Enhanced toluene removal from aqueous solutions using reed straw-derived biochar
Bibliographic record
Abstract
Abstract The escalating threat of pollutants, particularly aromatic hydrocarbons like benzene, toluene, ethylbenzene and xylene (BTEX), in aquatic environments necessitates effective remediation strategies. This study explores the potential of biochar derived from common reed (Phragmites australis) as a sustainable and multifaceted tool for the removal of toluene, a representative BTEX compound, from aqueous solutions. By harnessing reed straw as the precursor material for biochar production, this research showcases an environmentally friendly alternative to conventional disposal methods, such as incineration, offering the dual benefit of pollutant removal and carbon emissions reduction. The influence of pyrolysis temperature on biochar properties and its adsorption efficiency for toluene were rigorously examined, revealing a direct correlation between temperature and biochar’s pollutant sequestration capabilities. Results indicate that higher pyrolysis temperatures led to biochar (RB-750) with superior specific surface area (68.07 m2/g) and enhanced adsorption capabilities, demonstrating its potential as a powerful adsorbent in water treatment. The scanning electron microscope analysis revealed a complex, porous structure rich in active sites, validating the biochar’s suitability for pollutant adsorption. Optimal dosage was determined at 8 g/l, achieving an impressive toluene removal efficiency of 98.1%. Additionally, pH and initial toluene concentration significantly influenced removal efficiency. This study underscores the multifaceted potential of reed straw-derived biochar in combating water pollution while concurrently contributing to carbon emissions reduction through sustainable utilization of abundant wetland resources. Further research should delve into the impact of real-world conditions on its effectiveness, promising innovative solutions for environmental remediation efforts with a reduced carbon footprint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".