. 5 Molecular Modeling of Fulvic and Humic Acids: Charging Effects and Interactions with Al 3+ , Benzene, and Pyridine James D. Kubicki and Chad C. Trout CONTENTS 5.1 Introduction 5.2 Methods 5.2.1 Quantum Calculations 5.2.2 Classical Simulations 5.3 Results 5.3.1 Deprotonation and Complexation of Simple Organic Acids 5.3.2 Fulvic Acid: Charging and Solvation Effects on Structure 5.3.3 Comparison of Benzene and Pyridine Interactions with Aqueous FA 5.3.3.1 Al 3+ — Complexed Humic Acid 5.3.4 Pyridine Interaction with Al-Complexed HA 5.4 Conclusions and Future Work 5.5 Acknowledgments References 5.1 INTRODUCTION Soils are excellent examples of complex systems. The multitude of feedbacks occur- ring among the physical, chemical, and biological processes in soils creates an immense challenge for anyone attempting to understand soil formation and behavior. For example, organisms mine soils for essential nutrients, accelerating and modifying L1623_Frame_05.fm Page 113 Thursday, February 20, 2003 10:42 AM © 2003 by CRC Press LLC the rate of mineral weathering. In turn, death and decay of organisms leads to development of soil organic matter (SOM). The presence of soil organic matter then affects the soil quality (e.g., water and retention) and the types of weathering products that form because SOM influences dissolution and aqueous speciation. 1 Different mineral or amorphous solid types can then affect the turnover rates of SOM. 2 Such a bewildering interplay of soil components makes understanding the overall behavior of the system extremely difficult; however, important insights can be gleaned from isolating one or two components of the system and determining the key factors that control a given process. Each component of a soil may have significant complexity. This is especially true for SOM. 3 The result of partial decay of biomolecules, SOM contains numer- ous types of functional groups that range from hydrophobic to hydrophilic and that can form complexes with various metals. 4–7 Hence, sequestration of organic and metal contaminants is significantly affected by SOM chemistry. 8–11 Although this complexity leads to a wide variation in SOM between soils and even within a single soil, certain important components are common to most SOM. 5 We will never be able to model all this variation, but we can hope to focus on the most important components of the SOM and determine their roles in soil chemistry. Figure 5.1 schematically illustrates the role that molecular modeling can play in soil and environmental science. Thanks to the efforts of previous researchers, we have begun to see details of SOM molecular structure. 4,12–14 Determining the individual functional groups present with SOM is not a trivial task, but piecing together the larger-scale structure from FIGURE 5.1 Schematic representation of the role of molecular modeling in geochemistry shown above. Observations and constraints from field and laboratory studies are key in designing realistic molecular simulations. The feedback among the various approaches adds value to each component of the study. L1623_FrameBook.book Page 114 Thursday, February 20, 2003 9:36 AM © 2003 by CRC Press LLC these puzzle pieces is even more challenging. 12,15,16 Current structural models may not be perfect, and they may not reflect the diversity of SOM, but they are useful starting points for testing hypotheses with regard to SOM chemistry. Use and testing of this first generation of SOM models will lead to new insights and refinements of SOM structure. As molecular modeling techniques become more common among soil scientists, a larger array of model types can be studied and subtle chemical effects investigated. 17 We hope that this chapter will serve as a guidepost to important problems in modeling SOM chemistry and as a roadmap to useful modeling methods. Important papers have already been published in this area, but this area of research is relatively new and ripe for exploration. Schulten and Schnitzer 13 pub- lished some of the first papers on this topic. Early work focused on simple molecular mechanics calculations of neutral, isolated humic acid models. Although this approach neglects important factors such as charging and solvation, simplified mod- els can be used initially as a point of reference for more complex and realistic systems. Recently, inclusion of some of these complicating factors has led to more accurate descriptions of SOM models. 18 Diallo et al. 15,16 have taken a molecular modeling approach in their attempts to build SOM structural models. The use of new Fourier-transform-ion-cyclotron res- onance-mass spectrometry data and NMR spectroscopy has allowed these research- ers to piece together a more reliable picture of the large-scale humic acid struc- ture. 19–22 The two most important factors in producing worthwhile molecular simulations are an accurate theoretical model of bonding in the system (discussed in the Methods section) and a realistic description of the system to be modeled. The latter factor should encourage the modeler to use as much experimental data on the structure and chemistry of the system as he or she can. Too often, highly demanding and theoretically accurate computations may be carried out on a model system that does not reflect the true system of interest. Assumptions regarding important struc- tures can lead to useless model predictions. For instance, the catalysis field has long assumed that metal catalysts are controlled by the surface structure of the metal catalyst. Recent research has shown that in many instances, however, oxidation of the metal at the surface occurs before catalytic properties are present. 23 Thus, it is the metal oxide rather than the metal that is the catalyst. Molecular modeling studies that do not include all the important components of a reaction would never be able to predict the behavior of the true system. Other studies have focused on an important aspect of SOM chemistry: adsorption to mineral surfaces. 13,24,25 Adsorbed SOM is critical to understanding sequestration of contaminants in soils because adsorption can stabilize SOM and affect its sorptive properties. 26–30 Such simulations require knowledge of the SOM structure, the rele- vant mineral surface structure, and the nature of interaction between the two. Some recent experimental studies have addressed the nature of this interaction, but much more research needs to be performed on this topic. 31–33 Practically speaking, running simulations of a system, which includes a large organic molecule, mineral surface, and water molecules becomes computationally demanding because the number of atoms required to simulate the system will be large (>10,000). The work discussed in this chapter illustrates one approach to this large, complex problem. First, quantum mechanical calculations are used on small, simplified L1623_FrameBook.book Page 115 Thursday, February 20, 2003 9:36 AM © 2003 by CRC Press LLC systems to establish a link between models and experimental spectra (e.g., IR, Raman, NMR, etc.). Although oversimplifying the problem of SOM, this same approach is often used in experiments to gain a handle on the important functional groups involved in a given chemical reaction. 34,35 This step is key because force fields used in classical simulations are not always reliable. Moreover, it can be difficult to know when they are accurate and when they fail. Quantum mechanical results can be carried out with various levels of approximation but are generally more reliable than force fields, especially for unusual chemical bonding situations. When tested against experimental data, a reasonable degree of certainty can be associated with the molecular models used. Once these benchmarks are established, their results can be used to constrain structures of larger-scale classical simulations. In many of the questions regarding metal complexation by SOM, some aspects of the chemistry are more difficult to model than others. For example, if a model fulvic acid is complexed to Al 3+ , descriptions of the C-C and C-H bonds may be relatively easy to reproduce with classical force fields. 36 This is due to the fact that these bonds have been well studied and accurate parameters describing their interaction are built into the force field. Other interactions, such as Al-O bonding or H-bonding, are not accurately modeled by current force fields because parameterization of these species has not been as well tested. Thus, a combination of quantum mechanical and classical simulations can provide a maximum of information on these complex systems. 5.2 METHODS Two fundamental types of molecular modeling are discussed in this chapter: quantum mechanical calculations and classical mechanical simulations. The difference between the two is that quantum mechanical (or ab initio ) calculations describe the electron densities of atoms whereas classical mechanical simulations model atoms as particles connected to others via springs. Description of electron densities is computationally demanding, especially for heavier atoms, so quantum calculations are generally limited to fewer particles than classical simulations (Figure 5.2). The advantage quantum calculations enjoy is flexibility to model systems that are not well understood (i.e., bond lengths, energies, etc., are unknown). The difference between the two is so large that many workers use two different terms to describe these techniques: “computational chemistry” for quantum mechanics and “molecular modeling” for classical simulation. The intent is to associate the former with a more rigorous stature and the latter with more approximate results. In general, this sim- plified perception is fairly accurate, but quantum mechanical results can be useless and classical simulations can be accurate. The divide between these two end-members can be fuzzy in practice (Figure 5.2). Development of hybrid codes that employ each method on different components of a model has been a great advance in modeling larger-scale systems. 37 Termed “QM/MM” for quantum mechanics/molecular mechanics, this approach will likely enjoy widespread utilization and success in fields such as soil science, environmental chemistry, and geochemistry due to the nature and complexity of reactions in these fields. Furthermore, as computers become more powerful and software becomes more advanced, it becomes feasible to perform molecular simulations using quantum L1623_FrameBook.book Page 116 Thursday, February 20, 2003 9:36 AM © 2003 by CRC Press LLC mechanics to describe atomic interactions rather than the force field approximation. A few researchers, notably Lubin et al., 38 Weare et al., 39 and Iarlori et al. 40 have published excellent studies employing these techniques to systems of geochemical interest. Use of these codes is not yet commonplace, however, because they require a high level of computing power. For example, Hass et al. 41 used 32 nodes of an IBM SP2 for a period of 6 months to perform a simulation. Fortunately, the new generation of PC-based Linux clusters will make this type of simulation affordable for most researchers in the next decade. 5.2.1 Q UANTUM C ALCULATIONS All the ab initio quantum calculations presented in this chapter were performed with the program Gaussian 98. 42 The Gaussian series of programs has been developed over many years by a large number of researchers adding to and refining the original code. Gaussian was the brainchild of the Nobel laureate John Pople and is a standard program in the field of computational chemistry because of its reliability and flex- ibility. Other programs available to interested researchers include GAMESS (see Quantum Chemistry Program Exchange, Indiana University), Spartan (Wavefunc- tion, Inc.), Jaguar (Schrodinger, Inc.), Q-CHEM (Q-Chem, Inc.), Parallel Quantum Solutions (PQS, Inc.), HyperChem (Hypercube, Inc.), and DMol 3 (Accelrys, Inc.). Platforms for these calculations can range from a desktop PC to highly parallel FIGURE 5.2 Matrix representation of a fundamental problem in molecular modeling of geochemical systems. More accurate calculations are computationally more demanding, but larger model systems are needed to account for all the components in a geochemical system. Judicious use of each method can generate accurate and realistic molecular simulations. Level of Theory ≈≈ ≈≈ Cost of Calculation MM SE HF DFT MP2 G2 HΨΨ ΨΨ Number of Atoms 10 4 10 2 10 0 Molecules and test systems Clusters and complexes Bulk systems and interfaces L1623_FrameBook.book Page 117 Thursday, February 20, 2003 9:36 AM © 2003 by CRC Press LLC supercomputers, but commonly Unix-based workstations are used by molecular modelers because they are fast and affordable. This type of machine is rapidly being replaced by less expensive PC-based Linux (or “Beowulf”) clusters. As mentioned above, the two keys to useful quantum mechanical modeling are constructing an accurate initial model and choosing the appropriate level of theory. The first step will not be discussed here because this procedure varies from problem to problem. In some cases, a number of models may be constructed and tested to determine which one best fits available experimental data. The second step has been addressed with a simple scheme in the research presented in this chapter. One starts with the lowest level of theory possible and tests the results against experimental data and selected higher-level calculations. If the model results are satisfactory for the problem at hand, then the low level of theory is fine. If errors and inconsistencies are found, then higher level calculations must be performed for the suite of models under study. Two main considerations determine the vague “level of theory” mentioned above. First, the basis set used to describe the electron density must be adequate. Quantum calculations are approximations to the Schrodinger equation, H Ψ = E Ψ , where H is the Hamiltonian operator describing the kinetic and potential energy of electrons and nuclei, E is the energy of the system, and Ψ is the electronic wavefunction. Unfortunately, Ψ is not known, so we use various functions to approximate Ψ . Commonly, Gaussian functions, such as φ 1s (r, α ) = (2 α / π ) 3/4 exp[ −α r 2 ] where r is the electron-nucleus distance and α is the orbital exponent, are used for computa- tional reasons (which is the origin of the name for the Gaussian program). 43 Basis set notation is obtuse, but a general principle is that the larger number of Gaussian functions used, the more accurate the basis set. In addition, the Gaussian basis set can be split into different sets to describe core and valence electrons. This is helpful because of the different behaviors of electrons near and far from the nucleus. Typically, basis sets are split into sets of two or three, which gives rise to the terminology doubly- and triply-split basis sets. Triply-split basis sets are usually more accurate. For example, one could go from an STO-3G basis set with 3 Gaus- sians approximating each atomic orbital and no splitting between core and valence electrons to a 6–311G basis set with 6 Gaussians approximating each atomic orbital and a triply-split basis set. To confuse the issue even more, workers have found that addition of functions to describe formally unfilled atomic orbitals (e.g., d -orbitals on Al 3+ ) improves results considerably. 44 Seemingly extraneous orbitals provide for a more accurate descrip- tion of bonding because they help to account for polarization that occurs between two bonded atoms. A single set of d-orbitals on atoms heavier than H is designated with an asterisk (*); adding p-orbitals to H is designated with two asterisks (**). A more straightforward notation uses the number and type of orbitals included, which leads to a designation such as 6–311G(d,p). The last point regarding basis sets that will be important for the discussion here is the inclusion of diffuse functions. As the name implies, diffuse functions are used to describe electron density far from a nucleus. The role of electrons far from molecular nuclei is especially important in two cases. Anionic models require diffuse L1623_FrameBook.book Page 118 Thursday, February 20, 2003 9:36 AM © 2003 by CRC Press LLC functions because the electron density is spread over a greater volume compared to cations and neutral molecules. Models examining the interaction of two molecules via van der Waal’s forces or H-bonding also benefit greatly from the use of diffuse functions. Addition of diffuse functions is designated by a plus sign (+) for heavy atoms only and by two plus signs (++) for heavy atoms and H. For a more complete description of basis sets and their relationship to atomic orbitals, see McQuarrie and Simon. 43 The second consideration in choosing a method is the level of electron correla- tion. A range of methods from no electron correlation (Hartree-Fock methods) to full configuration interaction is available; however, the more extensive the electron correlation, the more computationally demanding the calculations become. Some electron correlation methods, such as the Møller-Plesset method, can scale as N 5 where N is the number of electrons. 45 One can imagine that such methods become impractical for larger model systems. A useful development has been the hybridization of molecular orbital theory and density functional theory. 46 The latter uses a relatively simple equation to estimate the electron correlation as a function of the electronic density. With the electronic density described by the basis sets discussed above, a quicker approxi- mation for electron correlation can be attained. There are numerous exchange and correlation functional pairs, but a commonly used set is the Becke 3-parameter exchange functional and the Lee-Yang-Parr correlation functional. 47,48 This approx- imation for electron exchange and correlation is simply designated B3LYP in Gaus- sian 98. 46 5.2.2 C LASSICAL S IMULATIONS Classical mechanical molecular simulations avoid calculation of electron densities altogether. Each atom is given a set of parameters that fit into analytical equations used to describe atomic interactions. For instance, ions affect one another through long-range Coulombic forces described by the equation φ ionic = Z i Z j / ε r ij (5.1) where φ ionic is the ionic potential energy, ε is the dielectric constant of the medium, Z i is the charge on ion i, and r ij is the distance between ions i and j. Many early simulations were performed with this type of interatomic potential alone (plus repulsion terms and perhaps van der Waal’s attraction terms). 49 Today, simulations generally reserve the ionic interaction terms for long-range, nonbonded forces, and any atoms directly bonded to one another interact through covalent terms. Choosing the atomic charges remains an important step in developing an interatomic potential, however. Charges are either determined empirically by adjusting charges within a model to fit experimental data, or they can be determined theoretically by adjusting atomic charges to fit electrostatic potentials around molecules in quantum mechan- ical calculations. 50 Other important nonbonded terms are van der Waal’s forces and hydrogen bonding. The latter is particularly important in determining the positions of H atoms L1623_FrameBook.book Page 119 Thursday, February 20, 2003 9:36 AM © 2003 by CRC Press LLC as solvation energies. A typical description of the van der Waal’s forces is the Lennard-Jones or 6–12 potential, so called because of its functional form φ vdW = A ij /r ij 12 − B ij /r ij 6 . (5.2) A ij /r ij 12 is a repulsive force (i.e., a positive contribution to the potential energy) and B ij /r ij 6 is an attractive force between nonbonded atoms i and j. Other exponential forms, such as the Buckingham potential, can also be used to describe atomic repulsion. 51 A similar equation can be used to describe H-bonds using different constants: φ H-bond = C ij /r ij 12 − D ij /r ij 10 . (5.3) Often, H-bonds are treated implicitly by electrostatic interactions; however, for simulations of solutions, clay minerals, and mineral-solution interfaces, explicit consideration of H-bonding should improve results. As stated above, ionic contributions to the energy are often reserved for nonbonded interactions. Bonded interactions are treated by harmonic approximations. Higher terms can be included and are necessary for configurations deviating from minimum energy structures. For the purpose of this introduction, however, simple harmonic equations will be used to illustrate the concepts behind this type of force field. Bond stretching and bond angle bending can be handled with equations of the form φ bond = φ ij = k ij (r ij − r 0 ) 2 (5.4) φ angle = φ ijk = k ijk ( θ ijk − θ 0 ) 2 (5.5) where the k’s are force constants defined by the atom types i, j, and k; r ij and θ ijk are the bond distance and angle in the present configuration; and r 0 and θ 0 are the minimum energy bond length and angle, respectively. Other forms, such as a Morse potential, have also been used successfully. 52 Often, the potential energy surface is followed until the most energetically stable configuration can be found. These “energy minimizations” occur at 0K and are useful for predicting structures and spectroscopic properties. 53 Energy minimizations are heavily influenced by the starting configuration of the model, however, and can end in local rather than global minima. Molecular dynamics simulations use the inter- atomic force field to predict positions as a function of time at a finite temperature. Time is explicitly included in the calculation and all the atoms can move in concert according to classical mechanics and their kinetic energies at a given temperature. A Boltzmann distribution of velocities is attained after atomic motions are scaled to a given temperature, which allows for some atoms to be moving with kinetic energies higher than the average value. 54 Molecular dynamics is the method of choice for studying dynamical properties of systems, such as diffusion or other time- dependent reactions. L1623_FrameBook.book Page 120 Thursday, February 20, 2003 9:36 AM © 2003 by CRC Press LLC The COMPASS force field used in the simulations reported here was developed explicitly for condensed-phase systems, which makes it unusual among classical force fields. 55 Generally, gas-phase data are used to constrain force constants and so on, which is a simpler approach, but this method leads to uncertainties regarding application in liquids and solids. The algorithm used to produce COMPASS (Con- densed-phase Optimized Molecular Potentials for Atomistic Simulation Studies) begins with gas-phase, ab initio calculations to estimate force field parameters, such as atomic charges and force constants. This is a reasonable approach because con- densation will act as a perturbation to the atomic parameters in a given molecule. 56 Once the parameters are fit to reproduce the ab initio results on gas-phase molecules, the parameters are then refined to fit both gas-phase and liquid-phase properties of the compounds. The process is iterated until a converged set of force field parameters is achieved that fits all the available results (both experimental and ab initio). Another important component of the COMPASS force-field development is the systematic fit to various types of compounds. First, alkane parameters are fit, and then held fixed while alkene parameters are derived. Each functional group is added by fitting to larger arrays of compounds, but the parameters derived in the previous level are not changed, so a self-consistent force field is created that describes a wide variety of functional groups. COMPASS has been used to model a number of condensed-phase organic systems. 57–59 Hence, we chose COMPASS as a likely candidate for modeling fulvic and humic acids; however, we caution the reader that classical mechanical force fields may be accurate for one system and not for another. The force field must be tested for each new application before the results can be considered reliable. 5.3 RESULTS 5.3.1 D EPROTONATION AND COMPLEXATION OF SIMPLE O RGANIC ACIDS To begin our investigation of fulvic and humic acid behavior, we first test our methods on simple, well-understood systems. Charging and metal complexation in naturally occurring organic acids is heavily influenced by benzoic acid-type functional groups within the larger acid. 60,61 Thus, we tested our methods on benzoic, salicylic, and phthalic acids, which are commonly used as simple analogs of important functional groups within fulvic and humic acids in experimental studies. 34,35,62 We are interested in modeling the charging behavior of fulvic and humic acids, so we must be able to model the various charged states of the above simple organic acids. To do this, the neutral and deprotonated species of each acid is modeled to predict its energy, structure and vibrational spectrum. (Note: The doubly deprotonated species of salicylic acid, C 6 H 4 OCOO 2− , was not modeled because the pK a for the phenol group is >13, so it should not deprotonate in most natural waters.) To account for solvation, we add H 2 O molecules around the organic acids such that the most hydrophilic functional groups are H-bonded. This approach has proven satisfactory for predicting vibrational frequencies of organic acids in aqueous solutions. 63,64 Figure 5.3 illustrates the minimum energy structures L1623_FrameBook.book Page 121 Thursday, February 20, 2003 9:36 AM © 2003 by CRC Press LLC FIGURE 5.3 Model configurations of aqueous organic molecules: (a) benzoic, (b) benzoate, (c) salicylic, (d) salicylate, (e) doubly de-protonated salicylate, (f) phthalic, (g) phthalate, and (h) doubly de-protonated phthalate. A key for complexation to metals is the orientation of the carboxylate groups with respect to the aromatic rings. Ligands with the carboxylate groups oriented in the plane of the aromatic ring (e.g., benzoate, salicylate) tend to be strong ligands. When O-O repulsion exists, then carboxylate groups tend to rotate out of the plane of the aromatic ring (e.g., doubly de-protonatated salicylate, phthalate, and doubly de-protonated phthalate), which limits the ability of the ligand to bind with a metal. a b c d e f g h L1623_FrameBook.book Page 122 Thursday, February 20, 2003 9:36 AM © 2003 by CRC Press LLC [...]... 17 15 1600 159 4 1 457 1438 1380 13 05 1 251 1 157 1142 1061 782 654 Phthalate-Al pH 4 Observed 1669 1600 1496 1 457 1383 1297 1267 1 159 10 45 8 75 653 Bridging Bidentate 1660 159 2 153 1 1463 1404 1301 1269 1 151 1026 870 654 Salicylic-Al pH 2 .5 Observed 1661 1608 159 2 155 3 1489 1464 1386 1327 1244 1149 1038 854 772 661 Monodentate 1 656 1609 158 8 156 3 1466 1 454 1370 1301 12 15 1 151 1022 862 769 663 Salicylate-Al... 159 5 155 5 14 95 1417 1400 1310 12 75 11 75 1160 1104 10 65 1020 159 6 156 6 1479 1433 1 353 1310 1292 1161 1116 1114 1066 1014 981 1667 1620 159 6 1489 1362 13 25 1248 1220 1167 1148 1067 1039 1648 1611 154 9 1474 1344 13 25 1244 1207 1160 1148 1114 1030 1618 159 5 157 9 1499 1463 1388 1362 1301 12 45 1228 1 150 1072 1038 10 35 1620 158 3 155 9 1482 1448 1393 1360 1307 1261 1221 1123 10 75 1022 997 1712 1690 1606 158 4... 1487 14 75 1298 1291 1274 1160 11 45 10 75 10 45 1 655 1634 156 4 1487 1449 1426 1281 1261 1187 1136 1086 1039 1023 1688 1647 16 05 158 1 154 0 1493 14 75 13 75 1362 1293 1274 1 158 1 050 159 2 157 1 155 3 1466 1448 14 25 1360 1293 1280 12 45 1 153 1116 1033 1604 158 6 1488 1446 1400 1363 1170 1160 1041 1023 155 3 1462 1422 1381 1347 1291 1126 1108 1029 943 From Varsányi, G.D., Assignments for Vibrational Spectra of Seven... bioavailability of copper in the environment.86–88 The main thrust of our research will be trying to incorporate mineral surfaces into the aqueous-phase models we have presented here Generating accurate models of systems containing water, NOM, metals, and mineral surfaces would begin to give us a molecular-level picture of soil chemistry 5. 5 ACKNOWLEDGMENTS JDK acknowledges the financial support of the Of ce of. .. chemistry of metals and NOM, a vast array of studies become possible As mentioned above, the complexity of NOM is almost endless Combined with the large number of metals of geochemical and environmental interest that may interact with NOM, the matrix of possible models is enormous A primary assumption in tackling this daunting problem is that a number of controlling parameters and guiding principles... February 20, 2003 9:36 AM 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Iarlori, S et al., Dehydroxylation and silanization of the surfaces of beta-cristobalite silica: An ab initio simulation, J Phys Chem B, 1 05, 8007, 2001 Hass, K.C et al., The chemistry of water on alumina surfaces: Reaction dynamics from first principles, Science, 282, 2 65, 1998 Frisch, M.J et al., Gaussian 98, Revision... consisting of each of the components discussed above: a model of HA complexed with Al3+ interacting with benzene and pyridine in water Charging and conformational changes in dissolved NOM discussed above may have a significant effect on the interaction of NOM with hydrophobic organic contaminants The development of more hydrophobic regions and coiling of the C backbone could increase the sorption of hydrophobic... amount of interaction between H atoms attached to the benzene ring (both in benzene and pyridine) and the O atoms in carboxylate groups (Figure 5. 9a). 65 The H-bonding donor capability of H2O molecules is much greater than that of aromatic H atoms, so the presence of the solvent overwhelms any potential H-bonding between the aromatic H and the carboxylate O atoms On the other hand, the acid H+ of the... initio results FIGURE 5. 5 A strong linear correlation between the calculated frequencies of Al- salicylate complex (Figure 5. 6(a)) and the observed UV-resonance Raman frequencies suggests that the molecular modeling is accurately representing this aqueous species The correlation has a slope of 1.00 and a standard deviation of ±12 cm−1 © 2003 by CRC Press LLC Phthalic-Al pH 2 .5 Observed 16 95 1634 156 4... adsorption interaction, and bonding should be similar between NOM and these two metals We are currently beginning to study more specific interactions, such as aqueous and surface complexation of Fe3+ with siderophores and the biochemistry of silica complexation. 85 Another area of great interest is the complexation of amino acids with metals such as Cu2+ because these complexes may play a role in the bioavailability . 159 8 1603 159 6 1667 1648 1618 1620 1712 1 655 1688 159 2 1604 155 3 16 05 157 6 159 5 156 6 1620 1611 159 5 158 3 1690 1634 1647 157 1 158 6 1462 158 5 1484 155 5 1479 159 6 154 9 157 9 155 9 1606 156 4 16 05. Observed Bridging Bidentate 16 95 17 15 1669 1660 1661 1 656 1668 1636 1634 1600 1600 159 2 1608 1609 1607 1607 156 4 159 4 1496 153 1 159 2 158 8 158 7 158 4 1487 1 457 1 457 1463 155 3 156 3 154 8 156 7 1449 1438. 1 454 1391 1388 1281 13 05 1267 1269 1386 1370 1327 1321 1261 1 251 1 159 1 151 1327 1301 1249 1246 1187 1 157 10 45 1026 1244 12 15 1149 1 154 1136 1142 8 75 870 1149 1 151 1086 1088 1039 1061 653 654