A comparative study of low-temperature dolomite formation driven by exopolymers from hypersaline microbial mats and clays
Bibliographic record
Abstract
Recent studies have shown that surfaces rich in functional groups can facilitate nucleation of low-temperature (low-T) dolomite. However, to date few experiments have investigated the details of the nucleation mechanisms nor determined how naturally occurring substances influence crystallization pathways of low-T dolomite. In this study we isolated and characterized extracellular polymers (EPS) from a hypersaline sabkha, as well as clay standards and performed mineralization experiments with these surfaces as seed material. Mineralization experiments were carried out in batch reactors in a solution supersaturated with respect to dolomite. Our results showed that over a five-month period the rate of low-t dolomite formation in samples seeded with EPS was significantly higher compared to those seeded with clay. The observed rates were also shorter than previously published experiments using bacterial cultures (e.g., Kenward et al., 2013, Deng et al., 2019). Precipitates from samples seeded with EPS show crystallization of dolomite pre-cursors after several days and assemblages of dolomite crystals from 10-days forward [Figure 1]. Measurements from EPS seeded samples showed significant depletion of Ca and Mg in solution within one week as well as elevated alkalinity that coincided with dolomite crystallization. Samples seeded with clay and control samples without seed materials showed little and no dolomite crystallization, respectively, during the same time-frame. Overall, the results of this work shows that EPS isolated from microbial mats are preferential nucleation surfaces for carbonate precipitation when compared to clays. Additionally, the findings reveal that the properties of nucleation surfaces such as functional group type and concentration are a key factor driving low-T dolomite precipitation.
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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) | 0.000 | 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 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".