Monte Carlo simulation on a gold nanoparticle irradiated by electron beams
Bibliographic record
Abstract
This study investigated the secondary electron production from a gold nanoparticle (GNP) irradiated by monoenergetic electron beams using Monte Carlo (MC) simulation. Spherical GNPs with diameters of 2, 50 and 100 nm in water were irradiated by monoenergetic electron beams with energies equal to 50 keV, 250 keV, 1 MeV and 4 MeV. MC simulations were performed using the Geant4 toolkit to determine the energy of the secondary electrons emitted from the GNPs. The mean effective range and deflection angle of the secondary electrons were tracked. Energy depositions inside and outside the nanoparticles due to the secondary electrons were also calculated. For comparisons, simulations were repeated by replacing the GNPs with water. Our results show that the mean effective range of secondary electrons increased with an increase of the GNP size and electron beam energy. For the electron beam energy and GNP size used in this study, the mean effective range was 0.5-15 µm outside the nanoparticle, which is approximately within the dimension of a living cell. The mean deflection angles varied from 78 to 83 degrees as per our MC results. The proportion of energy deposition inside the GNP versus that outside increased with the GNP size. This is different from the results obtained from a previous study using photon beams. The secondary electron energy deposition ratio (energy deposition for GNP/energy deposition for water) was found to be highest for the smallest GNP of 2 nm diameter in this study. For the energy deposited by the secondary electron, we concluded that the addition of GNPs can increase the secondary electron energy deposition in water, though most of the energy was self-absorbed by the large nanoparticles (50 and 100 nm). In addition, an electron source in the presence of GNPs does not seem to be better than photons as the yield of secondary electrons per unit mass of gold is less than water.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".