One‐step hydrothermal synthesis of a green <scp>NiCo‐LDHs‐rGO</scp> composite for the treatment of lead ion in aqueous solutions
Bibliographic record
Abstract
Abstract Herein, we have synthesized a microspherical nickel‐cobalt‐layered double hydroxides‐reduced graphene oxide composite (NiCo‐LDHs‐rGO) through a one‐step hydrothermal method and then used it as an adsorbent for the removal of Pb 2+ from aqueous solutions. Fourier transform infrared spectrophotometry (FT‐IR), field emission scanning electron microscopy (FESEM), mapping elemental analysis, electron dispersive x‐ray spectroscopy (EDX), x‐ray diffraction analysis (XRD), and the Brunauer–Emmett–Teller (BET) method were used for the characterization of the adsorbent. Factors affecting the adsorption of Pb 2+ ion such as solution pH, adsorbent dosage, contact time, competing ion, and regeneration were investigated in batch mode by the NiCo‐LDHs‐rGO. Under optimized conditions based on the Taguchi method (pH = 5.0, adsorbent dosage = 20 mg, and contact time = 30 min), the highest removal percentage was found to be 99.7% for 100 mg L −1 of Pb 2+ . According to the results, NiCo‐LDHs‐rGO exhibited a high preference for Pb 2+ over Cu 2+ , Zn 2+ , and Cd 2+ . This adsorbent was regenerated for several cycles (using 0.01 M HCl) with no significant deterioration in performance. Analyses of the adsorption isotherm models revealed that the adsorption of Pb 2+ follows Freundlich isotherm with a maximum adsorption capacity of 200 mg g −1 . Also, the kinetic data confirmed that pseudo second order kinetic equation is the best model for predicting the kinetics. Furthermore, the Simulink modelling illustrated that the adsorption kinetics of Pb 2+ onto NiCo‐LDHs‐rGO is done with high accuracy in a continuous stirred‐tank reactor. Finally, dual interactions of the effective parameters can be modelled by polynomial equations in MATLAB, and according to the Taguchi model, pH is clearly the most important feature among all effective parameters.
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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".