No abstract

Linear−bottlebrush−linear (LBBL) triblock copolymers represent an emerging system for creating multifunctional nanostructures. Their self-assembly depends on molecular architecture but remains poorly explored. We synthesize polystyrene-block-bottlebrush polydimethylsiloxane-block-polystyrene triblock copolymers with controlled molecular architecture and use them as a model system to study the self-assembly of LBBL polymers. Unlike classical stiff rodflexible linear block copolymers that are prone to form highly ordered nanostructures such as lamellae, at small weight fractions of the linear blocks, LBBL polymers self-assemble to a disordered sphere phase, regardless of the bottlebrush stiffness. Microscopically, characteristic lengths increase with the bottlebrush stiffness by a power of 2/3, which is captured by a scaling analysis. Macroscopically, the formed nanostructures are ultrasoft, reprocessable elastomers with shear moduli of about 1 kPa, two orders of magnitude lower than that of conventional polydimethylsiloxane elastomers. Our results provide insights on exploiting the self-assembly of LBBL polymers to create soft functional nanostructures.

3D printing elastomers enables the fabrication of many technologically important structures and devices such as tissue scaffolds, sensors, actuators, and soft robots. However, conventional 3D printable elastomers are intrinsically stiff; moreover, the process of printing often requires external mechanical support and/or post-treatment. Here, we exploit the self-assembly of a responsive linear-bottlebrush-linear triblock copolymer to create stimuli-reversible, extremely soft, and stretchable elastomers and demonstrate their applicability as inks for in situ directwrite printing 3D structures without the aid of external mechanical support or post-treatment. By developing a procedure for controlled synthesis of such architecturally designed block copolymers, we create elastomers with extensibility up to 600% and Young's moduli down to ∼10 2 Pa, 10 6 times softer than plastics and more than 10 2 times softer than all existing 3D printable elastomers. Moreover, the elastomers are thermostable and remain to be solid up to 180 °C, yet they are 100% solvent-reprocessable. Their extreme softness, stretchability, thermostability, and solvent-reprocessability bode well for future applications.

Focusing on the problem of multi-UAV cooperative air combat decision-making, a multi-UAV cooperative maneuvering decision-making approach is proposed based on multi-agent deep reinforcement learning (MARL) theory. First, the multi-UAV cooperative short-range air combat environment is established. Then, by combining the value-decomposition networks (VDNs) deep reinforcement learning theory with the embedded expert collaborative air combat experience reward function, an air combat cooperative strategy framework is proposed based on the networked decentralized partially observable Markov decision process (NDec-POMDP). The air combat maneuvering strategy is then optimized to improve the cooperative degree between UAVs in cooperative combat scenarios. Finally, multi-UAV cooperative air combat simulations are carried out and the results show the feasibility and effectiveness of the proposed cooperative air combat decision-making framework and method.

We consider the problem of estimating unknown transmittance η of a target bathed in thermal background light. As quantum estimation theory yields the fundamental limits, we employ lossy thermal noise bosonic channel model, which describes sensor-target interaction quantum-mechanically in many practical active-illumination systems (e.g., using emissions at optical, microwave, or radio frequencies). We prove that quantum illumination using two-mode squeezed vacuum (TMSV) states asymptotically achieves minimal quantum Cramér-Rao bound (CRB) over all quantum states (not necessarily Gaussian) in the limit of small input photon number. We characterize the optimal receiver structure for TMSV input and show its advantage over other receivers using both analysis and Monte Carlo simulation.