The nature of Cu bonding to natural organic matter
Introduction
The biogeochemistry of copper is largely controlled by its interactions with natural organic matter (NOM), not so much because of NOM’s abundance and polyfunctional character, as because of its remarkable affinity towards Cu(II) relative to other divalent cations (McLaren et al., 1983, Hering and Morel, 1988, Town and Powell, 1993, Ramos et al., 1994, Benedetti et al., 1996, McBride et al., 1997, Leenheer et al., 1998, Kinniburgh et al., 1999, Kogut and Voelker, 2001, Santos-Echeandia et al., 2008). The strong binding ability of NOM for Cu(II) likely results from the excellent match in size between the cupric ion and one or several ligands. The copper ligands must be well-defined structurally and chemically, and also numerous because NOM has a high sorption capacity and selectivity for Cu(II) over a large concentration range (Gao et al., 1997, Covelo et al., 2004). Since carboxylate moieties comprise the large majority of reactive sites below pH 7 in NOM, and can form a vast number of synthetic coordination complexes with Cu(II) (Melnik et al., 1998a, Melnik et al., 1998b, Melnik et al., 1999), the strongest bonds are expected to be with carboxyl ligands (Sposito et al., 1979, Boyd et al., 1981). The predominance of oxygen ligands does not exclude other electron donors from being involved in Cu bonding, such as nitrogen as suggested by electron spin resonance (ESR) spectroscopy (Boyd et al., 1983, Senesi and Sposito, 1984, Senesi et al., 1985, Luster et al., 1996). Based on solution chemistry the most likely ligands are dicarboxylate (malonate, log K = 5.04), mixed alcohol-carboxylate (citrate, malate, log K = 3.70 and 3.63), and aminocarboxylate (glutamate, log K = 8.32) groups attached to aliphatic chains (Fig. 1, Table 1; Gregor et al., 1989a, Gregor et al., 1989b, Town and Powell, 1993, Croué et al., 2003). Aromatic dicarboxylate (phthalate, log K = 3.22), alcohol-carboxylate (salicylate, log K = 2.22 for pH 5), and furan-carboxylate (furanate, log K = 1.10) ligands are less likely, because they have weaker binding strengths than aliphatic ligands. This interpretation is reinforced by infrared spectroscopy and two-dimensional NMR which showed that carboxylate moieties with –COOH, –OH, or –OR substituents on the α carbon from aliphatic chains or alicyclic structures constitute the majority of carboxyl structures, with the salicylate and furan-carboxylate aromatic structures being less reactive (Deshmukh et al., 2007, Hay and Myneni, 2007). Therefore, malonate, citrate/malate, and amino acid structures most likely play the important roles in Cu(II) chelation. Although stability constants can be used to predict the a priori nature of Cu(II)–NOM complexes, they do not provide unequivocal information on the true bonding environment of the metal in natural systems. They are, however, extremely valuable for making educated choices on the relevance of model compounds used in structural studies.
Using XANES and EXAFS spectroscopy, and Cu–glutamate as the best-fit structural analog, Karlsson and Skyllberg (2006) showed that Cu(II) forms a five-membered chelate ring with one amino nitrogen (α-NH2) or alcohol oxygen (α-OH) and one carboxylate oxygen from an α-substituted aliphatic carboxylic structure (Fig. 1). Copper is bridged to four O/N ligands (CuL2 complex) in a square-planar geometry at low metal concentration, and to two (CuL complex) at higher concentration. Today the glutamate-like model is the most detailed, but despite its own merit the topic is far from being exhausted. Here are some remaining open questions, which are addressed in this article:
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Is it possible to distinguish oxygen from nitrogen ligands, at least at low Cu concentration when heterogeneity is minimized?
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What are the limits of the glutamate model? Does it always provide an adequate fit to EXAFS data? Since NOM contains several well-defined types of carboxyl structures with specific protonation constants, the coordination mode of Cu(II) should vary with pH, metal to ligand ratio, and chemical composition of the NOM.
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At medium and high Cu concentration, can the data be modeled by a mixture of discrete ligands, and if so, how many are needed?
Answering these questions rests upon three capabilities (i) to detect small variations of the local structure about Cu atoms up to approximately 4 Å (i.e., second C atomic shell), and over two orders of magnitude in concentration (i.e., Cu/C ratio); (ii) to determine from a large dataset of multicomponent spectra the number of independent patterns (or “principal components”, PCs) that represent the number of identifiable Cu binding environments (i.e., species) present in the set of spectra; (iii) to build a spectral database of references of known identity and structure, which includes all distinct binding environments seen by EXAFS and plausibly present in NOM.
We recorded our data at liquid helium (LHe) temperature to enhance the signal from higher atomic shells by reducing the lability of organics, and by using a high-flux spectrometer at the European Synchrotron Radiation Facility (ESRF, Grenoble) equipped with a 30-element Ge detector for the study of highly diluted samples (concentration range 100–6500 mg/kg). The significant number of independent patterns accounting for 98–99% of variance in the dataset was determined with principal component analysis (PCA, also known as abstract factor analysis, Malinowski, 1991, Wasserman et al., 1999: Ressler et al., 2000, Manceau et al., 2002). Here, linear algebra can be used for quantification because the EXAFS response from a multispecies sample is the weighted-superposition of the response of each species present in a sample (Manceau et al., 1996). The PCs obtained from the PCA are not real spectra (i.e., they are abstract components), but the single species spectra which make up the multicomponent spectra in the dataset are linear combinations of PCs. Thus, the spectra of all unknown species contained in a sample can be identified from a database by target transformation, provided the unknown is present in the library of reference spectra. Care has been taken to include in the database all plausible Cu(II) species discussed in the literature and to not miss any major species. With such an extensive and representative database, target testing goes beyond the usual fingerprinting approach between known and unknown spectra, because the entire dataset is analyzed at one time in a statistically meaningful way for similarity to a specific structural reference.
Section snippets
Materials
Copper was sorbed on four types of organic materials considered good representatives of the diversity of NOM: two humic acid standards from the International Humic Substance Society (IHSS #1S102H from the Elliott Soil, and #1S103H from Pahokee Peat in Florida), one peat of heath vegetation (mostly Carex sp.) from Mazerolles, northeast of Nantes, France, and one moss peat (mostly Sphagnum sp.) from Estonia. The humic acid (HA) and fulvic acid (FA) fractions were extracted from the Carex (CP) and
Cu(II) reference compounds
When Cu is monovalent and coordinated to O (e.g., Cu2O), a well-defined absorption feature from the 1s to 4px,y transition occurs at 8982 eV (Kau et al., 1987) (Fig. 2a). This feature is less salient when Cu(I) is coordinated to S (e.g., Cu2S), because of the hybridization of Cu-d and S-p states. When S is bonded to Cu(II) (e.g., CuS) instead of Cu(I), it occurs at higher energy (8986 eV), partly because of the chemical shift of the 1s core level of Cu(II) relative to Cu(I). The high degree of
Nature of the five- and six-O-ring chelates
Despite the polyfunctional character of NOM and the many possible binding sites and coordination geometries, all samples can be described with variable proportions of only three key model ligands, all single metal ring chelates: malate, malonate, and thiolactate. There was no hint under our experimental conditions for dimerization of Cu, as observed for some phthalate, benzoate, acetate, and succinate molecular complexes (Koizumi et al., 1963, Cingi et al., 1970, Cingi et al., 1977, Cingi et
Acknowledgments
Olivier Proux and Jean Louis Hazemann from the FAME beamline at ESRF, and Matthew Marcus and Sirine Fakra from beamline 10.3.2 at the ALS, are thanked for their assistance during X-ray measurements. We thank Tourbières de France (http://www.tourbieres.com/) for providing the peat samples. This manuscript benefited from comments and suggestions by two anonymous reviewers. This research was funded by the EC2CO program from the CNRS, and supported by the Centre National de la Recherche
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