Mn3O4/ZnO-Al2O3-CeO2 mixed oxide catalyst derived from Mn-doped Zn-(Al/Ce)-LDHs: efficient visible light photodegradation of clofibric acid in water.

Mn3O4/ZnO-Al2O3-CeO2 catalyst was synthesized through a solid-state process from a 3% Mn-doped Zn-(Al/Ce) layered double hydroxide structure. Detailed structural and optical characterization using XRD, FTIR, UV-visible DRS, and TEM was conducted. By investigating clofibric acid (CA) degradation in aqueous solution, Mn3O4/ZnO-Al2O3-CeO2 photocatalytic activity was evaluated. The results show that the heterostructure mixed oxide catalyst has excellent CA photodegradation performance. Further, the characterization reveals that such photocatalytic efficiency can be attributed to two facts that are summarized in the optical properties and the synergic effect between Mn and Ce elements. The sample demonstrated a narrow band gap of 2.34 eV based on DRS. According to the experimental results of the photodegradation, after 120 min of irradiation, the photocatalyst exhibited the highest photocatalytic activity, with a degradation efficiency of 93.6%. Optimization outcomes indicated that maximum degradation efficiency was attained under the following optimum conditions: catalyst dose of 0.3 g/L, initial dye concentration of 20 mg/L, pH 3.86, and 120 min of reaction time. The quenching test demonstrates that photogenerated electrons and superoxide radicals are the most powerful reactive species. The catalyst could be useful in decreasing the photogenerated charges recombination, which offers more redox cycles simultaneously during the catalytic process. The strong Ce-Mn interaction and the formation of their different oxidation states offer a high degradation efficiency by facilitating electron-hole transfer. The introduction of Mn3O4 in the catalyst can effectively improve the visible absorption properties, which are beneficial in the photocatalytic process by reaching a high catalytic efficiency at a low cost.

ZnO-Zn2TiO4 heterostructure for highly efficient photocatalytic degradation of pharmaceuticals.

In this study, ZnO-Zn2TiO4 (ZTM) material was prepared through a novel synthesis method based on a ultrasound-assisted polyol-mediated process followed by calcination at a different temperature. Physical features of the samples were studied by using various analysis techniques including XRD, FT-IR, SEM/EDX, pHPZC, and UV-Vis DRS. Subsequently, the materials were employed as catalysts for the photocatalytic degradation of clofibric acid as a model pharmaceutical contaminant. The photocatalytic performance was evaluated under different conditions of calcination temperature, catalyst dosage, starting concentration, and initial pH of clofibric acid solution. The finding results revealed that hexagonal-tetragonal phases of ZnO-Zn2TiO4 calcined at 600 °C (ZTM-600) with an average crystallite size of 97.8 Å exhibited the best degradation efficiency (99%). The primary bands characteristic of ZnO and Zn2TiO4 were displayed by FT-IR analysis and the UV-visible DRS confirms the larger absorption capacity in UV-visible regions. The photogenerated electrons are the powerful reactive species involved in clofibric acid photodegradation process. This study shows a promising photocatalyst and provides new sight to rational design the facets of photocatalysis process for enhanced photocatalytic performances and effective wastewater treatment.

MgO-La2O3 mixed metal oxides heterostructure catalysts for photodegradation of dyes pollutant: synthesis, characterization and artificial intelligence modelling.

In the present work, we prepared MgO-La2O3-mixed-metal oxides (MMO) as efficient photocatalysts for degradation of organic pollutants. First, a series of MgAl-%La-CO3-layered double hydroxide (LDH) precursors with different contents of La (5, 10, and 20 wt%) were synthesized by the co-precipitation process and then calcined at 600 °C. The prepared materials were characterized by XRD, SEM-EDX, FTIR, TGA, ICP, and UV-vis diffuse reflectance spectroscopy. XRD indicated that MgO, La2O3, and MgAl2O4 phases were found to coexist in the calcined materials. Also, XRD confirms the orthorhombic-tetragonal phases of MgO-La2O3. The samples exhibited a small band gap of 3.0-3.22 eV based on DRS. The photocatalytic activity of the catalysts was assessed for the degradation of two dyes, namely, tartrazine (TZ) and patent blue (PB) as model organic pollutants in aqueous mediums under UV-visible light. Detailed photocatalytic tests that focused on the impacts of dopant amount of La, catalyst dose, initial pH of the solution, irradiation time, dye concentration, and reuse were carried out and discussed in this research. The experimental findings reveal that the highest photocatalytic activity was achieved with the MgO-La2O3-10% MMO with photocatalysts with a degradation efficiency of 97.4% and 93.87% for TZ and PB, respectively, within 150 min of irradiation. The addition of La to the sample was responsible for its highest photocatalytic activity. Response surface methodology (RSM) and gradient boosting regressor (GBR), as artificial intelligence techniques, were employed to assess individual and interactive influences of initial dye concentration, catalyst dose, initial pH, and irradiation time on the degradation performance. The GBR technique predicts the degradation efficiency results with R2 = 0.98 for both TZ and PB. Moreover, ANOVA analysis employing CCD-RSM reveals a high agreement between the quadratic model predictions and the experimental results for TZ and PB (R2 = 0.9327 and Adj-R2 = 0.8699, R2 = 0.9574 and Adj-R2 = 0.8704, respectively). Optimization outcomes indicated that maximum degradation efficiency was attained under the following optimum conditions: catalyst dose 0.3 g/L, initial dye concentration 20 mg/L, pH 4, and reaction time 150 min. On the whole, this study confirms that the proposed artificial intelligence (AI) techniques constituted reliable and robust computer techniques for monitoring and modeling the photodegradation of organic pollutants from aqueous mediums by MgO-La2O3-MMO heterostructure catalysts.

The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning.

During the last few years, important advances have been made in big data exploration, complex pattern recognition and prediction of complex variables. Machine learning (ML) algorithms can efficiently analyze voluminous data, identify complex patterns and extract conclusions. In chemical engineering, the application of machine learning approaches has become highly attractive due to the growing complexity of this field. Machine learning allows computers to solve problems by learning from large data sets and provides researchers with an excellent opportunity to enhance the quality of predictions for the output variables of a chemical process. Its performance has been increasingly exploited to overcome a wide range of challenges in chemistry and chemical engineering, including improving computational chemistry, planning materials synthesis and modeling pollutant removal processes. In this review, we introduce this discipline in terms of its accessible to chemistry and highlight studies that illustrate in-depth the exploitation of machine learning. The main aim of the review paper is to answer these questions by analyzing physicochemical processes that exploit machine learning in organic and inorganic pollutants removal. In general, the purpose of this review is both to provide a summary of research related to the removal of various contaminants performed by ML models and to present future research needs in ML for contaminant removal.

Chemically modified carbon-based electrodes for the determination of paracetamol in drugs and biological samples.

Paracetamol is a non-steroidal, anti-inflammatory drug widely used in pharmaceutical applications for its sturdy, antipyretic and analgesic action. However, an overdose of paracetamol can cause fulminant hepatic necrosis and other toxic effects. Thus, the development of advantageous analytical tools to detect and determine paracetamol is required. Due to simplicity, higher sensitivity and selectivity as well as costefficiency, electrochemical sensors were fully investigated in last decades. This review describes the advancements made in the development of electrochemical sensors for the paracetamol detection and quantification in pharmaceutical and biological samples. The progress made in electrochemical sensors for the selective detection of paracetamol in the last 10 years was examined, with a special focus on highly innovative features introduced by nanotechnology. As the literature is rather extensive, we tried to simplify this work by summarizing and grouping electrochemical sensors according to the by which manner their substrates were chemically modified and the analytical performances obtained.

Comparative overview of advanced oxidation processes and biological approaches for the removal pharmaceuticals.

Nowadays, pharmaceuticals are the center of significant environmental research due to their complex and highly stable bioactivity, increasing concentration in the water streams and high persistence in aquatic environments. Conventional wastewater treatment techniques are generally inadequate to remove these pollutants. Aiming to tackle this issue effectively, various methods have been developed and investigated on the light of chemical, physical and biological procedures. Increasing attention has recently been paid to the advanced oxidation processes (AOPs) as efficient methods for the complete mineralization of pharmaceuticals. Their high operating costs compared to other processes, however, remain a challenge. Hence, this review summarizes the current and state of art related to AOPs, biological treatment and their effective exploitation for the degradation of various pharmaceuticals and other emerging molecules present in wastewater. The review covers the last decade with a particular focus on the previous five years. It is further envisioned that this review of advanced oxidation methods and biological treatments, discussed herein, will help readers to better understand the mechanisms and limitations of these methods for the removal of pharmaceuticals from the environment. In addition, we compared AOPs and biological treatments for the disposal of pharmaceuticals from the point of view of cost, effectiveness, and popularity of their use. The exploitation of coupling AOPs and biological procedures for the degradation of pharmaceuticals in wastewater was also presented. It is worthy of note that an integrated AOPs/biological system is essential to reach the complete degradation of pharmaceuticals; other advantages of this hybrid technique involve low energy cost, an efficient degradation process and generation of non-toxic by-products.

Chemically modified carbon-based electrodes for the determination of paracetamol in drugs and biological samples / 药物分析学报

Paracetamol is a non-steroidal,anti-inflammatory drug widely used in pharmaceutical applications for its sturdy,antipyretic and analgesic action.However,an overdose of paracetamol can cause fulminant hepatic necrosis and other toxic effects.Thus,the development of advantageous analytical tools to detect and determine paracetamol is required.Due to simplicity,higher sensitivity and selectivity as well as costefficiency,electrochemical sensors were fully investigated in last decades.This review describes the advancements made in the development of electrochemical sensors for the paracetamol detection and quantification in pharmaceutical and biological samples.The progress made in electrochemical sensors for the selective detection of paracetamol in the last 10 years was examined,with a special focus on highly innovative features introduced by nanotechnology.As the literature is rather extensive,we tried to simplify this work by summarizing and grouping electrochemical sensors according to the by which manner their substrates were chemically modified and the analytical performances obtained.