Scavenging of “dry” electrons prior to hydration by azide ions: effect on the formation of H<sub>2</sub> in the radiolysis of water by <sup>60</sup>Co γ-rays and tritium β-electrons
Bibliographic record
Abstract
In this study, we use Monte Carlo track chemistry simulations to show that “dry” secondary electrons, precursors of the “hydrated” electron (e−aq), can be scavenged on the sub-picosecond time scale prior to hydration, by a high concentration (>0.1–1 M) of azide ions (N3−) in water irradiated with 60Co γ-rays and tritium β-electrons at 25 °C. This is a striking result, as N3− is known to react very slowly with e−aq. These processes tend to significantly reduce the yields of H2 as observed experimentally. For both energetic Compton electrons (“linear energy transfer”, LET ∼ 0.3 keV/µm), which are generated by the cobalt-60 γ-rays, and 3H β-electrons (LET ∼ 6 keV/µm), our H2 yield results confirm previous Monte Carlo simulations, which indicated the necessity of including the capture of the precursors to e−aq. Interestingly, our calculations show no significant changes in the scavenging of “dry” electrons at high azide concentrations in passing from γ-radiolysis to tritium β-radiolysis (i.e., with LET). This led us to the conclusion that the higher H2 yield observed experimentally for 3H β-electrons compared with 60Co γ-rays is mainly explained by the difference in the radiation track structures during the chemical stage (>1 ps). The higher LET of tritium β-electrons leads to more molecular products (H2 in this case) in tritium radiolysis than in γ-radiolysis. Finally, a value of ∼0.5 nm was derived for the reaction distance between N3− and the “dry” electron from the H2 yields observed in 60Co γ-radiolysis at high N3− concentrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".