Tough Interfacial Adhesion Enabled Extremely Durable Flexible Supercapacitors.

The interfacial void and delamination between the hydrogel electrolyte and flexible electrode caused by the inconformal contact and weak adhesion lead to serious performance degradation of solid-state-sandwiched supercapacitors (SCs) upon repetitive deformation. Herein, we propose a hydrogel polymer electrolyte (HPE) engineering strategy for enhancing the interfacial adhesion (Γ) to achieve extremely durable SCs via the soft, tough, and self-adhesive HPE. Using a self-cross-linked poly(N-hydroxyethyl acrylamide)/H3PO4 (PHEAA/H3PO4) HPE as the model, the interfacial adhesion between HPE and polyaniline (PANI)-modified carbon cloth (CC) electrode (CC/PANI) reaches up to 556 J/m2, leading to excellent durability of electrochemical performance under long-term repetitive deformations. The as-assembled sandwiched SC retains 94.14 and 93.62% of initial capacitance after 180° bending and twisting for 100,000 cycles, respectively. Furthermore, benefiting from the addition of H3PO4, the flexible sandwiched SC displays excellent tolerance to low temperatures and delivers a capacitance retention of 98.03% after 180° bending for 10,000 cycles at -20 °C. This work highlights the importance of interfacial adhesion engineering for the design of extremely deformation-tolerable SCs.

Predicting Long-Term Deformation of Soundproofing Resilient Materials Subjected to Compressive Loading: Machine Learning Approach.

Soundproofing materials are widely used within structural components of multi-dwelling residential buildings to alleviate neighborhood noise problems. One of the critical mechanical properties for the soundproofing materials to ensure its appropriate structural and soundproofing performance is the long-term compressive deformation under the service loading conditions. The test method in the current test specifications only evaluates resilient materials for a limited period (90-day). It then extrapolates the test results using a polynomial function to predict the long-term compressive deformation. However, the extrapolation is universally applied to materials without considering the level of loads; thus, the calculated deformation may not accurately represent the actual compressive deformation of the materials. In this regard, long-term compressive deformation tests were performed on the selected soundproofing resilient materials (i.e., polystyrene, polyethylene, and ethylene-vinyl acetate). Four levels of loads were chosen to apply compressive loads up to 350 to 500 days continuously, and the deformations of the test specimens were periodically monitored. Then, three machine learning algorithms were used to predict long-term compressive deformations. The predictions based on machine learning and ISO 20392 method are compared with experimental test results, and the accuracy of machine learning algorithms and ISO 20392 method are discussed.