Supercritical Fluid Extraction of Rare Earth Elements from Nickel Metal Hydride Battery
Bibliographic record
Abstract
Today’s world relies upon critical green technologies that are made of elements with unique properties irreplaceable by other materials. Such elements are classified under strategic materials; examples include rare earth elements that are in increasingly high demand but facing supply uncertainty and near zero recycling. For tackling the sustainability challenges associated with rare earth elements supply, new strategies have been initiated to mine these elements from secondary sources. Waste electrical and electronic equipment contain considerable amounts of rare earth elements; however, the current level of their recycling is less than 1%. Current recycling practices use either pyrometallurgy, which is energy intensive, or hydrometallurgy that rely on large volumes of acids and organic solvents, generating large volumes of environmentally unsafe residues. This study put emphasis on developing an innovative and sustainable process for the urban mining of rare earth elements from waste electrical and electronic equipment, in particular, a nickel metal hydride battery. The developed process relies on supercritical fluid extraction utilizing CO 2 as the solvent, which is inert, safe, and abundant. This process is very efficient in the sense that it is safe, runs at low temperature, and does not produce hazardous waste while recovering ∼90% of rare earth elements. Furthermore, we propose a mechanism for the supercritical fluid extraction of rare earth elements, where we considered a trivalent rare earth element state bonded with three tri- n -butyl phosphate molecules and three nitrates model for the extracted rare earth tri- n -butyl phosphate complex. The supercritical fluid extraction process has the double advantage of waste valorization without utilizing hazardous reagents, thus minimizing the negative impacts of process tailings.
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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".