Synthesis and Characterization of Bio-pitch from Bio-oil
Bibliographic record
Abstract
Coal-tar-pitch (CTP) is a fossil carbon material, currently used as a the binder in carbon anode manufacturing process. Regardless of the technical benefits of coal-tar-pitch, it contains polycyclic aromatic hydrocarbons (PAHs), known to be carcinogenic for humans and detrimental to the environment. To overcome this challenge, research was carried out to search for a suitable substitution with health- and environmental-friendly properties. Bio-pitch, produced from bio-oil, could be a potential alternative binder for the carbon anode manufacturing process, due to its similarity with CTP. However, the properties of bio-pitch could be significantly different from those of CTP depending on its origins and process conditions. This study focuses on the synthesis of bio-pitches from bio-oil under different vacuum extraction conditions and characterization of its physical and chemical properties, aiming at the determination of the conditions that may result in suitable properties for anode formulation. Both typical characterization and profound chemical analysis of bio-pitches were carried out, including the determination of density, softening point, coking value, quinoline insolubles, PAH content, molecular weight, viscosity, and elemental composition and the identification of chemical structures. In addition, the condensed fraction produced was also analyzed to identify the reaction mechanisms occurring during the pyrolysis 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 | 0.001 | 0.001 |
| 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".