Microbiologically Induced Calcite Precipitation Mediated by <em>Sporosarcina pasteurii</em>
Bibliographic record
Abstract
The particular bacterium under investigation here (S. pasteurii) is unique in its ability, under the right conditions, to induce the hydrolysis of urea (ureolysis) in naturally occurring environments through secretion of an enzyme urease. This process of ureolysis, through a chain of chemical reactions, leads to the formation of calcium carbonate precipitates. This is known as Microbiologically Induced Calcite Precipitation (MICP). The proper culture protocols for MICP are detailed here. Finally, visualization experiments under different modes of microscopy were performed to understand various aspects of the precipitation process. Techniques like optical microscopy, Scanning Electron Microscopy (SEM) and X-Ray Photo-electron Spectroscopy (XPS) were employed to chemically characterize the end-product. Further, the ability of these precipitates to clog pores inside a natural porous medium was demonstrated through a qualitative experiment where sponge bars were used to mimic a pore-network with a range of length scales. A sponge bar dipped in the culture medium containing the bacterial cells hardens due to the clogging of its pores resulting from the continuous process of chemical precipitation. This hardened sponge bar exhibits superior strength when compared to a control sponge bar which becomes compressed and squeezed under the action of an applied external load, while the hardened bar is able to support the same weight with little deformation.
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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".