Improved model for fullerene C 60 solubility in organic solvents based on quantum-chemical and topological descriptors

Abstract Fullerenes are sparingly soluble in many solvents. The dependence of fullerene's solubility on molecular structure of the solvent must be understood in order to manage efficiently this class of compounds. To find such dependency ab initio quantum-chemical calculations in combination with quantitative structure-property relationship (QSPR) tool were used to model the solubility of fullerene C 60 in 122 organic solvents. A genetic algorithm and multiple regression analysis (GA-MLRA) were applied to generate correlation models. The best performance is accomplished by the four-variable MLRA model with prediction coefficient r test 2 = 0.903. This study reveals a correlation of highest occupied molecular orbital energy (HOMO), certain heteroatom fragments, and geometrical parameters with solubility. Several other important parameters of solvents that affect the C 60 solubility have been also evaluated by the QSPR analysis. The employed GA-MLRA approach enhanced by application of quantum-chemical calculations yields reliable results, allowing one to build simple, interpretable models that can be used for predictions of C 60 solubility in various organic solvents

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD50). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R-2 = 0.88), validation set (R-2 = 0.95), and true external test set (R-2 = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors

Carbon Nanotubes’ Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra

[Abstract] This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of Jm showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of Jm for other CNTs was provided by random forest using eight features, obtaining test R-squared (R2) of 0.863 and test root-mean-square error (RMSE) of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG) transform with a potential applicability as predictive tools in nanotechnology and material risk assessmentsInstituto de Salud Carlos III; PI13/02020Instituto de Salud Carlos III; PI13/00280Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/025Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Ministerio de Economía y Competitividad; UNLC08-1E-002Ministerio de Economía y Competitividad ; UNLC13-13-3503Ministerio de Economía y Competitividad; CTQ2016-74881-PPaís Vasco.Gobierno; IT1045-16Brasil. Conselho Nacional de Desenvolvimento Científico e Tecnológico; 308539/2016-8Brasil. Conselho Nacional de Desenvolvimento Científico e Tecnológico; 454332/2014-

Genotoxicity of metal oxide nanomaterials: review of recent data and discussion of possible mechanisms

Nanotechnology has rapidly entered into human society, revolutionized many areas, including technology, medicine and cosmetics. This progress is due to the many valuable and unique properties that nanomaterials possess. In turn, these properties might become an issue of concern when considering potentially uncontrolled release to the environment. The rapid development of new nanomaterials thus raises questions about their impact on the environment and human health. This review focuses on the potential of nanomaterials to cause genotoxicity and summarizes recent genotoxicity studies on metal oxide/silica nanomaterials. Though the number of genotoxicity studies on metal oxide/silica nanomaterials is still limited, this endpoint has recently received more attention for nanomaterials, and the number of related publications has increased. An analysis of these peer reviewed publications over nearly two decades shows that the test most employed to evaluate the genotoxicity of these nanomaterials is the comet assay, followed by micronucleus, Ames and chromosome aberration tests. Based on the data studied, we concluded that in the majority of the publications analysed in this review, the metal oxide (or silica) nanoparticles of the same core chemical composition did not show different genotoxicity study calls (i.e. positive or negative) in the same test, although some results are inconsistent and need to be confirmed by additional experiments. Where the results are conflicting, it may be due to the following reasons: (1) variation in size of the nanoparticles; (2) variations in size distribution; (3) various purities of nanomaterials; (4) variation in surface areas for nanomaterials with the same average size; (5) differences in coatings; (6) differences in crystal structures of the same types of nanomaterials; (7) differences in size of aggregates in solution/media; (8) differences in assays; (9) different concentrations of nanomaterials in assay tests. Indeed, due to the observed inconsistencies in the recent literature and the lack of adherence to appropriate, standardized test methods, reliable genotoxicity assessment of nanomaterials is still challenging

Yilmaz H,Rasulev B,Leszczynski J, "Modeling the Dispersibility of Single Walled Carbon Nanotubes in Organic Solvents by Quantitative Structure-Activity Relationship Approach"

In this paper, we propose a novel distributed Interleaved Random Space-Time Code (IR-STC) designed for Multi-Source Cooperation (MSC) employing various relaying techniques, namely Amplify-Forward, Decode-Forward, Soft-Decode-Forward and Differential-Decode-Forward. We characterise the achievable slot utilisation efficiency and introduce a two-phase communication regime for our IR-STC aided MSC. A matrix based formalism is used for describing our IR-STC scheme and a novel Structured Embedded (SE) random interleaver generation method is proposed. Furthermore, the Bit Error Ratio (BER) performance of our IR-STC is characterised in conjunction with various relaying techniques under different inter-source Nakagamim fading channels

Modeling the Dispersibility of Single Walled Carbon Nanotubes in Organic Solvents by Quantitative Structure-Activity Relationship Approach

The knowledge of physico-chemical properties of carbon nanotubes, including behavior in organic solvents is very important for design, manufacturing and utilizing of their counterparts with improved properties. In the present study a quantitative structure-activity/property relationship (QSAR/QSPR) approach was applied to predict the dispersibility of single walled carbon nanotubes (SWNTs) in various organic solvents. A number of additive descriptors and quantum-chemical descriptors were calculated and utilized to build QSAR models. The best predictability is shown by a 4-variable model. The model showed statistically good results (R2training = 0.797, Q2 = 0.665, R2test = 0.807), with high internal and external correlation coefficients. Presence of the X0Av descriptor and its negative term suggest that small size solvents have better SWCNTs solubility. Mass weighted descriptor ATS6m also indicates that heavier solvents (and small in size) most probably are better solvents for SWCNTs. The presence of the Dipole Z descriptor indicates that higher polarizability of the solvent molecule increases the solubility. The developed model and contributed descriptors can help to understand the mechanism of the dispersion process and predictorganic solvents that improve the dispersibility of SWNTs