Development of Carbon Nanotube/Graphene‐Based Alginate Interpenetrating Hydrogels for Removal of Antibiotic Pollutants
Bibliographic record
Abstract
Abstract The extensive use of pharmaceutical antibiotics in treatment of human and animal infections has resulted in growing concerns about antibiotic pollution worldwide. In this work a novel interpenetrating polymer network (IPN) hydrogel has been developed to function as an effective and non‐selective adsorbent for various antibiotic pollutants in aqueous solution. This IPN hydrogel is made of multiple active components, including carbon nanotube (CNTs), graphene oxide (GO), and urea‐modified sodium alginate (SA). It can be readily prepared through efficient carbodiimide‐mediated amide coupling reaction followed by calcium chloride‐induced alginate cross‐linking. The structural properties, swellability, and thermal stability of this hydrogel have been investigated, while its adsorption properties towards an important antibiotic pollutant, tetracycline, was thoroughly characterized based on adsorption kinetic and isotherm analyses. With a BET surface area of 38.7 m2/g, the IPN hydrogel shows an excellent adsorption capacity of 84.28±4.2 mg/g towards tetracycline in water, while the adsorption capacity is decreased by only 18 % after four cycles of use, demonstrating very good reusability. Adsorptive performance in removing two other antibiotics, neomycin and erythromycin, has also been examined and compared. Overall, our studies disclose that this newly designed hybrid hydrogel is an effective and reusable adsorbent material for treating antibiotic pollution in the environment.
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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) | 0.000 | 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".