Electrochemical reactions between iron sulfide minerals and their implications for concrete durability
Bibliographic record
Abstract
It is well known that iron sulphides such as pyrite (FeS2) and pyrrhotite ((Fe1-xS, 0-x-0.125) are unstable in presence of oxygen and moisture, oxidizing to produce sulfuric acid and iron sulphate. Further reactions in concrete cause the formation of expansive reaction products such as ettringite and thaumasite, potentially leading to structural failure. However, the possibility of accelerated oxidation when pyrite and pyrrhotite come to contact with each other or with a steel rebar has not yet been investigated. This type of accelerated oxidation is more commonly known as corrosion when it happens in metals such as steel reinforcements. Since both pyrite and pyrrhotite are semiconductors and therefore sources of electrons, they can, however, promote this type of electrochemical reaction. Using electrochemical techniques, the electrochemical properties of pyrite, pyrrhotite, and steel and their open-circuit potentials were measured at different pH values. A zero resistance ammeter was also used to investigate the possibility of an accelerated oxidation reaction when any two of pyrite, pyrrhotite or steel are in electrical contact. The results show that pyrrhotite oxidation is accelerated in the presence of pyrite and both minerals could lead to an increased corrosion rate for steel rebar. These results are particularly significant as the Maskimo aggregate that was responsible for over $400 million dollars in damage to concrete in the Trois Rivières region of Quebec, Canada contains significant quantities of both pyrite and pyrrhotite. While pyrrhotite alone will cause damage to concrete through expansive reaction products, the combination of the two minerals may have contributed to both the extent of the damage and the small amount of pyrrhotite that was necessary to cause the damage. In addition to the results of the electrochemical studies, on-going work to investigate the electrochemical behaviour of the two minerals in concrete will also be outlined.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".