Spatiotemporal evolution of biomineralization in heterogeneous pore structure
Bibliographic record
Abstract
A fundamental understanding of the CaCO3 precipitation process in the pore network of geomaterials is important to uncover the mechanism behind the evolution of the engineering properties of geomaterials during the microbially induced carbonate precipitation (MICP) treatment. However, the details about the CaCO3 precipitation process and its interaction with the flow field at the pore scale are not well understood. In the current work, the CaCO3 precipitation process and flow field in a heterogeneous chip composed of two pore bodies, four pore throats of different sizes, and two dead-end pores are presented. The test results show that solutions can percolate through all four pore throats and diffuse into the dead-end pores at the beginning of the tests. As a result, CaCO3 can be precipitated across the chip with some difference in the number, shape, and size of crystals. Fine pore throats are more likely to be clogged, leading to solution percolating through coarse channels, thereby increasing the amount of CaCO3 in coarse channels. Precipitation in coarse channels is also ceased after a certain duration despite the continued injection of the solution. Our work provides insight into the CaCO3 precipitation process in a representative pore element, which can help to understand the mechanism behind the evolution of engineering properties and establish simulation models to predict the engineering properties of geomaterials treated by the MICP method.
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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".