Review: Electrochemiluminescence of Perovskite-Related Nanostructures

Perovskite nanostructures are promising nanomaterials for their possible application in electrochemiluminescent (ECL) analytical systems due to their unique optical, electronic, and chemical properties. This review focuses on the most recent advances in the application of perovskite and perovskite-related nanostructures, with different chemical compositions and modifications, in ECL with various media, coreactants, and reaction types. The most optimal methods of perovskite nanoparticle synthesis and electrode modification methods were reviewed. Possibilities and perspectives of the use of perovskite-related nanostructures for the ECL generation were demonstrated

OPTIMIZATION OF THE TECHNOLOGY AND COMPOSITION OF "SKUTEX" TABLETS

Using the symmetric composite uniform-plan of the 2nd order the theoretically-grounded composition of tablets based on dry extract of Scutellaria baicalensis has been received

Web-based Fragment Library

A new polarized force field BioEFP for modeling process in biology is far superior in accuracy to the common classical force fields. One of the main shortcomings of BioEFP is that the parameters are not readily available, thus it will take a lot of time to be calculated. Developing an online repository of pre-computed fragment parameters and a similarity algorithm will allow ascribing each fragment of a biological macromolecule to a pre-defined fragment. This study incorporates three parts to create the online repository. First, the visual design for the website using the Hypertext Markup Language and the Cascading Style Sheets to create the format for each part on the web page. Secondly, the Hypertext Preprocessor is used for the server side to run certain functions and building dynamic websites. Lastly, an online database is created by MySQL to sort and organize all the data file. The results from the project is allowing us to upload our own file with xyz coordinates and get the result as the efp file

Growing Neural Networks Using Nonconventional Activation Functions

In the paper, an ontogenic artificial neural network (ANNs) is proposed. The network uses orthogonal activation functions that allow significant reducing of computational complexity. Another advantage is numerical stability, because the system of activation functions is linearly independent by definition. A learning procedure for proposed ANN with guaranteed convergence to the global minimum of error function in the parameter space is developed. An algorithm for structure network structure adaptation is proposed. The algorithm allows adding or deleting a node in real-time without retraining of the network. Simulation results confirm the efficiency of the proposed approach

Water−Benzene Interactions: An Effective Fragment Potential and Correlated Quantum Chemistry Study

Structures and binding in small water−benzene complexes (1−8 water molecules and 1−2 benzene molecules) are studied using the general effective fragment potential (EFP) method. The lowest energy conformers of the clusters were found using a Monte Carlo technique. The binding energies in the smallest clusters (dimers, trimers, and tetramers) were also evaluated with second order perturbation theory (MP2) and coupled cluster theory (CCSD(T)). The EFP method accurately predicts structures and binding energies in the water−benzene complexes. Benzene is polarizable and consequently participates in hydrogen bond networking of water. Since the water−benzene interactions are only slightly weaker than water−water interactions, structures with different numbers of water−water, benzene−water, and benzene−benzene bonds often have very similar binding energies. This is a challenge for computational methods

Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?

Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions using quantum-mechanical formulations. Our group has proposed previously that the effective fragment potential (EFP) method could serve as an efficient alternative to solve this problem. However, one of the computational bottlenecks of the EFP method is obtaining parameters for each molecule/fragment in the system, before the actual EFP simulations can be carried out. Here we present a neural network model that is trained by pre-calculated EFP parameters for a set of fragment geometries, to predict the multipole moment parameters for the fragments with arbitrary geometries. We perform Monte Carlo simulation to assess accuracy of the model. The results demonstrate the ability to predict multipole moments within acceptable margin of error given that the training set is closely spaced. These results contribute towards extending the applicability of the EFP method to new types of chemistries and improving the accuracy and computational efficiency of describing non-covalent interactions

Laser Synthesis of Cerium-Doped Garnet Nanoparticles

The application of a pulsed laser ablation technique for the generation of cerium-doped garnet nanoparticles in liquids is investigated. The morphological and optical properties of the obtained nanoparticles are demonstrated. Features introduced by the single crystals of Gd3Al2.4Ga2.6O12:Ce3+, Lu3Al5O12:Ce3+, and Y3Al1.25Ga3.75O12:Ce3+ from which the nanoparticles are generated, as well as the parameters of a liquid media on the garnet nanoparticle generation are experimentally studied using TEM and UV-Vis spectroscopy methods. It is shown how the size, shape, and internal structure of the nanoparticles are related to the external laser ablation conditions, as well as to the laser melting processes of NPs in the colloidal solutions. This work provides important information about the generated nanoparticles, which can be used as building blocks for specially designed structures with predetermined optical properties