Metal-organic framework MIL-100(Fe) for dye removal in aqueous solutions: Prediction by artificial neural network and response surface methodology modeling

Co-doped Fe-MIL-100 as an adsorbent for tetracycline removal from aqueous solution

In the study, Fe-MIL-100 was modified by adding Co²⁺ in the synthesis process; Co/Fe-MIL-100 was successfully synthesized and used to adsorb tetracycline. The addition of Co²⁺ increased the thermal stability of Fe-MIL-100 without changing the crystal structure. It was found that Co/Fe-MIL-100 exhibited satisfactory performance in tetracycline removal, the tetracycline removal efficiency reached almost 100% in the initial concentration range of 10-40 mg/L, and it still reached 82.38% under the condition of 60 mg/L tetracycline. Besides, the factors of tetracycline concentration, pH and inorganic anion on removal efficiency were explored. The coexistence of inorganic anion decreased the adsorption capacity of tetracycline due to the competitive adsorption. CO₃^2- had a more obvious inhibition effect on the adsorption efficiency of tetracycline than Cl^-. The fitting correlation coefficient of Langmuir model was higher and the kinetics better fitted by pseudo-second-order, respectively. As a result of its high removal efficiency and excellent recycling performance, it has great potential in application fields such as removing tetracycline from wastewater.

Application of response surface methodology and artificial neural network for the preparation of Fe-loaded biochar for enhanced Cr(VI) adsorption and its physicochemical properties and Cr(VI) adsorption characteristics

In this study, we optimized and explored the effect of the conditions for synthesizing Fe-loaded food waste biochar (Fe@FWB) for Cr(VI) removal using the response surface methodology (RSM) and artificial neural network (ANN). The pyrolysis time, temperature, and Fe concentration were selected as the independent variables, and the Cr(VI) adsorption capacity of Fe@FWB was maximized. RSM analysis showed that the p-values of pyrolysis temperature and Fe concentration were less than 0.05, indicating that those variables were statically significant, while pyrolysis time was less significant due to its high p-value (0.2830). However, the ANN model results showed that the effect of pyrolysis time was more significant on Cr(VI) adsorption capacity than Fe concentration. The optimal conditions, determined by the RSM analysis with a lower sum of squared error than ANN analysis, were used to synthesize the optimized Fe@FWB (Fe@FWB-OPT) for Cr(VI) removal. From the equilibrium model fitting, the Langmuir model showed a better fit than the Freundlich model, while the Redlich-Peterson isotherm model overlapped. The Cr(VI) sorption capacity of Fe@FWB-OPT calculated from the Langmuir model was 377.71 mg/g, high enough to be competitive to other adsorbents. The kinetic Cr(VI) adsorption was well described by the pseudo-second-order and Elovich models. The XPS results showed that Cr adsorbed on the surface of Fe-FWB-OPT was present not only as Cr(VI) but also as Cr(III) by the reduction of Cr(VI). The results of Cr(VI) adsorption by varying the pH indicate that electrostatic attraction is a key adsorption mechanism.

Layer-by-layer coating of MIL-100(Fe) on a cotton fabric for purification of water-soluble dyes by the combined effect of adsorption and photocatalytic degradation

Efforts have been made for sustainable development of adsorbents to purify organic contaminants from wastewater. In this study, a MIL-100(Fe) based textile that acts as a reusable adsorbent and photocatalytic agent was developed by synthesizing MIL-100(Fe) onto a cotton fabric by the layer-by-layer (LBL) process using water-based solutions. As the number of LBL cycles increased, the add-on's of MIL-100(Fe) showed a drastic increase up to 8 cycles, then showed gradual increases with further treatments. The overall adsorption performance was enhanced with the increased MIL-100(Fe) add-on's, but the specific adsorption efficiency per unit mass of MIL-100(Fe) was reduced as the LBL cycles increased, implying the reduced average adsorption efficiency with a thicker coating. To examine the reusability of the adsorbent, desorption efficiency of RhB was measured. The desorption after the first-time adsorption was not efficient due to the strong binding inside the pores. For the later cycles of adsorption–desorption, desorption occurred more efficiently, probably because RhB molecules were adhered mostly at the outer surface of the MOF layer. Simultaneously, MIL-100(Fe)@cotton demonstrated the photocatalytic degradation performance against RhB in the presence of H₂O₂ by the Fenton reaction. With the combined effect of adsorption and photodegradation, the developed fabric attained 96% removal efficiency for RhB dissolved in water. This study demonstrates an environmentally responsible process of developing a MIL-100(Fe) coated fabric that is readily available for effective removal of organic foulants in water. This fabrication method can be applied as a scalable manufacturing of metal–organic framework-based photocatalytic adsorbent textiles.

A MIL-100(Fe)-based water purifying textile that functions by dual action of adsorption and photocatalytic activity is designed via a layer-by-layer process without using toxic organic solvents.

Physicochemical Modeling of the Adsorption of Pharmaceuticals on MIL-100-Fe and MIL-101-Fe MOFs

The adsorption of naproxen (NAP), diclofenac (DFC), and acetaminophen (APAP) molecules from aqueous solutions using MIL-100-Fe and MIL-101-Fe metal organic frameworks (MOFs) has been analyzed and modeled. Adsorption isotherms of these pharmaceuticals were experimentally quantified at 30 and 40°C and pH 7. Textural parameters and surface chemistry of these MOFs were analyzed, and results were utilized to explain the pharmaceutical adsorption mechanism. Density Functional Theory (DFT) calculations were performed to understand the reactivity of pharmaceutical molecules, and a statistical physics model was employed to calculate the main physicochemical parameters related to the adsorption mechanism. Results showed that the adsorption of these pharmaceuticals on MOFs was multimolecular and exothermic. Both MOFs displayed the highest adsorption capacities, up to 2.19 and 1.71 mmol/g, for NAP and DFC molecules, respectively. MIL-101-Fe showed better pharmaceutical adsorption properties than MIL-100-Fe due to its highest content of Fe-O clusters and mesopore volume. Adsorption mechanism of these organic molecules could involve hydrogen bond, van der Waals forces, and electrostatic interactions with MOF surfaces. In particular, MIL-101-Fe MOF is a promising material to prepare composites with competitive adsorption capacities for facing the water pollution caused by pharmaceutical compounds.

Combined Layer-by-Layer/Hydrothermal Synthesis of Fe3O4@MIL-100(Fe) for Ofloxacin Adsorption from Environmental Waters

A simple not solvent and time consuming Fe₃O₄@MIL-100(Fe), synthesized in the presence of a small amount of magnetite (Fe₃O₄) nanoparticles (27.3 wt%), is here presented and discussed. Layer-by-layer alone (20 shell), and combined layer-by-layer (5 shell)/reflux or /hydrothermal synthetic procedures were compared. The last approach (Fe₃O₄@MIL-100_H sample) is suitable (i) to obtain rounded-shaped nanoparticles (200–400 nm diameter) of magnetite core and MIL-100(Fe) shell; (ii) to reduce the solvent and time consumption (the layer-by-layer procedure is applied only 5 times); (iii) to give the highest MIL-100(Fe) amount in the composite (72.7 vs. 18.5 wt% in the layer-by-layer alone); (iv) to obtain a high surface area of 3546 m² g⁻¹. The MIL-100(Fe) sample was also synthesized and both materials were tested for the absorption of Ofloxacin antibiotic (OFL). Langmuir model well describes OFL adsorption on Fe₃O₄@MIL-100_H, indicating an even higher adsorption capacity (218 ± 7 mg g⁻¹) with respect to MIL-100 (123 ± 5 mg g⁻¹). Chemisorption regulates the kinetic process on both the composite materials. Fe₃O₄@MIL-100_H performance was then verified for OFL removal at µg per liter in tap and river waters, and compared with MIL-100. Its relevant and higher adsorption efficiency and the magnetic behavior make it an excellent candidate for environmental depollution.

An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions

The development of computer aid models for heavy metals (HMs) simulation has been remarkably advanced over the past two decades. Several machine learning (ML) models have been developed for modeling HMs over the past two decades with outstanding progress. Although there have been a noticeable number of diverse ML models investigations, it is essential to have an informative vision on the progression of those computer aid models. In the current short review covering the simulation of heavy metals in contaminated soil, water bodies and removal from aqueous solution, numerous aspects on the methodological and conceptual HMs modeling are reviewed and discussed in detail. For instance, the limitation of the classical analytical methods, types of heavy metal dataset, necessity for new versions of ML models exploration, HM input parameters selection, ML models internal parameters tuning, performance metrics selection and the types of the modelled HM. The current review provides few outlooks in understanding the underlying od the ML models application for HM simulation. Tackling these modeling aspects is significantly essential for ML developers and environmental scientists to obtain creditability and scientific consistency in the domain of environmental science. Based on the discussed modeling aspects, it was concluded several future research directions, which will promote environmental scientists for better understanding of the underlying HMs simulation.