The physicochemical characterisation data from a library of 69 engineered nanomaterials (ENMs) has been exploited in silico following enrichment with a set of molecular descriptors that can be easily acquired or calculated using atomic periodicity and other fundamental atomic parameters. Based on the extended set of twenty descriptors, a robust and validated nanoinformatics model has been proposed to predict the ENM ζ-potential. The five critical parameters selected as the most significant for the model development included the ENM size and coating as well as three molecular descriptors, metal ionic radius (r_ion), the sum of metal electronegativity divided by the number of oxygen atoms present in a particular metal oxide (Σχ/n_O) and the absolute electronegativity (χ_abs), each of which is thoroughly discussed to interpret their influence on ζ-potential values. The model was developed using the Isalos Analytics Platform and is available to the community as a web service through the Horizon 2020 (H2020) NanoCommons Transnational Access services and the H2020 NanoSoveIT Integrated Approach to Testing and Assessment (IATA).

来自 69 种工程纳米材料 (ENM) 库的物理化学表征数据已在计算机中被利用，随后富集了一组分子描述符，这些描述符可以使用原子周期性和其他基本原子参数轻松获取或计算。基于 20 个描述符的扩展集，已经提出了一个稳健且经过验证的纳米信息学模型来预测 ENM ζ 电位。被选为模型开发最重要的五个关键参数包括 ENM 尺寸和涂层以及三个分子描述符，金属离子半径 ( r _ion )，金属电负性的总和除以特定物质中存在的氧原子数金属氧化物 ( Σχ/n _O) 和绝对电负性 ( χ _abs )，对每一个都进行了深入讨论，以解释它们对 ζ 电位值的影响。该模型是使用 Isalos 分析平台开发的，并通过 Horizo​​n 2020 (H2020) NanoCommons 跨国访问服务和 H2020 NanoSoveIT 集成测试和评估方法 (IATA) 作为 Web 服务提供给社区。