Covalent index

TABLE 4. lono-covalent indexes of M4S1O4 orthosilicates with M = Li, Na and K. ( ) p = 4. ( ) p =3. The number of non-equivalent, sites in the structure is indicated in parentheses after the partial charges ranges. 

TABLE 5. lono-covalent, indexes of olivine structures M2Si04 (M = Mg, Ca, Sr, Ba). 

The (X is electronegativity and r is ionic radius) reflects the degree of covalent interactions in the metal-ligand complex relative to ionic interactions (Nieboer and Richardson 1980). In QSAR studies, the covalent index is commonly used in combination with ionic index T lr or with the constant for the first hydrolysis Hog KOHI (see Section 3.5). The subscript m refers to the most common (Mulliken) measure of electronegativity. Sometimes other measures are used in the literature and sometimes the m is omitted, e.g., the covalence index in Chapter 8 does not include the m subscript because the Pauling, Mulliken or Allred-Rochow scales for electronegativity might be pertinent. 

The constant for the first hydrolysis (Hog KqhI) was correlated also with the covalent index Xl r in QSAR studies (Newman and McCloskey 1996 Tatara et al. 1997, 1998). 

The influence of metal ionic characteristics on their biosorption capacity was analyzed using QSAR models. The waste biomass of Saccharomyces cerevisiae was used as biosotbent to adsorb 10 metal ions, and their maximum biosorption capacity (q was predicted by the models, values were correlated with 22 metal ionic characteristics. Among these, covalent index (X r) was crxrelated well with for all metal ions tested in the following equation q , =0.029 + 0.061 xl,r) (R =0.70). The biomass was shown to preferentially absorb soft ions, then borderline ions, and last hard ions. Classification of metal ions, for divalent ion or for soft-hard ion, could improve the linear relationship (R =0.89). 

The toxicity of 44 metals to the biofilms and planktonic cells of Pseudomonas fluorescens was measured and expressed as minimum inhibitory concentration, minimum bactericidal concentration, and minimum biofilm eradication concentration. Linear regression analyses wa-e conducted to determine the relationships between the measured toxicity values and the following physicochemical parameters standard reduction-oxidation potential, electronegativity, the solubility product of the corresponding metal-sulfide complex, the Pearson softness index, electron density, and the covalent index. Each of the physicochemical parameters was significantly (P < 0.05) correlated with one or more of the toxicity measurements. Heavy metal ions were found to show the strongest correlations between toxicity and physicochemical parameters. 

Tatara et al. (1997,1998) QSARs for predicting cation toxicity using standard reduction-oxidation potential alone or in combinations with standard reduction-oxidation potential, atomic number, ionization potential differential, covalent index, logarithm of the first hydrolysis constant, or Pearson and Mawby softness parameter alone or in combination with logarithm of the first hydrolysis constant... 

Mendes et al. (2010) QCARs for Predicting Cation Toxicity Using Standard Reduction-Oxidation Potential, Electronegativity, Pearson and Mawby (1967) softness parameter and in combination with the Logarithm of the First Hydrolysis Constant, Covalent Index, and Atomic Radius... 

Log of the equilibrium constant (log of the metal-ATP complex Covalent index (X r)... 

As discussed in Section 5.2.5, Van Kolck et al. (2008) developed 4 QSARs to predict the 96-hour LC50 values of 5 cations to the mussel Mytilis edulis and 4 QSARs to predict the 96-hour LC50 values of 6 cations to the mussel Perna viridis (Table 5.17). Six of these QSARs included 3 of the less numerous physical properties used to predict cation toxicity, viz., covalent index (x r), absolute value of the logarithm of the first hydrolysis constant (Hog XqhI), and ionic index (Z /r). The QSARs developed with the covalent index (x r) produced the highest value (Table 5.20). 

Zhou et al. (2011) evaluated 50 conditional binding constants (K) for biotic ligands from 27 studies and calculated mean ligand-specific conditional binding constants (K). They developed log K QSARs for five species using the Pearson and Mawby softeess parameter (a ), the covalent index (x r), the absolute value of... 

Van Kolck et al. (2008) developed 4 QSARs to predict the bioconcentration factors (BCF) of cations to the mussel Mytilus edulis, and 4 QSARs to predict the BCFs of cations to the mussel Perna viridis (Table 5.4). The BCFs for Mytilus edulis were developed for 8 cations and the QSARs with highest values were obtained using the ionic index (ZVr) and the covalent index (x i) (Table 5.18). The BCFs for Perna viridis were developed for 7 cations and the QSARs with highest values were obtained using the Pearson and Mawby softness parameter (Op) and the covalent index xit) (Table 5.18). 

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