Pyrolysis of low-value waste sawdust over low-cost catalysts: physicochemical characterization of pyrolytic oil and value-added biochar
Bibliographic record
Abstract
The present work deals with an experimental investigation into the generation and characterization of pyrolytic oil and biochar from Sal wood sawdust (SW). The pyrolysis experiment was performed in a semi-batch reactor at 500 oC and 80 oC/min heating rate with CaO, CuO, and Al2O3 catalysts. Further, the pyrolytic oil and biochar were investigated using different analyses, including proximate analysis, elemental analysis, thermal stability, GC-MS, FTIR, field emission scanning electron microscopy, electrical conductivity analysis, higher heating value (HHV), zeta potential analysis, and ash content analysis. Pyrolysis results revealed that compared to thermal pyrolysis (46.02 wt%), the pyrolytic oil yield was improved by catalytic pyrolysis with CaO and CuO (50.02 and 48.23 wt%, respectively). Further, the characterization of pyrolytic oil revealed that the loading of catalysts considerably improved the oil's properties by lowering its viscosity (69.50 to 22 cSt), ash content (0.26 to 0.11 wt%), and oxygen content (28.32 to16.60 %) while raising its acidity (4.2 to 9.6), heating value (25.66 to 36.09 MJ/kg), and carbon content (61.79 to 74.28%). According to the FTIR analysis, the pyrolytic oil contained hydrocarbons, phenols, aromatics, alcohols, and oxygenated compounds. Additionally, the GC-MS analysis showed that catalysts significantly reduced oxygenated fractions, phenols (20.23 to 15.26%), acids (12.23 to 6.56%), and increased hydrocarbons (12 to 16 wt%). Additionally, the results of the biochar analysis demonstrated that SW biochar was appropriate for a range of industrial applications, including in catalysts, supercapacitors, fuel cells, and bio-composite materials.
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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".