Microstructure Characterization of Cement Pastes with Recycled Aluminum Spent Pot Lining
Bibliographic record
Abstract
The use of locally available industrial by-products as supplementary cementitious materials and mineral fillers is vital for reducing the embodied carbon of modern concretes. Spent pot lining (SPL), a by-product of the aluminum industry, is massively produced worldwide. SPL treated with the low caustic leaching and liming process (LCLL-ash) is no more hazardous and can be used as cementitious material. This study aims to better understand the microstructure changes of cement paste incorporating aluminum smelter wastes, such as LCLL-ash and synthetic anhydrite. Ground LCLL-ash was used to partially replace cement in cement pastes with a constant water-to-binder ratio of 0.35. A small amount of anhydrite was added to some mixes. This study investigated chemo-micromechanical properties of cement paste systems through multiple techniques, including X-ray diffraction, thermogravimetric analysis, scanning electron microscopy, wavelength-dispersive spectroscopy, and microindentation test. The results showed that the reactive alumina from LCLL-ash modified the hydrated phases with the presence of the carbo-aluminate phases. The pastes containing LCLL-ash exhibited a higher CH, and C-S-H contents refer to the reference, suggesting that LCLL-ash has a slight nucleation effect. Moreover, LCLL cement paste showed an increase in the Ca/Si ratio of the C-S-H phase intermix. Finally, microindentation results revealed that adding anhydrite with 10% LCLL-ash enhanced the mechanical property of the cement paste at 28 days.
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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 | 0.000 |
| 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".