Facile synthesis of ZnONP/carbon-coated-eggshell nanocomposite: fast and efficient adsorbents for amoxicillin sequestration
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it