Enhancing U(VI) removal from water using nano-Kaolin and nano-Kaolin/MnFe<sub>2</sub>O<sub>4</sub> composite adsorbents
Bibliographic record
Abstract
Abstract The binding behavior of U(VI) ions onto nano-Kaolin (NK) and nano-Kaolin/MnFe2O4 composite (NK-MF) adsorbents was systematically investigated, focusing on the influence of pH, adsorbent mass, temperature, and contact time. Kinetic analysis, utilizing the pseudo-second-order model, revealed that both NK and NK-MF composites reach their maximum capacity of adsorption (qm) at pH 3. The maximum adsorption capacities were found to be (8.6) mg g−1 for NK and (14.79) mg g−1 for NK-MF at 25C°, indicating a significant enhancement due to the incorporation of MnFe2O4. The adsorption isotherms were examined using Langmuir, Freundlich, and Dubinin-Radushkevich models to characterize the adsorption mechanisms. The Langmuir and Freundlich models provided the best fit (R2 > 0.9), indicating monolayer and multilayer adsorption. Thermodynamics parameters, including enthalpy change (ΔH°), Gibbs free energy change (ΔG°), and entropy change (ΔS°), were derived from adsorption data across different temperatures. The values at 25 °C of ΔH° were (49.67) for NK and (70.97) for NK-MF; ΔG° values were (−5.06) kJ mol−1 for NK and (−7.39) kJ mol−1 for NK-MF; and ΔS° values were 187.36 J (mol·K)−1 for NK and (263.70) J (mol·K)−1 for NK-MF. The results indicate that the adsorption process is endothermic, with conditions that favor adsorption and a positive entropy change. These findings demonstrate the effectiveness and potential of NK and NK-MF composites as viable adsorbents for the uptake of U(VI) ions from water-based solutions. The incorporation of MnFe2O4 into NK improves adsorption capacity, making NK-MF a novel and practical material for uranium removal in environmental applications.
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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) | 0.000 | 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 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".