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We employ molecular dynamics (MD) simulations to investigate the mechanical behaviors of immiscible polymer interfaces enhanced by block copolymer compatibilizers. We show that the entanglement density at the interface, governed by the Flory–Huggins parameter χ, is critical for mechanical performance. Increasing immiscibility leads to sharper interfaces with reduced interfacial entanglements, resulting in easy chain pullout during tensile deformation and weaker interfacial strength. Adding block copolymer compatibilizers to the blends can switch the failure mechanism from interfacial chain pullout to bulk-phase crazing, substantially enhancing mechanical performance. Although long diblock and tetrablock copolymers only mildly increase the interfacial entanglement density, they can act as stress transmitters across the interface by entangling with chains in the bulk domains. Tetrablock copolymers are particularly effective for strengthening polymer blends by forming loops at the interface, making chain pullout topologically more difficult and promoting energy dissipation through crazing in the bulk regions. Our findings reveal the roles of both entanglement at interfaces and block copolymer architecture in the mechanical properties of immiscible polymer interfaces, which may guide the design of better compatibilizers for enhancing inhomogeneous polymer samples. 

There have been many decades of work on optimizing query processing in database management systems. Recently, modern machine learning (ML), and specifically reinforcement learning (RL), has gained increased attention as a means to develop a query optimizer (QO). In this work, we take a closer look at two recent state-of-the-art (SOTA) RL-based QO methods to better understand their behavior. We find that these RL-based methods do not generalize as well as it seems at first glance. Thus, we ask a simple question:How do SOTA RL-based QOs compare to a simple, modern, adaptive query processing approach?To answer this question, we choose two simple adaptive query processing techniques and implemented them in PostgreSQL. The first adapts an individual join operation on-the-fly and switches between a Nested Loop Join algorithm and a Hash Join algorithm to avoid sub-optimal join algorithm decisions. The second is a technique calledLookahead Information Passing(LIP), in which adaptive semijoin techniques are used to make a pipeline of join operations execute efficiently. To our surprise, we find that this simple adaptive query processing approach is not only competitive to the SOTA RL-based approaches but, in some cases, outperforms the RL-based approaches. The adaptive approach is also appealing because it does not require an expensive training step, and it is fully interpretable compared to the RL-based QO approaches. Further, the adaptive method works across complex query constructs that RL-based QO methods currently cannot optimize.