Materials Discovery最新文献

This work was aimed to formulate the atenolol loaded PLGA nanoparticles through optimizing formulation variables using 3³ factorial design. FTIR studies were performed to examine the interaction between the excipients used. These nanoparticles were characterized by size, shape, zeta potential, % drug entrapment efficiency, process yield and in-vitro drug release behavior. Different kinetics models were used to analyze the in-vitro drug release data. Preferred formulation showed particle size 192.6 ± 3.5 nm, PDI 0.234 ± 0.008, −32.4 mV zeta potential, drug entrapment efficiency 71.65 ± 1.8% and 78.32 ± 1.1% process yield. TEM results showed that these nanoparticles were spherical in shape and follow the Korsmeyer-Peppas model.

Polycrystalline CaS:Ce³⁺ and SrS:Ce³⁺ phosphors were successfully synthesized via the carbo-thermal reduction method. The XRD patterns of prepared phosphors were well matched with ICDD files and surface morphology of prepared phosphors were found to be in sub micron range. Photoluminescence emission spectra of CaS:Ce³⁺ and SrS:Ce³⁺ exhibits green and blue broad band emissions centered at 504 nm and 477 nm respectively. The concentration quenching phenomena was observed for prepared phosphors. CIE coordinates of Ce³⁺ doped CaS and SrS phosphors were calculated. The critical transfer distance (Rc) is calculated by equation proposed by Blasse. Thus prepared phosphors are suitable for pc-WLED.

In many subfields of chemistry and physics, numerous attempts have been made to accelerate scientific discovery using data-driven experimental design algorithms. Among them, Bayesian optimization has been proven to be an effective tool. A standard implementation (e.g., scikit-learn), however, can accommodate only small training data. We designed an efficient protocol for Bayesian optimization that employs Thompson sampling, random feature maps, one-rank Cholesky update and automatic hyperparameter tuning, and implemented it as an open-source python library called COMBO (COMmon Bayesian Optimization library). Promising results using COMBO to determine the atomic structure of a crystalline interface are presented. COMBO is available at https://github.com/tsudalab/combo.

Adsorption of organic contaminants on carbon nanotubes is a critical ecological criterion. Consequently, a predictive model for this endpoint is useful from the point of view of ecological risk assessment. Quantitative Structure–Property Relationships (QSPRs) built by the CORAL software package (http://www.insilico.eu/coral) are used to develop predictive models for adsorption (log K_∞) of organic contaminants by multi-walled carbon nanotubes (MWCNTs). The statistical characteristics of a CORAL model for external validation are: n = 30; r² = 0.8878; and RMSE = 0.475 (mg/g). The probabilistic scheme of the definition of the domain of applicability for the CORAL models are suggested and the practical aspects of using the CORAL software are discussed.

Crystallization behavior and polymorphism of calcium carbonate scale deposited from its sludge on aluminum and copper substrates in the presence of ethylene glycol-O,Oʼ-bis(2-aminoethyl)-N,N,Nʼ,Nʼ-tetraacetic acid (EGTA) as a chelating agent/scale inhibitor was investigated. The experiments were carried out at 60 and 100 °C and the samples were characterized by XRD, FTIR and SEM techniques. The data revealed that at 100 °C only calcite was found in the presence of copper, whereas with aluminum a ternary mixture containing calcite, aragonite and vaterite resulted. The data revealed that the polymorphic composition of CaCO₃ was influenced by metal present in the system.