Decamethonium Bromide

Machine learning models were applied to predict the scintillation performances of organic-inorganic hybrid metal halides (OIMHs), focusing on their photoluminescent quantum yield (PLQY).Random Forest and Decision Tree algorithms identified the most critical structural parameter of organic mols. influencing the M···M distance between metal ions and correlated PLQY value, with an optimal distance of approx. 8 Å correlating with enhanced luminescence efficiency.This prediction was exptl. validated through the synthesis of several OIMH compounds, demonstrating strong agreement between predicted and measured PLQY values.The machine learning approach not only enabled the screening of efficient compounds but also deepened the understanding of how structural factors, such as the structure of organic mols., govern scintillation properties.These findings underscore the potential of machine learning in accelerating the development of next-generation luminescent materials with improved performance, offering a powerful tool for future material design and optimization.

Decamethonium bromide (DDAB), as a class of quaternary ammonium-based disinfectants (QACs), was widely used during the COVID-19 outbreak.The abuse of DDAB is harmful to the health of aquatic organisms.The UV/H₂O₂ process is popularly utilized for the removal of trace contaminants from aqueous environments.Thus, in this paper, DDAB was selected as the model of QACs, and the degradation kinetics were simulated by Kintecus software.It was indicated that the effect of the degradation efficiency of DDAB was dominated by [·OH]_ss.The degradation efficiency of DDAB was greater than 95% when [·OH]_ss was higher than 1.04x10-¹² M.The high dosages of H₂O₂ (>100μM) and the less concentration of pollutants (<10μM) were more beneficial to the degradation of DDAB.The high dosages of NOM and CO^2-₃ in the system scavenged ·OH and thereby inhibited the degradation of DDAB.The interference of Cl^- on the degradation efficiency was negligible.Addnl., the optimal pH value for removing DDAB was between 4 and 7.Besides, the degradation pathway of DDAB was inferred by D. Functional Theory (DFT) and Ultra-high Performance Liquid Chromatog.-tandem Mass Spectrometry (UPLC-MS/MS).The results showed that the degradation of DDAB began with C-N bond breaking, then generated aldehydes, underwent demethylation reaction, and finally converted to low mol. weight mols.The results of Ecol. Structure Activity Relationships (ECOSAR) toxicity assessment demonstrated that the toxicity of all the degradation products was significantly low.Overall, this study provides theor. basis for the removal of QACs in the aqueous environment.