Layered double hydroxides possess positively charged surfaces; therefore, they may be ideal materials for removing viruses from contaminated waters (You et al., 2003). Layered double hydroxides are layered solids that are stacked, positively charged octahedral sheets. The general formula of LDHs is:
[M II 1-x M III x (OH)2]x+Am− x/m . nH2O
M II represent divalent cations Mg2+, Fe2+, Cd2+, Co2+, Zn2+ or Cu2+
M III represent trivalent cations Al3+, Cr3+, Fe3+ or Ga3+ in octahedral positions
A m− is an anion positioned between the interlayers CO32-, SO42-, NO3- or Cl-
x ranges from 0.2 to 0.33 (Figure 1).
According to You et al., (2003), the isomorphic substitution of trivalent metals by divalent metals which is stable by anions in the interlamellar space generates the net positive charge. Basically, LDH are rare in nature. Nevertheless, it can be synthesized by usig co-precipitation method of a solution of bivalent and trivalent metal salts with base (usually KOH or NaOH) under laboratory conditions (You et al., 2001).
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Cavani et al., (1991) and Miyata, (1983) found that the LDH have relatively large surface areas (0.02-0.12 km2/kg) and high anion exchange capacities (200-500 cmol/kg). Due to their unique properties, there has been increasing interests in investigating LDHs as potential sorbents for removing toxic anionic species, such as CrO42-, SeO42-, TcO4-, dicamba, and 2,4-D, from aqueous systems (Rhee et al., 1997; Kang et al., 1999; Lakraimi et al., 1999; You et al., 2001, 2002). The opposite charge properties of LDHs and viruses suggest that LDHs may have the potential to eliminate viruses from polluted waters.
According to Jin et al., (2007), layered double hydroxides nanocomposites are a group of anionic clay-like materials with unique layered structures. Synthesized LDH is highly crystalline and composed of well-defined hexagonal plate-shape crystals, with the diameter being approximately 100 nm. Due to the substitution of trivalent cation Al3+ by divalent cations Zn2+ and Mg2+ in octahedral structure, the LDH nanoparticles have positively charged sheets.
Hydrotalcite, a type of layered doubled hydroxide (LDH), occurs as a natural mineral and can be synthesized by reacting dilute aqueous solutions of magnesium and aluminium chlorides with sodium carbonate (Orthman et al., 2000). The material consists of stacks of mixed hydroxide layers of Mg and Al, [Mg1-x Alx (OH)2]x+, which are positively charged and require the presence of interlayer anion to maintain overall charge neutrality. Hydrotalcite is known as anionic clay as the interlayer anions, most commonly carbonate, can be exchanged with a wide range of inorganic and organic anions. The anion exchange capacity of the material is controlled by the Mg2+/Al3+ ratio.
Synthesis of Layered Double Hydroxide
There are a number of methods used to synthesize layered double hydroxide (Hickey, 2001) including:
deposition/ precipitation reactions;
anion exchange of a precursor LDH;
hydrolysis methods; and
direct synthesis by co-precipitation;
A common problem with all of these methods, excluding co-precipitation, is that in the preparation with anions other than carbonate, it is difficult to avoid contamination from carbon dioxide, since only carbonate is readily incorporated and tenaciously held in the interlayer (Newman, 1998). Thus co-precipitation is the most common method of synthesis.
In co-precipitation, all cations precipitate simultaneously in a ratio fixed by the starting solutions. This process can be carried out at high or low supersaturation, where high supersaturation increases the nucleation rate but prolongs washing of insoluble ions, while low supersaturation produces better crystalline material (Reichle, 1986). During the synthesis process the pH of the solution is very important and must be kept between10-12 for optimal hydrotalcite production (Hickey, 2001). If the pH exceeds 12, then dissolution of Al3+ ions can occur. If the pH is lower than 10, a more complex pathway for the precipitation exists and thus the process is not always complete.
Synthesis of LDH using co-precipitation method (Costantino et al., 2008)
M II + M III + Guest M II M III (Guest)-LDH M II M III (Guest)-LDH
solution (with improve crsystallinity)
According to You et al., (2001), magnesium-aluminium (Mg-Al) layered double hydroxide were prepared by mixing aqueous solutions of MgCl2 and AlCl3 (total metal concentration of 1M) were co-precipitated by adding drop wise a NaOH solution (2 M) to the mixture at 25 ± 1 °C. The molar ratio of Mg to Al ranged from 2.0 to 5.0. Nitrogen gas (N2) was bubbled throughout the co-precipitation process to minimize CO32- in solution. After co-precipitation, the suspensions were stirred for 6 hours with pH adjusted to 10 ± 0.3 and were then heated at 65°C for 16 hours. After cooling to room temperature, bottles containing the suspension were centrifuged and the precipitates were washed extensively using distilled water. The products were again dried at 65°C, ground, and stored in plastic bottles.
Structure of Layered Double Hydroxide
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Magnesium/Aluminium hydrotalcite (all subsequent references to hydrotalcite will be of the Mg/Al species) is a naturally occurring layered double hydroxide, which can also be easily and inexpensively synthesized by a number of methods. It is made up of stacks of mixed hydroxide layers of Magnesium and Aluminium which take the general form of [Mg1-x Alx(OH)2]x+ (Orthman et al., 2000). These layers have a structure similar to that of Brucite, Mg(OH)2, where each Mg2+ ion is octahedrally surrounded by six OH- ions (Trifirò et al., 1988). In Hydrotalcite, the Mg2+ ions are replaced by Aluminium, a cation of greater charge, but with similar radius. This gives the Brucite sheets a net positive charge. The Brucite structure and the structure of a hydrotalcite material are shown in Figure 4 (a) and Figure 4 (b):
(a) Brucite - Mg(OH)2; (b) Hydrotalcite Mg6Al(OH)16(CO32-).4H2O. (Hickey, 2001)
The octahedral Mg2+ and Al3+ structures share edges to form infinite sheets. These sheets are then stacked atop one another and held together by weak interactions through hydrogen (Trifirò et al., 1988). As mentioned previously, the layers carry a net positive charge. Thus, the presence of interlayer anions is required to achieve electrical neutrality. These interlayer anions along with the interstitial water are arranged randomly (Sharon, 1976). Figure 5 shows a diagrammatic representation of hydrotalcite layers and interlayer material:
Schematic representation of the hydrotalcite-type anionic clay structure (de Roy et al., 1992)
[M II1-xM IIIx (OH)2]x+Am−x/m.nH2O
Interlayer region (anions and water)
Brucite-like sheet (cations)
In hydrotalcite materials the Mg2+/Al3+ ratio determines both the number and arrangement of the charge balancing anions (Newman et al., 1998). This ratio typically ranges between 1 and 4. Recently, Orthman et al., (2000) found that the most efficient removal for melanoidin, an organic colour, occurred using hydrotalcite with Mg2+/Al3+ ratio of 3.
Ratio of hydrotalcite
Characterization of hydrotalcite
The resulted of hydrotalcite will be characterize using Powder X-Ray diffraction (PXRD), Fourier transform infrared (FTIR), Scanning Electron Microscopy (SEM) and Surface Area and Pores Size Analyzers (Brunauer, Emmett and Teller Theory - BET)
Powder X-Ray diffraction (PXRD)
Powder X-Ray diffraction analysis is the most important analytical technique for the characterization of hydrotalcite. PXRD is used for investigating the purity, composition and structural orientation of material. The technique involves directing X-rays to the crystals. The radiation will be either diffracted or reflected at different angles. An alumina sample holder is used to all solid samples. Some general features that are typical of all hydrotalcite are the presence of sharp and intense lines about 7.9 Çº. Basal spacing (d-spacing) was determined via powder technique.
PXRD is based on Bragg's law:
nλ = 2dhkl sin θ
n = integer representing the order of the reflection
λ = is the wavelength of the incident X-Ray beam,
d = the spacing between each lattice,
θ = the angle between the incident X-Ray beam and the reflecting crystal plan
hkl = diffraction or Miller indices of the plane where a, b and c are the axes.
Bragg's law indicates that the diffraction occurs when two conditions are satisfied:
1) The angle of the incidence = the angle of the scattering.
2) The path length difference is equal to an integer number of the wavelength.
3.5.2 Fourier transform infrared spectroscopy (FTIR)
FTIR has proven to be an extremely useful tool in the daily course of research on all the projects mentioned here. It is used as a tool for the evaluation of a molecule's structure by studying the vibration change occurring from the excitation of the bonds. The absorption peak shapes also indicate the environment that the vibration took place in; distortion from an ideal symmetry causes splitting of the relevant peak in the infrared (IR).
The hallmark of IR spectroscopy is that certain types of bonds between atoms invariably appear in certain regions of the infrared spectrum; we use this effect to determine the presence or absence of particular intercalates in the interlayer, and to check that the hydrotalcite layer material matches the expected spectrum. Since functional groups absorb at characteristic frequencies of infrared radiation, many compounds can be identified using only their spectra.
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A quick scan can be used to verify the purity and veracity of a sample before further experiments are carried out; higher-quality scans can be performed that give detailed information about the interlayer environment, the presence or absence of key functional groups in the system, the relative orderliness of the hydroxide layers, and the extent of ordering of water in the sample. We can also use IR to determine how much contamination (e.g. How much carbonate) is present in the system; if the hydrotalcite parent was a nitrate, we can also semi-quantitatively determine the extent of intercalation of our anion of interest.
Other characterization techniques
Application of Layered Double Hydroxide
Layered double hydroxides have a number of possible applications, which include:
as catalysts and catalyst supports (Newman et al., 1998);
as a novel environment for photochemical reactions of photoactive molecules (Kuroda et al., 1995);
as new modified electrodes in electrochemistry (Mousty et al., 1994); and
as adsorbent's of inorganic and organic molecules (Reichle, 1986);
Pollution of Water
The presence of colour in many industrial effluent streams is aesthetically undesirable (Orthman et al., 2000). Colour organic effluent is produced in the textile, paper, plastic, leather, food and mineral processing industries. Colour may arise from organic compounds that cause an increase in biological oxygen demand leading to polluted water. Other coloured dyes may significantly alter the photosynthetic activity in aquatic life due to reduced light penetration. The presence of heavy metals in dyes may also be toxic to some aquatic life. Although such environmental risks are acknowledged, most dyes exhibit low toxicity to mammals and aquatic organisms and mainly pose aesthetic problems to river systems.
The general public assumes that visible colour pollution from expended effluent indicates the presence of toxicants. Hence, there is increasing pressure on governments to tighten or introduce regulations for effluent colour levels. This can be specifically seen in Britain and Canada where, through new regulations, pressure is being placed on water companies to reduce the amount of colour in sewage effluent. Traditionally both biological and chemical methods have been employed for dye removal, but these techniques have not been very successful due to the essential nonbiodegradable nature of most dyes. That is, biological-chemical reactions do not work for colour removal due to the presence of large organic molecules and their very stable nature under micro-organisms.
Adsorption techniques have proven successful in removing coloured organic species, with activated carbon being the most widely used adsorbent due to its high capacity for the adsorption of organic materials. However, due to its high cost and the difficulty of regeneration, a search for cheap, effective adsorbents such as bentonite clay derivatives is needed.
Adsorption of dye by Hydrotalcite
Hydrotalcite occurs as a natural mineral and can be synthesized by reacting dilute aqueous solutions of magnesium and aluminium chlorides with sodium carbonate (Orthman et al., 2000). This adsorbent favours anion exchange with negatively charged species being either adsorbed on the ionic clay's surface or by entering the interlayer region of the clay hydrotalcite by anion exchange with carbonate groups. Hydrotalcite maybe an effective adsorbent of other organic molecules due to its hydrophobic nature and the accessibility of its interlayer region.
It is known that many substances in wastewater carry negative charges and account for large parts of pollutants in water systems. Examples include humic substances, dyes in the effluent of textile processes and biological species from food processing. With an affinity for anionic species it could be assumed that clay hydrotalcite would have a greater capacity to adsorb acidic dyes than basic species.
Hydrotalcite can be regenerated in the process whereby it is simply heated at 723K for four hours to burn off any adsorbed organic compounds. Although the hydrotalcite structure is lost during heating, it is restored when dispersed in aqueous solution. Hence, it could be expected that the uptake level of reused anion hydrotalcite is on par with the original clay.
Two mechanisms occur in the adsorption process. Firstly, dye species may be adsorbed on the ionic clay's surface. Such a mechanism can be observed for many sorbents with a considerable surface area such as activated carbons, porous silicas, activated alumina and resin. Clearly, the parameter of sorbent surface area plays a key role in this mechanism. A second mechanism involves anion exchange and is unique for a sorbent of anion clay. Coloured dyes that exhibit anionic properties can enter the interlayer region of the clay hydrotalcite by anion exchange with carbonate groups. This will give rise to a significant increase in the colour removal ability of the sorbents. The high efficiency by the sorbent in the removal of all three acid dyes may be explained by the combination of these two mechanisms.
Response Surface Methodology (RSM)
Response surface methodology (RSM) is defined as a statistical method that uses quantitative data from appropriate experimental designs to determine and simultaneously solve multivariate equations (Giovanni, 1983) and so generate a mathematical model that describe the overall process. It is preferred over conventional procedures because it enables the study of the effect of several variables, individually or in combination, by varying them simultaneously and systematically while carrying out only a limited number of experiments. This in turn reduces the time and cost required for determination of the optimum process and improves efficiency of the experimental process.
However, the effective use of RSM is dependent upon the following five assumptions (Giovanni, 1983):
The factors critical to the process are known.
The region of the interest where the factor levels influence the process is known.
The factors vary continuously throughout the experimental range tested.
There exists a mathematical function, which relates the factors to the measured response.
The response defined by this function is smooth surface.
Limitations in the use of RSM may vary from:
Large variations in the factors, which result in misleading conclusions.
Incorrect specification and the insufficient definition of the critical factors, which lead to inaccuracy of the optimum conditions.
Too narrow or broad a range of factor levels which prevents determination of the process optimum.
The misuse of statistical principles, which give biased, results that in turn lead to an incorrect mathematical model for the description of the optimum.
Four Step Procedures in RSM Determination
Response surface methodology (RSM) can be successfully implemented using the following four-step procedure.
Identification Factors (step 1)
Two or three critical factors that are most important or influence to the process during the study are identified (Giovanni, 1983) usually by conducting preliminary factorial analysis experiments. Factors are the characteristic of a process that can be varied within the system. In this reaction, factors include reaction time, concentration and pH of dye and dosage of hydrotalcite.
Definition of Factor Levels (step 2)
The range of factor levels which will determine the samples to be tested are defined (Giovanni, 1983). Factor levels are the degrees or quantity of the factors. They are initially set fairly broad and if necessary a second RSM experiment is conducted to yield more accurate representation of the optimum. Once the factor levels are set, a preliminary test with samples representing the midpoints of these levels is performed to ensure that the levels are appropriate.
Experimental Design and Selection of Test Samples (step 3)
The specific test samples are determined via experimental design and tested (Giovanni, 1983). Such design can be obtained from the literature or generated using computer software like statistical or design expert. They comprise of a selected subset of samples to be tested from the set of all possible samples that could be tested with emphasis on those closest to the midpoints of the factor levels. A design commonly used in optimization of enzymatic processes is the central composite rotatable design (CCRD). It consists of factorial, star and center points with the number of each depending on the number of factors and levels studied. Once the samples are specified, experiments are performed to test these samples and obtain quantitative data for use in subsequent statistical analysis.
Statistical Analysis (step 4)
The data from the experiment performed are analyzed using appropriate computer programs such as Microsoft excel, statistical or Design expert and interpreted. Three main analytical steps are usually performed; an analysis of variance a (ANOVA), a Regression analysis and a plotting of response surface. An ANOVA is first calculated to test a hypothesis on the parameters of a model and assess how well a particular model represents the data. The values for the Fischer variance ratio (F-ratio) and the coefficient of determination (R-squared) are important. The F-ratio provides information on how well the factors describe the statistical variation in the data from its mean.
The higher F-value from unity, the more certain the factors are in explaining the variation in the data about its mean. The R-squared evaluates the stability of the model in representing the real relationship among the factor studied; typically a value of 0.75 implies the model adequate for representing the real relationship among the factor while a value of >0.90 indicates that the model describes the real situation. A regression analysis is then performed to the generate coefficients (β0, β1…βq) for the selected empirical model.
The significance of the coefficients is evaluated using a student t-test. From this coefficients with P-values of <0.05 are generally considered highly significant in scientific literature and therefore included in the mathematical model. The model is often a first, second or third order polynomial function and when fitted to a set of sample data, characterize the relationship between the response y and the factors x1, x2…xy. Response is defined as the effect of the different factor levels on the process studied.
A model that can be represents the data well can be used to generate response surfaces. There are three-dimensional diagrams with the responses plotted onto the y-axis and the x1 and x2 axes each representing different factors in the different permutations. While response surfaces are commonly dome-shaped, those with cradle and saddle points are also possible.
The statistical information and response surface can be used for the following purposes (Giovanni, 1983);
To determine the combination of factor levels that will produce the optimum response for a given measurement.
To determine how a specific response is affected by changes in the factor levels.
To determine the factor levels that will simultaneously satisfy a set desired specification.
To describe the mutual interactions among the factors.