Abstract
Photocatalysis and electrocatalysis have emerged as promising technologies for addressing the energy crisis and environmental issues. However, the widespread application of these technologies is hampered by the challenge of scaling up the production of photo/electrocatalysts that are not only highly active and stable but also cost-effective and environmentally benign. This review delves into the latest advancements in the large-scale synthesis of photo/electrocatalysts. The factors to be considered in the large-scale production of catalysts are discussed first. The synthesis methods for batch preparation of photo/electrocatalysts are then comprehensively introduced, with a thorough discussion of their respective advantages and limitations. Moreover, the data analysis via machine learning techniques, which not only accelerates the identification and refinement of potential new catalysts but also offers insights for enhancing the high-throughput synthesis of catalysts, is introduced in detail. Then the representative examples are presented to illustrate the applications of large-scale catalysts in the field of industrial-level photo/electrocatalysis. Finally, the challenges and prospects in the development of large-scale production of photo/electrocatalysts are discussed. By bridging the gap between laboratory research and industrial application, this review aims to provide a reference for the future of large-scale preparation of photo/electrocatalysts in sustainable energy conversion and beyond. © 2024 Wiley-VCH GmbH.
| Original language | English |
|---|---|
| Article number | 2402441 |
| Journal | Advanced Energy Materials |
| Volume | 14 |
| Issue number | 45 |
| Online published | 17 Sept 2024 |
| DOIs | |
| Publication status | Published - 6 Dec 2024 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 9 Industry, Innovation, and Infrastructure
Research Keywords
- machine learning (ML)
- photo/electrocatalysts
- reactor designs
- scalable preparation
- synthetic routes
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