Advancements in Heavy Metal Stabilization: A Comparative Study on Zinc Immobilization in Glass-Portland Cement Binders
Bibliographic record
Abstract
Recent literature has exhibited a growing interest in the utilization of ground glass powder (GP) as a supplementary cementitious material (SCM). Yet, the application of SCMs in stabilizing heavy metallic and metalloid elements remains underexplored. This research zeroes in on zinc stabilization using a binder amalgam of GP and ordinary Portland cement (OPC). This study juxtaposes the stability of zinc in a recomposed binder consisting of 30% GP and 70% OPC (denoted as 30GP-M) against a reference binder of 100% CEM I 52.5 N (labeled reference mortar, RM) across curing intervals of 1, 28, and 90 days. Remarkably, the findings indicate a heightened kinetic immobilization of Zn at 90 days in the presence of GP—surging up to 40% in contrast to RM. Advanced microstructural analyses delineate the stabilization locales for Zn, including on the periphery of hydrated C3S particles (Zn–C3S), within GP-reactive sites (Si*–O–Zn), and amid C–S–H gel structures, i.e., (C/Zn)–S–H. A matrix with 30% GP bolsters the hydration process of C3S vis-à-vis the RM matrix. Probing deeper, the microstructural characterization underscores GP’s prowess in Zn immobilization, particularly at the interaction zone with the paste. In the Zn milieu, it was discerning a transmutation—some products born from the GP–Portlandite reaction morph into GP–calcium–zincate.
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 | 0.001 | 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".