Sustainable production of value-added sulfonated biochar by sulfuric acid carbonization reduction of rice husks
Bibliographic record
Abstract
Biochar is an effective vehicle for sequestering carbon and mitigating the greenhouse gas effect, as a way to combat climate change. Currently pyrolysis is the main treatment method to prepare biochar, and generally only half of the carbon (C) can be retained in the pyrolytic biochar. In this paper, we introduce a new low-cost method for the preparation of biochar from biomass by one-step carbonized reduced sulfuric acid. The results show that the carbonization reaction of rice husk with alkylated waste sulfuric acid can yield more than 80% of biochar, which is much higher than the pyrolysis biochar. Besides, the prepared biomass sulfonated char has abundant functional groups including SO3H, OH and exhibits excellent adsorption performance for Cd2+ with the maximum adsorption capacity of 93.98 mg/g. In conclusion, the method used in this paper to prepare sulfonated biochar has fewer steps, a lower processing cost and higher value-added products, thus making it more sustainable and economical, and supporting a wider range of sulfuric acid carbonization methods. It has the potential to inspire and lead the way for the safe, economical and sustainable preparation of value-added sulfonated carbon materials for commercial applications.
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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".