Scalable Nanocomposite Preparation In article number 2400341, Kwangjin An, Hong-Dae Kim, Seungho Cho, and co-workers report a scalable preparation of highly efficient thermocatalysts from well-distributed 2D precursors—layered double hydroxides and hexagonal boron nitride—by continuous microfluidization and bottom-up single-layer growth. This method also enables scalable production of various tailored two-dimensional material combinations for diverse technologies.

Wearable robots have been developed to assist the physical performance of humans. Specifically, exosuits have attracted attention due to their lightweight and soft nature, which facilitate user movement. Although several types of force controllers have been used in exosuits, it is challenging to control the assistive force due to the material's softness. In this study, we propose three methods to improve the performance of the basic controller using an admittance-based force controller. In method A, the cable was controlled according to the user's thigh motion to eliminate delays in generating the assistive force and improve the control accuracy. In method B, the stiffness feedforward model of the human exosuit was divided into two independent models based on the assistance phase for compensating the nonlinear stiffness more accurately. In method C, the real-time optimization method for the stiffness feedforward model with an adaptive moment estimation method optimizer was proposed. To validate these methods' effectiveness, we designed three new controllers, gradually combined the proposed methods with the basic controller, and compared their performances. We found that controller III, combining all three methods with the basic controller, showed the best performance. By applying controller III in the same exosuit, the root-mean-square error of the assistive force decreased from 39.84 N to 13.72 N, reducing the error by 65.56% compared with the basic controller. Moreover, the time delay for force generation in the gait cycle percentage decreased from 9.99% to 3.41%, reducing the delay by 65.87% compared with the basic controller.