Energy Deposition around Swift Carbon-Ion Tracks in Liquid Water

Ion-Type Dependence of DNA Electronic Excitation in Water under Proton, α-Particle, and Carbon Ion Irradiation: A First-Principles Simulation Study

Understanding how the electronic excitation of DNA changes in response to different high-energy particles is central to advancing ion beam cancer therapy and other related approaches, such as boron neutron capture therapy. While protons have been the predominant ions of choice in ion beam cancer therapy, heavier ions, particularly carbon ions, have drawn significant attention over the past decade. Carbon ions are expected to transfer larger amounts of energy according to linear response theory. However, molecular-level details of the electronic excitation under heavier ion irradiation remain unknown. In this work, we use real-time time-dependent density functional theory simulations to examine the quantum-mechanical details of DNA electronic excitations in water under proton, α-particle, and carbon ion irradiation. Our results show that the energy transfer does indeed increase for the heavier ions, while the excitation remains highly conformal. However, the increase in the energy transfer rate, measured by electronic stopping power, does not match the prediction by the linear response model, even when accounting for the velocity dependence of the irradiating ion's charge. The simulations also reveal that while the number of holes generated on DNA increases for heavier ions, the increase is only partially responsible for the larger stopping power. Larger numbers of highly energetic holes formed from the heavier ions also contribute significantly to the increased electronic stopping power.

Electron tracks simulation in water: Performance comparison between GPU CPU and the EUMED grid installation

PURPOSE

We explored different technologies to minimize simulation time of the Monte-Carlo method for track generation following the Geant4-DNA processes for electrons in water.

METHODS

A GPU software tool is developed for electron track simulations. A similar CPU version is also developed using the same collision models. CPU simulations were carried out on a single user desktop computer and on the computing grid France Grilles using 10 and 100 computing nodes. Computing time results for CPU, GPU, and grid simulations are compared with those using Geant4-DNA processes.

RESULTS

The CPU simulations better performs when the number of electrons is less than 10⁴ with 100 eV initial energy, this number decreases as the energy increases. The GPU simulations gives better results when the number of electrons is more than 10⁴ with initial energy of 100 eV, this number decreases to 10³ for electrons with 10KeV and increases back with higher energy. The use of the grid introduces an additional queuing time which slows down the overall simulation performance. Thus, the Grid gives better performance when the number of electrons is over 10⁵ with initial energy of 10KeV, and this number decreases as the energy increases.

CONCLUSIONS

The CPU is best suited for small numbers of primary incident electrons. The GPU is best suited when the number of primary incident particles occupies sufficient resources on GPU card in order to get an important computing power. The grid is best suited for simulations with high number of primary incident electrons with high initial energy.