A 90Sr/90Y-radioisotope battery based on betavoltaic and beta-photovoltaic dual effects

Materials Science in Semiconductor Processing(2024)

引用 0|浏览2
暂无评分
摘要
Betavoltaic batteries with strontium sources are expected to achieve higher output power because of their high energy density and low self-absorption rate with the development of MEMS. However, the high energy of beta particles emitted from 90Sr/90Y can prone to radiation damage of the semiconductor. In order to exploit the high energy of beta particles emitted from 90Sr/90Y and to avoid radiation damage to the semiconductor, this paper presents a 90Sr/90Y-radioisotope battery based on betavoltaic (BV) and beta-photovoltaic (BPV) dual effects. In the work, the energy deposition of beta particles in LYSO:Ce and GaAs was simulated by Monte Carlo code, and thickness of the scintillator was determined. And the doping concentrations and junction depth of semiconductor were optimized based on the theoretical calculations to obtain the best output performance of device. When the thickness of LYSO:Ce is 0.158 cm, the output power density Pm of the optimized dual-effect battery is 0.61 μW/cm2. And the conversion efficiency of the device is 0.92%. At this time, the doping concentrations are Na=1.58×1017 cm−3 and Nd=3.16×1018 cm−3, and the junction depth xj=0.05 μm. All calculated parameter values are considered as theoretical limit values. In addition, the contribution of BV effect and BPV effect to the output performance of the dual-effect radioisotope battery was investigated. Different scintillator thicknesses lead to different percentages of the two mechanics. In addition, the BV effect and BPV effect output proportion is also affected by the average energy of the radiation source. In the case that the average electron energy on the semiconductor surface is 0.27 MeV, the higher the radioactive source energy, the thicker the scintillator is required, resulting in more BPV effect and less BV effect.
更多
查看译文
关键词
Betavoltaic battery,Beta-photovoltaic battery,GaAs p–n junction,LYSO:Ce
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要