基于神经网络模型的纳米硫化亚铁绿色合成及盐酸土霉素的去除机理
- The Green Synthesis of Iron Sulfide Nanoparticles Based on Artificial Neural Network Models and the Removal Mechanisms of Oxytetracycline Hydrochloride
- 摘要：利用马尾松提取液制备的硫化亚铁纳米颗粒(PML-FeS NPs)可用于去除废水中的盐酸土霉素(OTC).然而,合成条件对PML-FeS NPs性能的具体影响尚未明确,且PML-FeS NPs作为活化剂活化过氧化氢(H2O2)降解OTC的去除机制未知.因此,本文运用人工神经网络(ANN)方法和随机森林(RF)模型对PML-FeS NPs性能进行了优化.结果表明,OTC的去除效率显著提升至100%.进一步的猝灭实验和电子自旋共振(EPR)分析揭示,在PML-FeS/H2O2体系中,活性氧物种(ROS)如羟基自由基(?OH)和单线态氧(1O2)共同参与了OTC的降解.通过密度泛函理论(DFT)计算和降解产物分析,阐明了OTC的降解途径,经毒性评估其降解产物显示出较低的毒性.本研究提出了一种基于废弃生物质合成绿色纳米材料的新方法,并为废水中污染物处理提供了有力的技术支持.
- Abstract：Green synthesized iron sulfide nanoparticles derived from pine extract (PML-FeS NPs) have demonstrated potential for chlortetracycline (OTC) removal from wastewater. However, the specific influence of synthesis conditions on the performance PML-FeS NPs remains unclear, and the mechanisms by which PML-FeS NPs activate hydrogen peroxide (H2O2) to degrade OTC are not well understood. In this study, artificial neural network (ANN) and random forest (RF) models were employed to enhance the performance of PML-FeS NPs. The results showed a remarkable improvement in OTC removal efficiency, reaching a full 100%. Further quenching experiments and electron paramagnetic resonance (EPR) analysis revealed that within the PML-FeS/H2O2 system, reactive oxygen species (ROS), including hydroxyl radicals (?OH) and singlet oxygen (1O2)， collaboratively contribute to OTC degradation. The degradation pathway of OTC was deciphered through density functional theory (DFT) calculations and the analysis of degradation products, which exhibited reduced toxicity post-assessment. This research proposes an innovative approach for synthesizing green nanomaterials from waste biomass and offers technical support for the wastewater treatment.
