Synergistic degradation of metronidazole and penicillin G in aqueous solutions using AgZnFe2O4@chitosan nano-photocatalyst under UV/persulfate activation

Environmental Technology & Innovation, Год журнала: 2024, Номер 35, С. 103724 - 103724

Опубликована: Июнь 26, 2024

In recent years, the persistence of pharmaceutical contaminants like metronidazole (MNZ) and penicillin G (PG) in water bodies has become a major environmental concern. The present research studied simultaneous degradation MNZ PG utilizing an AgZnFe2O4@Ch catalyst generated through co-precipitation technique as effective stimulator for persulfate (PS) existence UV light. structure was characterized using X-ray powder diffraction, Fourier transform infrared spectroscopy, Field emission scanning electron microscopy, vibrating-sample magnetometer, energy dispersive spectroscopy mapping. After 50 minutes reaction time under ideal operating conditions, which included 0.4 g/L catalyst, 4 mM PS, 5 mg/L PG, pH 5, highest 81.5 % 82.3 were obtained. Statistical parameters, including R2 values 0.985 0.981 indicate very good agreement between predicted observed values. Garson's method analysis revealed that PS dosage had greatest impact on degradation, while initial concentration exerted most significant influence degradation. Langmuir-Hinshelwood model surface rate constants (Kc) 0.954 (mg/L.min) adsorption equilibrium (KL-H) 0.032 (L/mg) both antibiotics, respectively. claimed mechanism illustrated by free radical scavenging studies, demonstrated SO•4- radicals main involved PG. A last investigation catalyst's regeneration it satisfactory chemical stability after five cycles usage approaches.

Язык: Английский

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Advances in Catalysts for Hydrogen Production: A Comprehensive Review of Materials and Mechanisms

Nanomaterials, Год журнала: 2025, Номер 15(4), С. 256 - 256

Опубликована: Фев. 8, 2025

This review explores the recent advancements in catalyst technology for hydrogen production, emphasizing role of catalysts efficient and sustainable generation. involves a comprehensive analysis various materials, including noble metals, transition carbon-based nanomaterials, metal–organic frameworks, along with their mechanisms performance outcomes. Major findings reveal that while metal catalysts, such as platinum iridium, exhibit exceptional activity, high cost scarcity necessitate exploration alternative materials. Transition single-atom have emerged promising substitutes, demonstrating potential enhancing catalytic efficiency stability. These underscore importance interdisciplinary approaches to design, which can lead scalable economically viable production systems. The concludes ongoing research should focus on addressing challenges related stability, scalability, integration renewable energy sources, paving way economy. By fostering innovation development, this work aims contribute towards cleaner solutions more resilient future.

Язык: Английский

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AI in single-atom catalysts: a review of design and applications

Journal of Materials Informatics, Год журнала: 2025, Номер 5(1)

Опубликована: Фев. 12, 2025

Single-atom catalysts (SACs) have emerged as a research frontier in catalytic materials, distinguished by their unique atom-level dispersion, which significantly enhances activity, selectivity, and stability. SACs demonstrate substantial promise electrocatalysis applications, such fuel cells, CO2 reduction, hydrogen production, due to ability maximize utilization of active sites. However, the development efficient stable involves intricate design screening processes. In this work, artificial intelligence (AI), particularly machine learning (ML) neural networks (NNs), offers powerful tools for accelerating discovery optimization SACs. This review systematically discusses application AI technologies through four key stages: (1) Density functional theory (DFT) ab initio molecular dynamics (AIMD) simulations: DFT AIMD are used investigate mechanisms, with high-throughput applications expanding accessible datasets; (2) Regression models: ML regression models identify features that influence performance, streamlining selection promising materials; (3) NNs: NNs expedite known structural models, facilitating rapid assessment potential; (4) Generative adversarial (GANs): GANs enable prediction novel high-performance tailored specific requirements. work provides comprehensive overview current status insights recommendations future advancements field.

Язык: Английский

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Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review

Next Materials, Год журнала: 2025, Номер 8, С. 100522 - 100522

Опубликована: Фев. 10, 2025

Язык: Английский

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Optimizing glycerol conversion to hydrogen: A critical review of catalytic reforming processes and catalyst design strategies

International Journal of Hydrogen Energy, Год журнала: 2025, Номер 109, С. 823 - 850

Опубликована: Фев. 14, 2025

Язык: Английский

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Machine Learning‐Based Surrogate Model Development for the Estimation of State‐of‐Charge and Minimization of Charging Time for Batteries of Lithium‐Ion in Electric Vehicles

Energy Storage, Год журнала: 2025, Номер 7(3)

Опубликована: Март 20, 2025

ABSTRACT Lithium‐ion batteries (LIBs) are the main energy source for electric vehicles (EVs), but they require sophisticated Battery Management Systems (BMS) optimal functionality. In response to this need, Python Mathematical Model (PyBaMM) was used apply Doyle–Fuller–Newman (DFN) electrochemical model, which provided detailed battery data. This research utilizes DFN model develop a surrogate based on machine learning precise state‐of‐charge (SoC) with predicted values of 15% 90%, is recommended value SoC in vehicle technology. The showed impressive accuracy, achieving 99.6% R‐score and mean squared error (MSE) 2.6%. Additionally, study implemented strategy integrated particle swarm optimization (PSO) determine charging parameters that reduce time while preserving health safety. These optimized decreased projected 130 s, although actual expected take around 225 s.

Язык: Английский

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Introducing an improved rime algorithm combined with gate current unit as an innovative stability monitoring and controlling model for flexible biogas-to-hydrogen/methanol system

Renewable Energy, Год журнала: 2025, Номер unknown, С. 123032 - 123032

Опубликована: Апрель 1, 2025

Язык: Английский

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Machine learning-assisted catalyst synthesis and hydrogen production via catalytic hydrolysis of sodium borohydride

International Journal of Hydrogen Energy, Год журнала: 2025, Номер 129, С. 130 - 149

Опубликована: Апрель 24, 2025

Язык: Английский

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Real-time monitoring using digital platforms for enhanced safety in hydrogen facilities – Current perspectives and future directions

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 98, С. 487 - 499

Опубликована: Дек. 10, 2024

Язык: Английский

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Direct partial oxidation of low-concentration methane to methanol with copper-based clay catalysts

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 81, С. 535 - 547

Опубликована: Июль 26, 2024

Язык: Английский

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