Analytische Chemie
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The mechanism of action of zirconium permanent modifiers on graphite surfaces was investigated in order to understand its influence on the analytical signal in atomic and molecular absorption spectrometry (AAS/MAS). For this, the molecule formation of CaF was studied, which is used for the indirect analytical determination of fluorine in high-resolution continuum source graphite furnace molecular absorption spectrometry (HR-CS-GFMAS). The kinetics of this reaction was established by monitoring its molecular spectrum at different atomisation temperatures. An Arrhenius plot showed a pseudo-first order reaction with respect to fluorine (n = 1). An intermediate state was isolated, and its structure was elucidated by spectroscopic methods: scanning electron microscopy with energy dispersive X-ray spectroscopy (SEMEDX), X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XANES and EXAFS), and Raman microspectroscopy. We propose here a mechanism, where ZrO2 acts as a heterogeneous catalyst: after a pyrolytic step, an intermediate state of ZrO(OCaF) is activated, and at higher temperatures, CaF(g) is released from the zirconium-coated graphite surface. No evidence of the formation of zirconium carbide was found. Consequently, as the CaF formation is catalysed by a heterogeneous catalyst, surface modifications with ZrO2 nanoparticles and ZrO xerogels were investigated in order to increase the surface area. Their influence was evaluated in the molecule formation of CaF, CaCl, CaBr, and CaI. Graphite furnace modification with zirconium oxide nanoparticles proves to be the best choice for fluorine analysis with a signal enhancement of more than eleven times with respect a non-coated graphite furnace. However, the influence of zirconium modifications in the analytical signals of Cl, and I is lower than the F signals or even negative in case of the Br. Understanding zirconium modifiers as heterogeneous catalysts offers a new perspective to AAS and MAS, and reveals the potential of surface analytical methods for development of improved permanent modifiers and graphite furnace coatings.
You Ask – ACEnano Replies
(2020)
The workshop starts with introductory information about the workshop and the H2020 project ACEnano, followed by two expert round tables, focussing on how the project could address regulator and industry needs, respectively. This is be followed by parallel sessions on tools (based on preferences expressed by those registered to attend, see “Questions”) and finally a question-and-answer session with the attendees.
The experts invited in Round Table 1 have been prepared to answer to questions related to obstacles and advantages for stakeholders such as SMEs to use the ACEnano approaches/tools. Standardisation needs are discussed.
The datasets from (Hard Energy) X-ray photoelectron spectroscopy, X-ray diffraction and Scanning Electron Microsopy are related to the publication
G. Chemello, X. Knigge, D. Ciornii, B.P. Reed, A.J. Pollard, C.A. Clifford, T. Howe, N. Vyas, V.-D. Hodoroaba, J. Radnik
"Influence of the morphology on the functionalization of graphene nanoplatelets analyzed by comparative photoelectron spectroscopy with soft and hard X-rays"
Advanced Materials Interfaces (2023), DOI: 10.1002/admi.202300116.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.
In this contribution, two large-area EDS detectors were tested according to the procedure proposed by Procop et al. (2015). In a first step, the optimal working distance (WD) in the two different SEM chambers was determined by moving the sample stage in the Z direction and monitoring the count rates at a magnification of 10,000 and a field of view of 25.6 µm. The WD at which the highest intensity was measured was selected as the optimal position, corresponding to the crossover between the EDS detector optical axis and electron beam optical axis. Next the Cu Kα peak was measured at different relative EDS positions while it was partially removed from the fully inserted position. The spectrum at each location was collected for 10 sec using the highest pulse rate and intermediate current to minimize pile up effects. The ‘inverse squared normalized intensities vs. relative EDS position’ used to extract the true detector – specimen distance shows a non-linear relationship even at the minimal relative positions, which indicates shadowing due to obstruction or use of an unsuitable and/or off-centered collimator. The normalized count rates measured as a function of the EDS distances, results in a too low GCE (too low true solid angles) for both tested detectors. The source of losses of signal was shadowing caused by collimators.
In this contribution, two large-area EDS detectors were tested according to the procedure proposed recently by Procop et al. In a first step, the optimal working distance (WD) in the two different SEM chambers was determined by moving the sample stage in the Z direction and monitoring the count rates from a field of view of 25.6 μm. The WD at which the highest intensity was measured was selected as the optimal position. Next the Cu Kα peak was measured at different relative EDS positions while it was partially removed from the fully inserted position. The spectrum at each location was collected for 10 sec using the highest pulse rate and intermediate current (2.3 nA) to minimize pile up effects (13% dead time). The ‘inverse squared normalized intensities vs. relative EDS position’ used to extract the true detector – specimen distance shows a non-linear relationship even at the minimal relative positions, which indicates shadowing due to obstruction or use of an unsuitable and/or off-centered collimator. The normalized count rates measured as a function of the EDS distances, results in a too low GCE (too low true solid angles) for both tested detectors. The search for sources of losses of signal due to possible shadowing effects is in progress.
Volume specific surface area (VSSA) by BET: concept and demonstration on industrial materials
(2017)
Volume specific surface area (VSSA) as measured by BET constituites a simple and reliable solution to (most) powders. Porous, coated, polydisperse/multimodal materials are to be treated with care, i.e. doubled by analysis with electron microscopy or more advanced BET analysis (e.g. t-plot)for each material in part.
We discuss the role of the volume specific surface area (VSSA) as determined from the specific surface area measured by the (extended) Brunauer-Emmett-Teller (BET) technique, in the identification process of powdered nano- and non-nanomaterials in line with the EU definition of nanomaterial. Results obtained in the NanoDefine project demonstrate that under appropriate conditions, VSSA can be used as proxy to the number-based particle size distribution. The extent of agreement between nano/non-nano classification by electron microscopy (EM) and classification by VSSA is investigated systematically on a large set of diverse particulate substances, representing most of the cases expected in regulatory practice. Thus, parameters such as particle shape, size polydispersity/multi-modality, and particle (inner or coating) porosity are evaluated. Based on these results, we derive a tiered screening strategy for powders, involving the use of VSSA for the purpose of implementing the definition of nanomaterial, and recommend it for inclusion in a technical guidance for the implementation of the definition.
Secondary fluorescence is an inevitable effect that has to be taken into account in any algorithm for quantitative electron probe microanalysis (EPMA) as an additional correction. Moreover, secondary fluorescence worsens spatial resolution of EPMA. Secondary fluorescence is excited both by characteristic radiation and by the X-ray continuum. In most cases the correction is small. There are, however, cases, e.g. the determination of low heavy metal concentration in a light matrix, where the contribution of secondary fluorescence exceeds 10% of the measured X-ray line intensity.
For secondary fluorescence correction the measured X-ray line intensity has to be divided by the correction factor (1+I_flchar/I_p +I_flcont/I_p )≈(1+I_flchar/I_p )(1+I_flcont/I_p ) in order to get those intensity I_p, which is excited only by the primary electrons. I_flchar and I_flcont mean the calculated characteristic and continuums fluorescence intensities. In order to get the intensity of fluorescence radiation, the absorption of the exciting radiation in the specimen, the photoionization probability and the self-absorption of the emitted line must be calculated. The critical quantity is the X-ray yield of the exciting atoms in case of fluorescence by characteristic radiation and the bremsstrahlung yield of the specimen in case of continuum fluorescence. In the former case it is reasonable to apply the same physical model to calculate I_flchar and I_p.
Secondary fluorescence is an inevitable effect that has to be taken into account in any algorithm for quantitative electron probe microanalysis (EPMA) as an additional correction. Moreover, secondary fluorescence worsens spatial resolution of EPMA, as discussed once more in two recent papers. Secondary fluorescence is excited both by characteristic radiation and by the X-ray continuum. In most cases the correction is small. There are, however, cases, e.g. the determination of low heavy metal concentration in a light matrix, where the contribution of secondary fluorescence exceeds 10% of the measured X-ray line intensity.
For secondary fluorescence correction the measured X-ray line intensity has to be divided by the correction factor (1+I_flchar/I_p +I_flcont/I_p )≈(1+I_flchar/I_p )(1+I_flcont/I_p ) in order to get those intensity I_p, which is excited only by the primary electrons and enables the determination of specimen composition. I_flchar and I_flcont mean the calculated characteristic and continuums fluorescence intensities. In order to get the intensity of fluorescence radiation, the absorption of the exciting radiation in the specimen, the photoionization probability and the self-absorption of the emitted line must be calculated. This can be performed in a straightforward way. The critical quantity is the X-ray yield of the exciting atoms in case of fluorescence by characteristic radiation and the bremsstrahlung yield of the specimen in case of continuum fluorescence. In the former case it is reasonable to apply the same physical model to calculate I_flchar and I_p.