Preparation of Biochars from Bio-Waste for Removing Pollutants from River Water
Bibliographic record
Abstract
As a low-cost adsorbent, biochar can be used as a great tool for water treatment. Instead of using expensive woody biomass for biochar production, this study aimed to investigate the feasibility of using biowaste such as sugarcane skins, orange peels, and peanut shells to produce biochars through pyrolysis at 700°C. The optimal time length, dosage, and temperature for water purification were explored afterwards in batch tests. The ammonia nitrogen (NH4+-N) removal efficiencies of sugarcane skin, orange peel, and peanut shells were 74.4%, 96.3% and 90.8%, respectively, and the simultaneous permanganate index removal efficiencies were 26.6%, 31.0% and 26.6%, respectively. There was no significant difference in NH4+-N and permanganate index removal efficiencies when the dosage of three kinds of biochars was higher than 1.0 g/100 ml water. Greater adsorption capacity of biochars was observed for pollutants when temperature was increased from 10 to 30°C. These results confirmed our assumption that biowaste could make for good raw materials in producing biochars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".