Solidification–stabilization of organic and inorganic contaminants using portland cement: a literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The treatment of hazardous wastes using cement-based solidification–stabilization (S–S) is of increasing importance as an option for remediating contaminated sites. Indeed, among the various treatment techniques, S–S is one of the most widely used methods for treating inorganic wastes. To enhance the application of S–S and to further develop this technology for site remediation, particularly for organic contaminants, it is important to have a better understanding of the mechanisms involved in the process. The primary objective of this review is to survey the current knowledge in this subject, focusing on (i) cement chemistry, (ii) the effects of inorganic (heavy metals) and organic compounds on cement hydration, and (iii) the mechanisms of immobilization of different organic and inorganic compounds. For heavy metals, cement-based S–S technology has been shown to be effective in immobilizing the contaminants, even without any additives. In applying cement-based S–S for treating organic contaminants, the use of adsorbents such as organophilic clay and activated carbon, either as a pretreatment or as additives in the cement mix, can improve contaminant immobilization in the solidified–stabilized wastes. The concept of degradative solidification–stabilization, which combines chemical degradation with conventional solidification–stabilization, seems promising, although further study is required to assess its technical and economic feasibility.Key words: cement, contaminated soil, immobilization, organics, precipitation, adsorption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it