Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia.
Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Av. Paisos Catalans 26, Tarragona 43007, Spain.
Nanoscale. 2016 Sep 15;8(36):16243-16250. doi: 10.1039/c6nr04376c.
While metal oxide nanoparticles (NPs) are one of the most commonly used nanomaterials, the theoretical models used to analyze and predict their behavior have been mostly based on just the chemical composition or the extrapolation from small metal oxide clusters' calculations. In this study, a set of novel, theoretical full-particle descriptors for modeling, grouping or read-across of metal oxide NP properties and biological activity was developed based on the force-field calculation of the potential energies of whole NPs. The capability of these nanodescriptors to group the nanomaterials acoording to their biological activity was demonstrated by Principal Component Analysis (PCA). The grouping provided by the PCA approach was found to be in good accordance with the algal growth inhibition data of well characterized nanoparticles, synthesized and measured inside the consortia of the EU 7FP framework MODERN project.
虽然金属氧化物纳米粒子（NPs）是最常用的纳米材料之一，但用于分析和预测其行为的理论模型主要基于化学组成或从小金属氧化物团簇的计算外推。在这项研究中，基于整个 NPs 势能的力场计算，开发了一套用于建模、分组或金属氧化物 NP 性质和生物活性读取的新型全粒子描述符。通过主成分分析（PCA）证明了这些纳米描述符根据生物活性对纳米材料进行分组的能力。PCA 方法提供的分组与在欧盟 7FP 框架 MODERN 项目联合体内合成和测量的具有良好特征的纳米颗粒的藻类生长抑制数据非常吻合。
