Nanomaterials in Environmental Catalysis

A special issue of Catalysts (ISSN 2073-4344). This special issue belongs to the section "Environmental Catalysis".

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 1674

Special Issue Editors


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Guest Editor
School of Environment and Chemical Engineering, Heilongjiang University of Science and Technology, Harbin 150080, China
Interests: research focuses on the application of environmental microbiology to water or wastewater treatment; preparation of functional materials for mining solid waste; preparation and application of activated carbon; advanced catalytic oxidation technology for water treatment
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Guest Editor
College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China
Interests: advanced oxidation processes in water and wastewater treatment; removal of emerging contaminants in water; development of environmental functional materials

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Guest Editor
School of Environment, Harbin Institute of Technology, Harbin 150090, China
Interests: membrane

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Guest Editor
Key Laboratory of Environmental Materials and Pollution Control, Education Department of Jilin Province, Siping 136000, China
Interests: advanced oxidation technology; environmental health effect

Special Issue Information

Dear Colleagues,

This Special Issue, “Nanomaterials in Environmental Catalysis”, welcomes both comprehensive reviews and original research articles on a variety of nanomaterials in environmental catalysis. Themes include, but are not limited to, the following:

  • Advanced nanomaterials for energy conversion and environmental catalysis application;
  • Catalysis for energy conversion;
  • Photocatalytic nanomaterials;
  • Electrocatalytic nanomaterials;
  • Heterogeneous catalysis in water/wastewater treatment processes;
  • Air treatment, such as catalytic conversion of greenhouse gases;
  • Nanomaterials in renewable feedstock production;
  • Nanocatalysts;
  • Metal–organic frameworks (MOFs);
  • Zeolite-based materials;
  • Catalytic water splitting;
  • Nanomaterial fabrication;
  • Environmental purification;
  • New techniques of nanomaterials characterization;
  • Removal of microbiological pollutants;
  • Self-cleaning surfaces;
  • Mechanism of pollutants’ decomposition;
  • Advanced oxidation technologies;
  • Green chemistry;
  • Waste recycling and repurposing via catalysis;
  • Biochar catalytic material;
  • Environmental bioenergy and processes.

Dr. Lixin Li
Prof. Dr. Lijie Xu
Prof. Dr. Langming Bai
Dr. Xin Ren
Guest Editors

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Keywords

  • nanomaterials
  • nanocatalysts
  • catalysis for energy conversion
  • photocatalytic nanomaterials
  • electrocatalytic nanomaterials

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Published Papers (2 papers)

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Research

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13 pages, 3394 KiB  
Article
Enhanced Diclofenac Removal from Constructed Wetland Effluent Using a Photoelectrocatalytic System with N-TiO2 Nanocrystal-Modified TiO2 Nanotube Anode and Graphene Oxide/Activated Carbon Photocathode
by Xiongwei Liang, Shaopeng Yu, Bo Meng, Xiaodi Wang, Chunxue Yang, Chuanqi Shi and Junnan Ding
Catalysts 2024, 14(12), 954; https://doi.org/10.3390/catal14120954 - 23 Dec 2024
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Abstract
This investigation reports on the efficacy of a photoelectrocatalysis (PEC) system enhanced by a nitrogen-doped TiO2 nanocrystal-modified TiO2 nanotube array (N-TiO2 NCs/TNTAs) anode paired with a graphene oxide/activated carbon (GO/AC) photocathode for diclofenac removal from effluent. The FE-SEM and EDX [...] Read more.
This investigation reports on the efficacy of a photoelectrocatalysis (PEC) system enhanced by a nitrogen-doped TiO2 nanocrystal-modified TiO2 nanotube array (N-TiO2 NCs/TNTAs) anode paired with a graphene oxide/activated carbon (GO/AC) photocathode for diclofenac removal from effluent. The FE-SEM and EDX analyses validated the elemental composition of the anode—27.56% C, 30.81% N, 6.03% O, and 26.49% Ti. The XRD results confirmed the anatase phase and nitrogen integration, essential for photocatalytic activity enhancement. Quantum chemical simulations provided a comprehensive understanding of the red-shifted absorption bands in N-TiO2, and UV-vis DRS demonstrated a red-shift in absorption to the visible spectrum, indicating improved light utilization. The PEC configuration achieved a photocurrent density of 9.8 mA/dm2, significantly higher than the unmodified and solely nitrogen-doped counterparts at 4.8 mA/dm2 and 6.1 mA/dm2, respectively. Notably, this system reduced diclofenac concentrations by 58% within 75 min, outperforming standard photocatalytic setups. These findings underscore the potential of N-TiO2 NCs/TNTAs-AC-GO/PTFE composite material for advanced environmental photoelectrocatalytic applications. Full article
(This article belongs to the Special Issue Nanomaterials in Environmental Catalysis)
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Review

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21 pages, 4524 KiB  
Review
Machine Learning-Assisted Catalysts for Advanced Oxidation Processes: Progress, Challenges, and Prospects
by Qinghui Yuan, Xiaobei Wang, Dongdong Xu, Hongyan Liu, Hanwen Zhang, Qian Yu, Yanliang Bi and Lixin Li
Catalysts 2025, 15(3), 282; https://doi.org/10.3390/catal15030282 - 17 Mar 2025
Viewed by 676
Abstract
Advanced oxidation processes (AOPs) are recognized as one of the most effective methods in the field of wastewater treatment, and the selection of catalysts in the oxidation process is very important. In the face of the traditional test trial-and-error method, the method of [...] Read more.
Advanced oxidation processes (AOPs) are recognized as one of the most effective methods in the field of wastewater treatment, and the selection of catalysts in the oxidation process is very important. In the face of the traditional test trial-and-error method, the method of screening advanced oxidation catalysts is time-consuming and inefficient. This paper examines approximately two decades’ worth of literature pertaining to the development of catalysts facilitated by machine learning. A synopsis of the various advanced oxidation processes and reactive oxygen species (ROS) is provided. Subsequently, it is posited that the swift advancement of machine learning (ML) and its algorithmic classification has significantly propelled the progress in ML-assisted catalyst screening, active site prediction, the discovery of acceleration mechanisms, and catalyst structural research, which are subsequently elucidated. Despite ML’s proven efficacy as a tool within the domain of AOPs’ catalysis, the article concludes by presenting challenges and outlining future development strategies, particularly in light of issues pertaining to data quality and quantity, as well as inherent model limitations. Full article
(This article belongs to the Special Issue Nanomaterials in Environmental Catalysis)
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