Influence of typical soil minerals on ureolytic bacteria‐induced carbonate precipitation
Bibliographic record
Abstract
Abstract Microbial‐induced carbonate precipitation (MICP) has been widely applied in soil remediation, stabilization and soil carbon storage. This study investigated the effects of different soil minerals on carbonate production during MICP. Aqueous sorption experiments, along with chemical and microstructural analyses, were conducted to monitor the progress and performance of MICP. Montmorillonite significantly enhanced carbonate production, while quartz, kaolinite and goethite adsorbed free Ca 2+ from the solution, slowing the formation of calcium carbonate without affecting the overall carbonate yield. Calcite and vaterite were identified as the primary carbonate phases, with vaterite dominating (accounting for ~90% of the crystalline calcium carbonate). However, montmorillonite substantially increased the proportion of calcite (from 9% to 52%). The enhancement may be attributed to the lower Ca 2+ adsorption capacity of montmorillonite, which helps maintain sufficient Ca 2+ in solution and promote direct calcite precipitation. Additionally, interactions between montmorillonite and ureolytic bacteria may also protect bacteria from being co‐precipitated with carbonate minerals. Soil minerals were also found to accelerate the urease activity of ureolytic bacteria, with goethite having the strongest effect. Since carbonate was not a limiting factor in the experiment, goethite had no significant impacts on carbonate yield. This study sheds light on the potential application of MICP under different soil mineral conditions.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".