Mehrtash Hadi, Konakbayeva Dinara, Tabtabaei Solmaz, Srinivasan Seshasai, Rajabzadeh Amin Reza
Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 3L8, Canada.
Department of Chemical Engineering, Howard University, Washington, DC 20060, USA.
Foods. 2022 Feb 26;11(5):693. doi: 10.3390/foods11050693.
This study explores a new perspective on triboelectrification that could potentially lead to the development of a non-destructive approach for the rapid characterization of powders. Sieved yellow pea powders at various particle sizes and protein contents were used as a model system for the experimental charge measurements of the triboelectrified powders. A tribocharging model based on the prominent condenser model was combined with a Eulerian-Lagrangian computational fluid dynamics (CFD) model to simulate particle tribocharging in particle-laden flows. Further, an artificial neural network model was developed to predict particle-wall collision numbers based on a database obtained through CFD simulations. The tribocharging and CFD models were coupled with the experimental tribocharging data to estimate the contact potential difference of powders, which is a function of contact surfaces' work functions and depends on the chemical composition of powders. The experimentally measured charge-to-mass ratios were linearly related to the calculated contact potential differences for samples with different protein contents, indicating a potential approach for the chemical characterization of powders.
本研究探索了摩擦起电的一个新视角，这可能会推动一种用于粉末快速表征的无损方法的发展。将不同粒径和蛋白质含量的过筛黄豌豆粉用作模型系统，用于对摩擦带电粉末进行实验电荷测量。基于突出的电容器模型的摩擦充电模型与欧拉-拉格朗日计算流体动力学（CFD）模型相结合，以模拟含颗粒流中的颗粒摩擦充电。此外，基于通过CFD模拟获得的数据库，开发了一个人工神经网络模型来预测颗粒与壁面的碰撞次数。摩擦充电模型和CFD模型与实验摩擦充电数据相结合，以估计粉末的接触电势差，该电势差是接触表面功函数的函数，并且取决于粉末的化学成分。对于不同蛋白质含量的样品，实验测量的荷质比与计算得到的接触电势差呈线性关系，这表明了一种用于粉末化学表征的潜在方法。
