Crop straw biochar enhances hydrocarbon adsorption in ground water
Bibliographic record
Abstract
Hydrocarbon fuel production and use can pose environmental risks, such as spills during extraction and transportation, which can contaminate soil, damage vegetation, adversely affect human and animal health, and contaminate ground water with soluble hydrocarbons, that could spread to surrounding areas. Our study evaluates the simultaneous adsorption capacity of canola straw biochar for 12 hydrocarbon pollutants in ground water from a northern peatland. This approach simulates the simultaneous contamination of multiple hydrocarbon classes in a complex aqueous matrix. In the laboratory, canola straw biochar remediated benzene, toluene, ethylbenzene, and xylene (BTEX), and linear chained and polycyclic aromatic hydrocarbons from ground water. BTEX concentrations significantly decreased with application of 1 g L −1 biochar, achieving a remediation efficiency of over 95 % within 7 days. Increasing application rates enhanced remediation efficiency, exceeding 99 % at a 2 g L −1 application rate. Hydrocarbon adsorption on biochar is a complex process involving surface interactions and diffusion-controlled steps, with the kinetic data fitting well to models indicative of chemisorption. X-ray photoelectron spectroscopy, BET/CO 2 porosimetry and Fourier transform infrared spectroscopy corroborated theoretical isotherm and kinetic models, indicating that functional groups on the biochar surface play a crucial role in adsorption, primarily through hydrophobic and π-π interactions. The results enhanced our understanding of adsorption mechanisms for multiple hydrocarbon classes in complex matrices under controlled laboratory conditions, and positioned canola straw biochar as an effective remediation technique for hydrocarbon water treatment. Biochar is made from waste agricultural materials and sequesters carbon, contributing to environmentally sustainable remediation and a circular economy.
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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".