Modeling adsorption of brominated, chlorinated and mixed bromo/chloro-dibenzo-p-dioxins on C60 fullerene using Nano-QSPR

Many technological implementations in the field of nanotechnology have involved carbon nanomaterials, including fullerenes such as the buckminsterfullerene, C60. The unprecedented properties of such organic nanomaterials (in particular their large surface area) gained extensive attention for their potential use as organic pollutant sorbents. Sorption interactions can be very hazardous and useful at the same time. This work investigates the influence of halogenation by bromine and/or chlorine in dibenzo-p-dioxins on their sorption ability on the C60 fullerene surface. Halogenated dibenzo-p-dioxins (PXDDs, where X = Br or Cl) are ever-present in the environment and accidently produced in many technological processes in only approximately known quantities. If all combinatorial Br and/or Cl dioxin substitution possibilities are present in the environment, the experimental characterization and investigation of sorbent effectiveness is more than difficult. In this work, we have developed a quantitative structure–property relationship (QSPR) model (R2 = 0.998), predicting the adsorption energy [kcal/mol] for 1,701 PXDDs adsorbed on C60 (PXDD@C60). Based on the QSPR model reported herein, we concluded that the lowest energy PXDD@C60 complexes are those that the World Health Organization (WHO) considers to be less dangerous with respect to the aryl hydrocarbon receptor (AhR) toxicity mechanism. Therefore, the effectiveness of fullerenes as sorbent agents may be underestimated as sorption could be less effective for toxic congeners than previously believed

MOESM1 of ILPC: simple chemometric tool supporting the design of ionic liquids

Additional file 1. A tool developed within this work, allowing for predefining the physicochemical properties of ionic liquids. Data concerning IL’s properties and names of the descriptors used is also included in this file

Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme

To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is DF4nanoGrouping scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property ‘lowest unoccupied molecular orbital’) need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low– one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were well-constructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the DF4nanoGrouping scheme that has been proposed before

Decision tree models to classify nanomaterials according to the <i>DF4nanoGrouping</i> scheme

<p>To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is <i>DF4nanoGrouping</i> scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property ‘lowest unoccupied molecular orbital’) need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low– one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were well-constructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the <i>DF4nanoGrouping</i> scheme that has been proposed before.</p