Synthesis and Magnetic Characterization of Metal-filled Double-sided Porous Silicon Samples

A magnetic semiconductor/metal nanocomposite with a nanostructured silicon wafer as base material and incorporated metallic nanostructures (Ni, Co, NiCo) is fabricated in two electrochemical steps. First, the silicon template is anodized in an HF-electrolyte to obtain a porous structure with oriented pores grown perpendicular to the surface. This etching procedure is carried out either in forming a sample with a single porous layer on one side or in producing a double-sided specimen with a porous layer on each side. Second, this matrix is used for deposition of transition metals as Ni, Co or an alloy of these. The achieved hybrid material with incorporated Ni- and Co-nanostructures within one sample is investigated magnetically. The obtained results are compared with the ones gained from samples containing a single metal

Investigation of a Mesoporous Silicon Based Ferromagnetic Nanocomposite

A semiconductor/metal nanocomposite is composed of a porosified silicon wafer and embedded ferromagnetic nanostructures. The obtained hybrid system possesses the electronic properties of silicon together with the magnetic properties of the incorporated ferromagnetic metal. On the one hand, a transition metal is electrochemically deposited from a metal salt solution into the nanostructured silicon skeleton, on the other hand magnetic particles of a few nanometres in size, fabricated in solution, are incorporated by immersion. The electrochemically deposited nanostructures can be tuned in size, shape and their spatial distribution by the process parameters, and thus specimens with desired ferromagnetic properties can be fabricated. Using magnetite nanoparticles for infiltration into porous silicon is of interest not only because of the magnetic properties of the composite material due to the possible modification of the ferromagnetic/superparamagnetic transition but also because of the biocompatibility of the system caused by the low toxicity of both materials. Thus, it is a promising candidate for biomedical applications as drug delivery or biomedical targeting

Lessons learned from the 1st Ariel Machine Learning Challenge: Correcting transiting exoplanet light curves for stellar spots

The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is to identify the effects of spots visually and correct for them manually or discard the affected data. This paper explores a first step towards fully automating the efficient and precise derivation of transit depths from transit light curves in the presence of stellar spots. The primary focus of the paper is to present in detail a diverse arsenal of methods for doing so. The methods and results we present were obtained in the context of the 1st Machine Learning Challenge organized for the European Space Agency’s upcoming Ariel mission. We first present the problem, the simulated Ariel-like data and outline the Challenge while identifying best practices for organizing similar challenges in the future. Finally, we present the solutions obtained by the top-5 winning teams, provide their code and discuss their implications. Successful solutions either construct highly non-linear (w.r.t. the raw data) models with minimal preprocessing –deep neural networks and ensemble methods– or amount to obtaining meaningful statistics from the light curves, constructing linear models on which yields comparably good predictive performance

Low-Temperature Preparation of Superparamagnetic CoFe2O4 Microspheres with High Saturation Magnetization

Based on a low-temperature route, monodispersed CoFe2O4 microspheres (MSs) were fabricated through aggregation of primary nanoparticles. The microstructural and magnetic characteristics of the as-prepared MSs were characterized by X-ray diffraction/photoelectron spectroscopy, scanning/transmitting electron microscopy, and vibrating sample magnetometer. The results indicate that the diameters of CoFe2O4 MSs with narrow size distribution can be tuned from over 200 to ~330 nm. Magnetic measurements reveal these MSs exhibit superparamagnetic behavior at room temperature with high saturation magnetization. Furthermore, the mechanism of formation of the monodispersed CoFe2O4 MSs was discussed on the basis of time-dependent experiments, in which hydrophilic PVP plays a crucial role

Lessons learned from the 1st Ariel Machine Learning Challenge: Correcting transiting exoplanet light curves for stellar spots

The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is to identify the effects of spots visually and correct for them manually or discard the affected data. This paper explores a first step towards fully automating the efficient and precise derivation of transit depths from transit light curves in the presence of stellar spots. The primary focus of the paper is to present in detail a diverse arsenal of methods for doing so. The methods and results we present were obtained in the context of the 1st Machine Learning Challenge organized for the European Space Agency’s upcoming Ariel mission. We first present the problem, the simulated Ariel-like data and outline the Challenge while identifying best practices for organizing similar challenges in the future. Finally, we present the solutions obtained by the top-5 winning teams, provide their code and discuss their implications. Successful solutions either construct highly non-linear (w.r.t. the raw data) models with minimal preprocessing –deep neural networks and ensemble methods– or amount to obtaining meaningful statistics from the light curves, constructing linear models on which yields comparably good predictive performance