Characterization and prediction of solute properties in methanol–water association by chemometrics analysis of solvation data

Abstract

Methanol-water binary mixtures have been characterized as a ternary or quaternary system composed of unassociated methanol (M) and water (W) and one or two methanol-water complex (MW). Identification of the methanol-water association was performed by chemometrics analysis of some solvent-related properties of binary methanol-water mixtures with compositions between 0 and 100% volume percent of methanol. Abstract factor analysis of the solvation data identified three chemical species in the solvent mixtures that can be attributed to the pure methanol and water solvents and a MW association. The stoichiometric molar ratio of the W:M associate was investigated by the multivariate curve resolution-alternative least squares (MCR-ALS) of the solvation data and the resulted concentration profiles of the solvent species implied a 1:1 molar ratio for the M:W association. The equilibrium constant of the formation of the M-W association was determined by MCR-ALS and iterative rank annihilation factor analysis (RAFA) to be equal to 13.72. The solvation data of the pure M-W cluster was calculated by target factor analysis (TFA) procedure. Higher hydrogen-bond basicity and lower hydrogen-bond acidity was obtained for the MW solvent in comparison with both the M and W solvents. The proposed model was employed to calculate the acidity constant and HPLC capacity factors of some solutes in the MW cluster. Interestingly, the solutes were found to be stronger acid in relative to pure methanol and water. Finally, the specific solute-solvent interactions and preferential solvation of the solutes were explained by the nonmodeled solute-related data.

Effect of zeolites on chitosan/zeolite hybrid membranes for direct methanol fuel cell

“…On the other hand, the free volume cavity size, r 3 , was 0.221 nm, which was a little larger than the radius of the penetrant molecules. The molar volume of the solution increased from 18.79 to 24.25 cm 3 mol −1 [21], and the diffusion resistance enhanced, accordingly. All these factors caused the methanol permeability decrease with the methanol concentration.…”

“…The increasing interaction would inhibit the solubility of methanol further. Meanwhile, the Richardt's polarity index reduced from 0.97 to 0.85 with the methanol concentration [21], so the solubility of methanol was weakened due to the hydrophilic nature of CS membrane. On the other hand, the free volume cavity size, r 3 , was 0.221 nm, which was a little larger than the radius of the penetrant molecules.…”

“…For methanol-water solution, methanol-methanol [18,19] and methanol-water associations [19][20][21][22][23] are often formed as a result of the hydrogen-bonding interactions. Within the hybrid membrane, most methanol molecules will travel through the organic bulk phase or the interface of polymer and zeolite [9].…”

Chemometrics investigation of the light-free degradation of methyl green and malachite green by starch-coated CdSe quantum dots

“…However, obtained results are not usually informative enough to draw conclusion about reaction kinetic and thus its applications in multi-component system is limited. Thanks to recent advancement in chemometrics, now it is possible to employ multivariate curve resolution-alternating least square (MCR-ALS) techniqueto analyze spectroscopic data of evolutionary processesin order to extract information regarding number of chemical species involved in the reaction, along with their concentration profiles and pure spectra of species [42][43][44][45][46][47]. This has potential to open up a new door for studying photocatalysis process, e.g.…”

Principal Component Analysis‐Ranking as a Variable Selection Method for the Simulation of 13C Nuclear Magnetic Resonance Spectra of Xanthones Using Artificial Neural Networks

“…This method is an extremely useful explorative tool which maps samples through scores and individual variables through loadings in a new vector space defined by the three high correlative PCs. Figure 1 shows the correlation coefficients of different PCs with the chemical shifts [31,32]. The order of the PCs based on their decreasing correlation coefficients is PC2 > PC1 > PC5 > PC3 > PC8 > PC12 > .…”