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Carbon dots (CDs), as a fascinating carbon nanomaterial, have important applications in various fields due to their unique properties. The physical and chemical properties of CDs can be fine-tuned using heteroatom doping and surface functionalisation. Here, we synthesised N-doped carbon dots (N-CDs) by reacting Citric acid, which serve as the carbon core, with twenty amino acids under microwave irradiation. The fluorescence quenching of each amino acid doped CDs by Hg(II) ions was experimentally measured. Then the effect of the molecular features and chemical properties of amino acids on the fluorescence quenching of N-CDs by Hg(II) ions was investigated by using the quantitative structure–property relationship (QSPR) method. Applying different machine learning techniques including correlation-based and ReliefF algorithm feature selection approaches to choose relevant descriptors, multi-linear regression, and support vector machine to construct QSPR model, some reliable and predictive models were developed. Based on the variables used throughout the final QSPR models, hydrophobic interactions, in addition to hydrogen bonding interactions, can be considered a major factor governing the photoluminescence behaviour of different N-CDs quenched by Hg(II) ions. N-CDs derived from amino acids bearing larger hydrophobic surfaces show greater fluorescence quenching, indicating that a greater capacity to interact with Hg(II) metal ions resulting in further fluorescence quenching. [ABSTRACT FROM AUTHOR]

Leachable investigations are routinely undertaken across a range of sectors (e.g., pharmaceuticals, medical devices, etc.) to determine whether chemicals from a container closure system transfer into a product under normal conditions of use. For Electronic Nicotine Delivery Systems (ENDS) the container closure system includes all materials in contact with the e-liquid that is aerosolized and subsequently inhaled by the user. Currently, there is no guidance for conducting leachable studies for ENDS products, however, there are relevant guidance documents for orally inhaled drug products that can be applied to an ENDS container closure system. We present a case study of the analytical investigation of two leachable compounds identified in simulated leachable studies using aged JUULpods filled with unflavored e-liquid (PG/VG/nicotine/ benzoic acid). Both compounds had limited toxicological information and were considered data deficient. A qualitative analysis of the aerosol collected from aged commercial JUULpods (Virginia Tobacco and Menthol), using a similar analytical method (LC-MS/MS) used in the simulated leachable studies, showed no trace or detectable levels of either leachable compound. Therefore, this qualitative analysis did not provide semi-quantitative values for the data-deficient leachable compounds necessary to support toxicological risk assessment. Further, no commercial authentic standards or reasonable synthetic route were available due to the molecular size and structural complexity of the compounds. Instead, method limits were established using an alternative approach to standard ICH guidelines. The experimentally determined method limit of quantitation, using spiked samples of simulated leachable e-liquid, provided conservative semi-quantitative values for each data deficient leachable compound in the aerosol that enabled a transfer efficiency from e-liquid to aerosol to be estimated. The transfer efficiency of each leachable compound was experimentally determined to be less than 3% based on the limit of quantitation, which then could be used to define a relevant exposure limit for the toxicological risk assessment. This work details a novel analytical approach for determining the transfer efficiency of data deficient leachable compounds from ENDS container closure systems into the ENDS aerosol to support toxicological health risk assessments. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) and two-dimensional (2D) quantitative structure-property relationship (QSPR) models were established for the computer-aided design of new phosphorus-containing podands extractants of the uranyl cation. GRIND methodology, where descriptors are derived from GRID molecular interaction fields (MIF), and Dragon-generated descriptors were used to perform QSPR modeling of the distribution coefficient (logD). The best model for 3D-QSPR has been obtained with R2 = 0.93 and Q2 = 0.79. Some simple 2D-QSPR models, able to correlate and predict the logD, are developed. The final models satisfied a set of rigorous validation criteria and performed well in the prediction of an external test set. The results reveal the role of size and steric hindrance of the hydrophobic part of the extractant molecule, as well as the importance of electrostatics and charge transfer interactions in the distribution coefficient of uranyl cation. This information could be very useful to design the most efficient ligands and find new extractants for uranyl ion extraction. [ABSTRACT FROM AUTHOR]

In this study, the maximum adsorption capacities (qmax) of multi-walled carbon nanotubes (MWCNTs) for 16 rare earth ions were estimated from the experimental data using Langmuir isotherm model. The Langmuir model fitted the experimental data very well, with R2 > 0.98. To explore the impact of ion properties of their adsorption onto MWCNTs, Quantitative Ion Character–Activity Relationships (QICAR) method was applied. The experimental values of qmax were used as dependent variable and the rare earth ions properties were considered as independent variables. Based on covalent index, a new variable was defined which is highly correlated to qmax (correlation coefficient of 0.776). Different variable selection techniques and wrapper models were applied to choose the best subset of variable. Among all developed linear and non-linear models constructed using support vector machine regression (SVR), neural networks, random forest and multiple linear regression, the SVR model presents the best statistical performance. The result of the Wrapper-SVR developed model, with R2c = 0.993 and R2p = 0.794, was excellent than those of other models. The importance of some thermodynamic parameters, Gibbs free energy (ΔG0f) and enthalpy of formation (ΔH0f) of the trivalent lanthanide ions (M+3) at 25°C, indicated the role of thermodynamic aspects on adsorption mechanism. Atomic radius, ionisation energy and electronegativity of rare earth ions should also be considered. [ABSTRACT FROM AUTHOR]

Knowing stability constants for the complexes HgII with extracting ligands is very important from environmental and therapeutic standpoints. Since the selectivity of ligands can be stated by the stability constants of cation–ligand complexes, quantitative structure–property relationship (QSPR) investigations on binding constant of HgII complexes were done. Experimental data of the stability constants in ML2 complexation of HgII and synthesized triazene ligands were used to construct and develop QSPR models. Support vector machine (SVM) and multiple linear regression (MLR) have been employed to create the QSPR models. The final model showed squared correlation coefficient of 0.917 and the standard error of calibration (SEC) value of 0.141 log K units. The proposed model presented accurate prediction with the Leave-One-Out cross validation (Q LOO 2 = 0.756) and validated using Y-randomization and external test set. Statistical results demonstrated that the proposed models had suitable goodness of fit, predictive ability, and robustness. The results revealed the importance of charge effects and topological properties of ligand in HgII - triazene complexation. [ABSTRACT FROM AUTHOR]

Stability constants prediction plays a critical role in the identification and optimization of ligand design for selective complexation of metal ions in solution. Thus, it is important to assess the potential of metal-binding ligand organic in the complex formation process. However, quantitative structure-activity/property relationships (QSAR/QSPR) provide a time-and cost-efficient approach to predict the stability constants of compounds. To this end, we applied a free alignment three-dimensional QSPR technique by generating GRid-INdependent Descriptors (GRINDs) to rationalize the underlying factors effecting on stability constants of transition metals; 105 (Y3+), 186 (La3+), and 66 (UO22+) with diverse organic ligands in aqueous solutions at 298 K and an ionic strength of 0.1 M. Kennard– Stone algorithm was employed to split data set to a training set of 75% molecules and a test set of 25% molecules. Fractional factorial design (FFD) and genetic algorithm (GA) applied to derive the most relevant and optimal 3 D molecular descriptors. The selected descriptors using various feature selection were correlated with stability constants by partial least squares (PLS). GA-PLS models were statistically validated ( R 2 = 0.96, q2 = 0.82 and R2pred=0.81 for Y3+; R 2 = 0.90, q2 = 0.73 and R2pred=0.82 for La3+ and R 2 = 0.95, q2 = 0.81 and R2pred=0.88 for UO22+), and from the information derived from the graphical results confirmed that hydrogen bonding properties, shape, size, and steric effects are the main parameters influencing stability constant of metal complexation. The provided information in this research can predict the stability constant of the new organic ligand with the transition metals without experimental processes. [ABSTRACT FROM AUTHOR]

The biogenic amine contents in fillets of three freshwater fish of kutum (Rutilus frisii), common carp (Cyprinus carpio), and Caspian salmon (Salmon caspius) were monitored during storage at 4°C for 20 days. The amounts of biogenic amines were determined at regular intervals using high performance liquid chromatography after derivatization with benzoyl chloride. Significant differences in the levels of selected biogenic amines among the fish samples were found (p< 0.05) during storage times. While the amount of putrescine, cadaverine, histamine, and tyramine in the fish fillets increased during the storage time, the changes in spermine and spermidine contents of the fish species were minor and both showed low and inverse correlations with storage time. For each of the three fish species, the sensory acceptability limit was found to be 4–8 days. The quality and biogenic amine indices of fish samples showed high correlation with storage time (r> 0.9). The principal component analysis of the results indicated that the formation of biogenic amines displays a certain behavioral pattern in all fish samples. [ABSTRACT FROM PUBLISHER]

Three-dimensional quantitative structure activity relationships (3D-QSAR) methods of comparative molecular field analysis (CoMFA), CoMFA region focusing (CoMFA-RF), and comparative molecular similarity indices analysis (CoMSIA) were successfully employed to predict and model of the separation factors of N-donor heterocyclic extractants in the separation of americium(III) from europium(III) ions from acidic aqueous solutions. An all-orientation search (AOS) strategy was used to acquire the best orientation and minimize the effect of the initial orientation of aligned compounds. The obtained 3D-QSAR models showed R2> 0.97 and an absolute error of > 0.1 in log unit of the separation factors. Validation, reliability, and robustness of 3D-QSAR models were evaluated by the prediction of external test sets, leave-one-out, leave-four-out, bootstrapping, and progressive scrambling approaches. The results confirmed that steric hindrance and electrostatic interactions besides hydrophobic effects of N-donor extractants play an important role in Am(III)/Eu(III) separation processes. The obtained information could be very useful in designing the most efficient ligands and finding new extractants for lanthanide and actinide separation. [ABSTRACT FROM PUBLISHER]

In this work, we have used two Molecular Interaction Fields (MIFs) based descrtipotrs, VolSurf and GRIND, as alignment-independent three-dimensional quantitative structure activity relationships (3D-QSAR) approaches to predict C60 solubilities in a diverse set of 132 organic solvents. GRIND methodology with fractional factorial design, and PLS analysis was carried out yielded a highly descriptive and predictive model. Genetic algorithm and successive projection algorithm (SPA) applied to feature selection and extract more informative VolSurf descriptors. A support vector machine (SVM) was used for model construction, and SPA-SVM-based VolSurf descriptors showed excellent performance in predicting C60 solubility. Validation, reliability and robustness of obtained models were evaluated by the prediction of external test sets, leave-one-out and progressive scrambling approach. The results confirmed that hydrophobic interactions besides steric effects are main factors influencing solubility of C60 in different organic solvents. [ABSTRACT FROM AUTHOR]

Stability constants prediction plays a critical role in the identification and optimization of ligand design for selective complexation of metal ions in solution. Thus, it is important to assess the potential of metal-binding ligand organic in the complex formation process. However, quantitative structure-activity/property relationships (QSAR/QSPR) provide a time-and cost-efficient approach to predict the stability constants of compounds. To this end, we applied a free alignment three-dimensional QSPR technique by generating GRid-INdependent Descriptors (GRINDs) to rationalize the underlying factors effecting on stability constants of transition metals; 105 (Y 3+ ), 186 (La 3+ ), and 66 (UO 2 2+ ) with diverse organic ligands in aqueous solutions at 298 K and an ionic strength of 0.1 M. Kennard- Stone algorithm was employed to split data set to a training set of 75% molecules and a test set of 25% molecules. Fractional factorial design (FFD) and genetic algorithm (GA) applied to derive the most relevant and optimal 3 D molecular descriptors. The selected descriptors using various feature selection were correlated with stability constants by partial least squares (PLS). GA-PLS models were statistically validated ( R 2  = 0.96, q 2  = 0.82 and R 2 pred =0.81 for Y 3+ ; R 2  = 0.90, q 2  = 0.73 and R 2 pred =0.82 for La 3+ and R 2  = 0.95, q 2  = 0.81 and R 2 pred =0.88 for UO 2 2+ ), and from the information derived from the graphical results confirmed that hydrogen bonding properties, shape, size, and steric effects are the main parameters influencing stability constant of metal complexation. The provided information in this research can predict the stability constant of the new organic ligand with the transition metals without experimental processes.