The Influence of Chemical Activity Models on the Description of Ion Transport through Micro-Structured Cementitious Materials
Bibliographic record
Abstract
The significance of ion activity in transport through a porous concrete material sample with steel rebar in its center and bathing solution is presented. For the first time, different conventions and models of ion activity are compared in their significance and influence on the ion fluxes. The study closes an interpretational gap between ion activity in a stand-alone (stagnant) electrolyte solution and ion transport (dynamic) through concrete pores. Ionic activity models developed in stationary systems, namely, the Debye–Hückel (DH), extended DH, Davies, Truesdell–Jones, and Pitzer models, were used for modeling the transport of ions driven through the activity gradient. The activities of ions are incorporated into a frame of the Nernst–Planck–Poisson (NPP) equations. Calculations were done with COMSOL software for a real concrete microstructure determined by X-ray computed tomography. The concentration profiles of four ions (Na+, Cl−, K+, OH−), the ionic strength, and the electric potential in mortar (with pores) and concrete samples (with aggregates and pores) are presented and compared. The Pitzer equation gave the most reliable results for all systems studied. The difference between the concentration profiles calculated with this equation and with the assumption of the ideality of the solution is negligible while the potential profiles are clearly distinguishable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".