RESUMO
Hot-melt pressure-sensitive adhesives (HMPSAs) are used in applications from office supplies to biomedical adhesives. The major component in HMPSA formulations is thermoplastic elastomers, such as styrene-based block copolymers, that provide both mechanical integrity and moldability. Since neat polymer networks are unable to establish an adhesive bond, large quantities of plasticizers and tackifiers are added. These additives enhance the adhesive performance but complicate the phase behavior and property stability of the pressure-sensitive adhesive. Herein, we introduce an alternative additive-free approach to HMPSA design based on self-assembly of bottlebrush graft-copolymers, where side chains behave as softness, strength, and viscoelasticity mediators. These systems maintain moldability of conventional thermoplastic elastomers, while architecturally disentangled bottlebrush network strands empower several benefits such as extreme softness for substrate wetting, low melt viscosity for molding and 3D-printing, and a broad frequency range of viscoelastic responses for adhesion regulation within almost four orders of magnitude. The brush graft-copolymers implement five independently controlled architectural parameters to regulate the Rouse time, work of adhesion, and debonding mechanisms.
RESUMO
Pressure sensitive adhesives (PSAs) are ubiquitous materials within a spectrum that span from office supplies to biomedical devices. Currently, the ability of PSAs to meet the needs of these diverse applications relies on trial-and-error mixing of assorted chemicals and polymers, which inherently entails property imprecision and variance over time due to component migration and leaching. Herein, we develop a precise additive-free PSA design platform that predictably leverages polymer network architecture to empower comprehensive control over adhesive performance. Utilizing the chemical universality of brush-like elastomers, we encode work of adhesion ranging 5 orders of magnitude with a single polymer chemistry by coordinating brush architectural parameters-side chain length and grafting density. Lessons from this design-by-architecture approach are essential for future implementation of AI machinery in molecular engineering of both cured and thermoplastic PSAs incorporated into everyday use.