Radiolysis of Sub- and Supercritical Water Induced by 10B(n,α)7Li Recoil Nuclei at 300–500 °C and 25 MPa
Bibliographic record
Abstract
(1) Background: Generation IV supercritical water-cooled reactors (SCWRs), including small modular reactor (SCW-SMR) variants, are pivotal in nuclear technology. Operating at 300–500 °C and 25 MPa, these reactors require detailed understanding of radiation chemistry and transient species to optimize water chemistry, reduce corrosion, and enhance safety. Boron, widely used as a neutron absorber, plays a significant role in reactor performance and safety. This study focuses on the yields of radiolytic species in subcritical and supercritical water exposed to 4He and 7Li recoil ions from the 10B(n,α)7Li fission reaction in SCWR/SCW-SMR environments. (2) Methods: We use Monte Carlo track chemistry simulations to calculate yields (G values) of primary radicals (e−aq, H•, and •OH) and molecular species (H2 and H2O2) from water radiolysis by α-particles and Li3⁺ recoils across 1 picosecond to 0.1 millisecond timescales. (3) Results: Simulations show substantially lower radical yields, notably e−aq and •OH, alongside higher molecular product yields compared to low linear energy transfer (LET) radiation, underscoring the high-LET nature of 10B(n,α)7Li recoil nuclei. Key changes include elevated G(•OH) and G(H2), and a decrease in G(H•), primarily driven during the homogeneous chemical stage of radiolysis by the reaction H• + H2O → •OH + H2. This reaction significantly contributes to H2 production, potentially reducing the need for added hydrogen in coolant water to mitigate oxidizing species. In supercritical conditions, low G(H₂O₂) suggests that H2O2 is unlikely to be a major contributor to material oxidation. (4) Conclusions: The 10B(n,α)7Li reaction’s yield estimates could significantly impact coolant chemistry strategies in SCWRs and SCW-SMRs. Understanding radiolytic behavior in these conditions aids in refining reactor models and coolant chemistry to minimize corrosion and radiolytic damage. Future experiments are needed to validate these predictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".