Bibliographic Details
| Title: |
The state-of-the-art review on biochar as green additives in cementitious composites: performance, applications, machine learning predictions, and environmental and economic implications |
| Authors: |
Ping Ye, Binglin Guo, Huyong Qin, Cheng Wang, Yang Liu, Yuyang Chen, Pengfei Bian, Di Lu, Lei Wang, Weiping Zhao, Yonggan Yang, Li Hong, Peng Gao, Peiyong Ma, Binggen Zhan, Qijun Yu |
| Source: |
Biochar, Vol 7, Iss 1, Pp 1-30 (2025) |
| Publisher Information: |
Springer, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Environmental sciences
LCC:Agriculture |
| Subject Terms: |
Biochar, Cement, Carbon neutral, Applications, Machine learning, Environmental sciences, GE1-350, Agriculture |
| More Details: |
Abstract Considerable carbon emissions from the cement industry pose a notable challenge to achieving long-term sustainable development and creating an enriched social environment. Biochar (BC) obtained from biomass pyrolysis can be used as a carbon-negative material, and it plays a crucial role in the reduction of global carbon emissions. The development of more efficient and cost-effective technologies to fully realize this potential and reduce the environmental impact of BC production and use remains a formidable challenge. The utilization of BC to prepare sustainable cementitious composites with economically value-added benefits has recently attracted much research interest. Therefore, this review analyzes factors influencing the physicochemical properties of BC and their optimization methods, as well as the impact of BC addition on various cement composites and their potential applications. Besides, recent advances in machine learning for predicting the properties of composites and the environmental-economic implications of material are reviewed. The progress and challenges of BC–cement composites are discussed and potential directions for exploration are provided. Therefore, it is recommended to explore commercialization pathways tailored to local conditions and to develop machine learning models for performance prediction and life-cycle analysis, thereby promoting the widespread application of BC in industry and construction. Graphical Abstract |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2524-7867 |
| Relation: |
https://doaj.org/toc/2524-7867 |
| DOI: |
10.1007/s42773-024-00423-1 |
| Access URL: |
https://doaj.org/article/078445a716dc44d295bde7b5d31ea61a |
| Accession Number: |
edsdoj.078445a716dc44d295bde7b5d31ea61a |
| Database: |
Directory of Open Access Journals |
