Rare Erath Elements: Their Geological Sources and the Potential of Organosulfonic Acids For Rare Earth Elements Leaching from Coal Ash and Process Optimization
Bibliographic record
Abstract
The orbital elactronic structure of Rare Earth Elements (REEs) contains many unpaired electrons which render them capable of storing large amount of magnetic energy in addition to being critical for hitech applications. Geological deposits are their conventional sources, and the current supply chain relies on production from these deposits. However, given their critical roles in the anticipated global energy transition, there is the need to explore other viable sources to supplement current and future supplies chains. REEs occur in coal as accessory minerals and their concentration in coal ash to levels that rival those of geological deposits has been estab;ished by sophisticated analytical chemical methods. Conmventional hydrometallurgical processes rely on acid leaching, using tioxic mineral acids. Meanwhile, organosulfonic acids have pKa values that rival those of conventional minerals acid and can, therefore, be used in hydrometallurgy but their uses in this regard are not well documented in the literature. In this extensive review, we have covered geological sources of REEs exaustively in addition to showing the potential of organosulfonic acids as environmentally benign lixiviants for REEs extraction from coal ash. We have also shown how process optimization can be achieved using advance technologies while using organisulfonic acids. Moreover, we have shown current and future global market trends regarding the production of select organosulfonic acids, and the anticipated global increase in their production motivates the use of organosulfonic acids as viable lixiviants for REEs extraction from caol ash deposits.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".