Optimization of Supercritical Fluid Extraction of Rare Earth Elements from Complex Ores Using a Tributyl Phosphate-Nitric Acid Adduct
Bibliographic record
Abstract
This study introduces a new approach to extracting rare earth elements (REEs) from a Canadian ore concentrate, employing supercritical fluid extraction (SCFE) with supercritical carbon dioxide (sc-CO 2 ) as the solvent and a tributyl phosphate-nitric acid (TBP-HNO 3 ) adduct as the chelating agent. Addressing the environmental and safety concerns of traditional extraction methods, this research explores an eco-friendly and efficient SCFE technique, enhanced by a preliminary NaOH cracking step, to achieve nearly complete extraction efficiency of REEs. Characterization of the ore pre- and postextraction was thoroughly carried out using X-ray diffraction (XRD), scanning electron microscopy energy dispersion spectroscopy (SEM-EDX), and Raman Spectroscopy, revealing significant alterations in the mineralogical structure that facilitate the SCFE process. Focusing on the distribution and accessibility of REEs in feed concentrate, NaOH cracked samples, and SCFE residue, this investigation reveals the predominant presence of REE-bearing minerals in the initial and cracked samples, particularly within zircon structures. A notable transformation of iron from hematite to magnetite, absent in the feed but present in postprocessing samples, suggests a reduction process facilitated by high-temperature NaOH cracking. The findings emphasize the complexity of REE extraction from mineral matrices and the potential of integrating SCFE with NaOH cracking for improved results. The study optimized the operational parameters for NaOH cracking and SCFE, demonstrating their crucial role in maximizing REE efficiencies. An empirical model was used to quantify how these parameters influence extraction efficiency, providing insights into the SCFE process mechanisms and identifying optimal conditions. Our findings highlight the potential of SCFE as a sustainable alternative for REE extraction from primary resources with complex matrices. By significantly reducing hazardous waste and potentially utilizing atmospheric CO 2, this method aligns with global sustainability goals. This research not only contributes to advancing REE extraction technologies but also highlights the importance of exploring green chemistry solutions in critical material recovery for future technologies.
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 | 0.000 | 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 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".