Facile synthesis of ZnONP/carbon-coated-eggshell nanocomposite: fast and efficient adsorbents for amoxicillin sequestration
Bibliographic record
Abstract
Abstract This article reports the synthesis and characterization of a novel eggshell/carbon/ZnO nanoparticles composite (ECZ) for efficient adsorption of amoxicillin (AMX) from aqueous solution. Systematic batch adsorption tests were conducted to compare and assess the AMX removal efficiency of ECZ with raw eggshell biochar (ESB). The most significant operating parameters, including solution pH, contact time, adsorbent dosage, temperature, and initial AMX concentration, were optimized. Maximum adsorption efficiency occurred at pH 5, and equilibrium was achieved in 80 min. ECZ composite possessed a significantly enhanced adsorption capacity of 37.91 mg g⁻1 at 313 K, which is nearly double that of ESB (18.73 mg g⁻1), illustrating the synergistic effect of ZnO nanoparticles and carbon modification. Equilibrium adsorption analysis according to Freundlich and Langmuir models determined that AMX adsorption on ECZ represented the Freundlich isotherm model, depicting multilayer adsorption over a heterogeneous surface, while ESB exhibited Langmuir representation, signifying monolayer coverage. The kinetic model ratified that pseudo-first-order representation efficiently captured the process of adsorption in both samples. Thermodynamic values (ΔG°, ΔH°, and ΔS°) determined in the temperature interval 298–313 K indicated that the adsorption process was spontaneous, endothermic, and entropy-stimulated with increased randomness at the solid–solution interface. In general, the ECZ composite is an excellent choice as a low-cost, effective, and eco-friendly adsorbent for the removal of pharmaceutical pollutants such as amoxicillin from wastewater, ensuring environmental protection and water purification.
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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".