An innovative method for quantifying residual cement paste content of recycled sand based on a salicylic acid-methanol solution: A case study on application
Source abstract
Abstract
Residual cement paste (RCP) is a key factor influencing the properties of recycled sand (RS). However, the lack of effective methods for quantifying the RCP content of RS limits its application potential. In this study, an innovative method based on a salicylic acid-methanol solution is proposed to quantify the RCP content of RS. The method relies on the selective dissolution of salicylic acid-methanol solution, effectively quantifying the RCP content of RS with minimal dissolution of the original sand. Dissolution testing of individual components in RS showed a solubility of 89.2% for cement paste, while the solubility of limestone and natural sand was below 5%. In addition, the results from X-CT scanning showed a deviation of less than 5% compared to the salicylic acid-methanol solution, further validating the applicability of this method for quantifying the RCP content of RS. Notably, ensuring the consistency of RS quality contributes to enhancing the accuracy of the salicylic acid-methanol solution in quantifying the RCP content, particularly for RS with larger particle sizes. The development of this method provides a theoretical basis for optimizing the processing parameters of RS, enabling customized treatment based on engineering requirements, thus improving processing efficiency and reducing energy consumption. Overall, this method, with its simple operation, facilitates rapid quantification of RCP content of RS both in laboratories and on-site. Its application is expected to enhance the quality control of RS and further promote widespread use of RS in the construction industry.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Recycled Aggregate Concrete Performance · Innovative concrete reinforcement materials · Concrete and Cement Materials Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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