Influence of zeta potential of <scp> ZrO <sub>2</sub> </scp> and <scp> Al <sub>2</sub> O <sub>3</sub> </scp> nanoparticles on removal of metal ions by hybrid electrospun polyamide 6 membrane: Kinetics of adsorption and fouling mechanisms
Bibliographic record
Abstract
Abstract Electrostatic charging of the adsorbent is a critical method for the selective removal of metal ions fromacidic wastewater. In this study, electrospun polyamide 6 (PA6) membranes were separately incorporated with Al 2 O 3 and ZrO 2 nanoparticles (NPs), and their ability in the removal of cations (eg, Al 3+ and Fe 3+ ) were evaluated by static adsorption and filtration methods. The impact of various parameters, namely microstructure, surface porosity, functional molecular groups, and hydrophobicity of membranes on the removal process, was investigated. By evaluating the zeta (ζ) potential of oxide ceramic NPs, it was found that the Al 2 O 3 and ZrO 2 NPs had positive and negative electrostatic charges inacidic solution, respectively. The results showed that the incorporation of negatively charged ZrO 2 NPs improved both the static adsorption and filtration removal capability of the PA6 membrane. The maximum adsorption efficiency of Al 3+ and Fe 3+ cations was 52.43% and 54.11%, and the highest filtration efficiency of the mentioned cations obtained by electrospun hybrid ZrO 2 incorporated PA6 membrane was 98.6% and 99.3%, respectively. It was also found that the adsorption of both Al 3+ and Fe 3+ cations correlated well with the pseudo‐second‐order kinetic model. The results also demonstrated that the dominant blocking models were switched with the increase in the initial concentration of metal ions from 5 ppm‐70 ppm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".