Calculation of the Absorbed Electron Energy 3D Distribution by the Monte Carlo Method, Presentation of the Proximity Function by Three Parameters α, β, η and Comparison with the Experiment

Svintsov, A. A.; Knyazev, M. A.; Zaitsev, S. I.

doi:10.3390/ma15113888

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…In the present paper all calculations are carried out for a few semiconductor materials and 20% purity 63 Ni and titanium tritide as radioisotope sources using in-house Monte Carlo program based on the algorithm described in [40,44]. The calculation algorithm is specially adapted for the fast simulation of beta particle energy deposition inside multilayer structures.…”

Section: Simulation Of Beta Particle Energy Depositionmentioning

confidence: 99%

Prediction of Betavoltaic Battery Parameters

Yakimov

2023

Energies

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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 63Ni 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.

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Section: Simulation Of Beta Particle Energy Depositionmentioning

confidence: 99%

Prediction of Betavoltaic Battery Parameters

Yakimov

2023

Energies

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EBIC Imaging of Conductive Paths Formed in Graphene Oxide as a Result of Resistive Switching

2023

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The electron-beam-induced current (EBIC) method is utilized in this work to visualize conductive channels formed in graphene oxide as a result of resistive switching. Using metal–insulator–semiconductor (MIS) structures, an increase in the electron beam induced current by a few orders of magnitude as compared with the EBIC signal in metal–insulator–metal (MIM) structures is achieved. The mechanism of the EBIC image formation related to the conductive channels is explained by the separation and collection of the e-beam generated excess carriers by rectifying barrier nanocontacts formed at the graphene oxide/Si interface during resistive switching. It is shown that the collection efficiency of the formed nanocontacts decreases with the beam energy, in agreement with the theoretical predictions for the Schottky-like nanocontacts. An important advantage of the EBIC method is demonstrated in its ability to monitor the generation and elimination of high density conductive channels even when the current–voltage measurements cannot detect and separate these processes. EBIC study of the dynamics of the conductive channel formation can help better understand the underlying physical mechanisms of their generation.

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Calculation of the Absorbed Electron Energy 3D Distribution by the Monte Carlo Method, Presentation of the Proximity Function by Three Parameters α, β, η and Comparison with the Experiment

Cited by 2 publications

References 25 publications

Prediction of Betavoltaic Battery Parameters

Prediction of Betavoltaic Battery Parameters

EBIC Imaging of Conductive Paths Formed in Graphene Oxide as a Result of Resistive Switching

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