Development of green concrete from industrial wastes and carbon dioxide
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
This thesis work successfully demonstrated the attainment of a fully waste-derived concrete material through the employment of industrial wastes and carbon dioxide. The aggregate and cement components of this green concrete were artificially synthesized from waste residues originating from the steelmaking and waste-incineration processes, respectively. The selection of these waste materials was based on their convenient abundance, compositional suitability, and as-received fineness. Carbonation was carried out successively through different stages of processing for the purpose of activating strength and converting gaseous CO2 into solid carbonates. Graded angular aggregates were generated from the carbonation of steelmaking slag, which was found to be composed mainly of β and γ di-calcium silicate polymorphs. After optimum processing, the aggregates resembled a very resilient, ceramic-like monolith material. Carbonation of steel slag generated a hardened C-S-H/CaCO3 paste, where C-S-H formed the binding medium while the nano-CaCO3 precipitates acted as the reinforcing composite. Additionally, waste-derived cement was produced from a stoichiometric mix of incinerator fly ash and waste-lime at 1000°C. The main reactive clinker phases generated were chloro-ellestadite and β di-calcium silicate. This binder did not possess hydraulic behaviour but effectively consolidated upon carbonation activation, forming a binding matrix comprised of gypsum, C-S-H, and CaCO3. The final green concrete product obtained from combining the two stable waste-derived components was comparable in performance to commercial benchmark concrete. Concrete so produced consumes no natural resources and can hypothetically sequester up to 12.6 million tons of CO2 per year if the Masonry block industries in United States and Canada were to adopt the prescribed methodologies.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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