Hot-electron mediated ion diffusion in proton-irradiated magnesium oxide
arXiv:1806.00443 · doi:10.1021/acs.nanolett.9b01214
Abstract
Highly energetic ions that impact materials have applications from semiconductor industry to medicine, and are fundamentally interesting as they trigger multi-length and time-scale processes. In particular, they excite electrons into non-thermalized energy distributions with subsequent non-equilibrium electron-electron and electron-ion dynamics. In order to achieve a quantitative description of these, we propose a general first-principles framework that bridges time scales from ultrafast electron dynamics directly after impact, to ion diffusion over migration barriers in semiconductors. We apply it to magnesium oxide under proton irradiation and discover a diffusion mechanism that is mediated by hot electrons. Our quantitative simulations show that this mechanism strongly depends on the projectile-ion velocity. This indicates that it may occur only at a specific penetration depth in the target and that it can be triggered by varying the kinetic energy of the particle radiation. Either of these predictions should facilitate direct experimental observation of this effect and significantly advances current understanding of non-equilibrium electron-ion dynamics.
References in corpus (7)
- Ultrafast carrier thermalization in lead iodide perovskite probed with two-dimensional electronic spectroscopy
- Electronic stopping power in insulators from first principles
- Silicon-carbon bond inversions driven by 60 keV electrons in graphene
- Ultrafast Hot Carrier Dynamics in GaN and its Impact on the Efficiency Droop
- Theory of Thermal Relaxation of Electrons in Semiconductors
- Examining real-time TDDFT non-equilibrium simulations for the calculation of electronic stopping power
- Electronic stopping power in a narrow band gap semiconductor from first principles
Cited by in corpus (3)
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- Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning