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We present how mesoporosity can be engineered in transition metal phosphate (TMPs) materials in a template-free manner. The method involves a transformation of a precursor metal phosphate phase, called M-struvite (NH4MPO4·6H2O, M = Mg2+, Ni2+, Co2+, NixCo1-x2+). It relies on the thermal decomposition of crystalline M-struvite precursors to an amorphous and simultaneously mesoporous phase, which forms while degassing of NH3 and H2O. The temporal evolution of mesoporous frameworks and the response of the metal coordination environment were followed with in-situ and ex-situ scattering and diffraction, as well as X -ray spectroscopy. Despite sharing the same precursor struvite structure, different amorphous and mesoporous structures were obtained depending on the involved transition metal. We highlight the systematic differences in absolute surface area, pore shape, pore size, and phase transitions depending on a metal cation present in the analogous M-struvites. The amorphous structures of thermally decomposed Mg-, Ni- and NixCo1-x-struvites exhibit high surface areas and pore volumes (240 m²g-1 and 0.32 cm-3 g-1 for Mg and 90 m²g-1 and 0.13 cm-3 g-1 for Ni). We propose that the low-cost, environmentally friendly M-struvites could be obtained as recycling products from industrial and agricultural wastewaters. These waste products could be then upcycled into mesoporous TMPs through a simple thermal treatment for further applications, for instance, in (electro)catalysis.
Since its isolation, graphene has received growing attention from academia and industry due to its unique properties. However, the “what is my material” barrier hinders further commercialization. X-ray photoelectron spectroscopy (XPS) is considered as a method of choice for the determination of the elemental and chemical composition. In this work the influence of the morphology of graphene particles on the XPS results is studied and investigated as a function of X-ray energy, using conventional XPS with Al K𝜶 radiation and hard X-ray photoemission spectroscopy (HAXPES) using Cr K𝜶 radiation. Thereby, the information depth is varied between 10 and 30 nm. For this purpose, two commercial powders containing graphene nanoplatelets with lateral dimensions of either ≈100 nm or in the micrometer range are compared. These larger ones exist as stack of graphene layers which is inspected with scanning electron microscopy. Both kinds of particles are then functionalized with either oxygen or fluorine. The size of the graphene particles is found to influence the degree of functionalization. Only the combination of XPS and HAXPES allows to detect the functionalization at the outermost surface of the particles or even of the stacks and to provide new insights into the functionalization process.
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.
Efficient water electrolysis requires highly active electrodes. The activity of corresponding catalytic coatings strongly depends on material properties such as film thickness, crystallinity, electrical conductivity, and chemical surface speciation. Measuring these properties with high accuracy in vacuum-free and nondestructive methods facilitates the elucidation of structure−activity relationships in realistic environments. Here, we report a novel approach to analyze the optical and electrical properties of highly active oxygen evolution reaction (OER) catalysts via spectroscopic ellipsometry (SE). Using a series of differently calcined, mesoporous, templated iridium oxide films as an example, we assess the film thickness, porosity, electrical resistivity, electron concentration, electron mobility, and interband and intraband transition energies by modeling of the optical spectra. Independently performed analyses using scanning electron microscopy, energy-dispersive X-ray spectroscopy, ellipsometric porosimetry, X-ray reflectometry, and absorption spectroscopy indicate a high accuracy of the deduced material properties. A comparison of the derived analytical data from SE, resonant photoemission spectroscopy, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy with activity measurements of the OER suggests that the intrinsic activity of iridium oxides scales with a shift of the Ir 5d t2g sub-level and an increase of p−d interband transition energies caused by a transition of μ1-OH to μ3-O species.