Viewpoint: Pavlovian Materials-Functional Biomimetics Inspired by Classical Conditioning

X-ray-Induced Photodegradation of Hydrogels by the Incorporation of X-ray-Activated Long Persistent Luminescent Nanoparticles

The development of on-demand degradable hydrogels remains an important challenge. Even though photodegradable hydrogels offer spatiotemporal control over degradation, it is difficult to use ultraviolet, visible, or near-infrared light as a tool for noninvasive triggering in vivo due to the poor tissue-penetration capacity. In contrast, X-ray irradiation can penetrate deep tissue and has virtually no penetration limitations for biological soft tissues. In this study, we propose an X-ray-photodegradation cascade system for hydrogel degradation by incorporating X-ray-activated persistent luminescence nanoparticles (X-PLNPs) into photodegradable hydrogels. A photodegradable 9,10-dialkoxyanthracene-based cross-linker was synthesized and used to prepare photodegradable hydrogels, of which the degradation behavior can be triggered by visible green light. Next, Tb3+-doped β-NaLuF4 was introduced as an X-PLNP that can convert X-rays into visible light centered at 544 nm. The afterglow can even be detected for 4 × 103 s after switching off the X-ray irradiation. The X-ray-induced green light emission was demonstrated to trigger photodegradation of the hydrogel. This proof-of-concept system for X-ray irradiation-induced on-demand hydrogel degradation was used to demonstrate X-ray-sensitive drug delivery inside a chicken breast as the in vitro tissue model. As this X-ray-induced cascade degradation of hydrogels can penetrate deep tissues, it is a promising platform for future in vivo applications requiring on-demand triggered hydrogel degradation, such as drug delivery or removal of hydrogel patches, hydrogel adhesives, or hydrogel tissue engineering scaffolds. It should, however, be noted that the hydrogel's X-ray and photoresponsiveness should be further improved to enable future in vivo use.

Stretchable hierarchical metal wire networks for neuromorphic emulation of nociception and anti-nociception

Among biomimetic technologies, the incorporation of sensory hardware holds exceptional utility in human-machine interfacing. In this context, devices receptive to nociception and emulating antinociception gain significance as part of pain management. Here we report, a stretchable two-terminal resistive neuromorphic device consisting of a hierarchical Ag microwire network formed using a crack templating protocol. The device demonstrates sensitivity to strain, where the application of strain induces the formation of gaps across active elements, rendering the device electrically open. Following activation by voltage pulses, the device exhibits potentiated states with finite retentions arising from filamentary growth across these gaps due to field migration. Remarkably, the strain-induced functioning alongside controllable gaps enables achieving user-controlled neuromorphic properties, desired for self-adaptive intelligent systems. Interestingly, in the neuromorphic potentiated state, the response to strain is enhanced by ∼106 due to higher sensitivities associated with nanofilaments. The device emulates basic neuromorphic functionalities such as threshold switching, and short-term (STP) and long-term potentiations (LTP). Furthermore, the sensitivity has been exploited in mimicking nociception through strain-induced changes in the potentiated state. Interestingly, repetition of the strain stimulus leads to endurance making the device restore its conductance, thereby emulating adaptation and habituation representing the antinociceptive behavior.

Transcriptional regulation of living materials via extracellular electron transfer

Engineered living materials combine the advantages of biological and synthetic systems by leveraging genetic and metabolic programming to control material-wide properties. Here, we demonstrate that extracellular electron transfer (EET), a microbial respiration process, can serve as a tunable bridge between live cell metabolism and synthetic material properties. In this system, EET flux from Shewanella oneidensis to a copper catalyst controls hydrogel cross-linking via two distinct chemistries to form living synthetic polymer networks. We first demonstrate that synthetic biology-inspired design rules derived from fluorescence parameterization can be applied toward EET-based regulation of polymer network mechanics. We then program transcriptional Boolean logic gates to govern EET gene expression, which enables design of computational polymer networks that mechanically respond to combinations of molecular inputs. Finally, we control fibroblast morphology using EET as a bridge for programmed material properties. Our results demonstrate how rational genetic circuit design can emulate physiological behavior in engineered living materials.

Trainable bioinspired magnetic sensitivity adaptation using ferromagnetic colloidal assemblies

Nature has already suggested bioinspired functions. Beyond them, adaptive and trainable functions could be the inspiration for novel responsive soft matter beyond the state-of-the-art classic static bioinspired, stimulus-responsive, and shape-memory materials. Here, we describe magnetic assembly/disassembly of electrically conducting soft ferromagnetic nickel colloidal particles into surface topographical pillars for bistable electrical trainable memories. They allow magnetic sensing with adaptable and rescalable sensitivity ranges, enabled by bistable memories and kinetic concepts inspired by biological sensory adaptations. Based on the soft ferromagnetism of the nanogranular composition and the resulting rough particle surfaces prepared via a solvothermal synthesis, triggerable structural memory is achieved by the magnetic field-driven particle assembly and disassembly, promoted by interparticle jamming. Electrical conversion from current to frequency for electrical spikes facilitates rescalable and trainable frequency-based sensitivity on magnetic fields. This work suggests an avenue for designing trainable and adaptable life-inspired materials, for example, for soft robotics and interactive autonomous devices.

Tailoring Classical Conditioning Behavior in TiO2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.

Thermally trainable dual network hydrogels

Inspired by biological systems, trainable responsive materials have received burgeoning research interests for future adaptive and intelligent material systems. However, the trainable materials to date typically cannot perform active work, and the training allows only one direction of functionality change. Here, we demonstrate thermally trainable hydrogel systems consisting of two thermoresponsive polymers, where the volumetric response of the system upon phase transitions enhances or decreases through a training process above certain threshold temperature. Positive or negative training of the thermally induced deformations can be achieved, depending on the network design. Importantly, softening, stiffening, or toughening of the hydrogel can be achieved by the training process. We demonstrate trainable hydrogel actuators capable of performing increased active work or implementing an initially impossible task. The reported dual network hydrogels provide a new training strategy that can be leveraged for bio-inspired soft systems such as adaptive artificial muscles or soft robotics.

Tuning the Phytoglycogen Size and Aggregate Structure with Solvent Quality: Influence of Water-Ethanol Mixtures Revealed by X-ray and Light Scattering Techniques

Phytoglycogen (PG) is a hyperbranched polysaccharide with promising properties for biomedical and pharmaceutical applications. Herein, we explore the size and structure of sweet corn PG nanoparticles and their aggregation in water-ethanol mixtures up to the ethanol mole fraction x_EtOH = 0.364 in dilute concentrations using small-angle X-ray scattering (SAXS) and dynamic light scattering (DLS) measurements. Between 0 ≤ x_EtOH ≤ 0.129, the conformation of PG contracts gradually decreasing up to ca. 80% in hydrodynamic volume, when measured shortly after ethanol addition. For equilibrated PG dispersions, SAXS suggests a lower PG volume decrease between 19 and 67% at the corresponding x_EtOH range; however, the inflection point of the DLS volume contraction coincides with the onset of reduced colloidal stability observed with SAXS. Up to x_EtOH = 0.201, the water-ethanol mixtures yield labile fractal and globular aggregates, as evidenced by their partial breakup under mild ultrasonic treatment, demonstrated by the decrease in their hydrodynamic size. Between 0.235 ≤ x_EtOH ≤ 0.364, PG nanoparticles form larger, more cohesive globular aggregates that are less affected by ultrasonic shear forces.

Magnetic field-driven particle assembly and jamming for bistable memory and response plasticity

Unlike classic synthetic stimulus-responsive and shape-memory materials, which remain limited to fixed responses, the responses of living systems dynamically adapt based on the repetition, intensity, and history of stimuli. Such plasticity is ubiquitous in biology, which is profoundly linked to memory and learning. Concepts thereof are searched for rudimentary forms of "intelligent materials." Here, we show plasticity of electroconductivity in soft ferromagnetic nickel colloidal supraparticles with spiny surfaces, assembling/disassembling to granular conducting micropillars between two electrodes driven by magnetic field B. Colloidal jamming leads to conduction hysteresis and bistable memory upon increasing and subsequently decreasing B. Abrupt B changes induce larger conduction changes than gradual B-changes. Periodic B pulsing drives to frequency-dependent facilitation or suppression of conductivity compared to exposing the same constant field. The concepts allow remotely controlled switching plasticity, illustrated by a rudimentary device. More generally, we foresee adaptive functional materials inspired by response plasticity and learning.

Some Characteristics and Arguments in Favor of a Science of Machine Behavior Analysis

Researchers and practitioners recognize four domains of behavior analysis: radical behaviorism, the experimental analysis of behavior, applied behavior analysis, and the practice of behavior analysis. Given the omnipresence of technology in every sphere of our lives, the purpose of this conceptual article is to describe and argue in favor of a fifth domain: machine behavior analysis. Machine behavior analysis is a science that examines how machines interact with and produce relevant changes in their external environment by relying on replicability, behavioral terminology, and the philosophical assumptions of behavior analysis (e.g., selectionism, determinism, parsimony) to study artificial behavior. Arguments in favor of a science of machine behavior include the omnipresence and impact of machines on human behavior, the inability of engineering alone to explain and control machine behavior, and the need to organize a verbal community of scientists around this common issue. Regardless of whether behavior analysts agree or disagree with this proposal, I argue that the field needs a debate on the topic. As such, the current article aims to encourage and contribute to this debate.

Bioinspired NEMS—Prospective of Collaboration with Nature

The fields of micro- and nanomechanics are strongly interconnected with the development of micro-electro-mechanical (MEMS) and nano-electro-mechanical (NEMS) devices, their fabrication and applications. This article highlights the biomimetic concept of designing new nanodevices for advanced materials and sensing applications.

Photonic artificial muscles: from micro robots to tissue engineering

Light responsive shape-changing polymers are able to mimic the function of biological muscles accomplishing mechanical work in response to selected stimuli. A variety of manufacturing techniques and chemical processes can be employed to shape these materials to different length scales, from centimeter fibers and films to 3D printed micrometric objects trying to replicate biological functions and operations. Controlled deformations shown to mimick basic animal operations such as walking, swimming or grabbing objects, while also controlling the refractive index and the geometry of devices, opens up the potential to implement tunable optical properties. Another possibility is that of combining artificial polymers with cells or biological tissue (such as intact cardiac trabeculae) with the aim to improve tissue formation in vitro or to support the mechanical function of damaged biological muscles. Such versatility is afforded by chemistry. New customized liquid crystalline monomers are presented here that modulate material properties for different applications. The role of synthetic material composition is highlighted as we demonstrate how using apparently similar molecular formulations, that liquid crystalline polymers can be adapted to different technological and medical challenges.

Stimulus-driven liquid metal and liquid crystal network actuators for programmable soft robotics

Sophisticated soft matter engineering has been endorsed as an emerging paradigm for developing untethered soft robots with built-in electronic functions and biomimetic adaptation capacities. However, the integration of flexible electronic components into soft robotic actuators is challenging due to strain mismatch and material incompatibilities. Herein, we report a general strategy to integrate electrically conductive liquid metals (LMs) and shape-morphing liquid crystal networks (LCNs) towards multifunctional and programmable soft robotics. A unique colloidal LM ink with superior adhesion and photothermal conversion efficiency was judiciously designed and fabricated by ultrasonicating LMs and miniature carboxylated gold nanorods (MiniGNR-COOH) in an aqueous suspension of biological bacterial cellulose. The designed nanocellulose-based colloidal LM ink is used for shape-deformable and electrically conductive LM-LCN soft robots that can be electro- and photo-thermally actuated. As proof-of-concept demonstrations, we present a light-fueled soft oscillator, an inchworm-inspired soft crawler and programmable robotic Shadow Play exhibiting multifunctional controllability. The strategy disclosed here could open up a new technological arena for advanced multifunctional soft materials with potential utility in bioinspired soft machines, integrated soft electronics, human-computer interaction and beyond.

Stimuli-responsive engineered living materials

Stimuli-responsive materials are able to undergo controllable changes in materials properties in response to external cues. Increasing efforts have been directed towards building materials that mimic the responsive nature of biological systems. Nevertheless, limitations remain surrounding the way these synthetic materials interact and respond to their environment. In particular, it is difficult to synthesize synthetic materials that respond with specificity to poorly differentiated (bio)chemical and weak physical stimuli. The emerging area of engineered living materials (ELMs) includes composites that combine living cells and synthetic materials. ELMs have yielded promising advances in the creation of stimuli-responsive materials that respond with diverse outputs in response to a broad array of biochemical and physical stimuli. This review describes advances made in the genetic engineering of the living component and the processing-property relationships of stimuli-responsive ELMs. Finally, the implementation of stimuli-responsive ELMs as environmental sensors, biomedical sensors, drug delivery vehicles, and soft robots is discussed.

Light-induced switching of polymer-surfactant interactions enables controlled polymer thermoresponsive behaviour

Hydroxypropyl cellulose (HPC) and arylazopyrazole (AAP) mixtures can be remotely controlled by light and temperature. We show that the hydrophobic interactions between HPC polymers with AAP surfactants can be drastically changed by changing the surfactants configuration through E/Z photo-isomerization. E-AAP interacts strongly with HPC which causes a dramatic increase of the critical temperature Tc of the polymers' phase transition and a loss of the coil-to-globule transition, while the hydrophobic interactions of HPC with Z-AAP are drastically reduced. As a result, E/Z photo-isomerization of AAP in mixtures with HPC offers remote control of the polymers phase transition, size and solution viscosity in an unprecedented way, and allows for new directions in colloid science.