Application of Nature-Based Nanotechnology for Enhancing Biocementation in Clay by Microbially Induced Calcium Carbonate Precipitation
Bibliographic record
Abstract
<title>Abstract</title> 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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".