How does the pore solution chemistry influence the passivation of reinforced alkali‐ and salt‐activated slag materials?
Bibliographic record
Abstract
Abstract Service life predictions of reinforced concrete structures are underpinned by the passivation film chemistry, structure, and thickness. In this work, we present how the formation of passive films at the steel–concrete interface of reinforced alkali‐ and salt‐activated slag materials can be affected by the pore solution chemistry, namely, pH, E h , and chemical composition. Thermodynamic simulations are used to illustrate the time‐dependent changes to the pore solution chemistry, where estimated electrical conductivities of the pore solution are used as a single‐value parameter to understand the solution complexity and capacity for charge transfer. A set of passivation reactions are proposed to understand the effects of OH − and HS − (reduced sulfur species) competition on the passivation pathways. These passivation reactions become more complex considering that a reducing pore solution might not establish until 3 days into the curing process—a crucial factor explaining the significant differences in the phase composition of passive films of activated slag materials (FeOOH, FeS). This study sheds light on recent progress in understanding these initial passivation reactions, emphasizing the essential role of material design and, therefore, the pore solution chemistry of these cements, along with significant insights for vital research and development concerning the corrosion durability of activated slag 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 | 0.001 |
| 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".