Dissolution and recrystallization behavior of microbially induced calcium carbonate: influencing factors, kinetics, and cementation effect
Bibliographic record
Abstract
Microbially induced calcium carbonate precipitation (MICP) is a nature-based biomineralization method with significant potential in geotechnical engineering. Despite the extensive previous studies on this technology, the stability of calcium carbonate crystals formed during the MICP process and its impact on cementation effectiveness remain unclear. This study investigates the effects of varying curing conditions and durations on the stability of microbially induced calcium carbonate crystals. Throughout the curing period, pH levels of the solutions and the mass of calcium carbonate samples were monitored. Crystal morphology, crystalline composition, and cementation properties were examined using scanning electron microscopy, X-ray diffraction, and ultrasonic oscillation tests. The findings reveal that vaterite remained stable in MICP or bacterial solution but quickly dissolved in deionized water. While most vaterite underwent dissolution and recrystallization within the first day of curing, the presence of organic matter in the crystals led to 10%–20% of the vaterite remaining undissolved after 28 days. The dissolution of vaterite tended to promote the growth of pre-existing calcite polymorph rather than forming new ones. Extended curing periods in deionized water increased the proportion of dissolved vaterite, resulting in higher calcite content and enhanced cementation properties, which provides new insights for optimizing MICP applications in geotechnical engineering.
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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 | 0.000 | 0.001 |
| 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".