Viewpoint: From Responsive to Adaptive and Interactive Materials and Materials Systems: A Roadmap

Abstract

Soft matter systems and materials are moving toward adaptive and interactive behavior, which holds outstanding promise to make the next generation of intelligent soft materials systems inspired from the dynamics and behavior of living systems. But what is an adaptive material? What is an interactive material? How should classical responsiveness or smart materials be delineated? At present, the literature lacks a comprehensive discussion on these topics, which is however of profound importance in order to identify landmark advances, keep a correct and noninflating terminology, and most importantly educate young scientists going into this direction. By comparing different levels of complex behavior in biological systems, this Viewpoint strives to give some definition of the various different materials systems characteristics. In particular, the importance of thinking in the direction of training and learning materials, and metabolic or behavioral materials is highlighted, as well as communication and information-processing systems. This Viewpoint aims to also serve as a switchboard to further connect the important fields of systems chemistry, synthetic biology, supramolecular chemistry and nano- and microfabrication/3D printing with advanced soft materials research. A convergence of these disciplines will be at the heart of empowering future adaptive and interactive materials systems with increasingly complex and emergent life-like behavior.

Keywords: adaptive materials; intelligent materials; interactive materials; learning materials; metamaterials; responsive materials; smart materials; systems chemistry; training materials.

Figure 1

Selected examples of biological responsive, adaptive and interactive behavior on increasing levels of complexity. The grey boxes highlight important concepts in the various examples. Reprinted and adapted with permissions from Ref.^{[, , , , –14]}

Figure 2

Responsive Systems: (a) Energy landscapes describe the switching of properties between State A and State B between two energy minima that are obtained by modulating the energy landscape of the environment after applying a trigger (e.g. pH, temperature, etc.). Note that State B could also sit in a metastable minimum. The switching process (trigger/countertrigger) is highly reversible and characterized by little to no fatigue. Adapted with permission from Refs. ^[18]

Figure 3

Adaptivity through training of a property change through rewiring of a switch (top, a,b) and training of properties (bottom, e). (c,d) Repeated application of the trigger/countertrigger switch leads to (c) adaptation to a new functional plateau or to (d) suppression of the property change. The change in the energy landscape in (b) corresponds to the adaptation in (c). (f-h) Intensity and frequency-dependent adaptation of a material property through repeated application of a signal, and forgetting of a property through relaxation from a metastable, trained state. The arrows in (a,b) and (e) indicate the changes to the energy landscape during repeated addition of trigger/countertrigger cycles or signals.

Figure 4. Adaptation to distinct functional plateaus as a function of signal strength or in the presence of a complex sensory landscape with various signal inputs that need to be correlated also in a non-linear manner.

Figure 5. Adaptation through associative learning (Pavlovian adaptation).

(a) The principles of conditioning of the salivation of a dog by interconnecting a ringing bell with the provision of food. (b) Unconditioned stimulus (food) leads to unconditioned response (salivation), (c) a neutral stimulus without conditioning leads to no conditioned response (no salivation) and (d) during conditioning and after conditioning the dog has connected both signals and later responds to the neutral stimulus (bell) with a conditioned response (salivation) even in absence of the unconditioned stimulus (food). (e) A rewiring of the signal processing pathways (W(US), W(NS) and W(CS)) occurs in a way that the neutral stimulus is interconnected to the processing of the unconditioned stimulus. A memory module (darker grey) is developed.

Figure 6

Adaptation of a material from a local stimulus to a global change can be achieved using chemically powered reaction/diffusion systems with autocatalytic amplification of the signal B using a proB substrate and an amplifying catalyst that becomes activated through the presence of B.

Figure 7. From adaptive materials to interactive materials and systems.