Biopitch as a Binder for Carbon Anodes: Impact on Carbon Anode Properties
Bibliographic record
Abstract
Aluminum industry depends on coal-tar-pitch as a binder to produce carbon anodes used for aluminum electrolysis. This binder is nonrenewable and mainly composed of polycyclic aromatic hydrocarbons which are harmful for the human health and the environment. The biomass-driven pitch is considered to be a green and abundant binder. In this study, we synthesized biopitch from bio-oil by heat treatment of the bio-oil at 160 and 180 °C. The biopitch was then baked at 1100 °C to produce its carbonized form. In comparison to the carbonized coal-tar-pitch, the baked biopitch showed amorphous microstructure, higher air reactivity and higher specific electrical resistivity. Biopitch is known to have high wettability and adhesion with the coke particles which could reduce the negative effect of inferior characteristics of its baked form on the resulting anodes. Biopitch was used to replace 100 % of the conventional coal-tar-pitch binder in the anode recipe. Green anodes were produced by mixing the coke aggregates with the biopitch at 178 °C. The baked anode properties were measured and compared to those of classical reference anodes (made of coal-tar-pitch and calcined coke). Although, the air and CO 2 reactivity of the biopitch anodes is slightly higher than that of the reference ones, the biopitch anodes showed similar density, coefficient of thermal expansion, specific electrical resistivity, mechanical strength, and lower air permeability in comparison to those of the reference anodes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".