Efficient Extraction of Valuable Metals from Polymetallic Shale Using Leaching and Adsorption Techniques
Bibliographic record
Abstract
Polymetallic black shale from the Buckton deposit in Northeastern Alberta, Canada, is an undeveloped resource for vanadium, rare earth elements (REEs), nickel, lithium, and a few other metals. The shale mainly contains quartz, calcite, illite and pyrite. The typical method to extract metals from shale is roasting followed by water/acid leaching. In this work, the valuable elements are extracted from polymetallic black shale using a low-temperature sulfation roasting-water leaching method. Sulfation roasting enables the destruction of illite releasing vanadium ions as well as releasing REEs, lithium and nickel from their respective mineral phases. We work on comparing microwave roasting and conventional roasting under varying roasting parameters including temperature, volume of sulfuric acid, and time, followed by water leaching, to determine the optimum leaching efficiencies of metals. Microwave roasting was found advantageous over conventional roasting in a scaled-up process and with a significant reduction in roasting temperature (by 40°C) and time (by 30 minutes), with an obvious reduction in energy consumption. A maximum leaching efficiency of 100% of gadolinium, 85% of ytterbium, 84% of cerium, 76% of nickel, 74% of vanadium, 59% of lithium, 34% of neodymium, 21% of yttrium, and 13% of lanthanum was achieved under optimum conditions. Extraction and recycling the use of rare earth elements (REEs) are very significant in meeting the worldwide demand for REEs. In view of recent advancements in adsorption technologies, amidoxime functionalized cellulose were synthesized and employed for removal of lanthanides (Nd3+, Ce3+, La3+, Y3+, Gd3+, and Yb3+) from aqueous solutions. The functionalized adsorbent was characterized by Fourier transform infrared spectroscopy (FTIR) and the concentration of REEs solution before and after adsorption were determined by inductively coupled plasma optical emission spectroscopy (ICP-OES). The adsorption kinetics of rare earth ions revealed that an uptake of 50% was achieved within the first 30-60 min. The kinetics of REEs adsorption was modelled better by a pseudo-second-order kinetic rate equation suggesting chemisorption rate-determining step.
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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) | 0.000 | 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".