The Application of Rare-Earth Element

2011 ◽  
Vol 233-235 ◽  
pp. 3005-3009
Author(s):  
Yu Cai Wu ◽  
Ming Yan

In this paper, the process of Cu-Ag contact wire with adding rare-earth elements was presented. The additive process of the rare-earth elements and the function of the rare earth were chiefly analyzed. Adding the rare-earth elements into melt alloy, the oxide and sulfur can be removed from the liquid, so we can get the purified alloy. At the same time, adding rare-earth can reduce the external crack flaws which produced during the casting and makes the grain refined, as the result, the properties of the Cu-Ag alloy contact wire can be greatly improved and meliorated. Such as the conductivity, the specific elongation, tensile strength and so on, are improved.

2020 ◽  
Vol 1009 ◽  
pp. 149-154
Author(s):  
Tanongsak Yingnakorn ◽  
Piamsak Laokhen ◽  
Loeslakkhana Sriklang ◽  
Tapany Patcharawit ◽  
Sakhob Khumkoa

High power neodymium magnets have been used extensively, such as components of hard disk drives, electric vehicles, and maglev trains. This type of magnet contains of high concentration of rare earth elements. After the device is out of service, the magnet will be removed and the rare earth element contained in the magnet will be extracted in order to reuse for any purposes. Recently, the study on extraction of rare earth elements (REE) from neodymium magnets is increased. However, there was only few research regarding to the extraction of rare earth metals by using a water leaching method. In this study, rare-earth elements were extracted from neodymium magnet scrap by using selective leaching technique. Initially, magnets were leached with 2 M of sulfuric acid for 24 hrs. Then, the leached solution was heated at 110°C in order to remove water and the green powder was remained. The green powder was further roasted in a muffle furnace at various temperatures from 750°C to 900°C for 2 hrs. and subsequently leached by water. Finally, the iron oxide residue was separated from rare earth element solution by filtration. Based on this experiment, it was found that the purity of the rare earth metals can be achieved up to 99.4%.


1999 ◽  
Vol 39 (2) ◽  
pp. 189 ◽  
Author(s):  
E. Diatloff ◽  
C. J. Asher ◽  
F. W. Smith

The foliar application of rare earth elements to plants has been reported to increase yields of a range of crops particularly when soils contain low levels of rare earth elements. A rare earth element fertiliser obtained from China was chemically analysed and found to contain 45.3% nitrate plus 8.7% lanthanum and 12.4% cerium; lanthanum and cerium were the most abundant rare earth elements measured. This fertiliser was applied once, as 0, 0.025, 0.05, 0.1, 0.5 and 1.0% (w/v) aqueous solutions to the foliage of 10-day-old maize (Zea mays L. cv. Hycorn 82) and 14-day-old mungbean [Vigna radiata (L.) Wilczek cv. Berken] plants grown in a nutrient-rich potting mix of low total rare earth element status. For comparison, a duplicate set of plants was sprayed with solutions containing analytical grade lanthanum and cerium nitrate at concentrations equivalent to those measured in the rare earth element fertiliser. No beneficial effects of the rare earth element treatments were observed. The shoots of maize and mungbean sprayed with ≤0.1% rare earth element fertiliser or equivalent appeared completely healthy throughout the experiment, but plants in the 0.5 and 1.0% treatments showed symptoms of leaf burn in maize, and small necrotic spots on mungbean leaves within 1–3 days of treatment. These symptoms became more severe over the next 5–9 days. The shoot dry weight of mungbean sprayed with 0.5 and 1.0% solutions was significantly (P<0.05) reduced by 27%. Symptoms observed on plants sprayed with lanthanum and cerium nitrate solutions were similar to those observed on plants sprayed with the rare earth element fertiliser, and similar growth reductions occurred also.


1996 ◽  
Vol 133 (1-4) ◽  
pp. 125-144 ◽  
Author(s):  
Kevin H. Johannesson ◽  
W.Berry Lyons ◽  
Mary A. Yelken ◽  
Henri E. Gaudette ◽  
Klaus J. Stetzenbach

2006 ◽  
Vol 57 (7) ◽  
pp. 725 ◽  
Author(s):  
Michael G. Lawrence ◽  
Stacy D. Jupiter ◽  
Balz S. Kamber

The rare earth elements are strong provenance indicators in geological materials, yet the potential for tracing provinciality in surface freshwater samples has not been adequately tested. Rare earth element and yttrium concentrations were measured at 33 locations in the Pioneer River catchment, Mackay, central Queensland, Australia. The rare earth element patterns were compared on the basis of geological, topographical and land-use features in order to investigate the provenancing potential of these elements in a small freshwater system. The rare earth element patterns of streams draining single lithological units with minor land modification show strongly coherent normalised behaviour, with a loss of coherence in agricultural locations. Evidence is reported for an anthropogenic Gd anomaly that may provide a useful hydrological tracer in this region since the introduction of magnetic resonance imaging in 2003. Several samples display a superchondritic Y/Ho mass ratio (up to 44), which is not explainable within the constraints imposed by local geology. Instead, it is suggested that the additional Y is derived from a marine source, specifically marine phosphorites, which are a typical source of fertiliser phosphorus. The data indicate that, under some circumstances, scaled and normalised freshwater rare earth patterns behave conservatively.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Sumin Ma ◽  
Wenhui Huang

Since the breakthrough of deep learning in object classification in 2012, extraordinary achievements have been made in the field of target detection, but the high time and space complexity of the target detection network based on deep learning has hindered the technology from application in actual product. To solve this problem, first of all, this paper uses the MobileNet classification network to optimize the Faster R-CNN target detection network. The experimental results on the rare earth element detection data set show that the MobileNet classification network is not suitable for optimizing the Faster R-CNN network. After that, this paper proposes a classification network that combines VGG16 and MobileNet, and uses the fusion network to optimize the Faster R-CNN target detection network. The experimental results on the rare earth element detection data set show that the Faster R-CNN target detection network optimized by the fusion classification network has the advantages of using VGG16 and MobileNet’s Faster R-CNN target detection network to detect rare earth elements. The innovation of this article is that the results on 5 time series data sets show that CDA-WR has better predictive performance than other ELM variant models. The effect of determining trace cerium elements in rocks and minerals is increased by more than 50%, based on deep learning. The algorithm studies the methods of target detection and recognition and integrates it into the intelligent robot used in this subject, giving the robot the ability to accurately detect the target object in real time.


2018 ◽  
Vol 238 ◽  
pp. 1044-1047 ◽  
Author(s):  
Evgenios Agathokleous ◽  
Mitsutoshi Kitao ◽  
Edward J. Calabrese

Author(s):  
Madalena Andrade ◽  
Amadeu M.V.M. Soares ◽  
Montserrat Solé ◽  
Eduarda Pereira ◽  
Rosa Freitas

Mycorrhiza ◽  
2020 ◽  
Vol 30 (6) ◽  
pp. 761-771
Author(s):  
Ruoyu Hu ◽  
Thierry Beguiristain ◽  
Alexis De Junet ◽  
Corinne Leyval

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