Anaerobic Corrosion Reaction Kinetics of Nanosized Iron
Bibliographic record
Abstract
Nanosized Fe0 exhibits markedly different anaerobic corrosion rates in water compared to that disseminated in moist quartz sand. In water, hydrogen production from corrosion exhibits an autocatalytic style, attaining a maximum rate of 1.9 mol kg(-1) d(-1) within 2 d of reaction. The rate then drops sharply over the next 20 d and enters a period of uniformly decreasing rate, represented equally well by first-order or diffusion-controlled kinetic expressions. In quartz sand, hydrogen production exhibits a double maximum over the first 20 d, similar to the hydration reaction of Portland cement, and the highest rate attained is less than 0.5 mol kg(-1) d(-1). We ascribe this difference in early time corrosion behavior to the ability of the released hydrogen gas to convect both water and iron particles in an iron/water system and to its inability to do so when the iron particles are disseminated in sand. By 30 d, the hydrogen production rate of iron in quartz sand exhibits a uniform decrease as in the iron/water system, which also can be described by first-order or diffusion-controlled kinetic expressions. However, the corrosion resistance of the iron in moist sand is 4 times greater than in pure water (viz. t1/2 of 365 d vs 78 d, respectively). The lower rate for iron in sand is likely due to the effect of dissolved silica sorbing onto iron reaction sites and acting as an anodic inhibitor, which reduces the iron's susceptibility to oxidation by water. This study indicates that short-term laboratory corrosion tests of nanosized Fe0/water slurries will substantially underestimate both the material's longevity as an electron source and its potential as a long-term source of hydrogen gas in groundwater remediation applications.
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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 | 0.001 |
| 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".