Low linear energy transfer radiolysis of supercritical water at 400 °C: <i>in situ</i> generation of ultrafast, transient, density-dependent “acid spikes”
Bibliographic record
Abstract
There is growing interest in the radiation chemistry of supercritical water (SCW), as its use as a coolant in a nuclear reactor (Generation IV) is the logical evolution of the current (Generation III or less) water-cooled reactors. However, current knowledge about the potential effects of water radiolysis in a Gen-IV supercritical water-cooled reactor (SCWR) is incomplete. In this work, Monte Carlo track chemistry simulations of the low linear energy transfer (LET) radiolysis of SCW (H2O) at 400 °C are used in combination with a spherical "spur" model to study the effect of water density on the in situ radiolytic formation of H3O+ ions and the corresponding abrupt, transient, highly acidic pH response ("acid spikes") that is observed immediately after irradiation. The magnitude and duration of this acidic pH effect depend on the water density in the considered range of 0.15-0.6 g cm-3. It is strongest at times less than a few tens of picoseconds with the pH remaining nearly constant at ∼1.6 and 1.9 for the highest ("liquid-like") and lowest ("gas-like") density, respectively. At longer times, the pH gradually increases for all densities and finally reaches a constant value corresponding to the non-radiolytic, pre-irradiation concentration of H3O+, due to the autoprotolysis of water. Our results show that the lower the density of the water, the longer the time required to reach this constant value, ranging from ∼50 ns at 0.6 g cm-3 (pH ∼ 5.6) to ∼1 μs at 0.15 g cm-3 (pH ∼ 8.5). The generation of these highly acidic pH fluctuations around the "native" radiation tracks, though local and transient, raises questions about the potential implications of this effect in proposed Gen-IV SCW-cooled reactors regarding corrosion and degradation of materials.
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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 | 0.000 |
| 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) | 0.001 | 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".