Artificial neural network modeling for Congo red adsorption on microwave-synthesized akaganeite nanoparticles: optimization, kinetics, mechanism, and thermodynamics.
Nguyen Vinh D,Nguyen Hoa T H,Vranova Valerie,Nguyen Linh T N,Bui Quy M,Khieu Tam T
Environmental science and pollution research international
This work aims to synthesize akaganeite nanoparticles (AKNPs) by using microwave and use them to adsorb Congo red dye (CR) from the aqueous solution. The AKNPs with an average particle size of about 50 nm in width and 100 nm in length could be fabricated in 20 min. The effects of pH, CR initial concentration, adsorption time, and adsorbent dosage on the adsorption process were investigated and the artificial neural network (ANN) was used to analyze the adsorption data. The various ANN structures were examined in training the data to find the optimal model. The structure with training function, TRAINLM; adaptation learning function, LARNGDM; transfer function, LOGSIG (in hidden layer) and PURELIN (in output layer); and 10 neutrons in hidden layer having the highest correlation (R = 0.996) and the lowest MSE (4.405) is the optimal ANN structure. The consistency between the experimental data and the data predicted by the ANN model showed that the behavior of the adsorption process of CR onto AKNPs under different conditions can be estimated by the ANN model. The adsorption kinetics was studied by fitting the data into pseudo-first-order, pseudo-second-order, Elovich, and intraparticle diffusion models. The results showed that the adsorption kinetics obeyed the pseudo-second-order model and governed by several steps. The adsorption isotherms at the different temperatures were studied by fitting the data to Langmuir, Freundlich, and Temkin isotherm models. The R obtained from the Langmuir model was above 0.9 and the highest value in three of four temperatures, suggesting that the adsorption isotherms were the best fit to the Langmuir model and the maximum adsorption capacity was estimated to be more than 150 mg/g. Thermodynamic studies suggested that the adsorption of CR onto AKNPs was a spontaneous and endothermic process and physicochemical adsorption. The obtained results indicated the potential application of microwave-synthesize AKNPs for removing organic dyes from aqueous solutions.
Toxicokinetic models and related tools in environmental risk assessment of chemicals.
Grech Audrey,Brochot Céline,Dorne Jean-Lou,Quignot Nadia,Bois Frédéric Y,Beaudouin Rémy
The Science of the total environment
Environmental risk assessment of chemicals for the protection of ecosystems integrity is a key regulatory and scientific research field which is undergoing constant development in modelling approaches and harmonisation with human risk assessment. This review focuses on state-of-the-art toxicokinetic tools and models that have been applied to terrestrial and aquatic species relevant to environmental risk assessment of chemicals. Both empirical and mechanistic toxicokinetic models are discussed using the results of extensive literature searches together with tools and software for their calibration and an overview of applications in environmental risk assessment. These include simple tools such as one-compartment models, multi-compartment models to physiologically-based toxicokinetic (PBTK) models, mostly available for aquatic species such as fish species and a number of chemical classes including plant protection products, metals, persistent organic pollutants, nanoparticles. Data gaps and further research needs are highlighted.
Combining exposure and effect modeling into an integrated probabilistic environmental risk assessment for nanoparticles.
Jacobs Rianne,Meesters Johannes A J,Ter Braak Cajo J F,van de Meent Dik,van der Voet Hilko
Environmental toxicology and chemistry
There is a growing need for good environmental risk assessment of engineered nanoparticles (ENPs). Environmental risk assessment of ENPs has been hampered by lack of data and knowledge about ENPs, their environmental fate, and their toxicity. This leads to uncertainty in the risk assessment. To deal with uncertainty in the risk assessment effectively, probabilistic methods are advantageous. In the present study, the authors developed a method to model both the variability and the uncertainty in environmental risk assessment of ENPs. This method is based on the concentration ratio and the ratio of the exposure concentration to the critical effect concentration, both considered to be random. In this method, variability and uncertainty are modeled separately so as to allow the user to see which part of the total variation in the concentration ratio is attributable to uncertainty and which part is attributable to variability. The authors illustrate the use of the method with a simplified aquatic risk assessment of nano-titanium dioxide. The authors' method allows a more transparent risk assessment and can also direct further environmental and toxicological research to the areas in which it is most needed. Environ Toxicol Chem 2016;35:2958-2967. © 2016 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
Risk assessment of heterogeneous TiO-based engineered nanoparticles (NPs): a QSTR approach using simple periodic table based descriptors.
Roy Joyita,Ojha Probir Kumar,Roy Kunal
Nowadays, the risk assessment of engineered nanoparticles (NPs) on human health and animals is of great importance. We have used here simple periodic table based descriptors for mixture compounds to predict the cytotoxicity for the heterogeneous NPs. We have developed mono parametric quantitative structure-toxicity relationship (QSTR) models for 34 TiO-based NPs modified with (poly) metallic clusters of noble metals (Au, Ag, Pt) to assess the cytotoxicity (-log EC) towards Chinese Hamster Ovary cell line. After critical statistical analysis of the developed five linear regression (LR) models, we found that the derived models are close to each other in terms of different metric values ( = 0.922-0.926; = 0.907-0.911; = 0.918-0.922; = 0.930-0.938; = 0.924-0.932). Thus, we have developed a partial least squares (PLS) model using the five descriptors obtained from the five LR models. The developed PLS model showed good predictivity and robustness in terms of both internal ( = 0.925; = 0.911) and external validation ( = 0.944; = 0.938) parameters. The descriptors, Electrochemical equivalent (E), 2nd ionization potential (2χpi), covalent radius (R), amount of Ag (Ag) and thermal conductivity (T) obtained from the final PLS model well explained the cause of cytotoxicity of the heterogeneous NPs without requiring any computationally expensive descriptors. The insights obtained from the developed models suggested that higher electronegativity, lower oxidation state, and release of metal cation from its oxide increase cytotoxicity through various mechanisms. Thus, these models can be used as efficient tools to assess the toxicity with physiological property of the new heterogeneous NPs in the future.