Value-added biocarbon production through slow pyrolysis of mixed bio-oil wastes: studies on their physicochemical characteristics and structure–property–processing co-relation
Bibliographic record
Abstract
Abstract In this work, mixed bio-oil (MBO) is transformed into valuable biocarbon through slow pyrolysis technique. MBO was accomplished in a semi-batch reactor at 600 and 900 °C temperature, 10 °C min−1 heating rate, and 30 min holding time under a non-oxidizing environment. The produced mixed bio-oil-derived biocarbon (MBOB) was characterized by its surface properties, thermal stability, elemental composition, thermal conductivity, BET surface area, surface morphology, and electrical conductivity. The pyrolysis outcomes established that the temperature has a predominant impact on the variation in yield and properties of MBOB. Characterization results of MBOB exposed increased properties (thermal stability, electrical and thermal conductivity, graphitic content, carbon content, and HHV) at 900 compared to 600 °C. Also, the elemental and EDS investigation of MBOB established a broad diminution in O2 and H2 at 900 than 600 °C. The purest form of carbon with enhanced thermal stability, higher carbon content, smoothness, and bigger particles of biocarbon (verified by SEM) is accomplished at 900 °C. The electrical and thermal conductivity (EC and TC) of MBOB increased with increasing the temperature from 600 to 900 °C due to the close contact of biocarbon particles. Finally, an investigation of the particle size of MBOB established that the majority of particles are within 1.5 to 1.7 µm. Graphical abstract
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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) | 0.000 | 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 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".