Palladium nanoparticles were synthesized by thermal decomposition of palladium(II) hexafluoroacetylacetonate (Pd(hfac)2), an atomic layer deposition (ALD) precursor, on a TiO2(110) surface. According to X-ray photoelectron spectroscopy (XPS), Pd(hfac)2 adsorbs on TiO2(110) dissociatively yielding Pd(hfac)(ads), hfac(ads), and adsorbed fragments of the hfac ligand at 300 K. A (2 × 1) surface overlayer was observed by scanning tunneling microscopy (STM), indicating that hfac adsorbs in a bidentate bridging fashion across two Ti 5-fold atoms and Pd(hfac) adsorbs between two bridging oxygen atoms on the surface. Annealing of the Pd(hfac)(ads) and hfac(ads) species at 525 K decomposed the adsorbed hfac ligands, leaving PdO-like species and/or Pd atoms or clusters. Above 575 K, the XPS Pd 3d peaks shift toward lower binding energies and Pd nanoparticles are observed by STM. These observations point to the sintering of Pd atoms and clusters to Pd nanoparticles. The average height of the Pd nanoparticles was 1.2 ± 0.6 nm at 575 K and increased to 1.7 ± 0.5 nm following annealing at 875 K. The Pd coverage was estimated from XPS and STM data to be 0.05 and 0.03 monolayers (ML), respectively, after the first adsorption/decomposition cycle. The amount of palladium deposited on the TiO2(110) surface increased linearly with the number of adsorption/decomposition cycles with a growth rate of 0.05 ML or 0.6 Å per cycle. We suggest that the removal of the hfac ligand and fragments eliminates the nucleation inhibition of Pd nanoparticles previously observed for the Pd(hfac)2 precursor on TiO2.
Identifying, counting and measuring particles is an important component of many research studies. Images with particles are usually processed by hand using a software ruler. Automated processing, based on conventional image processing methods (edge detection, segmentation, etc.) are not universal, can only be used on good-quality images and need to set a number of parameters empirically. In this paper, we present results from the application of deep learning to automated recognition of metal nanoparticles deposited on highly oriented pyrolytic graphite on images obtained by scanning tunneling microscopy (STM). We used the Cascade Mask-RCNN neural network. Training was performed on a dataset containing 23 STM images with 5157 nanoparticles. Three images containing 695 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in the verification set with 0.93 precision and 0.78 recall. Predicted contour refining with 2D Gaussian function was a proposed option. The accuracies for mean particle size calculated from predicted contours compared with ground truth were in the range of 0.87–0.99. The results were compared with outcomes from other generally available software, based on conventional image processing methods. The advantages of deep learning methods for automatic particle recognition were clearly demonstrated. We developed a free open-access web service “ParticlesNN” based on the trained neural network, which can be used by any researcher in the world.
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