The Effect of Competing Ions on the Sorption of Amoxicillin, Ciprofloxacin, and Sulfamethoxazole on Chitosan-Carbon Nanotube Hydrogel Beads
Bibliographic record
Abstract
The present work pertains to the synthesis of chitosan-carbon nanotube (CCNT) hydrogel beads using a two-step process for the uptake of amoxicillin (AMX), ciprofloxacin (CIP) and sulfamethoxazole (SMX) in the presence of varying concentration of sodium chloride (NaCl) and humic acid (HA) from 0 mg/L to 40 mg/L as competing ions.From the results obtained it was concluded that the increase in NaCl or HA concentration demonstrated antagonistic effects in the uptake of AMX, CIP and SMX on the synthesised CCNT hydrogel beads due to the formation of aggregates with an increase in ionic strength.Moreover, NaCl demonstrated the least effects on the uptake of the model antibiotics as compared to HA, indicating that NaCl ions exhibit minimal competitive effects with adsorbate molecules for active adsorption sites on CCNT hydrogel beads.Similarly, from the single factor analysis of variance results p-values of less than 0.05 were recorded for the uptake of AMX, CIP and SMX on CCNT hydrogel beads, explicitly indicating that there was a statistical difference between the means for the independent and dependent variables, thus cementing the negative effect of increasing ionic strength on the uptake of model adsorbates.Moreover, the findings of the present work suggest that the is need for a pretreatment stage aimed at eliminating co-existing contaminants prior to the application of solid-liquid adsorption for complete eradication of contaminants of emerging concern particularly antibiotics.
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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".