Accelerated discovery and mapping of block copolymer phase diagrams

Block copolymers are widely used in many applications due to their
spontaneous self-assembly into a variety of nanoscale morphologies.
However, a grand challenge in navigating this diverse and ever-growing
array of possible structures is the accelerated discovery, design, and
implementation of new materials. Here, we report a versatile and efficient
strategy to accelerate materials discovery by rapidly building expansive,
high-quality, and detailed block copolymer libraries through a combination
of controlled polymerization and chromatographic separation. To illustrate
the potential of this approach, a family of 16 parent diblock copolymers
was synthesized and separated, leading to over 300 distinct and
well-defined samples at the multigram scale. The resulting materials span
a wide range of compositions with exceptional resolution in volume
fraction and domain spacing that allows for the impact of monomer design
on polymer self-assembly to be elucidated. Phase behavior that can be
gleaned from these libraries includes the precise location of order–order
boundaries and the identification of morphologies with extremely narrow
windows of stability. This user-friendly, scalable, and automated approach
to discovery significantly increases the availability of well-defined
block copolymers with tailored molecular weights, molar-mass dispersities,
compositions, and segregation strengths, accelerating the study of
structure–property relationships in advanced soft materials.

嵌段共聚物 (Block copolymers) 因可自发自组装形成多种纳米级形貌，而被广泛应用于诸多领域。然而，在探索这一多样且持续增长的潜在结构阵列时，一项重大挑战在于加速新型材料的发现、设计与应用。在此，我们报道了一种通用且高效的策略：通过结合可控聚合 (controlled polymerization) 与色谱分离 (chromatographic separation) 技术，快速构建大规模、高质量且细节丰富的嵌段共聚物库，以此加速材料发现进程。为阐明该方法的应用潜力，我们合成并分离了16种母体二嵌段共聚物 (diblock copolymers)，最终得到超过300种克级规模、结构定义明确的不同样品。所获得的材料覆盖了宽泛的组成范围，在体积分数 (volume fraction) 与畴间距 (domain spacing) 上具备极高的分辨率，这使得我们能够清晰阐明单体设计对聚合物自组装过程的影响。从这些库中可获取的相行为包括有序-有序边界 (order–order boundaries) 的精确位置，以及稳定性窗口极窄的形貌的识别。这种易用、可规模化且自动化的材料发现方法，显著提升了具备定制化分子量、摩尔质量分散度 (molar-mass dispersities)、组成与链段分离强度 (segregation strengths) 的结构定义明确的嵌段共聚物的可获得性，从而加速了先进软材料中结构-性能关系 (structure–property relationships) 的研究。