Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach.

Nowadays, organic chemicals play an essential role in almost all walks of life and have become indispensable to modern society. However, the continually synthesized chemicals and the numerous potential adverse endpoints against living organisms increasingly promote the regulators regarding the computational approach as a crucial supplement and an alternative to the traditional animal tests in chemical risk assessment. In this present research, we evaluated the ecotoxicity of chemicals against four typical Gammarus species, which constituted a critical element in detritus cycle and also the recommended species for water monitoring. We first screened the molecular descriptors based on the Genetic Algorithm and then developed the Quantitative Structure-Activity Relationship models using the Multiple Linear Regression method. The statistical results from various validation metrics suggested that the obtained models were internally robust and externally predictive. The application domain analysis based on the leverage approach and standardized residual method demonstrated the broad application range of each model. The interpretation of molecular descriptors in each model suggested that the chemicals with higher polarity and hydrophilicity tend to be less toxic, whereas the lipophilic moieties would enhance the chemical toxicity. Meanwhile, the other selected descriptors, such as Chi-cluster, heterocyclic, and distance matrix descriptors, manifested that the chemical toxicity was also affected by molecular branching, connectivity, electrotopological state, and other various properties. In summary, the present work proposed well-performed QSAR models and clarified the possible toxic mechanism of chemicals against Gammarus species. The obtained models could help predict the toxicity data and conduct a preliminary risk assessment, thus guiding the subsequent animal tests and reducing the assessment cost.

Influence of Acidity and Oxidant Concentration on the Nanostructures and Electrochemical Performance of Polyaniline during Fast Microwave-Assisted Chemical Polymerization.

Polyaniline (PANI), a typical conducting polymer, has attracted great interest as an electrode material. A series of PANIs were prepared through fast microwave-assisted chemical oxidative polymerization with varying HCl and APS concentrations here. It was found that the microwave synthesized PANIs had ~4 times higher for the yields and 7~10 times higher for the electrical conductivity in comparison to PANI samples prepared using conventional method. PANI nanosheets could easily be fabricated in weakly acidic solution due to their oligomeric structure, which contained flat phenazine rings. By contrast, linear PANI chains produced in highly acidic solutions formed nanofibers. The APS concentration did not significantly affect the molecular structures of PANIs under the conditions here. However, increasing the concentration of APS produced nanofibers with shorter branches, which may be due to secondary nucleation during chain growth resulting from increases in active initiation centers. The electrical conductivity and electrochemical performance of PANIs were both improved with increasing HCl and APS concentrations. Improvements due to increases in HCl concentration may be attributed to additions in conjugation length and enrichment of doping levels, while improvements due to increases in APS concentration could be attributed to the increased crystallinity of PANI, which facilitates ion transport.