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 - Kromer, C. A1 - Schwibbert, Karin A1 - Gadicherla, A. K. A1 - Thiele, Dorothea A1 - Nirmalananthan-Budau, Nithiya A1 - Laux, P. A1 - Resch-Genger, Ute A1 - Luch, A. A1 - Tschiche, H. R. T1 - Monitoring and imaging pH in biofilms utilizing a fluorescent polymeric nanosensor N2 - Biofilms are ubiquitous in nature and in the man-made environment. Given their harmful effects on human health, an in-depth understanding of biofilms and the monitoring of their formation and growth are important. Particularly relevant for many metabolic processes and survival strategies of biofilms is their extracellular pH. However, most conventional techniques are not suited for minimally invasive pH measurements of living biofilms. Here, a fluorescent nanosensor is presented for ratiometric measurements of pH in biofilms in the range of pH 4.5–9.5 using confocal laser scanning microscopy. The nanosensor consists of biocompatible polystyrene nanoparticles loaded with pH-inert dye Nile Red and is surface functionalized with a pH-responsive fluorescein dye. Its performance was validated by fluorometrically monitoring the time-dependent changes in pH in E. coli biofilms after glucose inoculation at 37 °C and 4 °C. This revealed a temperature-dependent decrease in pH over a 4-h period caused by the acidifying glucose metabolism of E. coli. These studies demonstrate the applicability of this nanosensor to characterize the chemical microenvironment in biofilms with fluorescence methods. KW - Dye KW - Fluorescence KW - Signal enhancement KW - Sensor KW - Quantum yield KW - Synthesis KW - Nanoparticle KW - Nano KW - Polymer KW - Ph KW - Biofilm KW - MIC KW - Corrosion KW - Microorganism KW - Bacteria PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-550751 DO - https://doi.org/10.1038/s41598-022-13518-1 SN - 2045-2322 VL - 12 IS - 1 SP - 1 EP - 10 PB - Nature Publishing Group CY - London AN - OPUS4-55075 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 -