Enhancing the Mechanical Properties and Sustainability of Polymer-Modified Cementitious Tile Adhesives Using Recycled Materials
Bibliographic record
Abstract
The present scientific article aims to prepare a new cementitious tile adhesive with good mechanical properties, such as adhesion strength, shear adhesion strength, and slip resistance.In addition, the research highlights the significance of eco-friendly practices that utilize recycled resources in pre-packaged cementitious mortar products.These materials include sewage water sludge, cement kiln dust, and river soil.Mixing these elements into mortar recipes may help reduce construction waste, encourage the conservation of resources, and lessen the industry's impact on the environment by reusing and recycling materials that would otherwise go to landfills.Aside from improving the mortar's performance, this new method helps achieve sustainability in construction by recycling waste items and thereby decreasing the environmental impact of building materials.Therefore, commercial mortar formulations and three polymer-modified mortar formulations were prepared to investigate the effect of sewage water sludge, cement kiln dust, and river soil on the performance of polymer-modified cementitious adhesives In this work, the starting raw materials and hardened mortar specimens (28 days) of the prepared mortar formulations were well-characterized using various scientific techniques, including infrared Fourier transform spectrometry (FTIR) and X-ray fluorescence (XRF) for chemical oxide composition.To achieve the study's goals, percentages of sewage water sludge, cement kiln dust, and river soil were incorporated into the commercially prepared mortar formulations in the range of 6% to replace the silica sand used in the formulation.Additionally, different percentages of sewage water sludge were incorporated into the newly prepared mortar formulations in the range of 4%, 6%, 8%, and 10% to replace the silica sand used in the formulation.The XRF results indicated that the sewage water sludge, cement kiln dust, and river soil are mainly composed of sodium oxide (Na2O), aluminum oxide (Al2O3), silicon dioxide (SiO2), potassium oxide (K2O), calcium oxide (CaO), and iron oxide (Fe2O3), with an average particle size of 0.3-0.5 m.The results also show an improvement in the adhesion strength, shear adhesion strength, and slip resistance of the prepared polymer-modified cementitious adhesive mortar formulation when using the optimum recycled material (sewage water sludge) with an additional percentage of 10%.Moreover, the experimental results of the prepared mortar formulations demonstrate that increasing the sewage water sludge content enhances the adhesion strength, shear adhesion strength, and slip resistance, providing evidence in favor of its use as a sustainable building material that contributes to reducing waste and resource consumption.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".