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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.
This chapter first gives an introduction to the concepts of SSA and volume-specific surface area (VSSA) and an outline of the BET method. It continues with a discussion of the relationship between particle size, shape, and the VSSA, followed by an overview of instrumentation, experimental methods, and standards. Finally, sections on the use of the VSSA as a tool to identify nanomaterials and non-nanomaterials and its role in a regulatory context provide some insight on the importance of VSSA in the current Regulation of nanomaterials.
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.
The progress in activities on Microbeam Analysis under VAMAS/TWA 37 is reviewed. Particularly the liaison with the new projects within the ISO technical committee TC 202 is presented and discussed with respect to the possibility of identifying and launching related VAMAS projects. Need of more promotion for the engagement of more participants from industry and academia at national, European and international level is highlighted. Also, the competition with the other technical working areas (on 'nano' or materials-related) is critically discussed. Further, a short overview of the VAMAS areas of activities is given where Germany is involved.
An overview on the VAMAS technical working areas (TWA) and projects where Germany is active has been given. The role of VAMAS as an international pre-standardisation platform, including its intense collaboration with international bodies ISO, CEN, OECD and CCQM and national organizations like DIN has been highlighted.
Regional standardisation activities and how VAMAS can help in any way to promote activities are reported.
Activities related to organisational updates, government initiatives/priorities (especially related to Materials), details of any strategy documents publicly available, networks within Germany and how we engage are presented.
Following points are presented and discussed: i) nPSize as an EMPIR project on the nanoparticle size and shape distribution of (more) complex particles including sample preparation and machine learning approaches; ii) parallel development of ISO 21363 (PSSD by TEM), ISO 19749 (PSSD by SEM), ISO 22292 (3D TEM), ISO 52408 (NP Prep for AFM and EM), iii) VAMAS and pre-standardisation, TWA 34 „Nanoparticle Populations“ with two nPSize ILC projects P15 & P16 – results to be published and integrated in ISO 21363 (PSSD by TEM); iv) Remaining challenges: platelets, 2D materials (TiO2 and GR2M); v) Reference data sets as part of a complete Characterisation Workflow.