Effect of Red Mud Addition on Electrical and Magnetic Properties of Hemp-Derived-Biochar-Containing Epoxy Composites
Bibliographic record
Abstract
Waste stream valorization is a difficult task where the economic and environmental issues must be balanced. The use of complex metal-rich waste such as red mud is challenging due to the wide variety of metal oxides present such as iron, aluminum, and titanium. The simple separation of each metal is not economically feasible, so alternative routes must be implemented. In this study, we investigated the use of red mud mixed with hemp waste to produce biochar with high conductivity and good magnetic properties induced by the reduction of the metal oxides present in the red mud through carbothermal processes occurring during the co-pyrolysis. The resulting biochar enriched with thermally-reduced red mud is used for the preparation of epoxy-based composites that are tested for electric and magnetic properties. The electric properties are investigated under DC (direct current) regime with or without pressure applied and under AC (alternating current) in a frequency range from 0.5 up to 16 GHz. The magnetic measurements show the effective tailoring of hemp-derived biochar with magnetic structures during the co-pyrolytic process.
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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".