Towards defining new nano-descriptors: extracting morphological features from transmission electron microscopy images† Arafeh Bigdeli, a Mohammad Reza Hormozi-Nezhad, * ab Mehdi Jalali-Heravi, a Mohammad Reza Abedini c and Farzad Sharif-Bakhtiar d Due to the important role of surface-related properties of NPs in their biological behavior, simple and fast methods that could precisely demonstrate accurate information about NPs' surface, structure and morphology are highly desirable. In this study a set of surface morphological nano-descriptors (size, shape, surface area, agglomeration state, curvature, corner count and aspect ratio) have been defined and extracted from Transmission Electron Microscopy (TEM) images of nanoparticles (NPs) by Digital Image Processing methods. The extracted data represent a thorough description of the surface and morphologies of NPs lying beyond their TEM images and can supply the data required for a nano-QSAR approach for predicting toxicity profiles of NPs. These nano-descriptors can provide a framework to further understand the mechanisms which govern the adverse effects of NPs in biological systems. Metallic nanostructures (gold, silver, palladium.) with different sizes (10 to 100 nm), shapes (cube, sphere, rod.) and characteristics were taken into account for which physicochemical indexes were reported. To the best of our knowledge, this is the first ever study that presents numerical values for properties such as shape and agglomeration state which significantly affect NPs behavior. 1. Introduction Today with the developments in nanotechnology which has signicantly improved the quality of life for human beings, it is important to address the possible consequences, as with any emerging technology. Computational approaches play an essential role in this risk assessment procedure due to their fast, in-expensive and high throughput methods. However, it must be noted that the negative impacts of NPs should be carefully considered and evaluated by gathering specialists in both experimental and theoretical elds. The large number of NPs and the variety of their characteristics including various sizes, shapes and coatings suggest that the only rational approach which avoids testing every single NP is to nd a relationship between the physicochemical characteristics of NPs and their toxicity. 1,2 This approach, namely called Quantitative Structure– Activity Relationship of nanomaterials (nano-QSAR), statisti- cally establishes a mathematical relationship between a measured prole of a set of nanostructures and their physico- chemical properties (called “nano-descriptors”). Thus, through a nano-QSAR approach, one would be able to quantitatively predict the potential toxicity of a set of un-tested NPs based on experimental toxicological data available for a set of tested ones and therefore, prevent expensive and time-consuming empir- ical animal testing procedures for NPs risk assessment. However, since NPs signicantly differ to their bulk counter- parts, consequently, nano-QSAR differs to the well-known conventional QSAR approach (for which there are several commercial soware available and large sets of molecular descriptors are calculated) 3 and there is a need to develop QSAR models with a special insight to nanomaterials. Actually, some major obstacles impede the nano-QSAR approach such as: structural complexity and diversity of NPs, scarce and/or inconsistent empirical data and thus lack of available large scale datasets of NPs' toxicity, and nally lack of rational modeling procedures in describing the structural properties of these substances. 4 Therefore, nano-specic descriptors responsible in determining the toxicity of nanostructures are markedly required. Developing these novel nano-descriptors could be a great challenge for computational experts. A number of research groups have already expressed computa- tional and empirical nano-descriptors for revealing the behavior of nanomaterials. 5 For example, Puzyn et al. 6 presented a set of quantum mechanical descriptors for modeling the cytotoxicity of metal-oxide NPs to bacteria Escherichia coli. Martin et al. 7 a Department of Chemistry, Sharif University of Technology, Tehran, Iran. E-mail: hormozi@sharif.edu; Tel: +98 21 6616 5337 b Institute for Nanoscience and Nanotechnology (INST), Sharif University of Technology, Tehran, Iran c Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran d Department of Computer Engineering, Sharif University of Technology, Tehran, Iran † Electronic supplementary information (ESI) available. See DOI: 10.1039/c4ra10375k Cite this: RSC Adv. , 2014, 4, 60135 Received 13th September 2014 Accepted 5th November 2014 DOI: 10.1039/c4ra10375k www.rsc.org/advances This journal is © The Royal Society of Chemistry 2014 RSC Adv., 2014, 4, 60135–60143 | 60135 RSC Advances PAPER