Monte Carlo simulation on low-energy electrons from gold nanoparticle in radiotherapy
Bibliographic record
Abstract
This study investigated the low-energy electrons (LEEs) produced when a gold nanoparticle (GNP) is irradiated by photon beams. The secondary electrons emitted from a GNP (diameter = 100 nm), interacting with photon beams with energies equal to 35, 73.3 and 600 keV, were simulated using the Geant4 Monte Carlo code. The phase spaces of the secondary electrons were then used to simulate the LEEs in water using the NOREC Monte Carlo code. All secondary electrons emitted by the GNP, and all LEEs produced by each secondary electron were tracked in Monte Carlo simulations. It is found that the energy distributions of the LEEs from the GNP do not vary significantly between different photon beam energies. Moreover, the 660 keV photon beam produced more LEEs travelling to a longer range than photon beams of lower energies (35 and 73.3 keV). This higher energy deposition and longer range LEEs produced by the 660 keV photon beam can enhance the cell kill. Based on our Monte Carlo results, it is concluded that the unexpected close of the radiosensitization enhancement factors of the 35 (1.66) and 660 keV (1.18) photon beams from our previous measurements is because of the cell kill enhancement with the increased LEE yield and range in the 660 keV photon beam.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".