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Paper des Monats
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In this study, a new reliable, economic, and environmentally-friendly one-step synthesis is established to obtain carbon nanodots (CNDs) with well-defined and reproducible photoluminescence (PL) properties via the microwave-assisted hydrothermal treatment of starch and Tris-acetate-EDTA (TAE) buffer as carbon sources. Three kinds of CNDs are prepared using different sets of above mentioned starting materials. The as-synthesized CNDs: C-CND (starch only), N-CND 1 (starch in TAE) and N-CND 2 (TAE only) exhibit highly homogenous PL and are ready to use without need for further purification.
The CNDs are stable over a long period of time (>1 year) either in solution or as freeze-dried powder. Depending on starting material, CNDs with PL quantum yield (PLQY) ranging from less than 1% up to 28% are obtained. The influence of the precursor concentration, reaction time and type of additives on the optical properties (UV-Vis absorption, PL emission spectrum and PLQY) is carefully investigated, providing insight into the chemical processes that occur during CND formation. Remarkably, upon freeze-drying the initially brown CND-solution turns into a non-fluorescent white/slightly Brown powder which recovers PL in aqueous solution and can potentially be applied as fluorescent marker in bio-imaging, as a reduction agent or as a photocatalyst.
Advances in scanning electron microscopy (SEM) enable the high-resolution imaging of single nanoparticles (NPs) with sizes well below 10 nm. The SEM analysis in transmission mode (T-SEM) of NPs on thin film supports has many benefits when compared to the analysis of NPs on bulk substrates. The enhanced material (mass – thickness) contrast of the T-SEM imaging mode is well suited for in-depth and, particularly valuable, to very accurate, traceable, lateral dimensional measurements of NPs. Compared to samples prepared on bulk substrates, T-SEM with energy dispersive X-ray spectroscopy (EDS) achieves a drastically improved spatial resolution of the emitted X-rays. The poor signal-to-noise ratio of the X-ray spectra emitted by a single nanoparticle (NP) can be improved by the use of high-sensitivity (high collection solid angle) silicon drift (SDD), energy-dispersive X-ray spectrometers (EDS). The EDS spectral imaging of a single NP with a spatial resolution below 10 nm has become possible. This is demonstrated by means of various examples of nanostructures. Advanced data processing of T-SEM/EDS results sets the stage for the automated classification of NPs by feature analysis. This method combines the detection of morphological structures of interest by image processing of T-SEM micrographs with the chemical classification by EDS.
SASfit: a tool for small-angle scattering data analysis using a library of analytical expressions
(2015)
SASfit is one of the mature programs for small-angle scattering data analysis and has been available for many years. This article describes the basic data processing and analysis workflow along with recent developments in the SASfit program package (version 0.94.6). They include (i) advanced algorithms for reduction of oversampled data sets, (ii) improved confidence assessment in the optimized model parameters and (iii) a flexible plug-in system for custom user-provided models. A scattering function of a mass fractal model of branched polymers in solution is provided as an example for implementing a plug-in. The new SASfit release is available for major platforms such as Windows, Linux and MacOS. To facilitate usage, it includes comprehensive indexed documentation as well as a web-based wiki for peer collaboration and online videos demonstrating basic usage. The use of SASfit is illustrated by interpretation of the small-angle X-ray scattering curves of monomodal gold nanoparticles (NIST reference material 8011) and bimodal silica nanoparticles (EU reference material ERM-FD-102).
In this work, sample losses of silver nanoparticles (Ag NPs) in asymmetrical flow field-flow fractionation (AF4) have been systematically investigated with the main focus on instrumental conditions like focusing and cross-flow parameters as well as sample concentration and buffer composition. Special attention was drawn to the AF4 membrane. For monitoring possible silver depositions on the membrane, imaging laser ablation coupled to inductively coupled plasma mass spectrometry (LA-ICP-MS) was used. Our results show that the sample residue on the membrane was below 0.6% of the total injected amount and therefore could be almost completely avoided at low sample concentrations and optimized conditions. By investigation of the AF4 flows using inductively coupled plasma mass spectrometry (ICP-MS), we found the recovery rate in the detector flow under optimized conditions to be nearly 90%, while the cross-flow, slot-outlet flow and purge flow showed negligible amounts of under 0.5%. The analysis of an aqueous ionic Ag standard solution resulted in recovery rates of over 6% and the ionic Ag content in the sample was found to be nearly 8%. Therefore, we were able to indicate the ionic Ag content as the most important source of sample loss in this study.
A user-friendly open-source Monte Carlo regression package (McSAS) is presented, which structures the analysis of small-angle scattering (SAS) using uncorrelated shape-similar particles (or scattering contributions). The underdetermined problem is solvable, provided that sufficient external information is available. Based on this, the user picks a scatterer contribution model (or 'shape') from a comprehensive library and defines variation intervals of its model parameters. A multitude of scattering contribution models are included, including prolate and oblate nanoparticles, core-shell objects, several polymer models, and a model for densely packed spheres. Most importantly, the form-free Monte Carlo nature of McSAS means it is not necessary to provide further restrictions on the mathematical form of the parameter distribution; without prior knowledge, McSAS is able to extract complex multimodal or odd-shaped parameter distributions from SAS data. When provided with data on an absolute scale with reasonable uncertainty estimates, the software outputs model parameter distributions in absolute volume fraction, and provides the modes of the distribution (e.g. mean, variance etc.). In addition to facilitating the evaluation of (series of) SAS curves, McSAS also helps in assessing the significance of the results through the addition of uncertainty estimates to the result. The McSAS software can be integrated as part of an automated reduction and analysis procedure in laboratory instruments or at synchrotron beamlines.