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.     

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

Industrial-based application of supercritical CO₂ (SCCO₂) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C₁₅H₁₈O₃) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO₂, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10⁻⁴, 7.814 × 10⁻⁵, and 1.664 × 10⁻⁴ for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10⁻⁴, 6.197 × 10⁻⁵, and 8.777 × 10⁻⁵errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x₁ = 40.0, x₂ = 338.0, Y = 1.27 × 10⁻³) vector.