Mechanical, Structural, and Environmental Properties of Building Cements from Valorized Sewage Sludges
Bibliographic record
Abstract
Building materials can enable the recycling of sewage sludge from tannery wastewater treatment by infiltration/percolation over coal and clay waste. The process avoids energy-intensive operations and yields a stable and environmentally friendly product. The sludge under study is mainly composed of SiO2, CaO, Al2O3, and Fe2O3, which is convenient to replace the mortar in cement. Different mortars were made by substituting a variable amount of sludge, from 0 to 30%, into the standard cement. The microstructure and mechanical properties of the mortar specimens were characterized after curing for 7 days and 28 days. The best properties were obtained with 15% sludge. Above 15%, the strength decreases at an early stage, as confirmed by SEM and XRD analysis, with more voids and ettringites at larger sludge content. The leaching tests of the mortar confirm that the cumulative values of heavy metals are far below the Deutsch regulatory limits (NEN 7043), justifying retention of the metals in the matrix. Radiological assessment of the sludge mortars also confirms their safety with the values of naturally occurring radioactive materials, surface radon exhalation and annual effective dose far below the required limits. The study suggests that 15% sludge can be used to sustainably replace cement and meet building safety requirement standards.
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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 | 0.001 | 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".