Influence of reactant ratio and preheating conditions on aluminum oxidation in supercritical water for energy recovery
Bibliographic record
Abstract
Characterizing the rate of reaction of aluminum with supercritical water is crucial to the design of future reactors enabling the production of hydrogen and heat on demand. The goal of this study was to reconcile the differences in oxidation rates obtained from two distinct studies that reported significantly different rates while using the same samples reacting at similar temperatures and pressures. Two experimental configurations were used to test how preheating the reactants, either in contact or separately, affects the reaction. Additionally, aluminum samples of varying sizes and quantities were tested at 410 ∘ C and three different densities, to investigate how the ratio between surface area and the amount of water available would affect the oxidation rate. Changing the preheating conditions did not show an effect on reaction rates that could be quantified independently of variations in water availability caused by differences in water density. However, varying the reactant ratio induced up to a threefold reduction in the bulk oxidation rate observed. Increasing the amount of loosely packed aluminum slugs in the same reactor volume led to a reduction in overall linear oxidation rate assuming all surfaces reacted uniformly. These results highlight the importance of packing factor and water availability on the bulk oxidation rate of aluminum. These parameters should be considered in future work studying the kinetics of metal-water reactions, and for the design of practical systems aimed at the continuous production of heat and hydrogen. • Preheating the reactants did not have a significant effect on the oxidation rate. • Increasing the packing factor can reduce the bulk oxidation rate by up to threefold. • The oxidation rate of an individual slug can differ significantly from the bulk average.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".