QSAR model and mechanism research on color removal efficiency of dying wastewater by FeCl3 coagulation

Interpretable attention-based multi-encoder transformer based QSPR model for assessing toxicity and environmental impact of chemicals

The rising demand from consumer goods and pharmaceutical industry is driving a fast expansion of newly developed chemicals. The conventional toxicity testing of unknown chemicals is expensive, time-consuming, and raises ethical concerns. The quantitative structure-property relationship (QSPR) is an efficient computational method because it saves time, resources, and animal experimentation. Advances in machine learning have improved chemical analysis in QSPR studies, but the real-world application of machine learning-based QSPR studies was limited by the unexplainable 'black box' feature of the machine learnings. In this study, multi-encoder structure-to-toxicity (S2T)-transformer based QSPR model was developed to estimate the properties of polychlorinated biphenyls (PCBs) and endocrine disrupting chemicals (EDCs). Simplified molecular input line entry systems (SMILES) and molecular descriptors calculated by the Dragon 6 software, were simultaneously considered as input of QSPR model. Furthermore, an attention-based framework is proposed to describe the relationship between the molecular structure and toxicity of hazardous chemicals. The S2T-transformer model achieved the highest R2 scores of 0.918, 0.856, and 0.907 for logarithm of octanol-water partition coefficient (Log KOW), octanol-air partition coefficient (Log KOA), and bioconcentration factor (Log BCF) estimation of PCBs, respectively. Moreover, the attention weights were able to properly interpret the lateral (meta, para) chlorination associated with PCBs toxicity and environmental impact.

Methylene blue removal using modified poly(glycidyl methacrylate) as a low-cost sorbent in batch mode: kinetic and equilibrium studies

Industrial textile wastewater contains large amounts of cationic dye material. Therefore, a new adsorbent was synthesized as modified poly(glycidyl methacrylate) (mPGMA) with a fluorine group-containing compound 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP). mPGMA was characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectrometer (FTIR). The proposed adsorbent has been used to remove methylene blue (MB) from aqueous solutions by the adsorption process. In further experiments, the removal efficiency of adsorbent in both powder (˂600 μm) and granular form was compared from aqueous solutions by adsorption process. Furthermore, the effects of changing parameters such as adsorbent dosage, contact time, pH, temperature, and initial dye concentration on methylene blue adsorption were investigated. Also, Langmuir, Freundlich, and Temkin isotherms have been used to describe the equilibrium characteristics of adsorption. Finally, the experimental data fitted well by Langmuir isotherm with a maximum adsorption capacity of 17.5 mg g-1. The experimental data were applied to pseudo-first- and second-order models. The experimental results were better fitted for the pseudo-second-order model than the other model. Consequently, the experimental results showed that mPGMA is a suitable low-cost adsorbent with great potential benefit in removing methylene blue from aqueous solutions.