Development of Carbon Nanotube/Graphene‐Based Alginate Interpenetrating Hydrogels for Removal of Antibiotic Pollutants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 m 2 /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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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 | 0.001 |
| 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 it