Thermodynamic Consistency in the Modeling of Speciation in Self-Complexing Electrolytes

Novel Model for Predicting the Electrical Conductivity of Multisalt Electrolyte Solutions

This study presents a novel model to predict the electrical conductivity of multisalt electrolyte solutions by incorporating corrections to the ideal behavior due to relaxation and electrophoretic effects. The performance of the model is evaluated by comparing its predictions with the experimental data of 24 multisalt aqueous solutions. The comparison reveals good agreement for solutions with an ionic strength below 1 mol/L without adjusting any parameter to fit to the experimental data. However, the model tends to overestimate the molar conductivity at higher ionic strengths. The discrepancy is attributed to the neglect of the solvent structure and the formation of ion pairs. It has been speculated how the accuracy of the developed model could be improved in relation to these limitations. Furthermore, the performance of the model is rigorously tested in systems with ion complex formation. It has been demonstrated that when the distribution of ion complexes is calculated from a thermodynamic model and then used to predict the electrical conductivity with the developed model, a satisfactory level of accuracy is attained for these systems.

Enhanced virus inactivation by copper and silver ions in the presence of natural organic matter in water

Natural organic matter (NOM) is present in water matrix that serves as a drinking water source. This study examined the effect of low and high NOM concentrations on inactivation kinetics of a model RNA virus (MS2) and a model DNA virus (PhiX 174) by copper (Cu²⁺) and/or silver (Ag⁺) ions. Cu and Ag are increasingly applied in household water treatment (HHWT) systems. However, the impact of NOM on their inactivation kinetics remains elusive despite its importance for their application. The presence of NOM in water led to faster virus inactivation by Cu²⁺ but slower by Ag⁺. The fastest inactivation kinetics of MS2 (K_obs = 4.8 h^-1) were observed by Cu in water containing high NOM (20 mg C/L). Meanwhile, for PhiX 174, the fastest inactivation kinetics (av. K_obs = 3.5 h^-1) were observed by Cu and Ag synergism in water containing high NOM. Altogether, it can be concluded that the combination of Cu and Ag is promising as a virus disinfectant in treatment options allowing for multiple hours of residence time such as safe water storage tanks.

Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and ¹³C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between ¹³C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.

Biotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.