Current Materials Science - Volume 17, Issue 2, 2024

The primary intent of the study is to formulate the inclusion complex of lisinopril with the varied compositions of polymers like β-cyclodextrin for the enhancement of oral drug solubility and bioavailability using QbD approach.

The application of Box-behnken design to determine the optimized run from the prepared inclusion complexes. The physical kneading technique with β-cyclodextrin at varied amounts was used to create the inclusion complex of lisinopril.

The FT-IR analysis study confirmed the selected drug, polymers, and other excipients showed no physical interactions. The prepared inclusion complexes' particle sizes and encapsulation efficiency were between 802 to 3259µm, 19.22 to 93.28%. The optimized formulation batch (F5) showed 90.16% in vitro drug release at 24h compared to the pure drug. From the in vivo study, the pharmacokinetic parameters for the optimized formulation (F5) were found to be Cmax of 94.336 ng/ml, Tmax of 12h, and AUC 94.336 ng.h/ml, Kel of 0.0395h^-1 and t1/2 of 12h. After three months, stability studies for the optimized formulation batch indicate no change in drug entrapment efficiency and other parameters.

The β-cyclodextrin inclusion complex of lisinopril exhibited a 2-fold increase in the oral bioavailability of the model drug, which will be the novel drug-delivery strategy for the treatment of hypertension.

Metal surface roughness detection is an essential step of quality control in the metal processing industry. Due to the high manual involvement and poor efficiency of traditional roughness testing, rapid automated vision detection has received increasing attention in product quality control. Many methods have focused on extracting features related to roughness from images by means of mathematical statistics. However, these methods often rely on extensive experiments and complex calculations, while being sensitive to external environmental disturbances.

In this paper, a convolution neural network-based approach for metal surface roughness evaluation has been proposed. The convolutional neural network was initialized using a transfer learning strategy, and the data augmentation technique was applied to the benchmark dataset for sample expansion.

To evaluate this approach, samples of 4 types of roughness classes were prepared. The samples were divided into a training set, validation set, and test set in the ratio of 7:2:1. The accuracy of the neural network on the test set was found to be above 86%.

The effectiveness of the proposed approach and its superiority over manual detection have been demonstrated in the experiments.

Sm (Er) doping is an effective strategy for enhancing the photocatalytic activity of the semiconductor photocatalysts for the degradation of organic pollutants. BaSn-based nanorods possess wide band gap energy, which limits the photocatalytic application. It is important to research the feasibility of the improved photocatalytic performance of the BaSn-based nanorods by doping with Sm (Er).

The aim is to synthesize Sm (Er)-doped BaSn-based nanoscale materials through a simple hydrothermal process and research the photocatalytic performance of the Sm (Er)-doped BaSn-based nanoscale materials for the gentian violet degradation.

Sm (Er)-doped BaSn-based nanoscale materials with a polycrystalline structure were synthesized through a simple hydrothermal process. The Sm (Er)-doped composites were analyzed by X-ray diffraction, electron microscopy, solid diffuse reflectance spectrum, X-ray photoelectron spectroscopy, photoluminescence, and electrochemical impedance spectroscopy.

Sm (Er) doping induces the morphological evolution of the BaSn-based nanoscale materials from the nanorods to irregular nanoscale particles. Sm (Er) in the doped BaSn-based nanoscale materials exists in the form of the cubic Sm2Sn2O7 and orthorhombic ErF3 phases. The band gap value is decreased with increasing the Sm (Er) dopant contents. Sm (Er)-doped BnSnbased nanoscale materials with the Sm (Er) content of 8wt.% have the lowest band gap and show the strongest light absorption ability. Compared with the un-doped BaSn-based nanoscale materials, the Sm (Er)-doped BnSn-based nanoscale materials exhibit higher photocatalytic activity for the gentian violet degradation. 8wt.% Sm-doped BnSn-based nanoscale materials show the highest photocatalytic activity for the degradation of the gentian violet. 20 mL gentian violet solution (concentration of 10 mg·L^-1) can be totally degraded using 20 mg 8wt.% Sm-doped BnSn-based nanoscale materials under UV light illumination for 150 min.

The enhanced photocatalytic activity of the Sm (Er)-doped BnSn-based nanoscale materials can be attributed to the decreased band gap, enhanced light absorption ability, and decreased recombination of the photo-generated electron-hole pairs.