Synthesis and characterization of rice husk biochar via hydrothermal carbonization for wastewater treatment and biofuel production
Bibliographic record
Abstract
Abstract The recent implication of circular economy in Australia spurred the demand for waste material utilization for value-added product generations on a commercial scale. Therefore, this experimental study emphasized on agricultural waste biomass, rice husk (RH) as potential feedstock to produce valuable products. Rice husk biochar (RB) was obtained at temperature: 180 °C, pressure: 70 bar, reaction time: 20 min with water via hydrothermal carbonization (HTC), and the obtained biochar yield was 57.9%. Enhancement of zeta potential value from − 30.1 to − 10.6 mV in RB presented the higher suspension stability, and improvement of surface area and porosity in RB demonstrated the wastewater adsorption capacity. Along with that, an increase of crystallinity in RB, 60.5%, also indicates the enhancement of the catalytic performance of the material significantly more favorable to improve the adsorption efficiency of transitional compounds. In contrast, an increase of the atomic O/C ratio in RB, 0.51 delineated high breakdown of the cellulosic component, which is favorable for biofuel purpose. 13.98% SiO2 reduction in RB confirmed ash content minimization and better quality of fuel properties. Therefore, the rice husk biochar through HTC can be considered a suitable material for further application to treat wastewater and generate bioenergy.
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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) | 0.001 | 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".