Versatile Metal-Organic Framework Incorporating Ag2S for Constructing a Photoelectrochemical Immunosensor for Two Breast Cancer Markers

Breast cancer poses the significance of early diagnosis and treatment. Here, we developed an innovative photoelectrochemical (PEC) immunosensor characterized by high-level dual photocurrent signals and exceptional sensitivity. The PEC sensor, denoted as MIL&Ag2S, was constructed by incorporating Ag2S into a metal-organic framework of MIL-101(Cr). This composite not only enhanced electron-hole separation and conductivity but also yielded robust and stable dual photocurrent signals. Through the implementation of signal switching, we achieved the combined detection of cancer antigen 15-3 (CA15-3) and carcinoembryonic antigen (CEA) with outstanding stability, reproducibility, and specificity. The results revealed a linear range for CEA detection spanning 0.01-32 ng/mL, with a remarkably low detection limit of 0.0023 ng/mL. Similarly, for CA15-3 detection, the linear range extended from 0.1 to 320 U/mL, with a low detection limit of 0.014 U/mL. The proposed strategy introduces new avenues for the development of highly efficient, cost-effective, and user-friendly PEC sensors. Furthermore, it holds promising prospects for early clinical diagnosis, contributing to potential breakthroughs in medical detection and ultimately improving patient outcomes.

Interfacial Engineering Boosting the Activity and Stability of MIL-53(Fe) toward Electrocatalytic Nitrogen Reduction

The electrochemical nitrogen reduction reaction (eNRR) has emerged as a promising strategy for green ammonia synthesis. However, it suffers unsatisfactory reaction performance owing to the low aqueous solubility of N2 in aqueous solution, the high dissociation energy of N≡N, and the unavoidable competing hydrogen evolution reaction (HER). Herein, a MIL-53(Fe)@TiO2 catalyst is designed and synthesized for highly efficient eNRR. Relative to simple MIL-53(Fe), MIL-53(Fe)@TiO2 achieves a 2-fold enhancement in the Faradaic efficiency (FE) with an improved ammonia yield rate by 76.5% at -0.1 V versus reversible hydrogen electrode (RHE). After four cycles of electrocatalysis, MIL-53(Fe)@TiO2 can maintain a good catalytic activity, while MIL-53(Fe) exhibits a significant decrease in the NH3 yield rate and FE by 79.8 and 82.3%, respectively. Benefiting from the synergetic effect between TiO2 and MIL-53(Fe) in the composites, Fe3+ ions can be greatly stabilized in MIL-53(Fe) during the eNRR process, which greatly hinders the catalyst deactivation caused by the electrochemical reduction of Fe3+ ions. Further, the charge transfer ability in the interface of composites can be improved, and thus, the eNRR activity is significantly boosted. These findings provide a promising insight into the preparation of efficient composite electrocatalysts.

Integrative analysis of multi machine learning models for tetracycline photocatalytic degradation with MOFs in wastewater treatment

This study focuses on the utilization of connectionist models, specifically Independent Component Analysis (ICA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Genetic Algorithm-Particle Swarm Optimization (GAPSO) integrated with a least-squares support vector machine (LSSVM) to forecast the degradation of tetracycline (TC) through photocatalysis using Metal-Organic Frameworks (MOFs). The primary objective of this study was to evaluate the viability and precision of these connectionist models in estimating the efficiency of TC degradation, particularly within the context of wastewater treatment. The input parameters for these models cover essential MOF characteristics, such as pore size and surface area, along with critical operational factors, such as pH, TC concentration, catalyst dosage, and illumination duration, all of which are linked to the photocatalytic performance of MOFs. Sensitivity analysis revealed that the illumination duration is the primary influencer of TC photodegradation with MOF photocatalysts, while the MOFs' surface area is the second crucial parameter shaping the efficiency and dynamics of the TC-MOF photocatalytic system. The developed LSSVM models display impressive predictive capabilities, effectively forecasting the experimental degradation of TC with high accuracy. Among these models, the GAPSO-LSSVM model excels as the top performer, achieving notable evaluation metrics, including STD, RMSE, MSE, MRE, and R2 at values of 3.09, 3.42, 11.71, 5.95, and 0.986, respectively. In comparison, the PSO-LSSVM, ICA-LSSVM, and GA-LSSVM models yield mean relative errors of 6.18%, 7.57%, and 11.37%, respectively. These outcomes highlight the exceptional predictive capabilities of the GAPSO-LSSVM model, solidifying its position as the most accurate and dependable model for predicting TC photodegradation in this study. This study contributes to advancing photocatalytic research and effectively reinforces the importance of leveraging machine learning methodologies for tackling environmental challenges.

Nanotechnology-assisted treatment of pharmaceuticals contaminated water

The presence of pharmaceutical compounds in wastewater due to an increase in industrialization and urbanization is a serious health concern. The demand for diverse types of pharmaceutical compounds is expected to grow as there is continuous improvement in the global human health standards. Discharge of domestic pharmaceutical personal care products and hospital waste has aggravated the burden on wastewater management. Further, the pharmaceutical water is toxic not only to the aquatic organism but also to terrestrial animals coming in contact directly or indirectly. The pharmaceutical wastes can be removed by adsorption and/or degradation approach. Nanoparticles (NPs), such as 2D layers materials, metal-organic frameworks (MOFs), and carbonaceous nanomaterials are proven to be more efficient for adsorption and/or degradation of pharmaceutical waste. In addition, inclusion of NPs to form various composites leads to improvement in the waste treatment efficacy to a greater extent. Overall, carbonaceous nanocomposites have advantage in the form of being produced from renewable resources and the nanocomposite material is biodegradable either completely or to a great extent. A comprehensive literature survey on the recent advancement of pharmaceutical wastewater is the focus of the present article.

The colorimetry and smartphone determination of perfluorooctane sulfonate based on cytidine 5'-monophosphate-capped gold nanoclusters with peroxidase-like activity

Besides being a luminescent material, cytidine 5'-monophosphate-capped gold nanoclusters (AuNCs@CMP) also show superior peroxidase-like activity which can promote TMB oxidation in the presence of H₂O₂, causing the solution to turn efficiently from pale to blue. However, the presence of perfluorooctane sulfonate (PFOS) in the above system inhibited TMB oxidation and bluing of the solution, consequently establishing a colorimetric platform of AuNCs/H₂O₂/TMB for PFOS determination. The results showed that it responded to PFOS over a wide range of 2.0-50 μM, with a limit of detection (LOD) as low as 150 nM. Furthermore, in-depth mechanism investigation revealed that, rather than the active site of the catalyst being occupied by PFOS, such a hypochromatic effect originated from depletion of the reactive oxygen species (ROS) by PFOS degradation, thereby also offering a unique strategy to scavenge the lethal toxicity of PFOS. In addition, the colorimetric response of AuNCs/H₂O₂/TMB to PFOS was extended to smartphone determination conveniently based on RGB values. Finally, the established platform was applied to PFOS determination both in soil extracts and in tap water with good recovery, which supplies a novel colorimetric platform for visual determination of PFOS in practice. The method has the advantages of being rapid, sensitive and highly selective, which highlight the design and construction of more systems for determination and elimination of lethal pollutants in environmental water.

Fabricating Materials of Institute Lavoisier-53(Fe)/zeolite imidazolate framework-8 hybrid materials as high-efficiency and reproducible adsorbents for removing organic pollutants

Environmental pollution by emerging contaminants has become an urgent problem. Herein, novel binary metal-organic framework hybrids were constructed from Materials of Institute Lavoisier-53(Fe) (MIL-53(Fe)) and zeolite imidazolate framework-8 (ZIF-8) for the first time. A battery of characterizations were employed to determine the MIL/ZIF hybrids' properties and morphology. Furthermore, the MIL/ZIF towards toxic antibiotics (tetracycline, ciprofloxacin and ofloxacin) were studied to explore their adsorption abilities. The present work disclosed that the obtained MIL-53(Fe)/ZIF-8 = 2:3 possessed an eminent specific surface area with an admirable removal efficiency of tetracycline (97.4%), ciprofloxacin (97.1%) and ofloxacin (92.4%), respectively. The tetracycline adsorption process conformed to the pseudo-second-order kinetic model and this process was more compatible with the Langmuir isotherm model with the highest adsorption capacity of 215.0 mg g^-1. Moreover, the process of removing tetracycline was proved to be spontaneous and exothermic by the thermodynamic results. Furthermore, the MIL-53(Fe)/ZIF-8 = 2:3 towards tetracycline exhibited significant regeneration ability. The effects of pH, dosage, interfering ions and oscillation frequency on tetracycline adsorption capacity and removal efficiency were also investigated. The primary factors contributing to the decent adsorption ability between MIL-53(Fe)/ZIF-8 = 2:3 and tetracycline included electrostatic, π-π stacking, hydrogen bonding and weak coordination interactions. Additionally, we also investigated the adsorption ability in real wastewater. Thus, the proposed binary metal-organic framework hybrid materials can be deemed a promising adsorbent in wastewater purification.

Fabrication of a Heterojunction by Coupling a Metal-Organic Framework and N-Doped Carbon for the Photocatalytic Removal of Antibiotic Drugs with High Efficiency

Norfloxacin (NOR) and tetracycline (TC), two widely used antibiotic drugs released to the aquatic environment, induce harm to ecosystems. In this study, an effective method was developed successfully to remove NOR and TC by photocatalysis with a novel heterojunction NC/NH₂-MIL-53(Fe), which was fabricated by combining a metal-organic framework (MOF) material (NH₂-MIL-53(Fe)) and N-doped carbon (NC) nanoparticles via a facile solvent thermal method. The prepared product exhibits outstanding photocatalytic efficiencies toward degradation of NOR and TC that are 15 and 6 times higher than those of pure NH₂-MIL-53(Fe), respectively. Moreover, it is higher than those of the related materials reported previously. The greatly enhanced photocatalytic performance is assigned to the fabricated heterojunction with well-matched energy band gaps, where the NC acts as an efficient electron transfer/reservoir material to effectively promote the migration and transfer and restrain the recombination of charge carriers. In addition, the formed heterojunction increases specific surface area and light absorbance. The photocatalytic activity enhanced mechanism, degradation products, and pathway were investigated. The present study offers a novel strategy to significantly improve the photocatalytic performances of MOFs for highly efficient photocatalytic removal of antibiotic drugs in wastewater.

Insights into High-Performance and Selective Elimination of Cationic Dye from Multicomponent Systems by Using Fe-Based Metal-Organic Frameworks

Metal-organic frameworks (MOFs), especially Fe-MOFs, have shown prospective application in eliminating organic dyes from wastewater due to their well-developed pores, water stability, easy preparation, and economy. Herein, we synthesized four types of Fe-MOFs (such as MIL-88A, MIL-88B, MIL-100, and MIL-101) using the hydrothermal method. The products were analyzed with several methods. By comparing the adsorption effect of those four types of Fe-MOFs on three kinds of dyes, it has been shown that MIL-100 owns the best adsorption efficiency on cationic organic dyes methylene blue (MB) and Rhodamine B (RhB) in 180 min, while all MOFs have slight removal capacity on methyl orange (MO). MIL-100, as an adsorbent, was studied under various research conditions, and the maximum removal efficiencies to MB, RhB, and MO were found to be up to 97.36%, 88.75%, and 13.00%, respectively. Furthermore, cationic dye MB's removal by MIL-100 was fitted with a pseudo-second-order model and Langmuir isotherm model (Q_m = 411.041 mg/g) by adsorption kinetics and isotherms research, and MIL-100 could rapidly and selectively divide MB from a binary complex aqueous solution of MB and MO. The as-fabricated MIL-100 also exhibited excellent recyclability after four adsorption-desorption recycles and can be treated as a potential substance with high removal efficiency of cationic organic dye-containing industrial effluents.

An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique

Tetracyclines (TCs) have been extensively used for humans and animal diseases treatment and livestock growth promotion. The consumption of such antibiotics has been ever-growing nowadays due to various bacterial infections and other pathologic conditions, resulting in more discharge into the aquatic environments. This brings threats to ecosystems and human bodies. Up to now, several attempts have been made to reduce TC amounts in the wastewater, among which photocatalysis, an advanced oxidation process, is known as an eco-friendly and efficient technology. In this regard, metal organic frameworks (MOFs) have been known as the promising materials as photocatalysts. Thus, studying TC photocatalytic degradation by MOFs would help scientists and engineers optimize the process in terms of effective parameters. Nevertheless, the costly and time-consuming experimental methods, having instrumental errors, encouraged the authors to use the computational method for a more comprehensive assessment. In doing so, a wide-ranging databank including 374 experimental data points was gathered from the literature. A powerful machine learning method of Gaussian process regression (GPR) model with four kernel functions was proposed to estimate the TC degradation in terms of MOFs features (surface area and pore volume) and operational parameters (illumination time, catalyst dosage, TC concentration, pH). The GPR models performed quite well, among which GPR-Matern model shows the most accurate performance with R², MRE, MSE, RMSE, and STD of 0.981, 12.29, 18.03, 4.25, and 3.33, respectively. In addition, an analysis of sensitivity was carried out to assess the effect of the inputs on the TC photodegradation by MOFs. It revealed that the illumination time and the surface area play a significant role in the decomposition activity.

Recent advances in performance improvement of Metal-organic Frameworks to remove antibiotics: Mechanism and evaluation

Antibiotic pollution is a serious global problem, which may threaten the health of human and ecosystem. Thereinto, water pollution is the most common way. Thus, it is necessary to develop effective methods to remove antibiotics from the natural aqueous environments. Metal-organic Frameworks (MOFs) - based adsorption and photocatalysis strategies have been demonstrated to be efficient, environmental and promising methods to solve antibiotic pollution and repair the environment. In this review, several strategies to improve the properties of MOFs for removal were summarized and discussed. And the removal mechanisms were also discussed. Besides, new and more reliable evaluation methods of MOFs to remove antibiotics were presented, including preferential adsorption (q_p), quantum yields (QY), space time yields (SY) and figure of merit (FOM). This paper provides alternative perspectives for researchers to improve the properties of MOFs and raise analytic efficiency of antibiotic removal.

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.

A review of metal organic framework (MOFs)-based materials for antibiotics removal via adsorption and photocatalysis

Antibiotic abuse has led to serious water pollution and severe harm to human health; therefore, there is an urgent need for antibiotic removal from water sources. Adsorption and photodegradation are two ideal water treatment methods because they are cheap, simple to operate, and reusable. Metal organic frameworks (MOFs) are excellent adsorbents and photocatalysts because of their high porosity, adaptability, and good crystal form. The aim of this study is to suggest ways to overcome the limitations of adsorption and photocatalysis treatment methods by reviewing previous applications of MOFs to antibiotic adsorption and photocatalysis. The different factors influencing these processes are also discussed, as well as the various adsorption and photocatalysis mechanisms. This study provides a valuable resource for researchers intending to use MOFs to remove antibiotics from water bodies.

Insights into the Stability and Activity of MIL-53(Fe) in Solar Photocatalytic Oxidation Processes in Water

MIL-53(Fe) is a metal organic framework that has been recently considered a heterogeneous photocatalyst candidate for the degradation of water pollutants under visible or solar radiation, though stability studies are rather scarce in the literature. In this work, MIL-53(Fe) was successfully synthesized by a solvothermal method and fully characterized by X-ray diffraction (XRD), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), N2 adsorption–desorption isotherm, Thermogravimetric analysis coupled with mass spectrometry (TGA-MS), UV-visible diffuse reflectance spectroscopy (DRS), elemental analysis and wavelength dispersive X-ray fluorescence (WDXRF). The effects of pH, temperature, solar radiation and the presence of oxidants (i.e., electron acceptors) such as ozone, persulfate and hydrogen peroxide on the stability of MIL-53(Fe) in water were investigated. The as-synthetized MIL-53(Fe) exhibited relatively good stability in water at pH 4 but suffered fast hydrolysis at alkaline conditions. At pH 4–5, temperature, radiation (solar and visible radiation) and oxidants exerted negative effect on the stability of the metal–organic framework (MOF) in water, resulting in non-negligible amounts of metal (iron) and linker (terephthalic acid, H2BDC) leached out from MIL-53(Fe). The photocatalytic activity of MIL-53(Fe) under simulated solar radiation was studied using phenol and metoprolol as target pollutants. MIL-53(Fe) on its own removed less than 10% of the pollutants after 3 h of irradiation, while in the presence of ozone, persulfate or hydrogen peroxide, complete elimination of pollutants was achieved within 2 h of exposure to radiation. However, the presence of oxidants and the formation of some reaction intermediates (e.g., short-chain carboxylic acids) accelerated MIL-53(Fe) decarboxylation. The findings of this work suggest that MIL-53(Fe) should not be recommended as a heterogeneous photocatalyst for water treatment before carrying out a careful evaluation of its stability under actual reaction conditions.