Supercritical Fluid Extraction of Critical Metals from Postconsumer Products: Process Development and Mechanistic Investigation
Bibliographic record
Abstract
With the growing awareness to protect the urban environment, the use of green technologies has been widely promoted. These technologies rely upon strategic materials, such as rare earth elements (REEs), lithium, and Cobalt. They are essential in use and subject to supply risk. For building a more sustainable future and enabling the circular economy (make, use, recover), waste valorization and recycling of end-of-life products is imperative.Conventional recycling processes are based on pyrometallurgy or hydrometallurgy. The former is energy intensive, generating greenhouse gas emissions, while the latter relies on large volumes of acids and organic solvents; thus, generating hazardous wastes. There is a need for an environmentally sustainable and efficient process to enable green urban mining of secondary resources. Supercritical fluid extraction (SCFE) is a potential green alternative to convention processes because of the desirable properties of supercritical fluids as solvents. The SCFE is an emerging technology in the field of recycling of metals. There is a limited number of studies that investigated SCFE using end-of-life products as feed materials. The mechanism of SCFE and impacts of adding co-solvents and adducts to supercritical fluid are unclear. The SCFE involves multiple variables, which require a systematic approach for optimization. The main objective of this PhD project is to develop the SCFE process to recover strategic metals from end-of-life products. The first thrust focused on the recovery of rare earth elements (REEs) from neodymium iron boron (NdFeB) magnets and waste fluorescent lamp phosphors using SCFE. The second thrust focused on investigating the effect of organophosphorus ligand on the extraction of REEs from waste NdFeB magnets using SCFE. The third thrust focused on recovering lithium, cobalt, nickel, and manganese from waste lithium-ion battery using SCFE. The fourth thrust focused on elucidating the mechanism governing the complexation process during SCFE, focusing on elucidating the coordination environment of metal upon SCFE using X-ray absorption spectroscopy in collaboration with the Canadian Light Source. The fifth thrust focused on the techonoeconomic assessment of the SCFE process for the recovery of strategic materials from end-of-life products. Each thrust has led (or will lead) to a journal publication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".