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.

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.

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.

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.

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.

\"Green\" strategies to build up novel organic nanocarriers with bioperformance are modern trends in nanotechnology. In this way, the valorization of bio-wastes and the use of living systems to develop multifunctional organic and biogenic nanocarriers (OBNs) have revolutionized the nanotechnological and biomedical fields. This paper is a comprehensive review related to OBNs for bioactives\' delivery, providing an overview of the reports on the past two decades. In the first part, several classes of bioactive compounds and their therapeutic role are briefly presented. A broad section is dedicated to the main categories of organic and biogenic nanocarriers. The major challenges regarding the eco-design and the fate of OBNs are suggested to overcome some toxicity-related drawbacks. Future directions and opportunities, and finding \"green\" solutions for solving the problems related to nanocarriers, are outlined in the final of this paper. We believe that through this review, we will capture the attention of the readers and will open new perspectives for new solutions/ideas for the discovery of more efficient and \"green\" ways in developing novel bioperformant nanocarriers for transporting bioactive agents.

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.

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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.

Currently, in the field of interdisciplinary work in biology, there has been a significant push by the soft robotic community to understand the motion and maneuverability of hydrostats. This Review seeks to expand the muscular hydrostat hypothesis toward new structures, including plants, and introduce innovative techniques to the hydrostat community on new modeling, simulating, mimicking, and observing hydrostat motion methods. These methods range from ideas of kirigami, origami, and knitting for mimic creation to utilizing reinforcement learning for control of bio-inspired soft robotic systems. It is now being understood through modeling that different mechanisms can inhibit traditional hydrostat motion, such as skin, nostrils, or sheathed layered muscle walls. The impact of this Review will highlight these mechanisms, including asymmetries, and discuss the critical next steps toward understanding their motion and how species with hydrostat structures control such complex motions, highlighting work from January 2022 to December 2022.