Enhanced adsorption of ceftriaxone and cefotaxime using <scp>ZnAl</scp> ‐ <scp>LDH</scp> and graphene‐modified <scp>ZnAl</scp> ‐ <scp>LDH</scp> in wastewater treatment
Bibliographic record
Abstract
Abstract Antibiotics are frequently detected in trace amounts in pharmaceutical and hospital effluents, presenting significant risks to human health and aquatic ecosystems. Layered double hydroxides (LDHs) and their hybrids are gaining attention as affordable and efficient adsorbents for water treatment; herein, we investigate the eradication of two antibiotics, ceftriaxone (CFT) and cefotaxime (CFX), using calcined ZnAl‐LDH (LDH‐C) and a graphene‐loaded calcined ZnAl‐LDH (G‐LDH‐C) composite as an adsorbent. Microscopy analysis confirmed the uniform dispersion of graphene nanosheets over the LDH surface, while analysis (TGA) demonstrated the thermal stability of the synthesized adsorbent. Adsorption was optimized by varying pH, temperature, adsorbent dosage, contact time, and initial concentration. The G‐LDH‐C showed maximum capacities of 54.34 mg/g for CFT and 45 mg/g for CFX at pH 5 with a 10 mg/L dosage, reaching equilibrium at 60 and 45 min, respectively. Isothermal studies showed that CFT and CFX adsorption followed a monolayer and homogeneous process consistent with the Langmuir model. Kinetic analysis revealed that CFX fits well with the PFO model, while CFT aligned better with the PSO model. Thermodynamic results indicated that adsorption of both antibiotics was exothermic and spontaneous. Recyclability study showed that after seven cycles, there is a gradual decrease in adsorption capacity. CFT (G‐LDH‐C) adsorption capacity decreased from 26.5 to 15.4 mg/g, while CFT (LDH‐C) declined from 23.9 to 10.2 mg/g. Similarly, CFX (G‐LDH‐C) decreased from 44.9 to 22.7 mg/g, and CFX (LDH‐C) dropped from 37.9 to 16.3 mg/g, showing good potential as promising candidates for removing antibiotics from wastewater.
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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".