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Mechanism of Cd(II) and Cu(II) Adsorption onto Few-Layered Magnetic Graphene Oxide as an Efficient Adsorbent.

ABSTRACT: Heavy metal contamination caused by industrial discharge is a challenging environmental issue. Herein, an efficient adsorbent based on few-layered magnetic graphene oxide (FLMGO) was fabricated, characterized, and utilized to remove aqueous Cd(II) and Cu(II). Results present that the two components graphene oxide (GO) and Fe₃O₄ of FLMGO promote mutually, enabling FLMGO to outperform either GO or Fe₃O₄. Specifically, FLMGO adsorbs Cd(II) and Cu(II) with adsorption quantities of 401.14 and 1114.22 mg·g^-1 in 5 and 7 min, respectively. Moreover, FLMGO can be readily recovered via magnetic separation using a hand-held magnet. Adsorptions are spontaneous, endothermic, and entropy increasing, which are the best described by the Freundlich and pseudo-second-order model. The interaction mechanism is as follows: lone pair electrons in C=O- and C-O-related groups were coordinated toward Cd(II) and Cu(II) to induce chemical interaction. The high adsorption efficiency endows FLMGO with encouraging application potential in heavy metal remediation.

SUBMITTER: Guo T

PROVIDER: S-EPMC8246493 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Soils interact in many ways with metal ions thereby modifying their mobility, phase distribution, plant availability, speciation, and so on. The most prominent of such interactions is sorption. In this study, we investigated the sorption of Pb, Cd, and Cu in five natural soils of Nigerian origin. A relatively sparsely used method of modelling soil-metal ion adsorption, i.e. adaptive neuro-fuzzy inference system (ANFIS), was applied comparatively with multiple linear regression (MLR) models. The isotherms were well described by Freundlich and Langmuir equations (R2 ≥ 0.95) and the kinetics by nonlinear two-stage kinetic model, TSKM (R2 ≥ 0.81). Based on the values delivered by the Langmuir equation, the maximum adsorption capacities (Qm*) were found to be in the ranges 10,000-20,000, 12,500-50,000, and 4929-35,037 µmol kg-1 for Cd, Cu, and Pb, respectively. The study revealed significant correlations between Qm* and routinely determined soil parameters such as soil organic carbon (Corg), cation exchange capacity (CEC), amorphous Fe and Mn oxides, and percentage clay content. These soil parameters, combined with operational variables (i.e. solution/soil pH, initial metal concentration (Co), and temperature), were used as input vectors in ANFIS and MLR models to predict the adsorption capacities (Qe) of the soil-metal ion systems. A total of 255 different ANFIS and 255 different MLR architectures/models were developed and compared based on three performance metrics: MAE (mean absolute error), RMSE (root mean square errors), and R2 (coefficient of determination). The best ANFIS returned MAEtest 0.134, RMSEtest 0.164, and R2test 0.76, while the best MLR returned MAEtest 0.158, RMSEtest 0.199, and R2test 0.66, indicating the predictive advantage of ANFIS over MLR. Thus, ANFIS can fairly accurately predict the adsorption capacity and/or distribution coefficient of a soil-metal ion system a priori. Nevertheless, more investigation is required to further confirm the robustness/generalisation of the proposed ANFIS.

Dataset Information

Mechanism of Cd(II) and Cu(II) Adsorption onto Few-Layered Magnetic Graphene Oxide as an Efficient Adsorbent.

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