Prediction of Betavoltaic Battery Parameters

The approaches for predicting output parameters of betavoltaic batteries are reviewed. The need to develop a strategy for predicting these parameters with sufficient accuracy for the optimization of betavoltaic cell design without using the simple trial and error approach is discussed. The strengths and weaknesses of previously proposed approaches for the prediction are considered. Possible reasons for the difference between the calculated and measured parameters are analyzed. The depth dependencies of beta particles deposited energy for Si, SiC, GaN, and Ga2O3 and 20% purity Ni-63 and titanium tritide as radioisotope sources are simulated using the Monte Carlo algorithm taking into account the full beta energy spectrum, the isotropic angular distribution of emitted electrons and the self-absorption inside the radioisotope source for homogeneously distributed emitting points. The maximum short circuit current densities for the same semiconductors and radioisotope sources are calculated. The methodology allowing the prediction of betavoltaic cell output parameters with accuracy no worse than 30% is described. The results of experimental and theoretical investigations of the temperature dependence of betavoltaic cell output parameters are briefly discussed. The radiation damage by electrons with the subthreshold energy and the need to develop models for its prediction is considered.

Automated summary

This article reviews the approaches for predicting output parameters of betavoltaic batteries, discusses the need to develop a strategy for accurate prediction without using trial and error, and analyzes the strengths and weaknesses of previously proposed approaches. It also simulates the depth dependencies of beta particles deposited energy for different materials and radioisotope sources, calculates the maximum short circuit current densities, and describes a methodology for predicting betavoltaic cell output parameters with high accuracy.