Gustation-Inspired Dual-Responsive Hydrogels for Taste Sensing Enabled by Machine Learning.

Human gustatory system recognizes salty/sour or sweet tastants based on their different ionic or nonionic natures using two different signaling pathways. This suggests that evolution has selected this detection dualism favorably. Analogically, this work constructs herein bioinspired stimulus-responsive hydrogels to recognize model salty/sour or sweet tastes based on two different responses, that is, electrical and volumetric responsivities. Different compositions of zwitter-ionic sulfobetainic N-(3-sulfopropyl)-N-(methacryloxyethyl)-N,N-dimethylammonium betaine (DMAPS) and nonionic 2-hydroxyethyl methacrylate (HEMA) are co-polymerized to explore conditions for gelation. The hydrogel responses upon adding model tastant molecules are explored using electrical and visual de-swelling observations. Beyond challenging electrochemical impedance spectroscopy measurements, naive multimeter electrical characterizations are performed, toward facile applicability. Ionic model molecules, for example, sodium chloride and acetic acid, interact electrostatically with DMAPS groups, whereas nonionic molecules, for example, D(-)fructose, interact by hydrogen bonding with HEMA. The model tastants induce complex combinations of electrical and volumetric responses, which are then introduced as inputs for machine learning algorithms. The fidelity of such a trained dual response approach is tested for a more general taste identification. This work envisages that the facile dual electric/volumetric hydrogel responses combined with machine learning proposes a generic bioinspired avenue for future bionic designs of artificial taste recognition, amply needed in applications.

Designing Flexible but Tough Slippery Track for Underwater Gas Manipulation.

Lubricant-infused slippery surface exhibits a series of superior properties such as pressure tolerance, self-healing, oil-repellence, etc. Especially when being applied in an aqueous environment, the reliable bubble manipulating ability of slippery surface offers great opportunities to develop advanced systems in the field of gas transport, water splitting, etc. To improve the strength and the functionality of slippery surfaces, a sliced lubricant-infused slippery (SLIS) track is presented here, possessing both flexibility and toughness for underwater bubble manipulation. The rigid slippery slices with hydrophobic porous structure are linked by the liquid bridge of silicone oil, resulting in a continuous lubricant layer for bubble transfer. Taking advantage of this unique assembled structure, the in situ bubble controlling process, that is, pinning and moving, is achieved via the stretching/releasing of an elastic SLIS track. Besides, on the basis of the integrated design, a hypothesis of underwater gas mining is proved in the all-in-one process including the micro-bubble generation, bubble collection, and gas transport. The current design paves an avenue to reinforce the structure of slippery surfaces, and should promote the function of underwater bubble manipulation toward real-world applications.

Emerging Inter-Swarm Collaboration for Surveillance Using Pheromones and Evolutionary Techniques.

In this article, we propose a new mobility model, called Attractor Based Inter-Swarm collaborationS (ABISS), for improving the surveillance of restricted areas performed by unmanned autonomous vehicles. This approach uses different types of vehicles which explore an area of interest following unpredictable trajectories based on chaotic solutions of dynamic systems. Collaborations between vehicles are meant to cover some regions of the area which are unreachable by members of one swarm, e.g., unmanned ground vehicles on water surface, by using members of another swarm, e.g., unmanned aerial vehicles. Experimental results demonstrate that collaboration is not only possible but also emerges as part of the configurations calculated by a specially designed and parameterised evolutionary algorithm. Experiments were conducted on 12 different case studies including 30 scenarios each, observing an improvement in the total covered area up to 11%, when comparing ABISS with a non-collaborative approach.

Bio-inspired Design and Additive Manufacturing of Soft Materials, Machines, Robots, and Haptic Interfaces.

Soft materials possess several distinctive characteristics, such as controllable deformation, infinite degrees of freedom, and self-assembly, which make them promising candidates for building soft machines, robots, and haptic interfaces. In this Review, we give an overview of recent advances in these areas, with an emphasis on two specific topics: bio-inspired design and additive manufacturing. Biology is an abundant source of inspiration for functional materials and systems that mimic the function or mechanism of biological tissues, agents, and behaviors. Additive manufacturing has enabled the fabrication of materials and structures prevalent in biology, thereby leading to more-capable soft robots and machines. We believe that bio-inspired design and additive manufacturing have been, and will continue to be, important tools for the design of soft robots.

Bio-Inspired Renewable Surface-Initiated Polymerization from Permanently Embedded Initiators.

Herein, we describe a simple and robust approach to repeatedly modify surfaces with polymer brushes through surface-initiated atomic transfer radical polymerization (SI-ATRP), based on an initiator-embedded polystyrene sheet that does not rely on specific surface chemistries for initiator immobilization. The surface-grafted polymer brushes can be wiped away to expose fresh underlying initiator that re-initiates polymerization. This strategy provides a facile route for modification of molded or embossed surfaces, with possible applications in the preparation of fluidic devices and polymer-embedded circuits.

Filling polymersomes with polymers by peroxidase-catalyzed atom transfer radical polymerization.

Polymersomes that encapsulate a hydrophilic polymer are prepared by conducting biocatalytic atom transfer radical polymerization (ATRP) in these hollow nanostructures. To this end, ATRPase horseradish peroxidase (HRP) is encapsulated into vesicles self-assembled from poly(dimethylsiloxane)-block-poly(2-methyl-2-oxazoline) (PDMS-b-PMOXA) diblock copolymers. The vesicles are turned into nanoreactors by UV-induced permeabilization with a hydroxyalkyl phenone and used to polymerize poly(ethylene glycol) methyl ether acrylate (PEGA) by enzyme-catalyzed ATRP. As the membrane of the polymersomes is only permeable for the reagents of ATRP but not for macromolecules, the polymerization occurs inside of the vesicles and fills the polymersomes with poly(PEGA), as evidenced by (1) H NMR. Dynamic and static light scattering show that the vesicles transform from hollow spheres to filled spheres during polymerization. Transmission electron microscopy (TEM) and cryo-TEM imaging reveal that the polymersomes are stable under the reaction conditions. The polymer-filled nanoreactors mimic the membrane and cytosol of cells and can be useful tools to study enzymatic behavior in crowded macromolecular environments.

Evolution of Retinal Neuron Fractality When Interfacing with Carbon Nanotube Electrodes.

Exploring how neurons in the mammalian body interact with the artificial interface of implants can be used to learn about fundamental cell behavior and to refine medical applications. For fundamental and applied research, it is crucial to determine the conditions that encourage neurons to maintain their natural behavior during interactions with non-natural interfaces. Our previous investigations quantified the deterioration of neuronal connectivity when their dendrites deviate from their natural fractal geometry. Fractal resonance proposes that neurons will exhibit enhanced connectivity if an implant's electrode geometry is matched to the fractal geometry of the neurons. Here, we use in vitro imaging to quantify the fractal geometry of mouse retinal neurons and show that they change during interaction with the electrode. Our results demonstrate that it is crucial to understand these changes in the fractal properties of neurons for fractal resonance to be effective in the in vivo mammalian system.

Fractal Resonance: Can Fractal Geometry Be Used to Optimize the Connectivity of Neurons to Artificial Implants?

In parallel to medical applications, exploring how neurons interact with the artificial interface of implants in the human body can be used to learn about their fundamental behavior. For both fundamental and applied research, it is important to determine the conditions that encourage neurons to maintain their natural behavior during these interactions. Whereas previous biocompatibility studies have focused on the material properties of the neuron-implant interface, here we discuss the concept of fractal resonance - the possibility that favorable connectivity properties might emerge by matching the fractal geometry of the implant surface to that of the neurons.To investigate fractal resonance, we first determine the degree to which neurons are fractal and the impact of this fractality on their functionality. By analyzing three-dimensional images of rat hippocampal neurons, we find that the way their dendrites fork and weave through space is important for generating their fractal-like behavior. By modeling variations in neuron connectivity along with the associated energetic and material costs, we highlight how the neurons' fractal dimension optimizes these constraints. To simulate neuron interactions with implant interfaces, we distort the neuron models away from their natural form by modifying the dendrites' fork and weaving patterns. We find that small deviations can induce large changes in fractal dimension, causing the balance between connectivity and cost to deteriorate rapidly. We propose that implant surfaces should be patterned to match the fractal dimension of the neurons, allowing them to maintain their natural functionality as they interact with the implant.

Acoustic radiation characteristics of shark skin inspired surface modified plates.

This paper aims to evaluate the acoustic radiation characteristics of thin plates featuring a layer of small-scale biomimetic shark skin type additive surface treatment. The shark skin dermal denticles are modelled as point masses arranged in a bi-directional pattern on both the upper and lower surfaces of the plate. The governing equations are obtained through a variational approach, incorporating the Dirac Delta function in the derivation of the proposed semi-analytical model for the shark skin layer. A semi-analytical method based on the Rayleigh-Ritz formulation is utilized to analyze the vibrations of these plates with surface modification. The sound radiation characteristics are then derived from the solution of the Rayleigh integral. A comprehensive investigation is performed on the influence of surface modification on different vibro-acoustic characteristics, using a continuous structural mode and power transfer matrix-based approach. Notable observations include a reduction in peak vibro-acoustic responses with dense denticle arrangements, especially at resonance, demonstrating a direct relationship with mass ratios, i.e., the ratio of denticle mass to plate mass. The study further reveals a shift of vibro-acoustic responses towards low frequencies with an increase in mass ratios. A thorough comparative study indicates that while additive surface modifications inspired by shark skin may weaken sound radiation characteristics at resonance frequencies, a reverse effect can be observed at intermittent operational frequencies.

A Bio-Inspired Dopamine Model for Robots with Autonomous Decision-Making.

Decision-making systems allow artificial agents to adapt their behaviours, depending on the information they perceive from the environment and internal processes. Human beings possess unique decision-making capabilities, adapting to current situations and anticipating future challenges. Autonomous robots with adaptive and anticipatory decision-making emulating humans can bring robots with skills that users can understand more easily. Human decisions highly depend on dopamine, a brain substance that regulates motivation and reward, acknowledging positive and negative situations. Considering recent neuroscience studies about the dopamine role in the human brain and its influence on decision-making and motivated behaviour, this paper proposes a model based on how dopamine drives human motivation and decision-making. The model allows robots to behave autonomously in dynamic environments, learning the best action selection strategy and anticipating future rewards. The results show the model's performance in five scenarios, emphasising how dopamine levels vary depending on the robot's situation and stimuli perception. Moreover, we show the model's integration into the Mini social robot to provide insights into how dopamine levels drive motivated autonomous behaviour regulating biologically inspired internal processes emulated in the robot.

Trajectory Optimization to Enhance Observability for Bearing-Only Target Localization and Sensor Bias Calibration.

This study addresses the challenge of bearing-only target localization with sensor bias contamination. To enhance the system's observability, inspired by plant phototropism, we propose a control barrier function (CBF)-based method for UAV motion planning. The rank criterion provides only qualitative observability results. We employ the condition number for a quantitative analysis, identifying key influencing factors. After that, a multi-objective, nonlinear optimization problem for UAV trajectory planning is formulated and solved using the proposed Nonlinear Constrained Multi-Objective Gray Wolf Optimization Algorithm (NCMOGWOA). Simulations validate our approach, showing a threefold reduction in the condition number, significantly enhancing observability. The algorithm outperforms others in terms of localization accuracy and convergence, achieving the lowest Generational Distance (GD) (7.3442) and Inverted Generational Distance (IGD) (8.4577) metrics. Additionally, we explore the effects of the CBF attenuation rates and initial flight path angles.

Biosustainable Multiscale Transparent Nanocomposite Films for Sensitive Pressure and Humidity Sensors.

Light weight, thinness, transparency, flexibility, and insulation are the key indicators for flexible electronic device substrates. The common flexible substrates are usually polymer materials, but their recycling is an overwhelming challenge. Meanwhile, paper substrates are limited in practical applications because of their poor mechanical and thermal stability. However, natural biomaterials have excellent mechanical properties and versatility thanks to their organic-inorganic multiscale structures, which inspired us to design an organic-inorganic nanocomposite film. For this purpose, a bio-inspired multiscale film was developed using cellulose nanofibers with abundant hydrophilic functional groups to assist in dispersing hydroxyapatite nanowires. The thickness of the biosustainable film is only 40 µm, and it incorporates distinctive mechanical properties (strength: 52.8 MPa; toughness: 0.88 MJ m-3) and excellent optical properties (transmittance: 80.0%; haze: 71.2%). Consequently, this film is optimal as a substrate employed for flexible sensors, which can transmit capacitance and resistance signals through wireless Bluetooth, showing an ultrasensitive response to pressure and humidity (for example, responding to finger pressing with 5000% signal change and exhaled water vapor with 4000% signal change). Therefore, the comprehensive performance of the biomimetic multiscale organic-inorganic composite film confers a prominent prospect in flexible electronics devices, food packaging, and plastic substitution.

BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials.

The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.

Applications of a vacuum-actuated multi-material hybrid soft gripper: lessons learnt from RoboSoft manipulation challenge.

Soft grippers are garnering increasing attention for their adeptness in conforming to diverse objects, particularly delicate items, without warranting precise force control. This attribute proves especially beneficial in unstructured environments and dynamic tasks such as food handling. Human hands, owing to their elevated dexterity and precise motor control, exhibit the ability to delicately manipulate complex food items, such as small or fragile objects, by dynamically adjusting their grasping configurations. Furthermore, with their rich sensory receptors and hand-eye coordination that provide valuable information involving the texture and form factor, real-time adjustments to avoid damage or spill during food handling appear seamless. Despite numerous endeavors to replicate these capabilities through robotic solutions involving soft grippers, matching human performance remains a formidable engineering challenge. Robotic competitions serve as an invaluable platform for pushing the boundaries of manipulation capabilities, simultaneously offering insights into the adoption of these solutions across diverse domains, including food handling. Serving as a proxy for the future transition of robotic solutions from the laboratory to the market, these competitions simulate real-world challenges. Since 2021, our research group has actively participated in RoboSoft competitions, securing victories in the Manipulation track in 2022 and 2023. Our success was propelled by the utilization of a modified iteration of our Retractable Nails Soft Gripper (RNSG), tailored to meet the specific requirements of each task. The integration of sensors and collaborative manipulators further enhanced the gripper's performance, facilitating the seamless execution of complex grasping tasks associated with food handling. This article encapsulates the experiential insights gained during the application of our highly versatile soft gripper in these competition environments.

Biomimetic and bio-inspired robotics in electric fish research.

Weakly electric knifefish have intrigued both biologists and engineers for decades with their unique electrosensory system and agile swimming mechanics. Study of these fish has resulted in models that illuminate the principles behind their electrosensory system and unique swimming abilities. These models have uncovered the mechanisms by which knifefish generate thrust for swimming forward and backward, hovering, and heaving dorsally using a ventral elongated median fin. Engineered active electrosensory models inspired by electric fish allow for close-range sensing in turbid waters where other sensing modalities fail. Artificial electrosense is capable of aiding navigation, detection and discrimination of objects, and mapping the environment, all tasks for which the fish use electrosense extensively. While robotic ribbon fin and artificial electrosense research has been pursued separately to reduce complications that arise when they are combined, electric fish have succeeded in their ecological niche through close coupling of their sensing and mechanical systems. Future integration of electrosense and ribbon fin technology into a knifefish robot should likewise result in a vehicle capable of navigating complex 3D geometries unreachable with current underwater vehicles, as well as provide insights into how to design mobile robots that integrate high bandwidth sensing with highly responsive multidirectional movement.

Recent Advances in Sources of Bio-Inspiration and Materials for Robotics and Actuators.

Bionic robotics and actuators have made dramatic advancements in structural design, material preparation, and application owing to the richness of nature and innovative material design. Appropriate and ingenious sources of bio-inspiration can stimulate a large number of different bionic systems. After millennia of survival and evolutionary exploration, the mere existence of life confirms that nature is constantly moving in an evolutionary direction of optimization and improvement. To this end, bio-inspired robots and actuators can be constructed for the completion of a variety of artificial design instructions and requirements. In this article, the advances in bio-inspired materials for robotics and actuators with the sources of bio-inspiration are reviewed. The specific sources of inspiration in bionic systems and corresponding bio-inspired applications are summarized first. Then the basic functions of materials in bio-inspired robots and actuators is discussed. Moreover, a principle of matching biomaterials is creatively suggested. Furthermore, the implementation of biological information extraction is discussed, and the preparation methods of bionic materials are reclassified. Finally, the challenges and potential opportunities involved in finding sources of bio-inspiration and materials for robotics and actuators in the future is discussed.

Bio-Inspired Transparent Soft Jellyfish Robot.

Jellyfish are among the widely distributed nature creatures that can effectively control the fluidic flow around their transparent soft body, thus achieving movements in the water and camouflage in the surrounding environments. Till now, it remains a challenge to replicate both transparent appearance and functionalities of nature jellyfish in synthetic systems due to the lack of transparent actuators. In this work, a fully transparent soft jellyfish robot is developed to possess both transparency and bio-inspired omni motions in water. This robot is driven by transparent dielectric elastomer actuators (DEAs) using hybrid silver nanowire networks and conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)/waterborne polyurethane as compliant electrodes. The electrode exhibits large stretchability, low stiffness, high transmittance, and excellent conductivity at large strains. Consequently, the highly transparent DEA based on this hybrid electrode, with Very-High-Bond membranes as dielectric layers and polydimethylsiloxane as top coating, can achieve a maximum area strain of 146% with only 3% hysteresis loss. Driven by this transparent DEA, the soft jellyfish robot can achieve vertical and horizontal movements in water, by mimicking the actual pulsating rhythm of an Aurelia aurita. The bio-inspired robot can serve multiple functions as an underwater soft robot. The hybrid electrodes and bio-inspired design approach are potentially useful in a variety of soft robots and flexible devices.

Design for Robustness: Bio-Inspired Perspectives in Structural Engineering.

Bio-inspired solutions are widely adopted in different engineering disciplines. However, in structural engineering, these solutions are mainly limited to bio-inspired forms, shapes, and materials. Nature is almost completely neglected as a source of structural design philosophy. This study lists and discusses several bio-inspired solutions classified into two main classes, i.e., compartmentalization and complexity, for structural robustness design. Different examples are provided and mechanisms are categorized and discussed in detail. Some provided ideas are already used in the current structural engineering research and practice, usually without focus on their bio-analogy. These solutions are revisited and scrutinized from a bio-inspired point of view, and new aspects and possible improvements are suggested. Moreover, novel bio-inspired concepts including delayed compartmentalization, active compartmentalization, compartmentalization in intact parts, and structural complexity are also propounded for structural design under extreme loading conditions.

Early career scientists converse on the future of soft robotics.

During the recent decade, we have witnessed an extraordinary flourishing of soft robotics. Rekindled interest in soft robots is partially associated with the advances in manufacturing techniques that enable the fabrication of sophisticated multi-material robotic bodies with dimensions ranging across multiple length scales. In recent manuscripts, a reader might find peculiar-looking soft robots capable of grasping, walking, or swimming. However, the growth in publication numbers does not always reflect the real progress in the field since many manuscripts employ very similar ideas and just tweak soft body geometries. Therefore, we unreservedly agree with the sentiment that future research must move beyond "soft for soft's sake." Soft robotics is an undoubtedly fascinating field, but it requires a critical assessment of the limitations and challenges, enabling us to spotlight the areas and directions where soft robots will have the best leverage over their traditional counterparts. In this perspective paper, we discuss the current state of robotic research related to such important aspects as energy autonomy, electronic-free logic, and sustainability. The goal is to critically look at perspectives of soft robotics from two opposite points of view provided by early career researchers and highlight the most promising future direction, that is, in our opinion, the employment of soft robotic technologies for soft bio-inspired artificial organs.

Cephalopod-inspired polymer composites with mechanically tunable infrared properties.

Cephalopods have evolved an all-soft skin that can rapidly display colors for protection, predation, or communication. Development of synthetic analogs to mimic such color-changing abilities in the infrared (IR) region is pivotal to a variety of technologies ranging from soft robotics, flexible displays, dynamic thermoregulatory systems, to adaptive IR disguise platforms. However, the integration of tissue-like mechanical properties and rapid IR modulation ability into smart materials remains challenging. Here, by drawing inspiration from cephalopod skin, we develop an all-soft adaptive IR composite that can dynamically change its IR appearance upon equiaxial stretching. The biomimetic composite is built entirely from soft materials of liquid metal droplets and elastic elastomer, which are analogs of chromatophores and dermal layer of cephalopod skin, respectively. Driven by externally applied strains, the liquid metal inclusions transition between a contracted droplet state with corrugated surface and an expanded platelet state with relatively smooth surface, enabling dynamic variations in the IR reflectance/emissivity of the composite and ultimately resulting in reversible IR adaption. Strain-actuated flexible IR displays and pneumatically-driven soft devices that can dynamically manipulate their IR appearance are demonstrated as examples of the applicability of this material in emerging adaptive soft electronics.