Bibliographic record
Abstract
With the growing global demand for nickel (Ni) from energy storage and electric vehicles (EVs), and the depletion of high-grade Ni-sulphide ores, attention has turned to low-grade ultramafic sulphide ores as alternative Ni sources. High serpentine content complicates processing, as the serpentine coats the nickel-bearing pentlandite during flotation, reducing the amount of nickel recovery and grade while increasing the slurry viscosity. The overall goal of this thesis is to explore environmentally friendly methods that deal with the challenges posed by serpentine in these ores, including CO2 sequestration in ores through mineralization and using reagents from renewable sources. Initial studies explored the decarbonization of nickel processing by studying mineral interactions in carbonated and uncarbonated model systems followed by froth flotation with an improvement in nickel recovery of 26 and increased grade of 4 wt.%. There was also a decrease in slurry viscosity for the carbonated mineral system from 148 to 2.6 mPa.s. A techno-economic assessment showed that the process feasibility hinges on enhanced carbonation conversions. Further, CO2 mineralization of ore was performed to form magnesite, which demonstrated enhanced nickel separation for carbonated ore. Another approach focused on using renewable cellulose nanocrystals (CNCs) as a flotation reagent for pentlandite separation from serpentine. At 5-10 mg/g dosage, CNCs aggregated the serpentine particles, and above 20 mg/g, acted as dispersant. The mechanism was analyzed using a quartz crystal microbalance-dissipation (QCM-D). The dispersion enhanced the nickel recovery and grade by 15 and 5 wt.%, respectively. Low CNC dosages covered serpentine partly, causing aggregation as the planes of serpentine have a different charge; high dosages ensured full surface coverage, creating dispersion as all planes have a similar charge. The potential of CNC as a rheology modifier of ore to reduce viscosity and yield stress was also investigated. At a 2.5 mg/g dosage, viscosity and yield stress decreased and this trend was consistent across 30-50 wt.% suspensions. For instance, at 30 wt.%, viscosity rose from 0.12 Pa.s (0 mg/g) to 0.18 Pa.s (1 mg/g), then dropped to 0.10 Pa.s (2.5 mg/g), all at a 1 s-1. This positions CNCs as renewable solution for tailings dewatering, addressing mining's environmental challenges.
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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".