Synthesis of Supported Catalysts by Dry Impregnation in Fluidized Bed

The synthesis of catalytic or not composite materials by dry impregnation in fluidized bed is described. This process can be carried out under mild conditions from solutions of organometallic precursors or colloidal solutions of preformed nanoparticles giving rise to reproducible metallic nanoparticles containing composite materials with a high reproducibility. The adequate choice of the reaction conditions makes possible to deposit uniformly the metal precursor within the porous matrix or on the support surface. When the ratio between the drying time and the capillary penetration time (tsec/tcap) is higher than 10, the impregnation under soft drying conditions leads to a homogeneous deposit inside the pores of the particles of support. The efficiency of the metal deposition is close to 100%, and the size of the formed metal nanoparticles is controlled by the pores diameter. Finally, some of the presented composite materials have been tested as catalysts: iron-based materials were used in carbon-nanotubes synthesis, while Pd and Rh composite materials have been investigated in hydrogenation reactions

Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition

Dynamic mode decomposition (DMD) provides a practical means of extracting insightful dynamical information from fluids datasets. Like any data processing technique, DMD's usefulness is limited by its ability to extract real and accurate dynamical features from noise-corrupted data. Here we show analytically that DMD is biased to sensor noise, and quantify how this bias depends on the size and noise level of the data. We present three modifications to DMD that can be used to remove this bias: (i) a direct correction of the identified bias using known noise properties, (ii) combining the results of performing DMD forwards and backwards in time, and (iii) a total least-squares-inspired algorithm. We discuss the relative merits of each algorithm, and demonstrate the performance of these modifications on a range of synthetic, numerical, and experimental datasets. We further compare our modified DMD algorithms with other variants proposed in recent literature