Lifecycle-State Environmental Compliance of Sediment–Fly Ash One-Part Geopolymers: A Secondary Reanalysis Integrating Demolition, Carbonation, and Service-State Leaching
Bibliographic record
Abstract
Environmental qualification of waste-derived binders is commonly reported in separate test domains such as crushed material, carbonated material, and intact monoliths, even though these states correspond to different stages of one product life cycle. As a result, a practical question remains insufficiently resolved for circular construction: can a geopolymer be environmentally acceptable during service while becoming problematic after end-of-life fragmentation? This manuscript presents a transparent secondary reanalysis of a complete benchmark dataset for one-part sediment–fly ash geopolymers containing 0, 15, 30, and 50% dredged sediment. Rather than introducing new experiments, the study recalculates and integrates precursor batch leaching, crushed-mortar leaching at 7, 28, and 56 days, carbonation-conditioned crushed-mortar leaching at 60 and 180 days, and 64-day dynamic surface leaching of monoliths within a single lifecycle-state framework. Four state-sensitive metrics are used to organize the comparison: threshold utilization ratio, activation remobilization factor, carbonation recovery factor, and service-state safety margin. Internal cross-checking of the recalculated ratios confirms a clear divergence between demolition-state and service-state behavior. Mean crushed-state arsenic and selenium concentrations exceeded non-hazardous-waste thresholds by 21–29% and 20–38%, respectively, whereas molybdenum, antimony, and chromium remained well below the same limits. Relative to precursor powders, activation increased arsenic release by factors of 7.85–25.81 and selenium release by factors of 2.18–3.34, while chromium release fell to 0.04–0.17 of precursor values. Carbonation lowered pH by 2.61–2.71 units and reduced arsenic by 22.5–50.6%, but did not provide a comparable benefit for selenium at higher sediment contents. In contrast, all 64-day monolith releases remained below Dutch Soil Quality Decree limits; arsenic reached only 19.3–24.0% of the allowable value and sulfates 4.5–24.9%. The contribution is therefore methodological and regulatory rather than experimental: qualification protocols for sediment-based geopolymers should distinguish service-state safety from end-of-life fragmentation risk and treat carbonation as a selective, not universal, remediation pathway.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".