Literature DB >> 26583261

Prediction of 4H-SiC betavoltaic microbattery characteristics based on practical Ni-63 sources.

Gui Gui1, Kan Zhang1, James P Blanchard2, Zhenqiang Ma3.   

Abstract

We have investigated the performance of 4H-SiC betavoltaic microbatteries under exposure to the practical Ni-63 sources using the Monte Carlo method and Synopsys® Medici device simulator. A typical planar p-n junction betavoltaic device with the Ni-63 source of 20% purity on top is modeled in the simulation. The p-n junction structure includes a p+ layer, a p- layer, an n+ layer, and an n- layer. In order to obtain an accurate and valid predication, our simulations consider several practical factors, including isotope impurities, self-absorption, and full beta energy spectra. By simulating the effects of both the p-n junction configuration and the isotope source thickness on the battery output performance, we have achieved the optimal design of the device and maximum energy conversion efficiency. Our simulation results show that the energy conversion efficiency increases as the doping concentration and thickness of the p- layer increase, whereas it is independent of the total depth of the p-n junction. Furthermore, the energy conversion efficiency decreases as the thickness of the practical Ni-63 source increases, because of self-absorption in the isotope source. Therefore, we propose that a p-n junction betavoltaic cell with a thicker and heavily doped p- layer under exposure to a practical Ni-63 source with an appreciable thickness could produce the optimal energy conversion efficiency.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Keywords:  4H–SiC; Betavoltaic microbattery; Electron–hole pair; Monte carlo; Ni-63; Self-absorption

Year:  2015        PMID: 26583261     DOI: 10.1016/j.apradiso.2015.11.001

Source DB:  PubMed          Journal:  Appl Radiat Isot        ISSN: 0969-8043            Impact factor:   1.513


  1 in total

1.  Optimal Semiconductors for 3H and 63Ni Betavoltaics.

Authors:  Sergey I Maximenko; Jim E Moore; Chaffra A Affouda; Phillip P Jenkins
Journal:  Sci Rep       Date:  2019-07-26       Impact factor: 4.379

  1 in total

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