Versicherungsmarketing

Next-Generation Multifunctional Carbon–Metal Nanohybrids for Energy and Environmental Applications

“…For example, physical and hydrodynamic properties of porous media such as pore structure, particle size distribution, porosity, preferential flow/pathway, geometry, connectivity, and tortuosity can strongly affect the transport of NPs (e.g., CMNHs). 247New approaches of coupling mass transport measurements of CMNHs at the mesoscopic scale with direct measurements of physical and hydrodynamic proeprties of the porous media (e.g., by using a 3D X-ray computed tomography (CT) technique) (247,248) are needed to unravel the pore-scale processes. (249) Particularly, the nondestructive 3D X-ray CT technique can enable accurate characterization of pore network structure with regard to particle size distribution, porosity, and tortuosity of the porous media, all of which can influence CMNHs transport and retention in environmental media.…”

“…Alternatively, physically based mechanistic models like two-region, two-domain, and dual-permeability models can explicitly account for preferential flow and local/bulk transport in porous media at different scales (e.g., pore, representative elementary volume, and field scales). (247) To better correlate CMNHs' retention mechanisms with their newly emerging attributes (e.g., morphology, dimensionality, and MLC), porous media properties (e.g., physical and hydrodynamic properties), and environmental conditions (e.g., water content, water chemistry, and pore-water velocity), machine learning technique (i.e., decision tree) can be advantageous for identifying factors influencing CMNHs transport and retention in porous media. (253,254) This may facilitate the development of quantitative relationships between CMNHs mobility in porous media and their physicochemical properties using X-ray CT techniques, mathematical modeling, and machine learning techniques.…”