6 Materialchemie
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1-Propyl-3-methyl-imidazolium bis(trifluoromethylsulfonyl)imide (Solarpur®), an ionic liquid was purchased from Merck (formerly Sigma Aldrich), was analyzed using hard x-ray photoelectron spectroscopy (HAXPES). The liquid is UHV stable, vertically and laterally homogeneous, and is easy to clean; thus, it can be used as a reference material. The HAXPES spectra of Solarpur® obtained using monochromatic Cr Kα radiation at 5414.8 eV include a survey scan, high-resolution spectra of C 1s, O 1s, N 1s, S 2p, S 1s, S 2s, F 1s, and S KLL.
Both essential aspects of the surface of solid matter, its morphology and chemistry, are studied traditionally at BAM starting in the 60’s with different cyclical research focus areas, mostly related either to applicative research or method development. In the recent years, the focus has shifted almost exclusively to the nano-analytics of advanced materials such as complex nanoparticles, (ultra)thin films/coatings, nanocomposites, 2D materials, energy materials, etc. This is also the reason why BAM has established recently the new Competence Center nano@BAM (www.bam.de/Navigation/DE/Themen/Material/Nanotechnologie/sichere-nanomaterialien.html) with the five sub-fields nanoCharacterisation, nanoMaterial, nanoSafety, nanoData and nanoTechnology. The link to the BAM central guidelines to the safety in technology and chemistry is given by the development of reference products such as reference measurement procedures, reference (nano)materials, and newly reference data sets. Thus, an internationally well-networked group in surface analysis has been established @BAM, with regular contributions to integral analytical characterization with metrological and standardization background.
Examples of newly developed methodical approaches will be given with an emphasis on correlative nano-analysis of morphology and chemistry of nanomaterials. Correlative imaging by STEM-in-SEM with high-resolution SEM and EDX, and further with AFM or the new technique TKD (Transmission Kikuchi Diffraction) will be explained on various examples of nanostructures, both as starting materials and embedded/functionalized nanoparticles in products. The unique analytical benefits of the Auger electron probe as a veritable nano-tool for surface chemistry will be highlighted. The panoply of advanced surface characterization methods @BAM is completed by discussing examples of hybrid analysis of the bulk of nanomaterials by X-ray Spectroscopy with the highest surface-sensitive methods X-ray Photoelectron Spectroscopy (XPS) and Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS). Particularly for the analysis of the surface chemistry of nanostructures, such as the completeness of the shells of core-shell nanoparticles or in-depth and lateral gradients of chemistry within mesoporous thin layers, the latter methods are inherent.
Other special developments like approaches for the quantitative determination of the roughness of particle surface by electron microscopy or for the quantitative determination of the porosity of thin mesoporous layers by electron probe microanalysis (EPMA) with SEM will be presented in conjunction with the corresponding advanced materials studied.
Current research projects, promising ideas, including ongoing (pre-)standardization activities in the field of the challenging nano/surface analysis will be touched systematically, with the open goal of identifying future bilateral cooperation possibilities between EMPA and BAM.
A particle roughness analysis tool, based on electron microscopy (EM) images. The influence of various parameters on the calculated roughness was also investigated: the setting of the proper threshold, accelerating voltage, etc. The samples were gradually tilted to extend imaging information of more than only one projection. Furthermore, the measurement uncertainty of the profile roughness of particles associated to various orientations was estimated.
A particle roughness analysis tool, based on electron microscopy images (SEM and TEM). The influence of various parameters on the calculated roughness was also investigated: the setting of the proper threshold, accelerating voltage, etc. The samples were gradually tilted to extend imaging information of more than only one projection. Furthermore, the measurement uncertainty of the profile roughness of particles associated to various orientations was estimated.
Surface-supported metal-organic frameworks HKUST-1 (Hong Kong University of Science and Technology) were used as a model system for a development of a near ambient pressure (NAP) XPS based approach to investigate interaction with atmospheres of water, methanol or pyridine at pressures ranging from 1 to 4 mbar. The films were grown on a gold substrate functionalized with a COOH-terminated self-assembled monolayer using liquidphase epitaxy in a step-by-step fashion. Measurement protocols were developed and optimised for different gases in order to obtain spectra of similar quality in terms of signal intensity, noise and shape. Peak shapes were found to depend on the efficiency of charge compensation. Reference measurements in argon proved to be a useful strategy not only for the evaluation of the Cu(II)-fraction in pristine samples, but also to identify the contributions by the respective gas atmosphere to the C 1s and O 1s photoelectron spectra. Reduced copper was found during the exposition of HKUST-1 to water vapour and pyridine, but this effect was not observed in case of methanol. Additionally, it was established that there are no changes in relative Cu(II) percentage with increasing exposure time. This indicates that saturation was reached already at the lowest time of gas exposure. A detailed elucidation of the mechanism of Cu(II) reduction to Cu(I) in HKUST-1 mediated by water and pyridine is part of ongoing work and not in the scope of the present paper.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. 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. 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, and the segmented images can be used for automatically extracting and calculating various other particle size and shape descriptors.