Polymer aging genome: from lifetime prediction to lifetime control

Polymeric materials are playing an increasingly vital role in various branches of the national economy, as well as in production and daily life. In complex and variable application scenarios such as aerospace, transportation, electronics and new energy, the long-term stability and lifetime of equipment often depends on the performance deterioration process (aging) of polymeric materials. Unexpected failure may pose significant risks to operational safety and even lead to catastrophic consequences. Therefore, rapid and precise lifetime prediction is of great importance but faces a great challenge.The aging of polymeric materials is a multi-scale process that occurs throughout the entire life cycle of the material, involving multi-phase, multi-component complex systems under the influence of multiple factors. This complex process presents significant challenges to in-depth exploration of aging mechanisms. Consequently, lifetime prediction and regulation research largely relies on empirical approaches and leaves far behind the practical need. In this paper, we discuss existing challenges and propose future research perspectives: (1) Integration of experimental research with theoretical simulations to deeply understand and construct a panoramic view of the polymer aging process from molecular reactions, microscopic component and structure changes to macroscopic properties. Aging of polymer materials reflects how the external factors act on intrinsic characteristics during their full life cycle. Therefore, aging research of polymer materials covers polymerization, processing, application and recycling processes.(2) Development of aging mechanism-based, highly sensitive detection technologies for online aging status monitoring, efficient and precise aging evaluation and lifetime prediction of polymer materials. Based on the relationship between microscopic parameters and macroscopic properties, lifetime prediction models are expected to predict the deterioration of service performances under given conditions with time and guide the safe application of polymer materials.(3) Mechanism-driven lifetime regulation. By distinguishing the dominant and rate-determining steps during aging, corresponding strategies can be carried out to hinder or accelerate this process. On one hand, antioxidants and/or UV stabilizers can be introduced into materials or onto macromolecular chains to suppress initiation and propagation of radicals and aging reactions thereafter. On the other hand, embedding responsive species such as hydrolyzable esters or amides, and photoactive groups, with catalysts where appropriate, helps to trigger degradation under certain stimuli (e.g., ultraviolet light, temperature, or pH). In equipments, the lifetime of polymer materials in various components can be regulated to a similar value, so that maintenance frequency and cost can be reduced greatly. (4) Exploring the application of artificial intelligence (AI) in aging research and constructing an “aging genome” for polymer materials. A high-quality database and knowledge graph are the prerequisites. It provides a unified framework to integrate information from various sources in various formats, and outputs multi-scale models under a common ontology and metadata standard. This aging genome will greatly facilitate aging mechanism research, lifetime prediction, and lifetime regulation.