Novus fossilium index

Arkit: 1 arkintunnukseton lehti, F4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, C4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, A4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, L4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, E4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, G4 H2.

Quantitative structure-retention relationships analysis of retention index of essential oils

Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.

Capacidade de oxidação como índice de estabilidade da matéria orgânica de sedimentos de acordo com gradiente fluvial-estuarino do rio Paraíba do Sul

The hummus composition and it redox properties have been used to evaluate the organic matter quality from natural systems. The objectives of this study were the fractionation of the organic matter and the determination of the oxidation capacity of humic acids to evaluate the organic matter quality of sediments from a gradient fluvial-estuarine in the Brazilian Southeast. The carbon/nitrogen and humic acid/fulvic acid relationships of the organic matter and of the oxidation capacity of humic acids were positively correlated and followed the order: estuarine bordered mangroves ≅ fluvial bordered urban center and pasture > estuarine bordered pasture ≅ marine.

Dft theoretical study of energetic nitrogen-rich C4N6H8-n(NO2)n derivatives

Density functional theory (DFT) calculations at the B3LYP/6-31G** theoretical level were performed for a series of guanidine-fused bicyclic skeleton derivatives C4N6H8-n(NO2)n (n = 1 - 6). The heats of formation (HOFs) were calculated by isodesmic reactions, and the detonation properties were evaluated using the Kamlet - Jacobs equations. The bond dissociation energies were also analyzed to investigate the thermal stability and sensitivity of the compounds. The results show that all of the derivatives have high positive HOFs, compound G has the highest theoretical density, and compound F1 has the highest detonation velocity and detonation pressure. Considering both the detonation properties and thermal stabilities, compounds D1 and D4 (3 nitro substituents), E1 - E6 (4 nitro substituents), and G (6 nitro substituents) can be regarded as potential candidates for high-energy density materials.

Method for determining the photocatalytic potential of portland cement mortar containing TiO2 for decomposing the pollutant nitrogen monoxide

Photocatalytic materials can minimize atmospheric pollution by decomposing certain organic and inorganic pollutants using sunlight as an energy source. In this paper, the development of a methodology to measure the photocatalytic potential of mortar containing TiO2 nanoparticles is reported. The results indicate that up to 40% of NOx can be degraded by Portland cement mortar containing 30-50% of TiO2, which validates the method developed for evaluating the photocatalytic potential of materials.

PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.