Application Of Surface Complexation Model Research Articles

Application of surface complexation modeling on adsorption of uranium at water-solid interface: A review.

Precise prediction of uranium adsorption at water-mineral interface is of great significance for the safe disposal of radionuclides in geologic environments. Surface complexation modeling (SCM) as a very useful tool has been extensively investigated for simulating adsorption behavior of metals/metalloids at water-mineral interface. Numerous studies concerning the fitting of uranium adsorption on various adsorbents using SCM are well documented, but the systematic and comprehensive review of uranium adsorption using various SCM is not available. In this review, we briefly summarized the rationale of SCM, including constant-capacitance-model (CCM), diffuse-layer-model (DLM), triple-layer-model (TLM); The recent progress in the application of SCM on the fitting of uranium adsorption towards metal (hydr)oxides, clay minerals and soil/sediments was reviewed in details. This review hopefully provides the beneficial guidelines for predicting the transport and fate of uranium in geologic environments beyond laboratory timescales.

Pertechnetate/Perrhenate Surface Complexation on Bamboo Engineered Biochar.

The work deals with the evaluation of biochar samples prepared from Phyllostachys Viridiglaucescens bamboo. This evaluation consists of the characterization of prepared materials’ structural properties, batch and dynamic sorption experiments, and potentiometric titrations. The batch technique was focused on obtaining basic sorption data of 99mTcO4− on biochar samples including influence of pH, contact time, and Freundlich isotherm. ReO4−, which has very similar chemical properties to 99mTcO4−, was used as a carrier in the experiments. Theoretical modeling of titration curves of biochar samples was based on the application of surface complexation models, namely, so called Chemical Equilibrium Model (CEM) and Ion Exchange Model (IExM). In this case it is assumed that there are two types of surface groups, namely, the so-called layer and edge sites. The dynamic experimental data of sorption curves were fitted by a model based on complementary error function erfc(x).

Improved accuracy in multicomponent surface complexation models using surface-sensitive analytical techniques: Adsorption of arsenic onto a TiO2/Fe2O3 multifunctional sorbent

Novel composite materials are increasingly developed for water treatment applications with the aim of achieving multifunctional behaviour, e.g. combining adsorption with light-driven remediation. The application of surface complexation models (SCM) is important to understand how adsorption changes as a function of pH, ionic strength and the presence of competitor ions. Component additive (CA) models describe composite sorbents using a combination of single-phase reference materials. However, predictive adsorption modelling using the CA-SCM approach remains unreliable, due to challenges in the quantitative determination of surface composition. In this study, we test the hypothesis that characterisation of the outermost surface using low energy ion scattering (LEIS) improves CA-SCM accuracy. We consider the TiO2/Fe2O3 photocatalyst-sorbents that are increasingly investigated for arsenic remediation. Due to an iron oxide surface coating that was not captured by bulk analysis, LEIS significantly improves the accuracy of our component additive predictions for monolayer surface processes: adsorption of arsenic(V) and surface acidity. We also demonstrate non-component additivity in multilayer arsenic(III) adsorption, due to changes in surface morphology/porosity. Our results demonstrate how surface-sensitive analytical techniques will improve adsorption models for the next generation of composite sorbents.

Surface area of ferrihydrite consistently related to primary surface charge, ion pair formation, and specific ion adsorption

The specific surface area (SSA) of metal oxides is pivotal for scaling of surface phenomena. For ferrihydrite (Fh), the SSA can be assessed by probing the surface with ions that specifically adsorb (e.g. protons or phosphate). In the approach, an appropriate material with a known surface chemical behavior is used as reference, accounting for differences in e.g. surface sites and structure. As Fh is a nanomaterial, the size-dependency of many of its properties requires a consistent implementation for data analysis and modeling. In the present study, the proton adsorption of Fh was measured in NaNO3, NaCl, and NaClO4 solutions using a potentiometric titration methodology that leads to an internally consistent primary data set (H/Fe). For data interpretation, we employed a size-dependent molar mass, mass density, and chemical composition (FeO1.4(OH)0.2·nH2O), as well as a size-dependent surface curvature since the latter increases the value of the Stern layer capacitance. Using well-crystallized goethite as reference, state-of-the-art multisite complexation modeling discloses the underlying SSA of Fh. Similar to goethite, a significant variation in electrolyte affinity constants (logK) is found for Fh. This largely explains the differences in pHPZC reported in literature when using e.g. KNO3 or NaCl rather than NaNO3 as electrolyte solution. Our data collection was done for Fh materials with a known aging history. The same Fh samples were also probed with phosphate ions and the collected primary data (PO4/Fe) were interpreted with the CD model. This methodology yields SSA values that are consistent with those found by probing the surface of Fh with protons. As ion probing with phosphate is rapid and sensitive, it is recommended as a tool to determine the SSA of Fh materials. This enables the development of a consistent thermodynamic database for application of surface complexation modeling in natural systems.

Surface complexation model of Cd in paddy soil and its validation with bioavailability

In southern China, the paddy soils are mainly variable-charge soils, which have undergone high weathering processes and leaching processes. Due to deficiencies of carbonate and base cations, the variable-charge soil pH is normally less than 6.5, and decrease of soil pH can substantially enhance the mobility and bioavailability of heavy metals. Cadmium (Cd) is one of the most important pollutant influencing human health and environmental safety. Cd contamination is a serious problem in paddy soils, and the acidic condition could further aggravate the mobility and bioavailability of Cd. There is an urgent need to establish a model to investigate the adsorption characteristics of Cd and accurately assess the bioavailability of Cd in variable-charge paddy soils. In this study, the paddy soil was selected from agricultural regions in southern China. Batch experiments were conducted to investigate the Cd adsorption characteristics of the soils from pH 3 to 10. The results showed that Cd mainly existed in dissolved forms at pH 5.5, almost all Cd was adsorbed on the soil surface. Based on the potentiometric titration, the point of zero charge (pHpzc) was obtained when the surface charge density ( σ ) was equal to zero. The pHpzc of the paddy soil was 5.91. The 1-site/2-p K surface complexation model (SCM) was applied to further examine the acid-base properties of the soils by assuming the acid-base buffering system as a protonation-deprotonation process. The surface site concentration ( H s) and the surface site density ( D s) were obtained by Gran plot. The two acid equilibrium constants (p K a1 and p K a2) were determined by extrapolation and the calculated pHpzc were obtained by the average values of the p K a1 and p K a2. The value of pHpzc calculated from the model was 6.10, which was well matched with those from the potentiometric titration, indicating that it is feasible for the application of SCM to the variable-charge soils. The intrinsic equilibrium constants (lg K SOCd) was estimated from the Cd adsorption experimental data and acid-base parameters (p K a1, p K a2 and D s) using Visual MINTEQ software. The results showed that the adsorption of Cd on the paddy soil could be successfully modeled by the SCM. Based on the previous study, the pH was significantly negatively correlated with grain Cd content in paddy soil when pH was below 5.5 ( P 1 mol L−1 MgCl2 (exchangeable fraction) and 1 mol L−1 NaOAc/HOAc (bound to carbonate). Although the extracted Cd was positively correlated with Cd content in grain, the P value was much higher than 0.05, indicating the insignificant correlation. Compared with the extraction method, the dissolved Cd calculated from the SCM was significantly correlated with the grain Cd content (P

The impact of ionic strength on the proton reactivity of clay minerals

The reactivity of clay mineral surfaces exerts a fundamental control on elemental cycling in environmental systems. However, the effect of solution ionic strength (IS) on their reactivity is poorly understood. Here we quantified the effect of changes in IS on clay mineral reactivity through the application of surface complexation modeling (SCM) based on acid-base titration data for three different clay minerals (kaolinite, illite, and montmorillonite). The results demonstrate that the acidity constants (Ka values) for proton interactions on the clay surface vary linearly as a logarithmic function of solution IS, and that proton binding ability is inversely related to solution IS. Two possible explanations for the observed relationship are proposed: (1) solution electrolytes (e.g., Na+) competitively bind to clay surface sites relative to protons, and (2) that the surface electrostatic field is attenuated by solution electrolytes. We conclude that the impact of increasing solution IS on clay surface reactivity is the result of a weakening negative surface electric field that impacts the surface potential. This mechanism ultimately leads to different adsorption behavior for cations and anions under changing IS conditions. The empirical relationship between K values and IS provided here can be applied to predict clay surface reactivity under varied environmental IS conditions and has specific implications for trace metal behavior in environments, such as estuaries, where changes in IS can be significant.

Rare earth elements sorption to iron oxyhydroxide: Model development and application to groundwater

Iron oxyhydroxides are among the most important colloids that control rare earth elements (REE) concentrations and transport in natural hydrosystems. In this study, REE surface complexation to iron oxyhydroxides (Fe(OH)3(a)) was described by using the Donnan diffuse layer model and a two-site (i.e. ≡FesOH and ≡FewOH) model. The specific surface area and pH of zero charge were fixed as 100000 m2/mol and 8.0, respectively. The surface site density for weak and strong binding sites were fixed at 0.1 moL/mol Fe (≡FewOH) and 0.001 moL/mol Fe (≡FesOH) respectively. The two site types were used with pKa1int = 7.29 and pKa2int = 8.93. Using linear free energy relationship, the estimated equilibrium surface complexation constants (log K) increased from light REE (LREE) to heavy REE (HREE). Results of REE modeling calculation using the determined log K revealed a good fit of experimental data, showing an order of sorption on iron oxyhydroxides: HREE > MREE > LREE and preferential sorption of HREE at a lower pH. However, sorption edges only showed a slight change with ionic strength (0.1–0.7 moL/L) for the whole REE series. The generalized model was subsequently used to evaluate the impact of iron oxyhydroxides on REE speciation in groundwater. Application of the model to “model groundwater” showed that iron oxyhydroxide complexes of REE were significant in near neutral and weakly alkaline pH. This study contributes to putting forward a comprehensive database which would be useful for the application of surface complexation model to describe REE sorption by amorphous ferric hydroxides in nature.

Application of surface complexation modeling to trace metals uptake by biochar-amended agricultural soils

Biochar has emerged as a useful amendment to release nutrients into agricultural soil, to increase crop productivity, and as a sorbent to remediate metals and organics contamination. Since soils have heterogeneous physical properties across a given crop field, and even over a growing season, it is imperative to select the most appropriate biochar for the intended purpose and in defining the amendment level. In this study, we investigate the adsorption of Cd(II) and Se(VI) as model pollutant cations and anions, respectively, to two agricultural soils amended with a wood pin chip biochar (WPC). The proton reactivity of each sorbent was determined by potentiometric titration, and single-metal, single-sorbent experiments were conducted as a function of pH. The resulting data were modeled using a non-electrostatic surface complexation modeling (SCM) approach to determine the proton and metal binding constants and surface functional group concentrations of each soil and WPC. The SCM approach is a considerable advance over empirical modeling approaches because SCM models can account for changes in pH, ionic strength, temperature, and metal-to-sorbent ratio that may happen over the course of a growing season. The constants derived from the single-metal, single-sorbent experiments were then used to predict the extent of metal adsorption in more complex mixtures of Cd, Se, soil and WPC. Overall the SCM approach was successful in predicting metal distribution in multi-component mixtures. In cases where the predictions were poorer than expected, we identify reasons and discuss future experiments needed to further the application of SCM to sorbent mixtures containing biochar.

Application of surface complexation modeling on modification of hematite surface with cobalt cocatalysts: a potential tool for preparing homogeneously distributed catalysts

The knowledge to synthesize homogeneously distributed catalysts on the support is rather identical to understand the interactions between pollutants and adsorbents in the environmental chemistry.

Application of surface complexation models to anion adsorption by natural materials

Various chemical models of ion adsorption are presented and discussed. Chemical models, such as surface complexation models, provide a molecular description of anion adsorption reactions using an equilibrium approach. Two such models, the constant capacitance model and the triple layer model, are described in the present study. Characteristics common to all the surface complexation models are equilibrium constant expressions, mass and charge balances, and surface activity coefficient electrostatic potential terms. Methods for determining parameter values for surface site density, capacitances, and surface complexation constants also are discussed. Spectroscopic experimental methods of establishing ion adsorption mechanisms include vibrational spectroscopy, nuclear magnetic resonance spectroscopy, electron spin resonance spectroscopy, X-ray absorption spectroscopy, and X-ray reflectivity. Experimental determinations of point of zero charge shifts and ionic strength dependence of adsorption results and molecular modeling calculations also can be used to deduce adsorption mechanisms. Applications of the surface complexation models to heterogeneous natural materials, such as soils, using the component additivity and the generalized composite approaches are described. Emphasis is on the generalized composite approach for predicting anion adsorption by soils. Continuing research is needed to develop consistent and realistic protocols for describing ion adsorption reactions on soil minerals and soils. The availability of standardized model parameter databases for use in chemical speciation-transport models is critical.

Application of surface complexation modeling on the leaching of radionuclides from fly ashes

Surface complexation modeling based on diffuse double layer model (DDLM) has been employed to simulate the leaching behaviors of radionuclides (i.e., 60Co2+, 63Ni2+, 64Cu2+ and UO22+) on fly ashes from hospital wastes. The fly ashes were characterized by X-ray diffraction (XRD), potentiometric acid–base titration, and scanning electron microscopy (SEM). The effect of pH, the ratio of solid-to-liquid (RSL), and particle size on the leaching of radionuclides was also conducted under the batch techniques. According to XRD patterns analysis, the main components of fly ashes was determined to be quartz and clay minerals such as kaolinite, illite and smectite. The site density and pHPNZC (pH at point of net zero charge) of fly ashes calculated from potentiometric titration data was found to be 4.53 × 10−4 mol/g and 7.4, respectively. One can see that the small amount of metal oxides attached on the surface of fly ashes by SEM images, corrosion surface of fly ashes was observed after leaching experiments. Results of the batch leaching experiments indicated that the leaching amount of radionuclides in fly ashes significantly depended on pH, the ratio of solid-to-liquid, and particle size. It is found that the leaching amount of radionuclides decreased in the order of 64Cu2+ ≫ 63Ni2+ > 60Co2+ > UO22+ under the different pH, the RSL or particle size conditions. In this paper, DDLM has successfully been applied to describe the leaching behaviors of radionuclides from fly ashes. This research provides critical information for application of surface complexation modeling on the simulation of radionuclides leaching from fly ashes, which would clarify the leaching mechanism of radionuclides from fly ashes.

Surface complexation modeling for predicting solid phase arsenic concentrations in the sediments of the Mississippi River Valley alluvial aquifer, Arkansas, USA

The potential health impact of As in drinking water supply systems in the Mississippi River Valley alluvial aquifer in the state of Arkansas, USA is significant. In this context it is important to understand the occurrence, distribution and mobilization of As in the Mississippi River Valley alluvial aquifer. Application of surface complexation models (SCMs) to predict the sorption behavior of As and hydrous Fe oxides (HFO) in the laboratory has increased in the last decade. However, the application of SCMs to predict the sorption of As in natural sediments has not often been reported, and such applications are greatly constrained by the lack of site-specific model parameters. Attempts have been made to use SCMs considering a component additivity (CA) approach which accounts for relative abundances of pure phases in natural sediments, followed by the addition of SCM parameters individually for each phase. Although few reliable and internally consistent sorption databases related to HFO exist, the use of SCMs using laboratory-derived sorption databases to predict the mobility of As in natural sediments has increased. This study is an attempt to evaluate the ability of the SCMs using the geochemical code PHREEQC to predict solid phase As in the sediments of the Mississippi River Valley alluvial aquifer in Arkansas. The SCM option of the double-layer model (DLM) was simulated using ferrihydrite and goethite as sorbents quantified from chemical extractions, calculated surface-site densities, published surface properties, and published laboratory-derived sorption constants for the sorbents. The model results are satisfactory for shallow wells (10.6 m below ground surface), where the redox condition is relatively oxic or mildly suboxic. However, for the deep alluvial aquifer (21–36.6 m below ground surface) where the redox condition is suboxic to anoxic, the model results are unsatisfactory.

Nanoparticles in natural systems I: The effective reactive surface area of the natural oxide fraction in field samples

Information on the particle size and reactive surface area of natural samples is essential for the application of surface complexation models (SCM) to predict bioavailability, toxicity, and transport of elements in the natural environment. In addition, this information will be of great help to enlighten views on the formation, stability, and structure of nanoparticle associations of natural organic matter (NOM) and natural oxide particles. Phosphate is proposed as a natively present probe ion to derive the effective reactive surface area of natural samples. In the suggested method, natural samples are equilibrated (⩾10 days) with 0.5 M NaHCO 3 (pH = 8.5) at various solid–solution ratios. This matrix fixes the pH and ionic strength, suppresses the influence of Ca 2+ and Mg 2+ ions by precipitation these in solid carbonates, and removes NOM due to the addition of activated carbon in excess, collectively leading to the dominance of the PO 4–CO 3 interaction in the system. The data have been interpreted with the charge distribution (CD) model, calibrated for goethite, and the analysis results in an effective reactive surface area (SA) and a reversibly bound phosphate loading Γ for a series of top soils. The oxidic SA varies between about 3–30 m 2/g sample for a large series of representative agricultural top soils. Scaling of our data to the total iron and aluminum oxide content (dithionite–citrate–bicarbonate extractable), results in the specific surface area between about 200–1200 m 2/g oxide for most soils, i.e. the oxide particles are nano-sized with an equivalent diameter in the order of ∼1–10 nm if considered as non-porous spheres. For the top soils, the effective surface area and the soil organic carbon fraction are strongly correlated. The oxide particles are embedded in a matrix of organic carbon (OC), equivalent to ∼1.4 ± 0.2 mg OC/m 2 oxide for many soils of the collection, forming a NOM–mineral nanoparticle association with an average NOM volume fraction of ∼80%. The average mass density of such a NOM–mineral association is ∼1700 ± 100 kg/m 3 (i.e. high-density NOM). The amount of reversibly bound phosphate is rather close to the amount of phosphate that is extractable with oxalate. The phosphate loading varies remarkably ( Γ ≈ 1–3 μmol/m 2 oxide) in the samples. As discussed in part II of this paper series ( Hiemstra et al., 2010), the phosphate loading ( Γ) of field samples is suppressed by surface complexation of NOM, where hydrophilic, fulvic, and humic acids act as a competitor for (an)ions via site competition and electrostatic interaction.

Surface complexation modeling of Cu(II) adsorption on mixtures of hydrous ferric oxide and kaolinite

BackgroundThe application of surface complexation models (SCMs) to natural sediments and soils is hindered by a lack of consistent models and data for large suites of metals and minerals of interest. Furthermore, the surface complexation approach has mostly been developed and tested for single solid systems. Few studies have extended the SCM approach to systems containing multiple solids.ResultsCu adsorption was measured on pure hydrous ferric oxide (HFO), pure kaolinite (from two sources) and in systems containing mixtures of HFO and kaolinite over a wide range of pH, ionic strength, sorbate/sorbent ratios and, for the mixed solid systems, using a range of kaolinite/HFO ratios. Cu adsorption data measured for the HFO and kaolinite systems was used to derive diffuse layer surface complexation models (DLMs) describing Cu adsorption. Cu adsorption on HFO is reasonably well described using a 1-site or 2-site DLM. Adsorption of Cu on kaolinite could be described using a simple 1-site DLM with formation of a monodentate Cu complex on a variable charge surface site. However, for consistency with models derived for weaker sorbing cations, a 2-site DLM with a variable charge and a permanent charge site was also developed.ConclusionComponent additivity predictions of speciation in mixed mineral systems based on DLM parameters derived for the pure mineral systems were in good agreement with measured data. Discrepancies between the model predictions and measured data were similar to those observed for the calibrated pure mineral systems. The results suggest that quantifying specific interactions between HFO and kaolinite in speciation models may not be necessary. However, before the component additivity approach can be applied to natural sediments and soils, the effects of aging must be further studied and methods must be developed to estimate reactive surface areas of solid constituents in natural samples.

Application of surface complexation modeling to the reactivity of iron(II) with nitroaromatic and oxime carbamate contaminants in aqueous TiO 2 suspensions

This study reports on the application of surface complexation modeling to interpret observed kinetic trends for Fe II redox reactions with model nitroaromatic (4-chloronitrobenzene) and oxime carbamate (oxamyl) contaminants in aqueous TiO 2(s) suspensions. Pseudo-first-order rate constants for reduction of the two probe contaminants ( k red, s −1) vary by several orders of magnitude with changing conditions (100–500 μM Fe II, 0–15 g L −1 TiO 2(s), pH 2–9), but the relationship between reaction rates and Fe II speciation differs considerably for the two contaminants. For oxamyl, k red measurements are most strongly correlated with the volumetric total adsorbed Fe II concentration (moles Fe II adsorbed per liter of TiO 2(s) suspension), whereas k red measurements for 4-chloronitrobenzene are proportional to the concentration of the hydrolyzed Fe II surface complex ( TiOFe IIOH 0). The differing trends demonstrate that Fe II redox reactivity at the aqueous/TiO 2(s) interface is influenced, in part, by specific molecular interactions with the target oxidant. Results are also geochemically relevant in that they demonstrate unambiguously that mononuclear Fe II–metal (hydr)oxide surface complexes are sufficiently reactive species to reduce nitroaromatic contaminants, an issue that remained open following earlier studies in Fe III (hydr)oxide suspensions because structural Fe II species are simultaneously present in such systems because of interfacial Fe II-to-Fe III electron transfer processes that occur on Fe II adsorption.

Heterogeneous processes affecting metal ion transport in the presence of organic ligands: Reactive transport modeling

The development of models to accurately simulate metal ion transport through saturated systems under variable chemical conditions, e.g., in systems containing organic ligands (L) such as natural organic matter (NOM), has two essential aspects: (1) establishing the ability to simulate metal ion sorption to aquifer solids over a range of metal/ligand ratios; and (2) to incorporate this ability to simulate metal speciation over a range in chemical conditions (e.g., pH, ligand activity) into mass transport models. Modeling approaches to evaluate metal ion sorption and transport in the presence of NOM include: (1) isotherm-based transport models, and (2) multicomponent (MC) transport models. The accuracy of transport models depends on how well the chemical interactions affecting metal ion transport in the presence of organic ligands (e.g., metal/ligand complexation) are described in transport equations. The isotherm-based transport models often fail to accurately describe metal ion transport in the presence of NOM since these models treat NOM as a single solute despite the fact that NOM is a multicomponent mixture of subcomponents with different chemical and polyfunctional behavior. On the other hand, the calculations presented in this study suggest that a multicomponent reactive transport model, in conjunction with a mechanistic modeling approach for the description of metal ion binding by NOM in a manner conducive to the application of surface complexation modeling (SCM), can effectively be used as an important predictive tool in simulating metal ion sorption and transport under variable chemical conditions in the presence of NOM.

Determination of the parameters for the 1-p K triple-layer model of ion adsorption onto oxides from known parameter values for the 2-p K TLM

Prior knowledge of the model parameters describing the metal oxide/simple electrolyte interface is frequently crucial to successful application of surface complexation modeling. On the basis of the parameter values for the 2-p K triple-layer model recently published by Sverjensky, the parameters for the 1-p K TLM were determined. This was done by fitting (using the 1-p K model) the proton surface charge and ion adsorption isotherms generated by the 2-p K TLM. It was assumed that inner-layer capacitance was the same for both models. A strong correlation was found between ion adsorption constants of different electrolytes on the same oxide predicted by both models. Additionally, the 1-p K and 2-p K models predict different electrokinetic potentials in spite of very similar values of calculated surface charge and ion adsorption isotherms when the same value of outer-layer capacitance is assumed. Because the value of outer-layer capacitance practically does not influence surface charge and electrolyte adsorption isotherms, it can be treated as a freely adjusted parameter, the value of which can be estimated if we have measured the electrokinetic potential.

Lead sorption onto ferrihydrite. 3. Multistage contacting.

Few studies have demonstrated the practical application of surface complexation models, calibrated with fundamental macroscopic and spectroscopic metal sorption data, in helping to solve industrial trace metal emissions problems. In this work, multistage ferrihydrite sorption systems are evaluated for their effectiveness in reducing single-solute lead(II) [Pb(II)] concentrations in contaminated water streams to very low levels. Experimental data and modeling results indicate that a multistage sorption system can significantly reduce Pb(II) effluent concentrations for the same total amount of sorbent or, alternatively, dramatically lower total sorbent consumption for the same effluent Pb(II) concentration. Model predictions were generated using a steady-state, multistage, equilibrium adsorber model that was specifically developed for and integrated into 0LI Systems' Environmental Simulation Program. The modified triple-layer model was used to simulate Pb(II) surface-liquid equilibria within the adsorber model. Engineering screening evaluations indicate that a 2-3-stage sorption process can provide significant economic savings when compared to a 1-stage process operating with the same target effluent Pb(II) concentration. Additional equilibrium stages beyond 2 or 3 provide diminishing economic returns. The major economic driver for multiple contacting stages is reduced capital investment and operating costs for sludge handling, dewatering, and disposal.

On the Application of Surface Complexation Models to Ionic Adsorption

Surface complexation models (SCMs) have been used extensively to describe the acid–base properties and adsorption behaviors of inorganic and organic cations and anions on many single–phase adsorbents. The success of SCMs results from the conformation of adsorption to mass-action and material-balance laws and the flexibility in adjustable parameters. However, the assumptions made in the derivation of the SCMs are really not justified and the SCMs are not so thoroughly grounded in fundamental chemistry. The SCMs are really the data-fitting model, rather than the chemical model. The premises of application of SCMs are the accurate identification of surface species and the low surface coverage. Though the application of the SCMs comes into vogue, the SCMs should be applied with caution.

Surface Complexation Modeling: I. Strategy for Modeling Monomer Complex Formation at Moderate Surface Coverage

Surface complexation models (SCMs) have previously been shown to be capable of predicting metal ion sorption behavior onto mineral surfaces. Application of SCMs, however, requires a self-consistent approach for determining model parameter values. In this paper, titration data for α-Al2O3 and metal ion sorption data for Co(II) are used to determine the metal ion sorption parameters for the triple-layer model (TLM) version of the SCM. This was accomplished by calibrating the TLM parameters to a moderate-coverage (0.1%) data set and then using these values to predict data over a wide range of surface coverages (0.05 to 10%), pH (5 to 9), and ionic strength (0.001 to 0.1 M as NaNO3). The results of the analysis showed that a range of TLM parameter values could fit the calibration data set equally well. However, when metal ion sorption data covering a range of ionic strength and surface coverages were considered, an optimal set of TLM parameter values could be identified. For high surface coverage data (> 10%), the model was shown to underpredict the data, presumably due to the formation of multinuclear species. In order to extend the model predictions range to high surface coverage, polymer or surface precipitation TLM reactions are needed.