Research on Nano-Scale Material Evaluation Methods for Securing Advanced Construction Material Design Technologies
Research on Nano-Scale Material Evaluation Methods for Securing Advanced Construction Material Design Technologies
▲ Research Fellow Yun Tae-young, Department of Highway & Transportation Research, KICT
R&D Methods in Advanced Materials
In the advanced materials fields such as biotechnology, chemical engineering, semiconductor and battery technologies, where accuracy and speed are core factors in materials development competitiveness, there has been a shift away from traditional trial-and-error experimental methods. Instead, computational science and material informatics are now actively utilized for materials development. This approach replaces simple regression analysis methods that identify correlations between material names or compositions and their properties with the establishment of Quantitative Structure and Property Relationships (QSPR), which predict material properties by utilizing molecular structural characteristics, composition, and interactions. The key difference between these approaches is that while regression analysis methods cannot predict properties for materials not included in the database, QSPR methods can predict the properties of materials with similar molecular structures or molecular bonding characteristics, even if they weren't included in the original database. Figure 1 conceptually illustrates the differences and relationships between traditional research and development methodologies and recent research and development approaches for materials development.
Today, material research and development no longer relies solely on limited data obtained through experiments. The molecular structure and composition of materials for development are used to calculate the energy required for molecular bonding and separation through molecular dynamics or quantum mechanics. These dynamic theories can also be applied to calculate various physical properties, such as density, elastic modulus, viscosity, solubility, and adhesion strength. Figure 2 shows the solubility and adhesion strength resulting from interactions between non-crystalline and crystalline structures within materials, using molecular dynamics or quantum mechanics.
Application of Nanoscale Material Development Methods to Construction Materials
With the growing demand for higher safety, methods for evaluating the structural and functional adequacy of construction materials are becoming more refined. In addition, the growing interest in the environmental impact of construction materials and improved performance has made the development process for new materials more complex. For example, while simple engineering properties were previously used to evaluate asphalt binders for road pavements, mechanical properties, such as viscoelasticity and elastic recovery—which require complex equipment and theoretical understanding to ascertain—have been utilized since a U.S. research program was proposed in 1987. Furthermore, as heating materials like carbon nanotubes and graphite are being considered as road additives for snow melting functions, the complexity of experiments for evaluating materials performance is expected to increase. Highly complex materials with diverse functions tend to show sensitivity in their evaluation properties depending on experimental methods and procedures. Consequently, efforts to compensate for method and procedure-related issues, such as preferring experimental evaluations on full-scale components, significantly increase evaluation time and costs.
Nanoscale material evaluation methods that predict properties based on computational data utilizing nanoscale molecular structure and composition are relatively highly efficient in terms of time and cost. Figure 3 shows various qualitative variables that can be considered when using molecular dynamics for the asphalt mixtures used in road pavements, including material and additive types, aging effects, and moisture content. These qualitative variables are broken down into special quantitative information, such as molecular structure composition, and are used in machine learning along with properties like solubility, adhesion energy, tensile strength, viscosity, and elastic modulus to design new construction materials or predict the properties of new designs that weren't used in the training process.
Future of Nanoscale Construction Material Development Methods and Construction Material Technology
In the past, South Korea's construction technology development strategy took a “fast follower” approach, with companies quickly adopting and internalizing technologies first developed in advanced countries. This fast follower strategy is expected to continue due to Korea’s cultural characteristics that expect quick results, constraints on national budget, an economic-centered selection and concentration logic resulting from cultural characteristics, and limitations on the expandability of the domestic construction market. However, in a situation where information has become generalized and barriers between fields are lowering due to the universalization of convergent scientific and technological development methodologies, the construction field's technology development cannot continue to emphasize only system integration roles. To provide essential construction technologies to the public in a timely, efficient, and stable manner, which primarily serves the national interest, technology development in construction areas that are difficult for other fields to approach—due to low added value or high entry barriers to expertise—is necessary. It is anticipated that if nanoscale construction materials development technology, which is difficult to approach from other fields due to low homogeneity and complex environmental applications, is successfully implemented, it could become a good example of system integration including core technologies in the construction field.
References
- Yun Tae-young (2024) Research Methods Using Materials Informatics and Molecular Dynamics for the Development of Road Pavement Materials (I). Korean Society of Road Engineers (KSRE) v. 26, no. 4, pp. 45-58.
- Yun Tae-young, Moon Jae-pil, Shim Seung-bo, Joo Hyun-jin (2024) Genetic Algorithm–Partial Least Squares Regression Model for Predicting Density from Asphalt Binder Molecular Descriptors. Korean Society of Road Engineers (KSRE) v.26, no.4, pp.69-78.
- I. Jeon, J. Lee, T. Lee, T. Yun, S. Yang (2024) In Silico Simulation Study on Moisture-and Salt Water-induced Degradation of Asphalt Concrete Mixture, Construction and Building Materials v.417.