Insights into the Simultaneous Sorption of Ciprofloxacin and Heavy Metals Using Functionalized Biochar
Bibliographic record
Abstract
Biochar and chitosan are considered as green and cost-effective adsorbents for water purification; the combination of these two materials may lead to an improved adsorption capacity of the generated adsorbents. Most sorption studies have been focused on the ability to adsorb one contaminant or the same type of contaminants. Thus, this study aimed to produce chitosan-biochar beads (CH-BB) and test their efficiency in the simultaneous removal of a metal-complexing antibiotic, ciprofloxacin (CIP), and three metal(loid)s (As, Cd and Pb). Modification of raw pig manure biochar resulted in an increase in its adsorption capacity, except for Pb. The highest increment was observed for As (almost 6-fold) and the lowest was observed for CIP (1.1-fold). The adsorbent was able to simultaneously remove all targeted contaminants, individually and in the mixture. The adsorption capacity of CH-BB followed the order: Pb > Cd > >As > CIP. When Pb and As were present in the same mixture, their removal efficiency increased from 0.13 ± 0.01 to 0.26 ± 0.05 mg/g for As and from 0.75 ± 0.08 to 0.85 ± 0.02 mg/g for Pb due to their co-precipitation. The CIP–metal complexation probably resulted in a reduced adsorption ability for inorganics due to the decreased concentration of free ions. The presence of metals and metalloids led to alterations in CIP’s mobility.
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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 | 0.000 | 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) | 0.005 | 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".