Lignin–MgO-based loess stabilization incorporating CO<sub>2</sub> mineralization
Bibliographic record
Abstract
While increasing studies have incorporated carbonation into chemical stabilization of soils, a scarcity of research has focused on carbonation progress, carbonation effects on pH, dynamic properties, and brittleness of stabilized soils. None of these studies has been concerned with loess. To address these issues, this study proposes a novel and sustainable approach, which used lignin–MgO to integrate mineral carbonation into loess stabilization. This integrated approach enabled realizing mechanical property enhancement, alkalinity reduction, and CO2 sequestration. A comprehensive experimental program was conducted to evaluate the efficacy of this method. The results revealed that the proposed method could mineralize approximately 6% of CO2 relative to the dried mass of loess–binder mixture within 24 h of carbonation; addition of CO2 reduced pH values in stabilized loess from 10.3 to 8.2, and carbonation increased the early strength of stabilized loess by 50%. Furthermore, addition of lignin was found to reduce the brittleness and improve the freeze–thaw durability of stabilized loess. Finally, based on comprehensive mineralogical and microstructural analyses, a conceptual model was proposed to explain the mechanisms of carbonation and stabilization for loess.
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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".