The effect of linear energy transfer on the early, transient radiolytic oxygen depletion in the radiolysis of water by high-dose-rate irradiating protons
Bibliographic record
Abstract
Monte Carlo multi-track chemistry simulations were carried out to study, from a radiation chemistry perspective, the effect of “linear energy transfer” (LET) on the transient yields and concentrations of radiolytic oxygen consumption in the high-dose-rate (∼107 Gy/s) radiolysis of both pure air-saturated (0.25 mmol/L O2) and oxygenated (30 µmol/L O2) cell water, in the interval ∼1 ps–10 µs. Our simulation model consisted of randomly irradiating water with single pulses of 5 MeV (LET ∼ 8 keV/µm), 1.5 MeV (LET ∼ 19.5 keV/µm), and 0.7 MeV (LET ∼ 33 keV/µm) protons at 25 °C. Similar to what is observed with low-LET irradiation (∼300 MeV protons, LET ∼ 0.3 keV/µm), our calculations showed that, in pure, aerated water, the concentration of depleted oxygen, [−O2], exhibits a pronounced maximum around ∼0.1–0.2 µs for all three high-LET irradiating protons studied. This maximum increased markedly with increasing LET. As expected, the effect of adding competing scavengers of both hydrated electrons and •OH radicals on the radiolytic O2 depletion in oxygenated cell water (a more bio-mimetic model of cells) irradiated by 5 MeV protons delivered at the same dose rate led to a marked decrease in the maximum of [−O2] around 1 µs. However, contrary to what is observed for low-LET irradiation, we found that the transient O2 consumption is quite substantial under high-LET irradiation conditions. This is explained by the fact that, even though their underlying mechanism of action differs, high-LET particles affect radiolysis yields in a similar way to high dose rates.
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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 | 0.000 |
| 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 | 0.000 | 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".