Search

Abstract Formation of hetero‐contacts between particles of different materials in nanoparticle hetero‐aggregates can lead to new functional properties. Improvement of the functional behavior requires a detailed characterization of mixing between the two types of particles, in order to correlate different mixing with the performance of the material. Scanning transmission electron microscopy (STEM) is an option for this task. To obtain statistically relevant results, STEM‐images of many hetero‐aggregates have to be acquired and evaluated. This can be time‐consuming if it is done manually. In the present work, the applicability of convolutional neural networks for the automated analysis of STEM‐images acquired from TiO2–WO3 nanoparticle hetero‐aggregates is investigated. Hetero‐aggregates are obtained in a double flame spray pyrolysis (DFSP) setup, in which a variation of setup parameters is expected to affect the mixing of TiO2 and WO3. Mixing is investigated by a measurement of cluster sizes (the number of connected particles of the same material within an aggregate) and coordination numbers (the number of particle contacts with particles of the same or the different material). Results show that the distribution of measured values is wide for both quantities, rendering it challenging to correlate mixing with parameters varied in the DFSP setup.

Abstract Porous networks of Pt nanoparticles interlinked by bifunctional organic ligands have shown high potential as catalysts in micro‐machined hydrogen gas sensors. By varying the ligand among p‐phenylenediamine, benzidine, 4,4‘‘‐diamino‐p‐terphenyl, 1,5‐diaminonaphthalene, and trans‐1,4‐diaminocyclohexane, new variants of such networks were synthesized. Inter‐particle distances within the networks, determined via transmission electron microscopy tomography, varied from 0.8 to 1.4 nm in accordance with the nominal length of the respective ligand. While stable structures with intact and coordinatively bonded diamines were formed with all ligands, aromatic diamines showed superior thermal stability. The networks exhibited mesoporous structures depending on ligand and synthesis strategy and performed well as catalysts in hydrogen gas microsensors. They demonstrate the possibility of deliberately tuning micro‐ and mesoporosity and thereby transport properties and steric demands by choice of the right ligand also for other applications in heterogeneous catalysis.