Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering.

Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces the challenge of achieving superconductivity at room temperature. In recent years, Artificial Intelligence (AI) approaches have emerged as a promising tool for predicting such properties as transition temperature (Tc) to enable the rapid screening of large databases to discover new superconducting materials. This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, we perform various data pre-processing steps to derive the clean DataG dataset, containing 13,022 compounds. In another stage of the study, we apply the novel CatBoost algorithm to predict the transition temperatures of novel superconducting materials. In addition, we developed a package called Jabir, which generates 322 atomic descriptors. We also designed an innovative hybrid method called the Soraya package to select the most critical features from the feature space. These yield R2 and RMSE values (0.952 and 6.45 K, respectively) superior to those previously reported in the literature. Finally, as a novel contribution to the field, a web application was designed for predicting and determining the Tc values of superconducting materials.

Quince seed mucilage coated iron oxide nanoparticles for plasmid DNA delivery.

This study investigates the potential of iron oxide nanoparticles (Fe3O4) and quince seed mucilage as combined genetic carriers to deliver plasmid DNA (pDNA) through the gastrointestinal system. The samples are characterized by x-ray diffraction (XRD), zeta potential, dynamic light scattering, FT-IR spectroscopy, field emission scanning electron microscopy and vibrating sample magnetometry. The stability of pDNA loading on the nanocarriers and their release pattern are evaluated in simulated gastrointestinal environments by electrophoresis. The XRD patterns reveal that the nanocarriers could preserve their structure during various synthesis levels. The saturation magnetization (Ms) of the Fe3O4cores are 56.48 emu g-1without any magnetic hysteresis. Not only does the loaded pDNA contents experience a remarkable stability in the simulated gastric environment, but also, they could be released up to 99% when exposed to an alkaline environment similar to the intestinal fluid of fish. The results indicate that the synthesized nanoparticles could be employed as efficient low-cost pDNA carriers.