Green Intelligence Environmental School, Yangtze Normal University, Chongqing, 408100, China.
State Key Joint Laboratory of Environment Simulation and Pollution Control (SKJLESPC), Beijing Key Laboratory of Emerging Organic Contaminants Control (BKLEOCC), School of Environment, Tsinghua University, Beijing, 100084, China.
Chemosphere. 2021 Dec;284:131307. doi: 10.1016/j.chemosphere.2021.131307. Epub 2021 Jun 23.
Mechanochemical destruction of organic pollutants by high energy milling with inorganic reagents is considered a promising non-thermal technology to detoxify hazardous waste. However, due to complex nature of the physicochemical phenomena involved, pollutant destruction kinetics heavily depends on the used reagents and operating parameters, thus varying case by case. In the present work, a fractal model was validated as flexible tool to interpolate pollutant mechanochemical destruction data satisfactorily. In addition, such model was expanded to estimate the contributions of the inorganic reagent and the pollutant to the overall reaction rate. Specifically, the kinetic constant associated to mechanical activation of the co-milling reagent and that related to pollutant destruction reaction were calculated. Their values resulted to depend only on the specific compound, hence, the tabulated data could be used to predict the pollutant mechanochemical degradation rate for any kind of mixture.
用无机试剂通过高能球磨对有机污染物进行机械化学破坏被认为是一种很有前途的非热解毒危险废物技术。然而，由于涉及的物理化学现象的复杂性，污染物的破坏动力学严重依赖于所用的试剂和操作参数，因此情况各不相同。在本工作中，验证了分形模型作为插值污染物机械化学破坏数据的灵活工具的有效性。此外，还将该模型扩展到估计无机试剂和污染物对总反应速率的贡献。具体来说，计算了与共磨试剂机械活化相关的动力学常数和与污染物破坏反应相关的动力学常数。它们的值仅取决于特定的化合物，因此，列出的数据可用于预测任何混合物的污染物机械化学降解速率。
