Carbon Mineralization with North American PGM Mine Tailings—Characterization and Reactivity Analysis
Bibliographic record
Abstract
Global efforts to combat climate change call for methods to capture and store CO2. Meanwhile, the global transition away from fossil energy will result in increased production of tailings (i.e., wastes) from the mining of nickel and platinum group metals (PGMs). Through carbon mineralization, CO2 can be permanently stored in calcium- and magnesium-bearing mine tailings. The Stillwater mine in Nye, Montana produces copper, nickel, and PGMs, along with 1 Mt of tailings each year. Stillwater tailings samples have been characterized, revealing that they contain a variety of mineral phases, most notably Ca-bearing plagioclase feldspar. Increases in inorganic carbon in the tailings and ion concentration in the tailings storage facilities suggest carbonation has taken place at ambient conditions over time within the tailings storage facilities. Two experiments were performed to simulate carbon mineralization at ambient temperature and pressure with elevated CO2 concentration (10% with N2), revealing that less than 1% of the silicate-bound calcium within the tailings is labile, or easily released from silicate structures at low-cost ambient conditions. The Stillwater tailings could be useful for developing strategies of waste management as production of nickel and PGM minerals increases during the global transition away from fossil energy, but further work is needed to develop a process that can realize their full carbon storage potential.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".