The potential of electrospray deposition, for the controlled preparation of particles for imaging in electron microscopes, is evaluated on various materials: from mono-modal suspensions of spherical particles to multimodal suspensions and to real-world industrial materials. It is shown that agglomeration is reduced substantially on the sample carrier, compared with conventional sample preparation techniques. For the first time, it is possible to assess the number concentration of a tri-modal polystyrene suspension by electron microscopy, due to the high Deposition efficiency of the electrospray. We discovered that some suspension stabilizing surfactants form artifact particles during electrospraying. These can be avoided by optimizing the sprayed suspension.
The potential of electrospray deposition, for the controlled preparation of particles for imaging in electron microscopes, is evaluated on various materials: from mono-modal suspensions of spherical particles to multimodal suspensions and to real-world industrial materials. It is shown that agglomeration is reduced substantially on the sample carrier, compared with conventional sample preparation techniques. For the first time, it is possible to assess the number concentration of a tri-modal polystyrene suspension by electron microscopy, due to the high deposition efficiency of the electrospray. We discovered that some suspension stabilizing surfactants form artifact particles during electrospraying. These can be avoided by optimizing the sprayed suspension.
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.
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.