Improving mechanical performance and reducing CO2 emissions of cement-based composites via one-step synthesis of highly dispersed flake g-C3N4
Source abstract
Abstract
Graphitic carbon nitride (g-C 3 N 4 ) has demonstrated significant potential in enhancing the mechanical properties of cement-based materials. However, the poor dispersibility and complex preparation of bulk g-C 3 N 4 (B-CN), along with the lack of research on its environmental impact, remain major obstacles to its widespread application. To address this issue, this study employs a one-step method to synthesize highly dispersed flake g-C 3 N 4 (F-CN) for improving the mechanical properties and reducing the CO 2 emissions of cement-based composites, and investigates the underlying mechanisms. In addition, the CO 2 emissions of F-CN-modified cement are evaluated. Specifically, F-CN exhibits significantly higher dispersibility and a smaller particle size (20–80 nm), allowing it to be directly applied to cement-based systems without the need for additional dispersing agents. The highly dispersible F-CN is more favorable for accelerating cement hydration and refining the microstructure. Compared with the control and B-CN groups, F-CN increases the 28-day compressive strength of cement paste specimens by 8.65 % and 16.90 %, respectively. Moreover, compared with conventional cement and B-CN-modified cement, F-CN-modified cement reduces CO 2 emissions during production by 14.37 % and 6.37 %, respectively. This study proposes a simple and effective approach for improving both the performance and environmental profile of cement-based composites using highly dispersed flake g-C 3 N 4 , offering new insights into nanomaterial–cement interactions and contributing to low-carbon construction solutions.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Magnesium Oxide Properties and Applications · Concrete and Cement Materials Research · Advanced ceramic materials synthesis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (48)
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