RESUMO
Accurate 3D nanometrology of catalysts with small nanometer-sized particles of light 3d or 4d metals supported on high-atomic-number oxides is crucial for understanding their functionality. However, performing quantitative 3D electron tomography analysis on systems involving metals like Pd, Ru, or Rh supported on heavy oxides (e.g., CeO2) poses significant challenges. The low atomic number (Z) of the metal complicates discrimination, especially for very small nanoparticles (1-3 nm). Conventional reconstruction methods successful for catalysts with 5d metals (e.g., Au, Pt, or Ir) fail to detect 4d metal particles in electron tomography reconstructions, as their contrasts cannot be effectively separated from those of the underlying support crystallites. To address this complex 3D characterization challenge, we have developed a full deep learning (DL) pipeline that combines multiple neural networks, each one optimized for a specific image-processing task. In particular, single-image super-resolution (SR) techniques are used to intelligently denoise and enhance the quality of the tomographic tilt series. U-net generative adversarial network algorithms are employed for image restoration and correcting alignment-related artifacts in the tilt series. Finally, semantic segmentation, utilizing a U-net-based convolutional neural network, splits the 3D volumes into their components (metal and support). This approach enables the visualization of subnanometer-sized 4d metal particles and allows for the quantitative extraction of catalytically relevant structural information, such as particle size, sphericity, and truncation, from compressed sensing electron tomography volume reconstructions. We demonstrate the potential of this approach by characterizing nanoparticles of a metal widely used in catalysis, Pd (Z = 46), supported on CeO2, a very high density (7.22 g/cm3) oxide involving a quite high-atomic-number element, Ce (Z = 58).
RESUMO
A semi-industrial scale AnMBR plant was operated for more than 600 days to evaluate the long-term operation of this technology at ambient temperature (ranging from 10 to 27 âC), variable hydraulic retention times (HRT) (from 25 to 41 h) and influent loads (mostly between 15 and 45 kg COD·d-1). The plant was fed with sulfate-rich high-loaded municipal wastewater from the pre-treatment of a full-scale WWTP. The results showed promising AnMBR performance as the core technology for wastewater treatment, obtaining an average 87.2 ± 6.1 % COD removal during long-term operation, with 40 % of the data over 90%. Five periods were considered to evaluate the effect of HRT, influent characteristics, COD/SO42--S ratio and temperature on the biological process. In the selected periods, methane yields varied from 70.2±36.0 to 169.0±95.1 STP L CH4·kg-1 CODinf, depending on the influent sulfate concentration, and wasting sludge production was reduced by between 8 % and 42 % compared to conventional activated sludge systems. The effluent exhibited a significant nutrient recovery potential. Temperature, HRT, SRT and influent COD/SO42--S ratio were corroborated as crucial parameters to consider in maximizing AnMBR performance.
