Rare Earth Element Speciation in Coal and Coal Combustion Byproducts: A XANES and EXAFS Study
Bibliographic record
Abstract
Transitioning to a low-carbon economy, necessary to mitigate the impacts of anthropogenic climate change, will lead to a significant increase in demand for critical minerals such as rare earth elements (REE). Meeting these raw materials requirements will be challenging, so there is increasing interest in new sources of REE including coal combustion byproducts (CCBs). Extraction of REE from CCBs can be advantageous as it involves reusing a waste product, thereby contributing to the circular economy. While a growing body of literature reports on the abundance of REE in CCBs globally, studies examining the key factors which control their recovery, including speciation and mode of occurrence, are lacking. This study employed synchrotron-based X-ray absorption spectroscopy to probe the speciation and local bonding environment of yttrium in coals and their associated CCBs. Linear Combination Fitting identified silicate and phosphate minerals as the dominant REE-bearing phases. Taken together with the results of extended X-ray absorption fine structure (EXAFS) curve fitting, we find there is minimal transformation in the REE host phase during combustion, indicating it is transferred in bulk from the coals to the CCBs. Accordingly, these findings can be incorporated into the development of an efficient, environmentally conscious recovery process.
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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) | 0.001 | 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".