# Characterization of Nanoparticle Batch-To-Batch Variability
## 1 Introduction  
Establishment of standards in nanomaterial (NM) synthesis and processing to ensure reproducibility of biological responses is an important task with respect to the validity of scientific conclusions and the enforcement of legislation on NMs. Current industrial and laboratory processes for synthesis of NMs give rise to quite significant variability from batch-to-batch (B2B) [1; 2; 3]. Such variations are quite well known and considerable efforts are being made to reduce them for high added value applications. However, while such variability may not impact on the industrial applications of NMs (such as catalysis, material strengthening, energy conversion etc.) it may have significant implications for the evaluation of biological impacts of the NMs needed as part of responsible innovation, risk assessment and regulation. Thus, different detailed biological outcomes from different batches (even if none implies any real hazard) introduce a lack of certainty in the science [4; 5]. To some degree the (immediate) need for standard test materials (for example the OECD sponsorship program materials) can be addressed by purchase of a large single batch. However, it is clear that in the long term this will not work, as the shelf life of some NM dispersions may be as short as 3 months and full in vivo programs can take 2 years. There is also the question of how'representative' a single batch is of the NM properties across multiple batches. For these reasons, it is necessary to gain control over the reproducibility of batches (or have sufficiently'representative' batches in sufficient quantities) in order to really progress knowledge on the safety of NMs and thus remove uncertainty in the field. Thus, research is needed in order to identify the source of B2B variations within the synthesis processes [6] and to develop strategies to eliminate or reduce them, including evaluation of currently available methods (for example continuous flow) to address the problem. Understanding of the correlation between synthesis route and resulting physicochemical characteristics and toxicity, such as has been shown for colloidal versus flame synthesized silica [7] would also allow disentangling some of the apparently contradictory results present in current literature, where nominally similar materials are reported to have very different toxicities. Representative materials produced by different methods could then be utilized as positive and negative controls respectively, once their properties and toxicities are known [8]. For silica NMs, particle properties such as size, morphology, porosity, crystallinity [9], colloidal stability [3] and surface chemistry [10] are known factors in determining biological impact [4]. Indeed, synthesis route is well known to affect the properties, including toxicity, of resulting NMs, as has been shown for silica [7], ceria [6], silver [11] and many other NMs. Therefore, in order to study and hence design \"safer\" NM systems for practical applications such as cosmetics [12], silicon wafer polishing, electronics and so forth, high level of control and robust reproducible preparation methods are required. Additionally, in order to correctly study and interpret the biological impact of NMs, it is vital to reach a state where exquisite control can be applied to NM synthesis in order to achieve the level of reproducibility required. For NMs that are applied as viable biospecific carrier systems for therapeutic delivery, B2B reproducibility is also a prerequisite. Given the surface sensitivity of the dynamic biological environment, small variations at even the sub-nano level may lead to unpredictable outcomes. Consequently, only with a sufficient level of synthetic control we may achieve any useful predefined and designed-in biological impact. For example, three different particles of the same bulk material (ceria) revealed different protein adsorption patterns in alveolar lining fluid, despite having similar surface area and surface chemistry [13].