Wood fly ash and blast furnace slag management by alkali-activation: Trace elements solidification and composite application
Bibliographic record
Abstract
Wood and biomass are burned in many industries as a sustainable energy source. The large quantities of fly ash produced must be landfilled, leading to environmental concerns. Precipitator wood fly ash (PFA) and ground granulated blast furnace slag (BFS) have been used in this study to prepare alkali-activated composites to manage and recycle the fly ash. After an essential characterization, the influence of parameters such as PFA and BFS content, alkaline activator content (silica moduli of 0, 0.82, 1.32), curing method, and curing duration on the mechanical, chemical, and microstructural properties of the samples have been studied through compressive strength, density, FTIR, and SEM-EDS investigations. The environmental safety and influence of polycondensation on heavy metal stabilization have been examined through ICP-MS. The results prove that oven and hydrothermal curing obtain the early age strength. Despite the variations of strength with duration and type of curing, the compressive strength of samples after 28 days of curing tends to close values for a constant PFA/BFS ratio, due to which the need for energy-intensive curing methods is addressed. ICP-MS shows that the composites can suitably solidify As, Cd, Ba, Cr, Pb, Mo, Se, Hg, Sr, Cu, and Zn. On the other hand, the composites were almost incapable of stabilizing Co and V. Unlike the case for mechanical properties; higher PFA content favours hazardous metal stabilization through polycondensation.
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.
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".