Insights from experiment and machine learning for enhanced TiO2 coated glazing for photocatalytic NOx remediation-Reference-Cited by-同舟云学术

Insights from experiment and machine learning for enhanced TiO₂ coated glazing for photocatalytic NO_x remediation

Published:2024 Issue:22 Volume:12 Page:13281-13298
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Lin Zhi-Peng¹,Li Yuankai¹,Haque Saif A.¹^ORCID,Ganose Alex M.¹²^ORCID,Kafizas Andreas¹³^ORCID

Affiliation:

1. Department of Chemistry, Molecular Science Research Hub, Imperial College London, White City, London, W12 0BZ, UK

2. Thomas Young Centre, The London Centre for the Theory and Simulation of Materials, Royal School of Mines, Imperial College London, South Kensington, London, UK

3. London Centre for Nanotechnology, Imperial College London, SW7 2AZ, UK

Abstract

TiO2-coated glass for photocatalytic NOx removal was optimised. Correlations between the synthesis parameters, coating properties, and photocatalytic activity was systematically studied through experimentation and machine learning tools for the first time.

Funder

Engineering and Physical Sciences Research Council

Royal Society

University of Southampton

Science and Technology Facilities Council

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/TA/D4TA01319K

Reference74 articles.

1. Control of combustion-generated nitrogen oxide emissions: Technology driven by regulation

2. Global partitioning of NOx sources using satellite observations: Relative roles of fossil fuel combustion, biomass burning and soil emissions

3. Climate change in a changing world: Socio-economic and technological transitions, regulatory frameworks and trends on global greenhouse gas emissions from EDGAR v.5.0

4. SOx/NOx Removal from Flue Gas Streams by Solid Adsorbents: A Review of Current Challenges and Future Directions

5. Perspectives in Adsorptive and Catalytic Mitigations of NO_x Using Metal–Organic Frameworks