Microbial and abiotic approaches to carbonating stevensite: what works and what doesn’t
Bibliographic record
Abstract
Although Mg-rich clays are common in sediments, rocks and mineral wastes produced by mining, little is known about how they might contribute to carbon dioxide (CO 2 ) sequestration. The purpose of this study is to use previously tested mineral carbonation techniques at ambient temperature and pressure on the low-temperature Mg-rich clay mineral, stevensite [(Na 2 y · n H2O)(Mg 3– y X y )Si 4 O 10 (OH) 2 ]. This study tests the hypothesis that authigenic 2:1 Mg-clays, while less reactive to CO 2 than other Mg-rich minerals ( e.g., brucite and serpentines), can transform into carbonates under high alkalinity conditions (100–1187 mEq/L, pH = 9–11). Abiotic experiments using either CO 2(g) bubbling or addition of HCl into alkaline lake water and deionized water followed by addition of NaOH to induce a pH swing did not result in carbonation of synthetic stevensite. However, the abiotic experiments gave important insights into the role of silicate dissolution for alkalinity generation and carbon sequestration. Incubation of a natural biofilm with stevensite in highly alkaline lake water resulted in precipitation of the hydrated magnesium carbonate mineral, dypingite [Mg 5 (CO 3 ) 4 (OH) 2 ·∼5H2O], on the surface of synthetic stevensite as observed with X-ray diffraction and scanning electron microscopy. A Mg-rich clay phase was also precipitated in an incubation experiment containing biofilm but without the addition of synthetic stevensite. These results provide important insights and observations that can contribute towards geochemical models of environments where we observe carbonation of clay minerals, such as alkaline lakes, which can serve as natural analogues for industrial carbon sequestration projects.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".