Estimation of molecular diffusivity of pure chemicals in water: a quantitative structure-property relationship study

The aggregation of Aspergillus spores and the impact on their inactivation by chlorine-based disinfectants

The formation of fungal biofilm goes through some different states, including monodisperse state, aggregated state, germinated state, hyphal and biofilm. The aggregation of spores is a primary step of fungal biofilm development in aquatic systems. Previous studies on the inactivation of fungi were mostly performed in the monodisperse state of fungal spores and biofilm state, however, the inactivation of aggregated fungal spores is still unclear. In this study, the aggregated characteristics of fungal spores (Aspergillus fumigatus and Aspergillus flavus) at different pH values were firstly studied, and the inactivation efficiency of fungal spores at different aggregation degree by chlorine-based disinfectants was also clarified. The results showed that the aggregation degree of Aspergillus fumigatus was the highest at pH 9.0 while it was the lowest at pH 5.0. Aggregation between fungal spores was mainly mediated by occasional adhesin-adhesin interactions and electrostatic interactions. Compared with monodisperse spores, fungal spores were more resistant to chlorine-based disinfectants with the increase of spore aggregation degree. The inactivation rate constants of Aspergillus fumigatus at 30% and 63% aggregation degree were 1.5- and 4-folds lower than that of monodisperse spores, respectively. The lower proportion of membrane damage and higher intracellular reactive oxygen species level for aggregated spores than monodisperse spores was observed according to the flow cytometric results after chlorine-based disinfectants treatment. The reasons for the lower inactivation efficiency of aggregated spores are as following: the protection of outer layer spores and signals between aggregates lead to the increase of resistance for aggregated spores. This study is meaningful for the control of the fungal spores at different states in water.

Predictive Models for the Binary Diffusion Coefficient at Infinite Dilution in Polar and Nonpolar Fluids

Experimental diffusivities are scarcely available, though their knowledge is essential to model rate-controlled processes. In this work various machine learning models to estimate diffusivities in polar and nonpolar solvents (except water and supercritical CO₂) were developed. Such models were trained on a database of 90 polar systems (1431 points) and 154 nonpolar systems (1129 points) with data on 20 properties. Five machine learning algorithms were evaluated: multilinear regression, k-nearest neighbors, decision tree, and two ensemble methods (random forest and gradient boosted). For both polar and nonpolar data, the best results were found using the gradient boosted algorithm. The model for polar systems contains 6 variables/parameters (temperature, solvent viscosity, solute molar mass, solute critical pressure, solvent molar mass, and solvent Lennard-Jones energy constant) and showed an average deviation (AARD) of 5.07%. The nonpolar model requires five variables/parameters (the same of polar systems except the Lennard-Jones constant) and presents AARD = 5.86%. These results were compared with four classic models, including the 2-parameter correlation of Magalhães et al. (AARD = 5.19/6.19% for polar/nonpolar) and the predictive Wilke-Chang equation (AARD = 40.92/29.19%). Nonetheless Magalhães et al. requires two parameters per system that must be previously fitted to data. The developed models are coded and provided as command line program.

A simple modelling approach for prediction of standard state real gas entropy of pure materials

The performance of an energy conversion system depends on exergy analysis and entropy generation minimisation. A new simple four-parameter equation is presented in this paper to predict the standard state absolute entropy of real gases (SSTD). The model development and validation were accomplished using the Linear Genetic Programming (LGP) method and a comprehensive dataset of 1727 widely used materials. The proposed model was compared with the results obtained using a three-layer feed forward neural network model (FFNN model). The root-mean-square error (RMSE) and the coefficient of determination (r(2)) of all data obtained for the LGP model were 52.24 J/(mol K) and 0.885, respectively. Several statistical assessments were used to evaluate the predictive power of the model. In addition, this study provides an appropriate understanding of the most important molecular variables for exergy analysis. Compared with the LGP based model, the application of FFNN improved the r(2) to 0.914. The developed model is useful in the design of materials to achieve a desired entropy value.

Linear and non-linear QSAR modelling of juvenile hormone esterase inhibitors

A tight control of juvenile hormone (JH) titre is crucial during the life cycle of a holometabolous insect. JH metabolism is made through the action of enzymes, particularly the juvenile hormone esterase (JHE). Trifluoromethylketones (TFKs) are able to inhibit this enzyme to disrupt the endocrine function of the targeted insect. In this context, a set of 96 TFKs, tested on Trichoplusia ni for their JHE inhibition, was split into a training set (n = 77) and a test set (n = 19) to derive a QSAR model. TFKs were initially described by 42 CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) descriptors, but a feature selection process allowed us to consider only five descriptors encoding the structural characteristics of the TFKs and their reactivity. A classical and spline regression analysis, a three-layer perceptron, a radial basis function network and a support vector regression were experienced as statistical tools. The best results were obtained with the support vector regression (r(2) and r(test)(2) = 0.91). The model provides information on the structural features and properties responsible for the high JHE inhibition activity of TFKs.

A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition

Evaluation of diffusion coefficients of pure compounds in air is of great interest for many diverse industrial and air quality control applications. In this communication, a QSPR method is applied to predict the molecular diffusivity of chemical compounds in air at 298.15K and atmospheric pressure. Four thousand five hundred and seventy nine organic compounds from broad spectrum of chemical families have been investigated to propose a comprehensive and predictive model. The final model is derived by Genetic Function Approximation (GFA) and contains five descriptors. Using this dedicated model, we obtain satisfactory results quantified by the following statistical results: Squared Correlation Coefficient=0.9723, Standard Deviation Error=0.003 and Average Absolute Relative Deviation=0.3% for the predicted properties from existing experimental values.

Impact of virus aggregation on inactivation by peracetic acid and implications for other disinfectants

Viruses in wastewater and natural environments are often present as aggregates. The disinfectant dose required for their inactivation, however, is typically determined with dispersed viruses. This study investigates how aggregation affects virus inactivation by chemical disinfectants. Bacteriophage MS2 was aggregated by lowering the solution pH, and aggregates were inactivated by peracetic acid (PAA). Aggregates were redispersed before enumeration to obtain the residual number of individual infectious viruses. In contrast to enumerating whole aggregates, this approach allowed an assessment of disinfection efficiency which remains applicable even if the aggregates disperse in post-treatment environments. Inactivation kinetics were determined as a function of aggregate size (dispersed, 0.55 and 0.90 μm radius) and PAA concentration (5-103 mg/L). Aggregation reduced the apparent inactivation rate constants 2-6 fold. The larger the aggregate and the higher the PAA concentration, the more pronounced the inhibitory effect of aggregation on disinfection. A reaction-diffusion based model was developed to interpret the experimental results, and to predict inactivation rates for additional aggregate sizes and disinfectants. The model showed that the inhibitory effect of aggregation arises from consumption of the disinfectant within the aggregate, but that diffusion of the disinfectant into the aggregates is not a rate-limiting factor. Aggregation therefore has a large inhibitory effect if highly reactive disinfectants are used, whereas inactivation by mild disinfectants is less affected. Our results suggest that mild disinfectants should be used for the treatment of water containing viral aggregates.