TY - JOUR A1 - Rühle, Bastian A1 - Hodoroaba, Vasile-Dan T1 - Towards Automated Electron Microscopy Image Segmentation for Nanoparticles of Complex Shape by Convolutional Neural Networks N2 - In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary. KW - Nanoparticles KW - Convolutional neural networks KW - Image segmentation KW - Electron microscopy KW - Automatisation PY - 2020 DO - https://doi.org/10.1017/S1431927620017262 VL - 26 IS - S2 SP - 1188 EP - 1189 PB - Cambridge University Press CY - Cambridge, UK AN - OPUS4-51773 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Modena, Mario M. A1 - Rühle, Bastian A1 - Burg, Thomas P. A1 - Wuttke, Steffan T1 - Nanoparticle characterization: What to measure? N2 - What to measure? is a key question in nanoscience, and it is not straightforward to address as different physicochemical properties define a nanoparticle sample. Most prominent among these properties are size, shape, surface charge, and porosity. Today researchers have an unprecedented variety of measurement techniques at their disposal to assign precise numerical values to those parameters. However, methods based on different physical principles probe different aspects, not only of the particles themselves, but also of their preparation history and their environment at the time of measurement. Understanding these connections can be of great value for interpreting characterization results and ultimately controlling the nanoparticle structure–function relationship. Here, the current techniques that enable the precise measurement of these fundamental nanoparticle properties are presented and their practical advantages and disadvantages are discussed. Some recommendations of how the physicochemical parameters of nanoparticles should be investigated and how to fully characterize these properties in different environments according to the intended nanoparticle use are proposed. The intention is to improve comparability of nanoparticle properties and performance to ensure the successful transfer of scientific knowledge to industrial real‐world applications. KW - Nanoparticle characterization KW - Nanoparticles KW - Porosity KW - Shape KW - Size PY - 2019 DO - https://doi.org/10.1002/adma.201901556 SN - 0935-9648 SN - 1521-4095 VL - 31 IS - 32 SP - 1901556, 1 EP - 26 PB - Wiley AN - OPUS4-49129 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Rühle, Bastian T1 - Surface Functional Group Quantification on Micro- and Nanoparticles N2 - Organic and inorganic micro- and nanoparticles are increasingly used as drug carriers, fluorescent sensors, and multimodal labels in the life and material sciences. Typically, these applications require further functionalization of the particles with, e.g., antifouling ligands, targeting bioligands, stimuli-responjsive caps, or sensor molecules. Besides serving as an anchor point for subsequent functionalization, the surface chemistry of these particles also fundamentally influences their interaction with the surrounding medium and can have a significant effect on colloidal stability, particle uptake, biodistribution, and particle toxicity in biological systems. Moreover, functional groups enable size control and tuning of the surface during the synthesis of particle systems. For these reasons, a precise knowledge of the chemical nature, the total number of surface groups, and the number of groups on the particle surface that are accessible for further functionalization is highly important. In this contribution, we will will discuss the advantages and limitiations of different approaches to quantify the amount of commonly used surface functional groups such as amino,[1,2] carboxy,[1,2] and aldehyde groups.[3] Preferably, the quantification is carried out using sensitive and fast photometric or fluorometric assays, which can be read out with simple, inexpensive instrumentation and can be validated by complimentary analytic techniques such as ICP-OES and quantitative NMR. T2 - NANAX Hamburg CY - Hamburg, Germany DA - 16.09.2019 KW - Microparticles KW - Nanoparticles KW - Quantitative Analysis KW - Surface KW - Funtional Groups PY - 2019 AN - OPUS4-49616 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mrkwitschka, Paul A1 - Abram, Sarah-Luise A1 - Rühle, Bastian A1 - Hodoroaba, Vasile-Dan T1 - Understanding Correlative Electron Microscopy Imaging with SEM, STEM-in-SEM and TEM for the Accurate Characterization of Size and Shape of Iron Oxide Nanoparticles N2 - The recently certified reference material (CRM) BAM-N012 as cubical iron oxide FeOx nanoparticles (NPs) of 8 nm area equivalent square edge length (ESL) and the RM candidate BAM-N013 as nearly spherical NPs of 22 nm size were analyzed in detail by electron microscopy (EM). For the metrological characterization with SEM, STEM in SEM and TEM, the understanding of the imaging contrasts and sensitivities, and the correct interpretation of the (art)effects which are inherent to each detection mode is necessary. The same sample areas with NPs deposited on a TEM grid were analyzed by two SEM acquisition modes, i. e. SE InLens and STEM in SEM using a dedicated transmission sample holder, and further, correlatively, analyzed with TEM. With increasing kV, SE InLens shows increasing particle size (unless overcharging at the particle boundaries is filtered), as a known effect. For STEM-in-SEM the particle size decreases significantly and individual particles are identified easier (at 2 kV only a few single particles can be detected automatically). ❑ Documentation of the sample preparation and measurement conditions (including optimization process) is important for reproducibility. ❑ Plasma cleaning, analysis in the transmission mode at SEM is recommended for FeOx NPs. ❑ Selection of the threshold algorithm can significantly alter the reported ECD. KW - Nanoparticles KW - Iron oxide KW - Electron microscopy KW - Reference materials KW - Particle size distribution KW - Imaging PY - 2024 DO - https://doi.org/10.1093/mam/ozae044.339 VL - 30 IS - Supplement_1 SP - 710 EP - 711 PB - Oxford University Press (OUP) AN - OPUS4-61007 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -