Mineral Carbonation for Carbon Sequestration: A Case for MCP and MICP
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mineral carbonation is a prominent method for carbon sequestration. Atmospheric carbon dioxide (CO2) is trapped as mineral carbonate precipitates, which are geochemically, geologically, and thermodynamically stable. Carbonate rocks can originate from biogenic or abiogenic origin, whereby the former refers to the breakdown of biofragments and the latter precipitation out of water. Carbonates can also be formed through biologically controlled mechanisms (BCMs), biologically mediated mechanisms (BMMs), and biologically induced mechanisms (BIMs). Microbial carbonate precipitation (MCP) is a BMM occurring through the interaction of organics (extracellular polymeric substances (EPS), cell wall, etc.) and soluble cations facilitating indirect precipitation of carbonate minerals. Microbially induced carbonate precipitation (MICP) is a BIM occurring via different metabolic pathways. Enzyme-driven pathways (carbonic anhydrase (CA) and/or urease), specifically, are promising for the high conversion to calcium carbonate (CaCO3) precipitation, trapping large quantities of gaseous CO2. These carbonate precipitates can trap CO2 via mineral trapping, solubility trapping, and formation trapping and aid in CO2 leakage reduction in geologic carbon sequestration. Additional experimental research is required to assess the feasibility of MICP for carbon sequestration at large scale for long-term stability of precipitates. Laboratory-scale evaluation can provide preliminary data on preferable metabolic pathways for different materials and their capacity for carbonate precipitation via atmospheric CO2 versus injected CO2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it