An Improved Metallicity Calibration with UBV Photometry

We used the data of 701 stars covering the colour index interval 0.32<B-V<=1.16, with metallicities -1.76<=[Fe/H]<=+0.40 dex, which were taken from PASTEL catalogue and estimated metallicity dependent guillotine factors which provide a more accurate metallicity calibration. We reduced the metallicities of 11 authors to the metallicities of Valenti & Fischer (2005), thus obtained a homogeneous set of data which increased the accuracy of the calibration, i.e. [Fe/H]=-14.316*delta^{2}_{0.6}-3.557*delta_{0.6}+0.105. Comparison of the metallicity residuals, for two sets of data, based on the metallicity dependent guillotine factors with the ones obtained via metal free guillotine factors, shows that metallicities estimated by means of new guillotine factors are more accurate than the other ones. This advantage can be used in the metallicity gradient investigation of the Galactic components, i.e. thin disc, thick disc and halo.Comment: 12 pages, including 10 figures and 6 tables, accepted for publication in PAS

Electronic depth profiles with atomic layer resolution from resonant soft x-ray reflectivity

The analysis of x-ray reflectivity data from artificial heterostructures usually relies on the homogeneity of optical properties of the constituent materials. However, when the x-ray energy is tuned to an absorption edge, this homogeneity no longer exists. Within the same material, spatial regions containing elements at resonance will have optical properties very different from regions without resonating sites. In this situation, models assuming homogeneous optical properties throughout the material can fail to describe the reflectivity adequately. As we show here, resonant soft x-ray reflectivity is sensitive to these variations, even though the wavelength is typically large as compared to the atomic distances over which the optical properties vary. We have therefore developed a scheme for analyzing resonant soft x-ray reflectivity data, which takes the atomic structure of a material into account by "slicing" it into atomic planes with characteristic optical properties. Using LaSrMnO4 as an example, we discuss both the theoretical and experimental implications of this approach. Our analysis not only allows to determine important structural information such as interface terminations and stacking of atomic layers, but also enables to extract depth-resolved spectroscopic information with atomic resolution, thus enhancing the capability of the technique to study emergent phenomena at surfaces and interfaces.Comment: Completely overhauled with respect to the previous version due to peer revie

The Synchrotron Spectrum of Fast Cooling Electrons Revisited

We discuss the spectrum arising from synchrotron emission by fast cooling (FC) electrons, when fresh electrons are continually accelerated by a strong blast wave, into a power law distribution of energies. The FC spectrum was so far described by four power law segments divided by three break frequencies $\nu_{sa} < \nu_{c} < \nu_{m}$. This is valid for a homogeneous electron distribution. However, hot electrons are located right after the shock, while most electrons are farther down stream and have cooled. This spatial distribution changes the optically thick part of the spectrum, introducing a new break frequency, $\nu_{ac} < \nu_{sa}$, and a new spectral slope, $F_{\nu }\propto \nu^{11/8}$ for $\nu_{ac} < \nu < \nu_{sa}$. The familiar $F_{\nu }\propto \nu ^{2}$ holds only for $\nu < \nu_{ac}$. This ordering of the break frequencies is relevant for typical gamma-ray burst (GRB) afterglows in an ISM environment. Other possibilities arise for internal shocks or afterglows in dense circumstellar winds. We discuss possible implications of this spectrum for GRBs and their afterglows, in the context of the internal-external shock model. Observations of $F_{\nu} \propto \nu^{11/8}$ would enable us to probe scales much smaller than the typical size of the system, and constrain the amount of turbulent mixing behind the shock.Comment: 9 pages, 1 figure. Revised version, Accepted for Publication in the Astrophysical Journal Letter

Perovskite Solar Cells: Developing a simple, fast and low-cost Fabrication Technology

Solar energy is the most abundant renewable resource and is regarded as the most promising for the sustainability of our society. Perovskites are a class of semiconductor materials with unique properties since they allow films fabrication with high electronic quality using non-vacuum solution techniques. Therefore, such materials are interesting for a wide range of opto-electronic applications. Perovskites allow rapid, simple and low-cost solar cell manufacturing, being nowadays considered the most promising material to compete with silicon in photovoltaics technology. However, the production of homogeneous MAPbI3 films by Spin Coating is challenging, as it requires precise control of several factors that influence the films’ properties. In this work, the influence of the main deposition parameters on the MAPbI3 thin films manufacture was studied to find the best processing conditions that enable obtaining films as homogeneous and uniform as possible. This allowed attaining MAPbI3 polycrystalline films with state-of-art quality, having grain sizes between 3 and 13 μm and UV-Visible absorption of 85-90 %. The remaining layers (i.e. selective contacts) of the Perovskite cell structure were investigated as well, allowing the fabrication of sets of full solar cells with a maximum VOC of 0.77 V and JSC of 7.65 mA.cm-2

Machine Learning Optimization of p-Type Transparent Conducting Films

p-Type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu-Zn-S thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (&gt;80%) as well as excellent hole conductivity (&gt;1000 S cm-1). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multidimensional parameter space such that optimization for a specific application is challenging and time-consuming. Here we show that strategic design of experiment combined with machine learning (ML) allows for the efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of four experimental CBD factors. The ML model is based on support vector regression employing a radial basis function as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. The optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters