TY - JOUR A1 - Afantitis, A. A1 - Melagraki, G. A1 - Isigonis, P. A1 - Tsoumanis, A. A1 - Varsou, D. D. A1 - Valsami-Jones, E. A1 - Papadiamantis, A. A1 - Ellis, L.-J. A. A1 - Sarimveis, H. A1 - Doganis, P. A1 - Karatzas, P. A1 - Tsiros, P. A1 - Liampa, I. A1 - Lobaskin, V. A1 - Greco, D. A1 - Serra, A. A1 - Kinaret, P. A. S. A1 - Saarimäki, L. A. A1 - Grafström, R. A1 - Kohonen, P. A1 - Nymark, P. A1 - Willighagen, E. A1 - Puzyn, T. A1 - Rybinska-Fryca, A. A1 - Lyubartsev, A. A1 - Jensen, K. A. A1 - Brandenburg, J. G. A1 - Lofts, S. A1 - Svendsen, C. A1 - Harrison, S. A1 - Maier, D. A1 - Tamm, K. A1 - Jänes, J. A1 - Sikk, L. A1 - Dusinska, M. A1 - Longhin, E. A1 - Rundén-Pran, E. A1 - Mariussen, E. A1 - El Yamani, N. A1 - Unger, Wolfgang A1 - Radnik, Jörg A1 - Tropsha, A. A1 - Cohen, Y. A1 - Lesczynski, J. A1 - Hendren, C. O. A1 - Wiesner, M. A1 - Winkler, D. A1 - Suzuki, N. A1 - Yoon, T. H. A1 - Choi, J.-S. A1 - Sanabria, N. A1 - Gulumian, M. A1 - Lynch, I. T1 - NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment N2 - Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational ‘safe-by-design’ approaches to facilitate NM commercialization. KW - Nanoinformatics KW - Hazard assessment KW - (Quantitative) Structure-Active Relationships KW - Safe-by-design KW - Predictive modelling PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-505952 DO - https://doi.org/10.1016/j.csbj.2020.02.023 VL - 18 SP - 583 EP - 602 PB - Elsevier B.V. AN - OPUS4-50595 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Peters, R. A1 - Elbers, I. A1 - Undas, A. A1 - Sijtsma, E. A1 - Briffa, S. A1 - Carnell-Morris, P. A1 - Siupa, A. A1 - Yoon, T.-H. A1 - Burr, L. A1 - Schmid, D. A1 - Tentschert, J. A1 - Hachenberger, Y. A1 - Jungnickel, H. A1 - Luch, A. A1 - Meier, F. A1 - Kocic, J. A1 - Kim, J. A1 - Park, B. C. A1 - Hardy, B. A1 - Johnston, C. A1 - Jurkschat, K. A1 - Radnik, Jörg A1 - Hodoroaba, Vasile-Dan A1 - Lynch, I. A1 - Valsami-Jones, E. T1 - Benchmarking the ACEnano toolbox for characterisation of nanoparticle size and concentration by interlaboratory comparisons N2 - ACEnano is an EU-funded project which aims at developing, optimising and validating methods for the detection and characterisation of nanomaterials (NMs) in increasingly complex matrices to improve confidence in the results and support their use in regulation. Within this project, several interlaboratory comparisons (ILCs) for the determination of particle size and concentration have been organised to benchmark existing analytical methods. In this paper the results of a number of these ILCs for the characterisation of NMs are presented and discussed. The results of the analyses of pristine well-defined particles such as 60 nm Au NMs in a simple aqueous suspension showed that laboratories are well capable of determining the sizes of these particles. The analysis of particles in complex matrices or formulations such as consumer products resulted in larger variations in particle sizes within technologies and clear differences in capability between techniques. Sunscreen lotion sample analysis by laboratories using spICP-MS and TEM/SEM identified and confirmed the TiO2 particles as being nanoscale and compliant with the EU definition of an NM for regulatory purposes. In a toothpaste sample orthogonal results by PTA, spICP-MS and TEM/SEM agreed and stated the TiO2 particles as not fitting the EU definition of an NM. In general, from the results of these ILCs we conclude that laboratories are well capable of determining particle sizes of NM, even in fairly complex formulations. KW - Nanomaterials KW - Benchmarking KW - Inter-laboratory comparison KW - ACEnano KW - Characterisation KW - Size KW - Concentration PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-531852 DO - https://doi.org/10.3390/molecules26175315 SN - 1420-3049 VL - 26 IS - 17 SP - 1 EP - 23 PB - MDPI CY - Basel AN - OPUS4-53185 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Peters, R. A1 - Elbers, I. A1 - Undas, A. A1 - Sijtsma, E. A1 - Briffa, S. A1 - Carnell-Morris, P. A1 - Siupa, A. A1 - Yoon, T.-H. A1 - Burr, L. A1 - Schmid, D. A1 - Tentschert, J. A1 - Hachenberger, Y. A1 - Jungnickel, H. A1 - Luch, A. A1 - Meier, F. A1 - Kocic, J. A1 - Kim, J. A1 - Park, B. C. A1 - Hardy, B. A1 - Johnston, C. A1 - Jurkschat, K. A1 - Radnik, Jörg A1 - Hodoroaba, Vasile-Dan A1 - Lynch, I. A1 - Valsami-Jones, E. T1 - Correction: Peters et al. Benchmarking the ACEnano Toolbox for Characterisation of Nanoparticle Size and Concentration by Interlaboratory Comparisons. Molecules 2021, 26, 5315 N2 - This is a corrigendum to the original article "Benchmarking the ACEnano toolbox for characterisation of nanoparticle size and concentration by interlaboratory comparisons" that was published in the journal "Molecules", vol. 26 (2021), no. 17, article 5315. PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-554381 DO - https://doi.org/10.3390/molecules27154849 VL - 27 IS - 4849 SP - 1 EP - 3 PB - MDPI CY - Basel AN - OPUS4-55438 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Radnik, Jörg A1 - Hodoroaba, Vasile-Dan A1 - Jungnickel, H. A1 - Tentschert, J. A1 - Luch, A. A1 - Sogne, V. A1 - Maier, F. A1 - Burr, L. A1 - Schmid, D. A1 - Yoon, T.-H. A1 - Petters, R. A1 - Briffa, S.M. A1 - Valsami-Jones, E. T1 - Automation and Standardization—A Coupled Approach Towards Reproducible Sample Preparation Protocols for Nanomaterial Analysis N2 - Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by au-tomation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been de-veloped, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air–liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data. KW - Sample preparation KW - Automation KW - Nanomaterial analysis KW - Standardization PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-543988 DO - https://doi.org/10.3390/molecules27030985 VL - 27 IS - 3 SP - 1 EP - 22 PB - MDPI AN - OPUS4-54398 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -