Insight into the performance of novel kaolinite-cellulose/cobalt oxide nanocomposite as green adsorbent for liquid phase abatement of heavy metal ions: Modelling and mechanism
Bibliographic record
Abstract
A cost-efficient kaolinite-cellulose/cobalt oxide green nanocomposite (Kao-Cel/Co3O4 NC) was successfully synthesized, and utilized as a promising material for removing Pb2+ and Cd2+ from aqueous solution. The fabricated nanocomposite has been characterized by Fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy-energy dispersive X-ray, high-resolution transmission electron microscopy, and Brunauer-Emmett-Teller analysis. The batch methodology was exploited for optimization of process parameters and the optimized conditions were found to be adsorbent dosage (2.0 g/L), extraction time (50 min), initial concentration (60 mg/L), and initial solution pH (6). Kao-Cel/Co3O4 NC displayed excellent adsorption properties and achieved maximum saturation capacity (Qm) of 293.68 mg Pb2+/g and 267.85 mg Cd2+/g, with an equilibration time of 50 min at 323 K. The Langmuir model best expressed the isotherm data recommending the adsorption onto energetically homogeneous NC surface, while the compatibility of kinetics data with pseudo-second-order model revealed the dependency of adsorption rate on adsorption capacity, and probable involvement of chemisorption in the rate-controlling step. Electrostatic interaction and ion exchange mechanism were responsible for the uptake of Pb2+ and Cd2+ by Kao-Cel/Co3O4 NC as demonstrated by Fourier transform infrared spectroscopy and pH studies. Thermodynamic parameters confirmed the physical, spontaneous, and endothermic sequestration processes. Real water investigation specified that the present adsorbent could be effectively used for liquid phase decontamination of Pb2+ and Cd2+. The nanocomposite exhibited high reusability, which could be utilized efficiently for five runs with sustainable results. In summary, this study portrayed the present nanocomposite as an emerging material for the adsorption of heavy metal ions particularly Pb2+ and Cd2+.
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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 | 0.001 | 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 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".