Application of Nature-Based Nanotechnology for Enhancing Biocementation in Clay by Microbially Induced Calcium Carbonate Precipitation
Bibliographic record
Abstract
Abstract Microbially induced calcium carbonate precipitation (MICP) is a nature-based soil stabilization technique, which has been developed for the past 20 years. Nevertheless, the application of the MICP method for stabilization of clays has received less attention in the literature as it is necessary to boost its effect in various ways to ensure its efficiency. Simultaneous use of the MICP method with natural nanomaterials can be a way to enhance the performance of MICP. In this study, the effect of nano-CaCO3 and nano-SiO2 on enhancing the MICP processes in a kaolinite clay is investigated. The nanomaterials and bacteria and cementation solutions were added to the host soil with different percentages. A series of unconfined compressive strength (UCS) tests was conducted to study the effect of nano-enhanced bio-cementation on soil strength after different curing times. Furthermore, the microstructure of the treated soils was investigated by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), Raman spectroscopy, chemical decomposition and X-ray diffraction (XRD) analyses. It was observed that the amount of calcium carbonate and UCS increased in all nano-bio-treated samples with curing time. SEM images of the modified samples showed that the soil texture becomes flocculated with the addition of nano-SiO2 with the MICP method and calcium carbonate was formed in the voids between the clay minerals, which increased the strength of the soil. XRD analyses and Raman spectroscopy confirmed the presence of calcium carbonate in the soil texture.
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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 | 0.000 |
| 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".