Sonocatalytic degradation of tetracycline hydrochloride using SnO hollow-nanofiber decorated with UiO-66-NH.

In this study, a porous hollow nanofiber SnO was decorated with UiO-66-NH nanoparticles with straightforward solvothermal method and utilized for sonocatalytic degradation of tetracycline (TC) by ultrasonic irradiation (USI). The prepared materials were characterized using different techniques such as SEM, EDS, FTIR, XRD, BET, XPS, UV-DRS, EIS, and zeta potential. SnO PHNF/UiO-66-NH nanocomposite offered the highest apparent rate constant of 0.

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

A series of FeO@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.

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.

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.

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

In this study, a porous nanocontainer from UiO-66-NH/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-NH/CNTs nanocomposite. The corrosion protection performance of the coated panels was investigated by electrochemical impedance spectroscopy (EIS), salt spray, and contact angle measurement.

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.

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 (HS). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for HS capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the HS solubility in ILs.

Modeling of CO 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.

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.

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.

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-FeO@SiO@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.

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, NH-MIL-125(Ti) (NMT) as one of the known stable metal-organic frameworks (MOFs) in aqueous solution was successfully magnetized with CoFeO nanoparticles through the hydrothermal method. The Ag/AgCl as a plasmonic photocatalyst was assembled on the CoFeO/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.

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.

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).

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.

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.

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.