Intralanthanide Separation on Layered Titanium(IV) Organophosphate Materials via a Selective Transmetalation Process

The lanthanides (Ln) are an essential part of many advanced technologies. Our societal transformation toward renewable energy drives their ever-growing demand. The similar chemical properties of the Ln pose fundamental difficulties in separating them from each other, yet high purity elements are crucial for specific applications. Here, we propose an intralanthanide separation method utilizing a group of titanium(IV) butyl phosphate coordination polymers as solid-phase extractants. These materials are characterized, and they contain layered structures directed by the hydrophobic interaction of the alkyl chains. The selective Ln uptake results from the transmetalation reaction (framework metal cation exchange), where the titanium(IV) serves as sacrificial coordination centers. The “tetrad effect” is observed from a dilute Ln3+ mixture. However, smaller Ln3+ ions are preferentially extracted in competitive binary separation models between adjacent Ln pairs. The intralanthanide ion-exchange selectivity arises synergistically from the coordination and steric strain preferences, both of which follow the reversed Ln contraction order. A one-step aqueous separation of neodymium (Nd) and dysprosium (Dy) is quantitatively achievable by simply controlling the solution pH in a batch mode, translating into a separation factor of greater than 2000 and 99.1% molar purity of Dy in the solid phase. Coordination polymers provide a versatile platform for further exploring selective Ln separation processes via the transmetalation process.Peer reviewe

Building Detection on Aerial Images Using U-NET Neural Networks

This article presents research results of two convolutional neural networks for building detection on satellite images of Planet database. To analyze the quality of developed algorithms, there was used Sorensen-Dice coefficient of similarity which compares results of algorithms with tagged masks. The masks were generated from json files and sliced on smaller parts together with respective images before the training of algorithms. This approach allows to cope with the problem of segmentation for aerial high-resolution images efficiently and effectively. The problem of building detection on satellite images can be put into practice for urban planning, building control, etc

Characterization of ion-irradiation-induced defects in multi-walled carbon nanotubes

Peer reviewe

Facial expression recognition algorithm based on deep convolution neural network

This paper presents algorithms for smile detection and facial expression recognition. The developed algorithms are based on the implementation of a relatively new approach in the field of deep machine learning - a convolutional neural network. The aim of this network is to classify facial images into one of the six types of emotions. The studying of algorithms was carried using face images from the CMU MultiPie database. To accelerate the neural network operation, the training and testing processes were performed parallel, on a large number of independent streams on GPU. Fo r developed models there were given metrics of quality

Comparison of Different Convolutional Neural Network Architectures for Satellite Image Segmentation

Convolutional neural networks for detection geo- objects on the satellite images from DSTL, Landsat-8 and PlanetScope databases were analyzed. Three modification of convolutional neural network architecture for implementing the recognition algorithm was used. Images obtained from the Landsat-8 and PlanetScope satellites are used for estimation of automatic object detection quality. To analyze the accuracy of the object detection algorithm, the selected regions were compared with the areas by previously marked by experts. An important result of the study was the improvement of the detector for the class "Forest". Segmentation of satellite images has found application at urban planning, forest management, climate modelling, etc