Microstructure and physical properties of nano charcoal ash as binder
Bibliographic record
Abstract
Waste coconut shell (CS) was used to produce nano charcoal ash (NCA) as a potential modifier material in an asphalt binder. This study focused on the microstructural and physical properties of NCA. Thermogravimetric analysis and derivative thermogravimetric analysis (TGA/DTA), field emission scanning electron microscopy (FESEM), X-ray fluorescence, particle size analysis (PSA), penetration tests, softening point tests and dynamic shear rheometer (DSR) tests were performed. The TGA/DTA results revealed 490°C to be a suitable CS burning temperature to form carbon and to reduce impurities. The morphology determined by FESEM showed that charcoal CS presents a smooth, porous and irregular shape. The carbon content on the surface of the material was 77·6%, as indicated by energy-dispersive X-ray spectroscopy. PSA showed that the optimum size of the charcoal CS obtained after several grinding cycles was 148 nm. Test results indicated that adding NCA from coconut shell to bitumen improved the binder stiffness up to 47% and significantly increased the softening point up to 12% compared with virgin binder. The DSR test revealed that the optimum size of NCA enhanced the bitumen by increasing the resistance to rutting until a temperature of 76°C was reached, prior to failure at a temperature of 82°C.
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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 | 0.000 |
| 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) | 0.001 | 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".