Microbially mediated carbon dioxide removal for sustainable mining
Bibliographic record
Abstract
The climate crisis and rising demand for critical minerals necessitate the development of novel carbon dioxide removal and ore processing technologies.Microbial processes can be harnessed to recover metals from and store carbon dioxide within mine tailings to transform the mining industry for a greener and more sustainable future.The rise of vascular land plants (380-350 Ma) led to an immense transfer of carbon dioxide (CO 2 ) from the atmosphere to the geosphere via photosynthesis and organic matter burial, leading to fossil fuel formation [1].In a matter of centuries, humans have largely reversed this natural carbon storage through petroleum extraction and combustion, having emitted 2400 ± 240 Gt of CO 2 to the atmosphere since 1850 [2].Anthropogenic CO 2 and other greenhouse gases (GHG) have already caused a 1.1˚C increase above pre-industrial global surface temperatures [2].The most recent Intergovernmental Panel on Climate Change (IPCC) report indicates that even if warming can be limited to 1.5˚C, we will face multiple climate impacts in the near-term [2].Therefore, we must urgently phase out fossil fuels and recapture the excess CO 2 we have already emitted to the atmosphere.Commensurate to the climate crisis is the rising demand for critical minerals with the development of green energy and technologies, including solar panels, electric vehicles, and batteries.Consequently, society will demand greater mineral resources while expecting the mining sector to achieve carbon neutrality.While the IPCC 2022 Mitigation of Climate Change report outlines several carbon dioxide removal (CDR) pathways [2], one option that warrants greater attention is the consumption of CO 2 through enhanced weathering and carbonation of minerals at mine sites.CO 2 reacts with major rock-forming minerals to release dissolved cations (Ca 2+ , Mg 2+ , K + ) and inorganic carbon (HCO 3 -), which are eventually transported to the oceans, storing carbon as alkalinity and solid carbonate minerals (e.g., CaCO 3 ) such as those in corals and limestone [3].Weathering is the breakdown of rocks through chemical and physical processes and is an important regulator of atmospheric CO 2 .Silicate and carbonate rock weathering naturally consumes an estimated 1.2 to 3.0 Gt CO 2 /yr globally, a number that will increase in response to warmer temperatures and higher CO 2 concentrations [4,5].Microbes contribute to weathering by attaching to mineral surfaces, generating rock-dissolving acids through fermentation and respiration, releasing chelating compounds, and using minerals as starting materials for energetically favourable redox reactions.Different microbes target different minerals, and their growth depends on
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".