Irradiation of gold nanoparticles by x‐rays: Monte Carlo simulation of dose enhancements and the spatial properties of the secondary electrons production
Bibliographic record
Abstract
PURPOSE: The aim of this study is to understand the characteristics of secondary electrons generated from the interaction of gold nanoparticles (GNPs) with x-rays as a function of nanoparticle size and beam energy and thereby further the understanding of GNP-enhanced radiotherapy. METHODS: The effective range, deflection angle, dose deposition, energy, and interaction processes of electrons produced from the interaction of x-rays with a GNP were calculated by Monte Carlo simulations. The GEANT4 code was used to simulate and track electrons generated from a 2, 50, and 100 nm diameter GNP when it is irradiated with a 50 kVp, 250 kVp, cobalt-60, and 6 MV photon beam in water. RESULTS: When a GNP was present, depending on the beam energies used, secondary electron production was increased by 10- to 2000-fold compared to an absence of a GNP. Low-energy photon beams were much more efficient at interacting with the GNP by two to three orders of magnitude compared to MV energies and increased the deflection angle. GNPs with larger diameters also contributed more dose. The majority of the energy deposition was outside the GNP, rather than self-absorbed by the nanoparticle. The mean effective range of electron tracks for the beams tested ranged from approximately 3 microm to 1 mm. CONCLUSIONS: These simulated results yield important insights concerning the spatial distributions and elevated dose in GNP-enhanced radiotherapy. The authors conclude that the irradiation of GNP at lower photon energies will be more efficient for cell killing. This conclusion is consistent with published studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".