Fluid transport in finite-sized nanoporous materials is critically affected by apparent interfacial barriers, which severely restrict efficiency improvement on reduction in system size; however, the mechanism leading to this effect even in defect-free materials is unknown. Using large-scale atomistic simulations emulating carbon nanotube (CNT) and zeolite membranes up to a micrometer thick, we demonstrate that transport coefficients in finite nanomaterials are non-uniform and increase with distance from the external surface, attaining the bulk system value far from the surface. Attenuation of transport occurs in a finite entrance region of developing fluid flow, in which the fluid momentum decorrelates from that at the entry and whose extent depends on the texture of the pore surface. This effect disguises as interfacial resistance in nanomaterials and has remarkable effects on membrane selectivity. Thus, finite CNTs can be selective for H 2 over CH 4 , in contrast to the corresponding infinite tube that is selective for CH 4 , due to the smaller apparent interfacial resistance for H 2 . The simulations, covering a wide range of conditions, reveal a quantitative correlation between the apparent relative interfacial resistance and Maxwell reflection coefficient for CNT membranes. These insights will enable optimal design of ultra-thin membranes, sensors, and other applications using nanoscale materials.

The quest to reduce transport resistance in separations using nanomaterials has led to considerable interest in nanoscale adsorbents and ultrathin membranes. It is now established that interfacial resistance limits the performance of such nanosized materials; however, the origin of this resistance is uncertain. While it is associated with surface pore blockages and distortions in some materials, its existence even in ideal materials is largely putative. Here, we report equilibrium molecular dynamics (EMD) simulations with ideal zeolite-based nanosheets, indicating the transport resistance to be entirely distributed within the solid, without contribution from an interfacial effect. We demonstrate the presence of an internal entry region over which fluid decorrelation occurs, and in which the local transport coefficient inside the crystal is nonuniform and position-dependent, increasing to the uniform value in the bulk material at larger distances. Our EMD-based diffusivity profiles within the nanomaterial enable us to unequivocally determine the entry length, and reveal an internal excess resistance, frequently assumed to be an interfacial resistance, due to significant reduction of the internal transport coefficient in the entrance and exit regions. A decrease in the entry length with loading in PON zeolite nanosheets is seen. We demonstrate a reduction in external resistance in the external bulk chambers used in simulations, triggered by the interplay of incomplete decorrelation in the nanosheet and periodic boundary conditions imposed on the system comprising the nanosheet and surrounding bulk reservoirs when the nanosheet thickness is less than the entry length. Our analysis of the transport dynamics within the nanosheet demonstrates that, at least for ideal systems, decomposition of the inhomogeneous diffusivity-based internal resistance into an interfacial and a uniform transport coefficient-based internal contribution is not appropriate for finite-sized systems. Our results will enable the improved design of nanoscale membranes and materials for applications in separation and other processes.

This study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.

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