Self-limited oxide formation in Ni(111) oxidation

The oxidation of the Ni(111) surface is studied experimentally with low energy electron microscopy and theoretically by calculating the electron reflectivity for realistic models of the NiO/Ni(111) surface with an ab-initio scattering theory. Oxygen exposure at 300 K under ultrahigh-vacuum conditions leads to the formation of a continuous NiO(111)-like film consisting of nanosized domains. At 750 K, we observe the formation of a nano-heterogeneous film composed primarily of NiO(111)-like surface oxide nuclei, which exhibit virtually the same energy-dependent reflectivity as in the case of 300 K and which are separated by oxygen-free Ni(111) terraces. The scattering theory explains the observed normal incidence reflectivity R(E) of both the clean and the oxidized Ni(111) surface. At low energies R(E) of the oxidized surface is determined by a forbidden gap in the k_parallel=0 projected energy spectrum of the bulk NiO crystal. However, for both low and high temperature oxidation a rapid decrease of the reflectivity in approaching zero kinetic energy is experimentally observed. This feature is shown to characterize the thickness of the oxide layer, suggesting an average oxide thickness of two NiO layers.Comment: 10 pages (in journal format), 9 figure

Dynamics of the Interaction Between Ceria and Platinum During Redox Processes

The work is focused on understanding the dynamics of the processes which occur at the interface between ceria and platinum during redox processes, by investigating an inverse catalytic model system made of ceria epitaxial islands and ultrathin films supported on Pt(111). The evolution of the morphology, structure and electronic properties is analyzed in real-time during reduction and oxidation, using low-energy electron microscopy and spatially resolved low-energy electron diffraction. The reduction is induced using different methods, namely thermal treatments in ultra-high vacuum and in H2 as well as deposition of Ce on the oxide surface, while re-oxidation is obtained by exposure to oxygen at elevated temperature. The use of two different epitaxial systems, continuous films and nanostructures, allows determining the influence of platinum proximity on the stabilization of the specific phases observed. The factors that limit the reversibility of the observed modifications with the different oxidation treatments are also discussed. The obtained results highlight important aspects of the cerium oxide/Pt interaction that are relevant for a complete understanding of the behavior of Pt/CeO2 catalysts

Self-organized 2D nanopatterns after low-coverage Ga adsorption on Si (1 1 1)

The evolution of the Si(1 1 1) surface after submonolayer deposition of Ga has been observedin situby low-energy electron microscopy and scanning tunnelling microscopy. A phase separation of Ga-terminated-R 30° reconstructed areas and bare Si(1 1 1)-7 × 7 regions leads to the formation of a two-dimensional nanopattern. The shape of this pattern can be controlled by the choice of the surface miscut direction, which is explained in terms of the anisotropy of the domain boundary line energy and a high kink-formation energy. A general scheme for the nanopattern formation, based on intrinsic properties of the Si(1 1 1) surface, is presented. Experiments performed with In instead of Ga support this scheme

adsorbate induced self ordering of germanium nanoislands on si 113

The impact of Ga preadsorption on the spatial correlation of nanoscale three-dimensional (3D) Ge-islands has been investigated by low-energy electron microscopy and low-energy electron diffraction. Submonolayer Ga adsorption leads to the formation of a 2D chemical nanopattern, since the Ga-terminated (2×2) domains exclusively decorate the step edges of the Si(113) substrate. Subsequent Ge growth on such a partially Ga-covered surface results in Ge 3D islands with an increased density as compared to Ge growth on clean Si(113). However, no pronounced alignment of the Ge islands is observed. Completely different results are obtained for Ga saturation coverage, which results in the formation of (112) and (115) facets regularly arranged with a periodicity of about 40 nm. Upon Ge deposition, Ge islands are formed at a high density of about 1.3×1010 cm−2. These islands are well ordered as they align at the substrate facets. Moreover, the facet array induces a reversal of the Ge islands' shape anisotropy as compared to growth on planar Si(113) substrates

Strain as driving force for interface roughening of δ\delta-doping layers

The growth of smooth and abrupt heteroepitaxial semiconductor interfaces is limited by strain originating from the lattice mismatch of the different materials. Employing measurements of crystal truncation rods, we have performed an extensive study of the impact of intra-layer strain on structural properties of ultra-thin (δ) doping layers. The use of molecular beam epitaxy and related methods, i.e., the use of surfactants and low growth temperatures with subsequent annealing (solid phase epitaxy) allows the preparation of Bi, Sb, Ge and Si1−xCx δ layers with doping profiles of monolayer width and high peak layer concentration (>10%). We find a linear dependence of the interface roughness on the intra-layer strain

Epitaxial, well-ordered ceria/lanthana high-k gate dielectrics on silicon

It is shown that the growth of epitaxial lanthana films on silicon may be achieved by substrate prepassivation using an atomic layer of chlorine, which prevents silicon oxide and silicate formation at the oxide–silicon interface. Postdeposition of two layers of cerium oxide facilitates the healing of structural defects within the La2O3 film, strongly increasing its crystallinity at the expense of a slightly more oxidized interfacial layer below. Together, the approach of combining Cl prepassivation and the ceria overgrowth results in an epitaxial, high-quality ceria/lanthana gate stack suitable for high-k integration in a gate-last process

Low-energy electron microscopy intensity-voltage data -- factorization, sparse sampling, and classification

Low-energy electron microscopy (LEEM) taken as intensity-voltage (I-V) curves provides hyperspectral images of surfaces, which can be used to identify the surface type, but are difficult to analyze. Here, we demonstrate the use of an algorithm for factorizing the data into spectra and concentrations of characteristic components (FSC3) for identifying distinct physical surface phases. Importantly, FSC3 is an unsupervised and fast algorithm. As example data we use experiments on the growth of praseodymium oxide or ruthenium oxide on ruthenium single crystal substrates, both featuring a complex distribution of coexisting surface components, varying in both chemical composition and crystallographic structure. With the factorization result a sparse sampling method is demonstrated, reducing the measurement time by 1-2 orders of magnitude, relevant for dynamic surface studies. The FSC3 concentrations are providing the features for a support vector machine (SVM) based supervised classification of the types. Here, specific surface regions which have been identified structurally, via their diffraction pattern, as well as chemically by complementary spectro-microscopic techniques, are used as training sets. A reliable classification is demonstrated on both exemplary LEEM I-V datasets.Comment: 13 pages, 7 figure

Low-energy electron microscopy intensity-voltage data – factorization, sparse sampling, and classification

Low-energy electron microscopy (LEEM) taken as intensity-voltage (I-V) curves provides hyperspectral images of surfaces, which can be used to identify the surface type, but are difficult to analyze. Here, we demonstrate the use of an algorithm for factorizing the data into spectra and concentrations of characteristic components () for identifying distinct physical surface phases. Importantly, is an unsupervised and fast algorithm. As example data we use experiments on the growth of praseodymium oxide or ruthenium oxide on ruthenium single crystal substrates, both featuring a complex distribution of coexisting surface components, varying in both chemical composition and crystallographic structure. With the factorization result a sparse sampling method is demonstrated, reducing the measurement time by 1-2 orders of magnitude, relevant for dynamic surface studies. The concentrations are providing the features for a support vector machine (SVM) based supervised classification of the surface types. Here, specific surface regions which have been identified structurally, via their diffraction pattern, as well as chemically by complementary spectro-microscopic techniques, are used as training sets. A reliable classification is demonstrated on both exemplary LEEM I-V datasets