Synthesis of magnetic layered double hydroxide (Fe3O4@CuCr-LDH) decorated with ZIF-8 for efficient sonocatalytic degradation of tetracycline.

A series of Fe3O4@CuCr-LDH hybrids decorated with different amount of ZIF-8 (FLZ, 10-40 wt%) was prepared using simple methods and characterized with different techniques. The activity of the synthesized nanocomposites was investigated in the sonocatalytic degradation of tetracycline (TC) antibiotic from wastewater. When the content of ZIF-8 in the nanocomposite structure was 20 wt%, the FLZ-20 sonocatalyst exhibited the high performance in the sonocatalytic removal of TC. At optimum conditions (0.7 g/L catalyst dosage, pH of 7, 50 mg/L initial concentration of antibiotic, and 15 min sonication time) of the sonocatalytic removal of TC approached to 91.4% under ultrasonic irradiation (USI) using FLZ-20. This efficiency was much higher than those of obtained results by Fe3O4@CuCr-LDH and pristine ZIF-8. The formed ●OH and ●O2- exhibited the major roles in the sonocatalytic TC degradation process. The excellent performance of FLZ-20 can be attributed to the heterojunctions created between composite components, which could improve the electron transfer ability and effectively separate e-/h+ pairs. In addition, FLZ-20 showed the superior reusability and stability during three successive recycling. Moreover, the facile magnetically separation of the sonocatalyst from the aqueous solution was another outstanding feature, which prevents the formation of secondary pollutants. It can be concluded that the fabrication of heterojunctions is an efficient procedure to promote the sonocatalytic acting of the catalyst.

Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches.

Light olefins, as the backbone of the chemical and petrochemical industries, are produced mainly via steam cracking route. Prediction the of effects of operating variables on the product yield distribution through the mechanistic approaches is complex and requires long time. While increasing in the industrial automation and the availability of the high throughput data, the machine learning approaches have gained much attention due to the simplicity and less required computational efforts. In this study, the potential capability of four powerful machine learning models, i.e., Multilayer perceptron (MLP) neural network, adaptive boosting-support vector regression (AdaBoost-SVR), recurrent neural network (RNN), and deep belief network (DBN) was investigated to predict the product distribution of an olefin plant in industrial scale. In this regard, an extensive data set including 1184 actual data points were gathered during four successive years under various practical conditions. 24 varying independent parameters, including flow rates of different feedstock, numbers of active furnaces, and coil outlet temperatures, were chosen as the input variables of the models and the outputs were the flow rates of the main products, i.e., pyrolysis gasoline, ethylene, and propylene. The accuracy of the models was assessed by different statistical techniques. Based on the obtained results, the RNN model accurately predicted the main product flow rates with average absolute percent relative error (AAPRE) and determination coefficient (R2) values of 1.94% and 0.97, 1.29% and 0.99, 0.70% and 0.99 for pyrolysis gasoline, propylene, and ethylene, respectively. The influence of the various parameters on the products flow rate (estimated by the RNN model) was studied by the relevancy factor calculation. Accordingly, the number of furnaces in service and the flow rates of some feedstock had more positive impacts on the outputs. In addition, the effects of different operating conditions on the propylene/ethylene (P/E) ratio as a cracking severity factor were also discussed. This research proved that intelligent approaches, despite being simple and straightforward, can predict complex unit performance. Thus, they can be efficiently utilized to control and optimize different industrial-scale units.

Machine learning approaches for predicting arsenic adsorption from water using porous metal-organic frameworks.

Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, its eliminating is highly necessary. In this study, the ability of different novel and robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Decision Tree, and Random Forest was implemented to predict the adsorptive removal of arsenate [As(V)] from wastewater over 13 different metal-organic frameworks (MOFs). A large experimental dataset was collected under various conditions. The adsorbent dosage, contact time, initial arsenic concentration, adsorbent surface area, temperature, solution pH, and the presence of anions were considered as input variables, and adsorptive removal of As(V) was selected as the output of the models. The developed models were evaluated using various statistical criteria. The obtained results indicated that the LightGBM model provided the most accurate and reliable response to predict As(V) adsorption by MOFs and possesses R2, RMSE, STD, and AAPRE (%) of 0.9958, 2.0688, 0.0628, and 2.88, respectively. The expected trends of As(V) removal with increasing initial concentration, solution pH, temperature, and coexistence of anions were predicted reasonably by the LightGBM model. Sensitivity analysis revealed that the adsorption process adversely relates to the initial As(V) concentration and directly depends on the MOFs surface area and dosage. This study proves that ML approaches are capable to manage complicated problems with large datasets and can be affordable alternatives for expensive and time-consuming experimental wastewater treatment processes.

State-of-the-art progress of metal-organic framework-based electrochemical and optical sensing platforms for determination of bisphenol A as an endocrine disruptor.

Considering the low concentration levels of bisphenol compounds present in environmental, food, and biological samples, and the difficulty in analyzing the matrices, the main challenge is with the cleanup and extraction process, as well as developing highly sensitive determination methods. Recent advances in the field of metal-organic frameworks (MOFs) due to their large surface area, low weight, and other extraordinary physical, chemical, and mechanical features have made these porous materials a crucial agent in developing biosensing assays. This review focuses on MOFs across their definition, structural features, various types, synthetic routes, and their significant utilization in sensing assays for bisphenol A (BPA) determination. Additionally, recent improvements in characteristics and physio-chemical features of MOFs and their functional applications in developing electrochemical and optical sensing assays via different recognition elements for detecting BPA are comprehensively discussed. Finally, the existing boundaries of the current advances including future challenges concerning successful construction of sensing approaches by employing functionalized MOFs are addressed.

Improvement of anti-corrosion performance of an epoxy coating using hybrid UiO-66-NH2/carbon nanotubes nanocomposite.

In this study, a porous nanocontainer from UiO-66-NH2/CNTs nanocomposite with an excellent barrier characteristics was constructed through amine-functionalized Zr-based metal organic framework. The characterization of the prepared nano-materials were performed using different analyses such as FTIR, XRD, SEM, EDS, TEM, and BET and the results proved the successful synthesize of UiO-66-NH2/CNTs nanocomposite. The corrosion protection performance of the coated panels was investigated by electrochemical impedance spectroscopy (EIS), salt spray, and contact angle measurement. The EIS results revealed that unmodified and UiO-66-NH2 containing coating in 3.5 wt.% NaCl electrolyte were failed after 45 days but the corrosion was negligible in UiO-66-NH2/CNTs coating due to high pore resistance values even after 45 days. Salt spray and contact angle measurements confirmed that UiO-66-NH2/CNTs containing coating acts as an efficient barrier against wet saline environment even at long exposure times. This is attributed to uniform dispersion in the epoxy matrix and formation of a uniform nanocomposite coating.

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids.

Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H2S solubility in ILs and possesses R2, RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H2S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM+ and Tf2N-, i.e., [OMIM][Tf2N] ionic liquid, is the best choice for H2S capture among the investigated absorbents. The H2S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

State of the art on the ultrasonic-assisted removal of environmental pollutants using metal-organic frameworks.

The environmental and health issues of drinking water and effluents released into nature are among the major area of contention in the past few decades. With the growth of ultrasound-based approaches in water and wastewater treatment, promising materials have also been considered to employ their advantages. Metal-organic frameworks (MOFs) are among the porous materials that have received great attention from researchers in recent years. Features such as high porosity, large specific surface area, electronic properties like semi-conductivity, and the capacity to coordinate with the organic matter have resulted in a substantial increase in scientific researches. This work deals with a comprehensive review of the application of MOFs for ultrasonic-assisted pollutant removal from wastewater. In this regard, after considering features and synthesis methods of MOFs, the mechanisms of several ultrasound-based approaches including sonocatalysis, sonophotocatalysis, and sono-adsorption are well assessed for removal of different organic compounds by MOFs. These methods are compared with some other water treatment processes with the application of MOFs in the absence of ultrasound. Also, the main concern about MOFs including environmental hazards and water stability is fully discussed and some techniques are proposed to reduce hazardous effects of MOFs and improve stability in humid/aqueous environments. Economic aspects for the preparation of MOFs are evaluated and cost estimates for ultrasonic-assisted AOP approaches were provided. Finally, the future outlooks and the new frontiers of ultrasonic-assisted methods with the help of MOFs in global environmental pollutant removal are presented.

Estimation of tetracycline antibiotic photodegradation from wastewater by heterogeneous metal-organic frameworks photocatalysts.

In this work, the potential ability of various modern and powerful machine learning methods such as Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Gradient-Boosted Decision Trees (GBDT), Extra Tree (ET), Decision Trees (DT), and Random Forest (RF) were investigated to estimate tetracycline (TC) photodegradation from wastewater by 10 different metal-organic frameworks (MOFs). A comprehensive databank was gathered, including 374 data points from the photodegradation percentage of MOFs in various practical conditions. The inputs of the employed models were chosen as catalyst dosage, antibiotic concentration, Illumination time, solution pH, and specific surface area and pore volume of the investigated MOFs, and the output was TC degradation efficiency. Different statistical criteria were calculated for the validation of the developed models. Average absolute percent relative error (AAPRE) and standard deviation error (STD) values of 1.19% and 0.0431, 3.07% and 0.0628, 2.88% and 0.0751, 2.86% and 0.1304, 8.73% and 0.2751, 4.24% and 0.1024, 2.83% and 0.0934, and 11.56% and 0.4459 were obtained for CatBoost, LightGBM, XGBoost, AdaBoost, GBDT, ET, DT, and RF approaches, respectively. Among all implemented models, the CatBoost was found to be the most trustable model. Moreover, this model followed the expected trends of the TC degradation process with variation of catalyst dosage, initial TC concentration, and reaction pH. The developed CatBoost model predicted the removal of TC by MOFs accurately, which proved the capability of this approach in solving complex problems with numerous data points and its straightforwardness and cost-effectiveness for environmental applications.

Modeling of CO2 adsorption capacity by porous metal organic frameworks using advanced decision tree-based models.

In recent years, metal organic frameworks (MOFs) have been distinguished as a very promising and efficient group of materials which can be used in carbon capture and storage (CCS) projects. In the present study, the potential ability of modern and powerful decision tree-based methods such as Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) was investigated to predict carbon dioxide adsorption by 19 different MOFs. Reviewing the literature, a comprehensive databank was gathered including 1191 data points related to the adsorption capacity of different MOFs in various conditions. The inputs of the implemented models were selected as temperature (K), pressure (bar), specific surface area (m2/g) and pore volume (cm3/g) of the MOFs and the output was CO2 uptake capacity (mmol/g). Root mean square error (RMSE) values of 0.5682, 1.5712, 1.0853, and 1.9667 were obtained for XGBoost, CatBoost, LightGBM, and RF models, respectively. The sensitivity analysis showed that among all investigated parameters, only the temperature negatively impacts the CO2 adsorption capacity and the pressure and specific surface area of the MOFs had the most significant effects. Among all implemented models, the XGBoost was found to be the most trustable model. Moreover, this model showed well-fitting with experimental data in comparison with different isotherm models. The accurate prediction of CO2 adsorption capacity by MOFs using the XGBoost approach confirmed that it is capable of handling a wide range of data, cost-efficient and straightforward to apply in environmental applications.

Ag-doped magnetic metal organic framework as a novel nanostructured material for highly efficient antibacterial activity.

In the last decades, numerous attempts have been made to prevent microbial pollution spreading, using antibacterial agents. Zeolitic imidazolate framework-8 (ZIF-8) belongs to a subgroup of metal organic frameworks (MOFs) merits of attention due to the zinc ion clusters and its effective antibacterial activity. In this work, Ag-doped magnetic microporous γ-Fe2O3@SiO2@ZIF-8-Ag (FSZ-Ag) was successfully synthesized by a facile methodology in room temperature and used as an antibacterial agent against the growth of the Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus bacteria. Several characterization methods were applied to analyze the properties of the materials, and the results confirmed the accuracy of the synthesis procedure. Silver ions have employed to enhance the efficiency of antibacterial activity. As the results illustrated, FSZ-Ag nanostructured material had superior performance to inactive E. coli and S. aureus in growth inhibition test in liquid media. The best antibacterial activity as minimum inhibitory concentration (MIC) was 100 mg/L of FSZ-Ag against both bacteria. Leaching rates of silver ions showed that 80% of Ag released in the solutions, which was responsible for inhibiting the growth of bacteria. Also, fluorescence microscopy was used to investigate bacterial viability after 20 h contacting FSZ-Ag to distinguish live and dead bacteria by staining with DAPI and PI fluorescence stains. This novel magnetic nanostructured material is an excellent promising candidate to use in biological applications as high potential bactericidal materials.

In situ deposition of Ag/AgCl on the surface of magnetic metal-organic framework nanocomposite and its application for the visible-light photocatalytic degradation of Rhodamine dye.

Herein, NH2-MIL-125(Ti) (NMT) as one of the known stable metal-organic frameworks (MOFs) in aqueous solution was successfully magnetized with CoFe2O4 nanoparticles through the hydrothermal method. The Ag/AgCl as a plasmonic photocatalyst was assembled on the CoFe2O4/NMT (CFNMT) at room temperature by in situ deposition, and photo-reduction methods to improve the photocatalytic activity of CFNMT under LED visible light. The prepared materials were fully characterized by SEM/EDX, TEM, FTIR, XRD, UV-DRS, and VSM analysis. Rhodamin B (RhB) was selected as the pollutant model. The results showed that the Ag/AgCl@CFNMT had super-fast degradation ability of RhB molecule due to the synergetic effect between Ag/AgCl and CFNMT in comparison with NMT and CFNMT. The introduced Ag/AgCl on the surface of CFNMT increased absorption of photons in the visible region and enhanced the transfer and separation of the produced charge on the contact area between Ag/AgCl and CFNMT. Also, after seven times recycling, besides the simple magnetic separation of Ag/AgCl@CFNMT from liquid media, the composite still showed high photodegradation ability (89%).

Activated carbon/metal-organic framework nanocomposite: Preparation and photocatalytic dye degradation mathematical modeling from wastewater by least squares support vector machine.

Herein, Kiwi peel activated carbon (AC), Materials Institute Lavoisier (MIL-88B (Fe), and AC/MIL-88B (Fe) composite were synthesized and used as catalysts to degrade Reactive Red 198. The material properties were analyzed by the FTIR, BET-BJH, XRD, FESEM, EDX, TGA, and UV-Vis/DRS. The BET surface area of AC, MIL-88B (Fe) and AC/MIL-88B (Fe) was 1113.3, 150.7, and 199.4 m2/g, respectively. The band gap values (Eg) estimated by Tauc plot method, were obtained 5.06, 4.19 and 3.79 eV for AC, MIL-88B (Fe) and AC/MIL-88B (Fe), respectively. The results indicated that the AC/MIL-88B (Fe) composite had higher photocatalytic activity (99%) than that of pure AC (79%) and MIL-88B (Fe) catalysts (87%). The decolorization kinetic was matched well with the second-order model. Moreover, the data were modeled using least squares support vector machine which optimized with Cuckoo optimization algorithm. The optimal parameters were found 0.837 and 3.49e+02 based on σ2 and γ values, respectively. The mean square error (MSE) and correlation coefficient (R2) values were obtained 3.97 and 0.948. Therefore, the attained data, materials characterization and prediction of modeling validate the composite form of MIL-88B(Fe) with new AC, had better photocatalytic activity in comparison with the individual form.

Synthesis of amine-modified zeolitic imidazolate framework-8, ultrasound-assisted dye removal and modeling.

The present research is focused on the ultrasound assisted adsorption of Acid blue 92 (AB92) and Direct red 80 (DR80) as anionic dyes in single and binary systems onto zeolitic imidazolate framework (ZIF-8) functionalized with 3-Aminopropyltrimethoxysilane (APTES). Different techniques such as Fourier transform infrared (FTIR), scanning electron microscope (SEM), field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) and thermogravimetric analyses (TGA) were used to characterize the prepared adsorbent. The individual effects and possible interactions between the various parameters including adsorbent dosage, sonication time, initial dye concentrations and pH on dyes removal efficiency were investigated by response surface methodology (RSM). The optimized experimental conditions were fixed at adsorbent dosage 0.005g for AB92 and 0.01g for DR80, pH 2.1, sonication time 15min, and initial dyes concentration 15mgL-1 to get maximum removal percentage (>95.0%). A reliable and intelligent model based on least-squares support vector machine (LS-SVM) was developed to predict dye removal efficiency. The root mean square error (RMSE) of 0.604, 0.734 and 1.549 with high determination coefficient (R2) of 0.999, 0.996 and 0.997 for AB92, DR80 and binary system, respectively, were able to predict and model the adsorption process. The presented model illustrates better performance in predicting dye removal efficiency compared to the kinetic models. The results showed that the adsorption process had better conformation with pseudo-second order model. The adsorption equilibrium data was investigated by Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich isotherm models and the data were well fitted by Langmuir model with maximum adsorption capacity of 633.4 and 500.2mgg-1 for AB92 and DR80 dyes, respectively. APTES@ZIF-8 was regenerated and found to be reusable after four successive cycles without considerable loss in adsorption capacity.

Laccase immobilized manganese ferrite nanoparticle: synthesis and LSSVM intelligent modeling of decolorization.

Laccase was immobilized onto manganese ferrite nanoparticle (MFN) and dye decolorization from single and binary systems was studied. The characteristics of laccase immobilized manganese ferrite nanoparticle (LIMFN) were investigated using Fourier transform infrared (FTIR) and scanning electron microscopy (SEM). Direct red 31 (DR31), Acid blue 92 (AB92) and Direct green 6 (DG6) were used. A least square support vector machine (LSSVM) was developed to predict the decolorization efficiency of various single and binary systems based on the obtained laboratory data under different experimental conditions. Statistical and graphical quality measures were also employed to evaluate the performance and accuracy of the developed intelligent models. It is shown that the predictions of the designed LSSVM models are in close agreement with the experimental data. The effects of LIMFN dosage, pH and dye concentration on dye decolorization from single and binary systems were evaluated. Decolorization kinetics followed Michaelis-Menten Model.

Direct dyes removal using modified magnetic ferrite nanoparticle.

The magnetic adsorbent nanoparticle was modified using cationic surface active agent. Zinc ferrite nanoparticle and cetyl trimethylammonium bromide were used as an adsorbent and a surface active agent, respectively. Dye removal ability of the surface modified nanoparticle as an adsorbent was investigated. Direct Green 6 (DG6), Direct Red 31 (DR31) and Direct Red 23 (DR23) were used. The characteristics of the adsorbent were studied using Fourier transform infrared (FTIR), scanning electron microscopy (SEM) and X-ray diffraction (XRD). The effect of adsorbent dosage, initial dye concentration and salt was evaluated. In ternary system, dye removal of the adsorbent at 90, 120, 150 and 200 mg/L dye concentration was 63, 45, 30 and 23% for DR23, 97, 90, 78 and 45% for DR31 and 51, 48, 42 and 37% for DG6, respectively. It was found that dye adsorption onto the adsorbent followed Langmuir isotherm. The adsorption kinetic of dyes was found to conform to pseudo-second order kinetics.

Gemini polymeric nanoarchitecture as a novel adsorbent: synthesis and dye removal from multicomponent system.

In this paper, gemini polymeric nanoarchitecture (GPN) as a novel adsorbent was synthesized, and its dye removal ability from single and multicomponent (ternary) systems was investigated. The physical characteristics of GPN were studied using Fourier transform infrared (FTIR). Acid Blue 92 (AB92), Direct Green 6 (DG6), and Direct Red 31 (DR31) were used as model compounds. The isotherm and kinetic of dye adsorption from single and multicomponent (ternary) systems were studied. The effect of operational parameter such as adsorbent dosage, dye concentration, and salt on dye removal was evaluated. The maximum dye adsorption capacity (Q0) of GPN was 1000 mg/g, 1428 mg/g, and 1250 mg/g for AB92, DG6, and DR31, respectively. It was found that adsorption of AB92, DG6, and DR31 onto GPN followed with Langmuir, Freundlich, and Langmuir isotherms, respectively. Adsorption kinetic of dyes followed pseudo-second order kinetics. The results showed that the GPN as an insoluble polymeric adsorbent with high dye adsorption capacity might be a suitable alternative to remove dyes from colored wastewater.