Efficient removal of amoxycillin antibiotics onto magnetic graphene oxide: adsorption performance, mechanism, and regeneration exploration
Bibliographic record
Abstract
The present study was done to synthesise an adsorbent, i.e. magnetic graphene oxide (MGO) nanocomposite, which was performed based on a facile precipitation method and was utilised in experiments for removing amoxycillin (AMX). The characteristics of the prepared adsorbent were defined based on commonly utilised analyses (SEM, XRD, BET, TEM, FTIR, VSM, and pHpzc). According to kinetic studies, the PSO model was found as an applicable model for describing data. Moreover, the two-step diffusion process, i.e. diffusion in the boundary layer and the porous structures, was perceived for the evaluated process based on the IPD model. The isotherm models, including Langmuir, Freundlich, Temkin, and D–R, were employed for fitting data and calculating AMX adsorption capacity, among which Langmuir was the best one; using this model, the maximum adsorption capacities for MGO were 91.4, 103.9, 112.3, and 122.5 mg/g, which were achieved at 20, 30, 40, and 50°C. In addition, a feasible, spontaneous, and endothermic process was found for the adsorption of AMX ions, according to thermodynamic studies. The highest percentage of removal (100%) was obtained for the initial concentration of 25 mg/L at 50°C using the adsorbent dose of 1.5 g/L at a pH of 5 and a contact time of 90 min. The values of 74.4 m2/g and 27.74 emu/g were detected for the specific surface area and saturation magnetisation values of the MGO, respectively. The overall results were representative of the suitability of the MGO as an adsorbent for removing AMX from aqueous media.
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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) | 0.000 | 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 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".