Impact of Pitch Modification on Anode Properties: Effect of Additive Type
Bibliographic record
Abstract
Aluminum is one of the major industries in Canada. The main challenges facing the aluminum industry are carbon loss, energy use, greenhouse gas emissions, cell performance, and production costs, especially for high-amperage cells. The quality of carbon anodes plays a major role in the stability of cell operation and energy consumption. Anodes are made from petroleum coke, rejected green and baked anodes and butts, as well as coal tar pitch, which binds all of the particles. Although the industry depends on a steady supply of high-quality anodes, the availability of quality anode raw materials-coke and pitch-has decreased. A means of improving raw material quality is to modify their properties. In this work, using two additives, low- and high-quinoline insoluble (LQI and HQI) pitches were modified. These additives enrich the surface functional groups of the pitch, thereby increasing coke-pitch interactions. Various additive concentrations and pitch percentages were assessed. It is found that the choice of additive type has a marked effect on pitch properties, with different additives improving different pitches. Additive 1 is suitable for the HQI pitch, whereas additive 2 modifies the LQI pitch better. Anode properties are improved by modifying one of the pitches, whereas modifying the other pitch affects the anode quality to a lesser extent. Thus, the results showed that the modification of an already good-quality pitch (LQI pitch) does not significantly affect the anode quality. On the other hand, the modification of the inferior-quality pitch (HQI pitch) improved the anode quality and decreased the optimum pitch percentage necessary to obtain good anodes compared to the percentage of the LQI pitch needed. This would help decrease the anode production cost. The wettability tests give an indication of if the additive has the potential to improve the coke-pitch interactions, but it cannot predict the effect of pitch percent.
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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 | 0.001 |
| 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) | 0.002 | 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".