Low Reynolds Number Interactions between Colloidal Particles near the Entrance to a Cylindrical Pore

The interaction between stable colloidal particles arriving at a pore entrance was studied using a numerical method for the case where the particle size is smaller than but of the same order as the pore size. The numerical method was adapted from a front-tracking technique developed for studying incompressible, multifluid flow by S. O. Unverdi and G. Tryggvason (J. Comp. Phys. 100, 25, 1992). The method is based on the finite difference solution of Navier-Stokes equation on a stationary, structured, Cartesian grid and the explicit representation of the particle-liquid interface using an unstructured grid that moves through the stationary grid. The simulations are in two dimensions, considering both deformable and nondeformable particles, and include interparticle colloidal interactions. The interparticle and particle-pore hydrodynamic interactions, which are very difficult to determine using existing analytical and semi-numerical, semi-analytical techniques in microhydrodynamics, are naturally accounted for in our numerical method and need not be explicity determined. Two- and three-particle motion toward a pore has been considered in our simulations. The simulations demonstrate how the competition between hydrodynamic forces and colloidal forces acting on particles dictate their flow behavior near the pore entrance. The predicted dependence of the particle flow behavior on the flow velocity and the ratio of pore size to particle size are qualitatively consistent with the experimental observations of V. Ramachandran and H. S. Fogler (J. Fluid Mech. 385, 129, 1999). Copyright 2000 Academic Press.     

Studies on the Stereochemistry of 2-(Nitromethylidene)-Heterocycles

The ¹H-NMR spectra of 2-(nitromethylidene)pyrrolidine ( 7 ), 1-methyl-2-(nitromethylidene)imidazolidind ( 10 ) and 3-(nitromethylidene)tetrahydrothiazine ( 11 ) in CDCl₃ and (CD₃)₂SO indicate that these compounds have the intramolecularly H-bonded structures (Z)- 7 , (E)- 10 and (Z)- 11 while the N-methyl derivative 8 of 7 is (E)-configurated in both solvents. 1-Benzylamino-1-(methyltio)-2-nitroehtylene ( 13 ), an acylic model, has the H-bonded configuration (E)- 13 in CDCl₃ and in (CD₃)₂SO. 2-(Nitromethylidene)thiazolidine ( 3 ) has the (E)-configuration in CDCl₃ but exists in (CD₃)₂SO as a mixture of (Z)- and (E)-isomers with the former predominating. Both species are detected to varying proportions in a mixture of the two solvents. ¹⁵N-NMR spectroscopy of 3 ruled out unambiguously the nitronic acid structure 6 and the nitromethyleimine structure 5 . The N-methyl derivative 4 of 3 is (Z)-configurated in (CD₃)₂SO. Comparison of the olefinic proton shifts of (Z)- 3 and (Z)- 4 with those of analogues and also of 1,1-bis(methylti)-2-nitroethylene ( 12 ) shows decreased conjugation of the lone pair of electrons of the ring N-atom in (Z)- 3 and (Z)- 4 . This is also supported by ¹³C-NMR studies. Plausible explanations for the phenomenon are offered by postulating that the ring N-atoms are pyramidal in (Z)- 3 and (Z)- 4 and planar in other cases or, alternatively, that the conjugated nitroenamine system gets twisted due to steric interaction between the NO₂-group and the ring S-atom. Single-crystal X-ray studies of 3 and 8 show that the former exists in the (Z)-configuration and the latter in (E)-configuration; the ring N-atom in the former has slightly more pyramidal character than in the latter.     

Quantitative Analysis of Solar Photovoltaic Panel Performance with Size-Varied Dust Pollutants Deposition Using Different Machine Learning Approaches

In this paper, the impact of dust deposition on solar photovoltaic (PV) panels was examined, using experimental and machine learning (ML) approaches for different sizes of dust pollutants. The experimental investigation was performed using five different sizes of dust pollutants with a deposition density of 33.48 g/m² on the panel surface. It has been noted that the zero-resistance current of the PV panel is reduced by up to 49.01% due to the presence of small-size particles and 15.68% for large-size (ranging from 600 µ to 850 µ). In addition, a significant reduction of nearly 40% in sunlight penetration into the PV panel surface was observed due to the deposition of a smaller size of dust pollutants compared to the larger size. Subsequently, different ML regression models, namely support vector machine (SVMR), multiple linear (MLR) and Gaussian (GR), were considered and compared to predict the output power of solar PV panels under the varied size of dust deposition. The outcomes of the ML approach showed that the SVMR algorithms provide optimal performance with MAE, MSE and R² values of 0.1589, 0.0328 and 0.9919, respectively; while GR had the worst performance. The predicted output power values are in good agreement with the experimental values, showing that the proposed ML approaches are suitable for predicting the output power in any harsh and dusty environment.