On the nature of metallic nanoparticles obtained from molecular Co3Ru-carbonyl clusters in mesoporous silica matrices

We report on the impregnation of THF solutions of the low-valent heterometallic cluster NEt(4)[Co(3)Ru(CO)(12)] into two mesoporous silica matrices, amorphous xerogels and ordered MCM-41, and a study of its thermal decomposition into metallic nanoparticles by X-ray diffraction, transmission electron microscopy and in situ magnetic measurements under controlled atmospheres. The decomposition of the cluster was monitored as a function of temperature by examining the chemical composition of the particles, their size distributions and their structures as well as their magnetic properties. Treatment under inert atmosphere (i.e. argon) at temperatures below 200 degrees C resulted in the formation of segregated spherical particles of hcp-ruthenium (2.3 +/- 1.0 nm) and hcp-cobalt (3.1 +/- 0.9 nm). The latter is transformed to fcc-cobalt (3.2 +/- 1.0 nm) above 270 degrees C. At higher temperatures, Co-Ru alloying takes place and the Ru content of the particles increases with increasing temperature to reach the nominal composition of the molecular precursor, Co(3)Ru. The particles are more evenly distributed in the MCM-41 framework compared to the disordered xerogel and also show a narrower size distribution. Owing to the different magnetic anisotropy of hcp- and fcc-cobalt, which results in different blocking temperatures, we were able to clearly identify the products formed at the early stages of the thermal decomposition procedure.     

Computable infinite-dimensional filters with applications to discretized diffusion processes

Let us consider a pair signal–observation ((_xn,_yn),n≥0)$((x_n,y_n),n\ge 0)$ where the unobserved signal (_xn)$\left(x_n\right)$ is a Markov chain and the observed component is such that, given the whole sequence (_xn)$\left(x_n\right)$, the random variables (_yn)$\left(y_n\right)$ are independent and the conditional distribution of _yn$y_n$ only depends on the corresponding state variable _xn$x_n$. The main problems raised by these observations are the prediction and filtering of (_xn)$\left(x_n\right)$. We introduce sufficient conditions allowing us to obtain computable filters using mixtures of distributions. The filter system may be finite or infinite-dimensional. The method is applied to the case where the signal _xn=_XnΔ$x_n=X_{n\Delta }$ is a discrete sampling of a one-dimensional diffusion process: Concrete models are proved to fit in our conditions. Moreover, for these models, exact likelihood inference based on the observation (_y0,…,_yn)$(y_0,\dots ,y_n)$ is feasible.