Composition characteristics and evaluation indexes of molecular energy for asphalt components based on principal component analysis
Source abstract
Abstract
To further explain the molecular behavior of asphalt components, principal component analysis (PCA) was used to study their molecular energy composition characteristics and evaluation indexes. Initially, the molecular models of asphalt components were established, including unaged, short-term aged and long-term aged, and the reliability of its structure were proved. Subsequently, the energy composition of asphalt 12-component molecules was calculated, and the change characteristics of energy composition during the aging process can be analyzed. Finally, the energy composition characteristics of asphalt component molecules were investigated based on PCA, and the principal component indexes and evaluation method obtained were verified by cluster analysis. The results show that the asphaltene component molecules have relatively high energy (-114.252 kcal/mol), and the molecular energy (bond energy, non-bond energy) can be changed continuously due to aging behavior, and the bond energy of pyrrole molecule can be reduced from -105.723 kcal/mol to -179.447 kcal/mol. The energy composition of asphalt component molecules can be reduced to energy synthesis index (F1), bond energy cross-term - van der Waals force index (F2), bond energy diagonal term - electrostatic force index (F3) and electrostatic energy index (F4). Principal component indexes can effectively evaluate the energy composition characteristics of asphalt component molecules. The research results can provide the exploration direction for the study of asphalt components molecular structure, and provide a theoretical basis for optimizing their chemical structure and performance expression.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Asphalt Pavement Performance Evaluation · Polymer Synthesis and Characterization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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