Theoretical prediction of output performance of 63NiO-Si heterojunction betavoltaic cell

doi:10.1063/5.0100186

CIOMP OpenIR

	Theoretical prediction of output performance of 63NiO-Si heterojunction betavoltaic cell
	Y. Wang; R. Zheng; J. Lu; X. Li; Z. Chen; X. Zhang; Y. Zhang; L. Liang; Y. Zeng; L. Qin and Y. Liu
	2022
发表期刊	Applied Physics Letters
ISSN	36951
卷号	121 期号:8
摘要	For the 63NiO-Si heterojunction betavoltaic nuclear battery, the energy deposition of the energy conversion material itself was simulated by Monte Carlo simulation, and the structure of the 63NiO-Si heterojunction was optimized based on the theoretical calculation results. When the thickness of 63NiO is 4 m and the doping concentration of Si is 1 1015 cm-3, the short-circuit current density, open-circuit voltage, fill factor, and maximum output power density of the nuclear battery are 1.22 A cm-2, 3.17 V, 0.95, 3.67 W cm-2. In addition, the output performance of 63Ni/NiO-Si heterojunction betavoltaic nuclear cell was calculated in this study. Under the condition that the activity of the radioactive source and the thickness of NiO(63NiO) are the same in the two structures, the proposed structure (63NiO-Si) has greatly improved the output performance of the nuclear battery by reducing the energy lost from radioactive source self-absorption. 2022 Author(s).
DOI	10.1063/5.0100186
URL	查看原文
收录类别	ei
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/67165
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Y. Wang,R. Zheng,J. Lu,et al. Theoretical prediction of output performance of 63NiO-Si heterojunction betavoltaic cell[J]. Applied Physics Letters,2022,121(8).
APA	Y. Wang.,R. Zheng.,J. Lu.,X. Li.,Z. Chen.,...&L. Qin and Y. Liu.(2022).Theoretical prediction of output performance of 63NiO-Si heterojunction betavoltaic cell.Applied Physics Letters,121(8).
MLA	Y. Wang,et al."Theoretical prediction of output performance of 63NiO-Si heterojunction betavoltaic cell".Applied Physics Letters 121.8(2022).

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Theoretical predicti（2460KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载