A negative contains all the information for the final photographic print. But held up against the light, the negative often appears glaring and garish—certainly unfamiliar and sometimes difficult to recognize. Immediately after exposure in the quiet acrid environment of the darkroom, the positive enlargement is initially invisible and then starts to appear so faintly that it is scarcely detectable; only with patience as it reacts to the harsh chemical baths into which it was plunged does it emerge into a sharp, detailed, and nuanced image. In the shadows cast by the negative, as the resultant positive emerges from the nearly unrecognizable past, it is surprising and gratifying to discover a memory of the initial moment of exposure. This metaphor serves as an apt though abstract description of the experiences described in this essay.
To explore information's role in the physics of living systems, experimentalists have recently turned to making materials from robots. These systems offer unique opportunities because they are easy to control and interpret, yet each machine retains the capacity to sense and compute. Here, we review recent work on robotic matter, emphasizing how internal states, local sensing, and feedback at the microscale enable macroscale properties that are fundamentally distinct from traditional condensed matter or other active matter systems. We argue that this field is poised to evolve rapidly, thanks to technological innovations including semiconductor miniaturization, heterogeneous materials integration, and low-power computation. Finally, we highlight outstanding experimental and theoretical challenges that robots are well positioned to address, including the tradeoffs between robot size and intelligence and the difficulty of preserving information flows when robot actions are coarse-grained into macroscopic variables.
In this review, we introduce granular materials as a condensed matter system and briefly discuss their general properties. We then focus on particle segregation in rapid, dense granular flows, a phenomenon that occurs more readily in granular materials than in other condensed matter systems. Our primary emphasis is on the development of continuum models to describe segregation in these systems. Over the years, numerous approaches have been proposed, each offering different perspectives on how to construct such models. Rather than providing an exhaustive review of any single approach, we compare and contrast various modeling strategies, highlighting their commonalities and respective advantages. By doing so, we aim to establish a clearer connection between different approaches, facilitating closer comparisons and potential synergies between them. We believe that bridging these approaches is essential for advancing our understanding and improving predictive capabilities in granular segregation modeling in the future.
This review provides an introduction to the essential physics of soft adhesion, including the thermodynamics of adhesion and wetting, the mechanics of contact with deformable materials, and the material properties that most affect interfacial interactions with soft solid gels and elastomers. Throughout, we emphasize both foundational physics and current experimental and theoretical research in these areas. We conclude with a practical overview of standard experimental test methods for characterizing soft adhesion. The physical understanding developed herein provides the basis for understanding the mechanics of contact with soft materials.
Soft and active condensed matter represent a class of fascinating materials that we encounter in our everyday lives -- and constitute life itself. Control signals interact with the dynamics of these systems, and this influence is formalized in control theory and optimal control. Recent advances have employed various control-theoretical methods to design desired dynamics, properties, and functionality. Here we provide an introduction to optimal control aimed at physicists working with soft and active matter. We describe two main categories of control, feedforward control and feedback control, and their corresponding optimal control methods. We emphasize their parallels to Lagrangian and Hamiltonian mechanics, and provide a worked example problem. Finally, we review recent studies of control in soft, active, and related systems. Applying control theory to soft, active, and living systems will lead to an improved understanding of the signal processing, information flows, and actuation that underlie the physics of life.
