/ZnO, SnO2/ZnO/TiO2 and Co3O4/ZnO) [
For the reason that, the nanocomposite improves light absorption effectiveness, prevention of photo corrosion, increases sun light absorption, new functionalities get up at the interfacial, change in surface acid-base behavior and better suppression of electron hole recombination [
In addition to the electron-hole recombination and visible light response, deep awareness on factors affecting photo-degradation, as well as, adsorption effectiveness, such as: catalyst/dosage loading (which controls light scattering, screening effects, turbidity and agglomeration) [
Among different metal oxide nanomaterial synthesis methods, the solution-based approach is the simplest, least energy consuming and easy to control the morphology, as well as, sizes of the nanomaterials by handling the experimental factors such as type of solvents with different dielectric constant, starting materials (precursor) concentration, reaction solution pH, mineral acids, etc. [
impregnation method [
Among these approaches, the sol-gel procedure is the most attractive method towards low production cost, good, high reliability, repeatability, simplicity of the process, low process temperature, ease of control of physical characteristics & morphology of nanoparticles, good compositional homogeneity and optical properties [
Photo-catalysis defined as “catalysis driven acceleration of a light-induced reaction”, the detailed scheme of semiconductor photo-catalyst reaction was summarized in
(H2O2) and hydroxyl radicals ( OH • ). The resulting powerful oxidizing agents ( OH • ) leads to partial or complete mineralization (CO2, H2O and mineral acids) of organics pollutants adsorbed on the surface of the semiconductor [
Depending on the CB and VB potentials and bandgap energies the electronic arrangement of the semiconductor heterojunctions are categorized into (a) straddling gap (b) staggered gap and (c) broke gap heterojunctions as shown in
The heterojunction composed of an n-type semiconductor as a donor and a p-type semiconductor as an acceptor is so-called p-n heterojunction. As shown in (
Similarly, n-n heterojunctions (
Apart from the binary nanocomposites, recently combination of large band gap materials with various semiconductors to develop ternary or multi-component heterostructures resulted in great improvement in photocatalytic performance of photocatalysts under visible-light/solar irradiation. As indicated the studied ZnO based ternary composites are grouped into four classes, based on: 1) band gap engineering, 2) plasmonic, 3) p-n-n and n-n-n heterojunctions, and 4) magnetic properties.
According to band gap engineering the difference between the energy (CB) of the joint semiconductors forces a rapid transfer of the photoexcited electrons from one semiconductor to other, thus accelerating the separation of e−/h+ pairs and enhancing the photocatalytic efficacy. The enhancement photocatalytic efficacy was confirmed by Chen et al. [
The synergistic effects of the internal electric fields formed in the n-n-n or p-n-n heterojunctions between constituents of the ternary nanocomposites are more efficient than the binary n-n or p-n heterojunctions for substantially enhanced separation of the photogenerated e−/h+ pairs and further prolonging lifetime of the charge carriers. Thus, the enhanced photocatalytic performances for the photocatalysts with n-n-n or p-n-n heterojunctions are much more than those of the photocatalysts with n-n or p-n heterojunctions [
Now a days since recovering of the engaged photocatalysts from the solution is practically reduce cost and avoids the secondary contamination, especially on large-scale applications, magnetically separable photocatalysts are promising materials. In sight of this, a number of researchers have been investigated Fe3O4, owing to its outstanding magnetic properties, being environmentally kind and not expensive. Some of ternary composites investigated based on ZnO and Fe3O4 includes: ZnO/Ag2O/Fe3O4 [
Different characterization techniques have been practiced for the analysis of various physicochemical properties of nanomaterials. Among many characterization techniques, the following are the most important techniques that should be conducted during analysis: 1) Morphological characterizations such as: polarized optical microscopy (POM), transmission electron microscopy (SEM), transmission electron microscopy (TEM), 2) Structural characterizations such as: X-ray diffraction (XRD) [
Finally, beside many industrial and medical applications of those different modified, as well as, un modified nano structured materials, there are certain toxicities which are allied with nanomaterials [
Before applying the adsorption and photcatalysis techniques for water/wastewater treatment, appropriate understanding on their working mechanisms is essential. Mostly since the Langmuir and Freundlich models fit well with different experimental data, extracting final interpretation depending on only those models became erroneous. In addition, numbers of theoretical explanations have been proposed for PFO and PSO models. However, effective fitting of these models alone is no assurance to predict whether the mechanism of reaction is under control of physical adsorption or chemisorption, as well as, either adsorption-reaction or adsorption-diffusion mechanism. Therefore, applying both adsorption-reaction and adsorption-diffusion mechanism is the key on kinetics of adsorption. In addition, both linear and non-liner fitness of the adsorption data should be conducted. On photocatalysis, progress has been done only with simple semiconductors, which is insufficient because of high electron-hole recombination and poor visible light absorption ability. Therefore, modification concerning electron-hole recombination and visible light response got effectiveness on degradation of pollutants. Finally, optimization of different parameters/reaction conditions also ought to be taken into consideration.
Authors acknowledge Adama Science and Technology University (ASTU) for offering financial support.
The authors declare no conflicts of interest regarding the publication of this paper.
Abebe, B., Murthy, H.C.A. and Amare, E. (2018) Summary on Adsorption and Photocatalysis for Pollutant Remediation: Mini Review. Journal of Encapsulation and Adsorption Sciences, 8, 225-255. https://doi.org/10.4236/jeas.2018.84012