Adsorption of Organic Pollutants From Wastewater Using Biochar: A Mechanistic Study on Competitive Adsorption Behavior
Bibliographic record
Abstract
This study investigates the adsorption of methyl orange (MO), methylene blue (MB), and acetaminophen (ACT) using biochars produced from Douglas fir and Miscanthus at different temperatures and with different post-pyrolysis treatments that added surface functional groups. Compounds were adsorbed separately and in mixtures to examine the competitive nature of the adsorption processes. MO is known to interact with MB and ACT, whereas MB and ACT are not likely to interact due to both having electron-donating groups. When comparing the biochar adsorption capacities for these compounds when alone and mixed, biochars with both hydroxyl and carbonyl surface functional groups had higher adsorption capacities for the tested compounds when they were in mixed solutions. Biochars with only hydroxyl groups exhibited competing adsorption mechanisms and poorer adsorption capacities of aromatic compounds in complex solutions. This provides an understanding of how competing adsorption mechanisms of aromatic compounds by biochars vary depending on the dominant adsorption mechanisms of the biochar, which will allow for more effective real-world applications for water purification in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".