Surface speciation as a function

Figure 9.24. (a) Calculated surface speciation as a function of pH at ionic strength 0.1 (1 1 electrolyte) for a 10 M hydrous ferric oxide suspension, (b) Calculated equilibrium speciation as a function of pH for zinc in a 10 M suspension of hydrous ferric oxide TOTZn = 10 M, / = 0.1 M. (Adapted from Dzombak and Morel, 1990.)... 

Correcting for Coulombic Interaction. The surface speciation as a function of solution variables can be computed if we can correct our equibrium constants for electrostatic attraction or repulsion. Westall (21, 22, 23) has developed a computer program that permits one to compute iteratively the composition of the surface and its charge from a set of equilibrium constants. Figures 12 and 13 illustrate the application of this computation to the interaction of o-phosphate with goethite ( -FeOOH). This interaction is rather involved because various mono-dentate and bidentate surface species have to be assumed to account for the experimental observations (18, 24) ... 

The acid-hase characteristics of the surface groups (relative speciation of surface groups as a function of pH in upper figure) determine the pH of zero potential (point of zero proton condition MeOHt = =MeO ). The Nernst equation—a surface potential dependence on pH of (RT/Fj In 10 (= 59 mvolt at 25°C)—is not fulfilled. The lines in the lower figure were calculated from alkalimetric and acidimetric titration curves using... 

The large differences seen in Cu(II) adsorption capacity for thiol- and amino-modified silicas does not occur for Pb(II) adsorption (Figure 7). While it is tempting to argue these differences in adsorption behavior based on hard-soft acid-base theory the hardness of Cu2+ and Pb2+ are very similar, both generally categorized as intermediate cases.18 It is more likely that Pb(II) adsorption behavior under the conditions of this study is dictated largely by solution behavior and speciation of Pb2+ in aqueous solution at pH 5. Indeed it was found that these solutions were unstable and prone to precipitation slowly over time under these conditions. Further work will be needed as a function of pH, surface functionality, and metal ion to determine the role of various factors on adsorption behavior of these systems. 

Fig. 15.5. Calculated metal sorption curves for Pb, Cu and Cd onto the bacterium Bacillus subtilis, shown as a function of pH versus the concentration of sorbed metal. Curves are calculated based on experimental metal sorption data of Fein et al. (1997), and were computed using the geochemical speciation programme JCHESS. The solution depicted contains 1 g 1 bacteria dry wt (155 m g surface area, 8.0 Cm electrical double layer capacitance), 1 mM dissolved CaC03 and 1 iM dissolved lead, copper and cadmium. Adsorption was calculated using a CCM treatment.

Beryllium is an alkaline-earth elements whose behavior drastically differs from that of the other alkaline-earth elements. Its low mobility in natural waters is attributed to its affinity for surfaces. Laboratory experiments have been performed to examine the partitioning of Be between sediments from natural systems and water (You et al, 1989). The partition coefficient depends strongly on pH in the range 2-7. The curve of as a function of pH (Figure 24) can be explained by a thermodynamic model by taking into account beryllium speciation in freshwater and the... 

Figure 6. Experimental dissolution rate (mol/m2 per hour) as a function of surface speciation (eq 17). Insert dissolution rates (mol/m2 per hour) for hematite, goethite, lepidocrocite, and magnetite as a function of the free energy (kj/mol of electrons) of the reduction reactions...

Figure 8. The effect of selenite on the EDTA-promoted dissolution of y-FeOOH 0.5 gIL). Part a At low pH the dissolution rate is increased by selenite at pH 7 it is strongly inhibited. Concentration of the ligands is given in inol/L. Part b Surface speciation on lepidocrocite as a function of pH according to Sposito et al. (35). These data suggest that binuclear selenite surface complexes are formed in the neutral pH range (from reference 33).

We have shown above that dissolution rates of multiple oxides can be related to the abundance and speciation of hydrogen and hydroxyl radicals at different metal centers at the surface. Since dissolution of most complex oxides is nonstoichiometric, the identity of these centers varies as a function of time and experimental conditions. The selective removal of some cations from the solid surface creates a reacted layer that is depleted in those elements that dissolve rapidly (i.e, modifying cations during basalt dissolution or sodium, calcium, and aluminum in the case of feldspars). As steady-state dissolution is controlled by the dismantling of these altered layers, it is critical to know their chemical characteristics and to identify the main mechanisms that control their formation. Two important findings obtained via microbeam techniques will be presented here. 

The oxidation of Co(II)EDTA to Co(III)EDTA by pyrolusite-coated silica under dynamic flow conditions exhibited breakthrough characteristics (Figure 1) that were consistent with earlier research (Jardine and Taylor, 1995). The oxidation was initially rapid and slowed with continual exposure of Co(II)EDTA to the Mn-oxide. Jardine and Taylor (12) showed that the loss of oxidative potential was not caused by the accumulation of Mn on the surface and postulated that an intermediate Mn(III)-oxide solid phase, Mn203, was formed that impeded the redox reaction. XANES spectroscopy was used to determine the speciation of the solid-phase as a function of reaction time in order to identify the inhibition mechanism. 

The surface speciation in Model 3 simulations for systems equilibrated with air and 1% CO2 is shown in Fig. 4-11 and 4-12. The ternary carbonate complexes in Model 3 were of greater importance in comparison to Model 2. This emphasizes again the significance of collecting adsorption data as a function of the partial pressure of CO2, in order to accurately represent the effect of carbonate complexation on both aqueous and surface speciation. Model 3 speciation better represented the experimental data not only as a function of CO2, but also as a function of pH in the pH range from 5 to 7. 

Figure 2. Distribution of DNOC (open symbols), and TNT (filled symbols) between aqueous and solid phases of typical aquifer and soil materials. Shown is the fraction of sorbed species as a function of pH and K" -saturation of the clay minerals. The relative importance of the two dominating sorption mechanisms are compared hydrophobic partitioning into sediment organic matter (O, ) and specific EDA-complex formation with clay mineral surfaces (O, ) triangles (A, A) represent the overall speciation for DNOC and TNT respectively. Sediment parameters were chosen as follows fraction of organic carbon 4. (soil) = 0.05 f (aquifer) = 0.001 fraction of clay minerals f igy (soil) = 0.35 fciay (aquifer) = 0.05 porosity e(soil) = 0.4 (aquifer) = 0.3 bulk density p(soil) = p(aquifer) = 2.5 kg L water saturation 0(soil) = 0.5 0(aquifer) = 1 fraction of K -saturation of clay minerals = 0.001 (when pH was used as system variable) pH = 7.0 (when f(, +, was used as system variable). Linear adsorption isotherms of TNT and... 

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