Maw Mitchell, Dashtimoghadam Erfan, Keith Andrew N, Morgan Benjamin J, Tanas Alexander K, Nikitina Evgeniia, Ivanov Dimitri A, Vatankhah-Varnosfaderani Mohammad, Dobrynin Andrey V, Sheiko Sergei S
Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-3290, United States.
Lomonosov Moscow State University, Leninskie Gory 1, 119991, Moscow, Russian Federation.
ACS Cent Sci. 2023 Feb 1;9(2):197-205. doi: 10.1021/acscentsci.2c01407. eCollection 2023 Feb 22.
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.
压敏胶粘剂（PSA）是一类广泛应用的材料，涵盖从办公用品到生物医学设备等多个领域。目前，PSA满足这些不同应用需求的能力依赖于各种化学品和聚合物的反复试验混合，由于组分迁移和浸出，这本质上会导致性能不精确且随时间变化。在此，我们开发了一个精确的无添加剂PSA设计平台，该平台可预测地利用聚合物网络结构来全面控制胶粘剂性能。利用刷状弹性体的化学通用性，我们通过协调刷状结构参数——侧链长度和接枝密度，用单一聚合物化学方法编码了5个数量级的粘附功。这种基于结构设计方法的经验教训对于未来将固化和热塑性PSA纳入日常使用的分子工程中人工智能机械的实施至关重要。
