Accurate characterization of graphite in ores and black mass using an innovative sample preparation method for automated mineralogy
Bibliographic record
Abstract
Automated mineralogy systems are widely used for the mineralogical characterization of powder samples for mineral processing purposes. This characterization method requires the mineral powder to be embedded in a resin (polished block (PB) preparation). Very quickly, some problems linked to polished block preparation arose. This particularly involves the mineral settlement in the liquid resin due to differences in mineral size and density. Several authors have suggested solutions to overcome the error results due to the PB preparation method, such as vertical section, the addition of sized graphite, dynamic hardening, and the addition of black carbon (BC) to increase resin viscosity avoiding mineral settlement. Only the BC method resolved all the errors associated with the PB preparation. Indeed, it eliminated the mineral settlement and provided excellent spatial dispersion of particles on the observation surface, ensuring better mineral quantification and liberation/association estimation, except for the graphite-bearing samples. In fact, as graphite shows no contrast with resin under back-scattered electron-based based images, it cannot be characterized by the automated mineralogy systems. few studies have addressed this problem by the addition of carnauba wax or iodoform to contrast resin and graphite. The iodoform was easy to use and provided better contrast compared to carnauba wax. This work presents an innovative polished block preparation method that combines CB and iodoform to prevent both particle settlement and to contrast the resin and graphite which was very challenging. The obtained results are highly satisfying and comparable to those of standard characterization techniques such as XRD and chemical assay. This new preparation method is highly useful for graphite-bearing black mass (obtained from battery recycling) characterization by automated mineralogy systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".