Self-assembled MgxZn1−xO quantum dots (0 ≤ x ≤ 1) on different substrates using spray pyrolysis methodology

By using the spray pyrolysis methodology in its classical configuration we have grown self-assembled MgxZn1−xO quantum dots (size [similar]4–6 nm) in the overall range of compositions 0 ≤ x ≤ 1 on c-sapphire, Si (100) and quartz substrates. Composition of the quantum dots was determined by means of transmission electron microscopy-energy dispersive X-ray analysis (TEM-EDAX) and X-ray photoelectron spectroscopy. Selected area electron diffraction reveals the growth of single phase hexagonal MgxZn1−xO quantum dots with composition 0 ≤ x ≤ 0.32 by using a nominal concentration of Mg in the range 0 to 45%. Onset of Mg concentration about 50% (nominal) forces the hexagonal lattice to undergo a phase transition from hexagonal to a cubic structure which resulted in the growth of hexagonal and cubic phases of MgxZn1−xO in the intermediate range of Mg concentrations 50 to 85% (0.39 ≤ x ≤ 0.77), whereas higher nominal concentration of Mg ≥ 90% (0.81 ≤ x ≤ 1) leads to the growth of single phase cubic MgxZn1−xO quantum dots. High resolution transmission electron microscopy and fast Fourier transform confirm the results and show clearly distinguishable hexagonal and cubic crystal structures of the respective quantum dots. A difference of 0.24 eV was detected between the core levels (Zn 2p and Mg 1s) measured in quantum dots with hexagonal and cubic structures by X-ray photoemission. The shift of these core levels can be explained in the frame of the different coordination of cations in the hexagonal and cubic configurations. Finally, the optical absorption measurements performed on single phase hexagonal MgxZn1−xO QDs exhibited a clear shift in optical energy gap on increasing the Mg concentration from 0 to 40%, which is explained as an effect of substitution of Zn2+ by Mg2+ in the ZnO lattice.

A sharing-based approach to supporting adaptation in service compositions

Data-related properties of the activities involved in a service composition can be used to facilitate several design-time and run-time adaptation tasks, such as service evolution, distributed enactment, and instance-level adaptation. A number of these properties can be expressed using a notion of sharing. We present an approach for automated inference of data properties based on sharing analysis, which is able to handle service compositions with complex control structures, involving loops and sub-workflows. The properties inferred can include data dependencies, information content, domain-defined attributes, privacy or confidentiality levels, among others. The analysis produces characterizations of the data and the activities in the composition in terms of minimal and maximal sharing, which can then be used to verify compliance of potential adaptation actions, or as supporting information in their generation. This sharing analysis approach can be used both at design time and at run time. In the latter case, the results of analysis can be refined using the composition traces (execution logs) at the point of execution, in order to support run-time adaptation.