Transforming the Anthropomorphic Passive Free-Flow Foot Prosthesis Into a Powered Foot Prosthesis With Intuitive Control and Sensation (Bionic FFF).

INTRODUCTION: Approximately 89% of all service members with amputations do not return to duty. Restoring intuitive neural control with somatosensory sensation is a key to improving the safety and efficacy of prosthetic locomotion. However, natural somatosensory feedback from lower-limb prostheses has not yet been incorporated into any commercial prostheses. MATERIALS AND METHODS: We developed a neuroprosthesis with intuitive bidirectional control and somatosensation and evoking phase-dependent locomotor reflexes, we aspire to significantly improve the prosthetic rehabilitation and long-term functional outcomes of U.S. amputees. We implanted the skin and bone integrated pylon with peripheral neural interface pylon into the cat distal tibia, electromyographic electrodes into the residual gastrocnemius muscle, and nerve cuff electrodes on the distal tibial and sciatic nerves. Results. The bidirectional neural interface that was developed was integrated into the existing passive Free-Flow Foot and Ankle prosthesis, WillowWood, Mount Sterling, OH. The Free-Flow Foot was chosen because it had the highest Index of Anthropomorphicity among lower-limb prostheses and was the first anthropomorphic prosthesis brought to market. Conclusion. The cats walked on a treadmill with no cutaneous feedback from the foot in the control condition and with their residual distal tibial nerve stimulated during the stance phase of walking.

Bionic e-skin with precise multi-directional droplet sliding sensing for enhanced robotic perception.

Electronic skins with deep and comprehensive liquid information detection are desired to endow intelligent robotic devices with augmented perception and autonomous regulation in common droplet environments. At present, one technical limitation of electronic skins is the inability to perceive the liquid sliding information as realistically as humans and give feedback in time. To this critical challenge, in this work, a self-powered bionic droplet electronic skin is proposed by constructing an ingenious co-layer interlaced electrode network and using an overpass connection method. The bionic skin is used for droplet environment reconnaissance and converts various dynamic droplet sliding behaviors into electrical signals based on triboelectricity. More importantly, the two-dimensional sliding behavior of liquid droplets is comprehensively perceived by the e-skin and visually fed back in real-time on an indicator. Furthermore, the flow direction warning and intelligent closed-loop control of water leakage are also achieved by this e-skin, achieving the effect of human neuromodulation. This strategy compensates for the limitations of e-skin sensing droplets and greatly narrows the gap between artificial e-skins and human skins in perceiving functions.

Affective computing for human-machine interaction via a bionic organic memristor exhibiting selective in situ activation.

Affective computing, representing the forefront of human-machine interaction, is confronted with the pressing challenges of the execution speed and power consumption brought by the transmission of massive data. Herein, we introduce a bionic organic memristor inspired by the ligand-gated ion channels (LGICs) to facilitate near-sensor affective computing based on electroencephalography (EEG). It is constructed from a coordination polymer comprising Co ions and benzothiadiazole (Co-BTA), featuring multiple switching sites for redox reactions. Through advanced characterizations and theoretical calculations, we demonstrate that when subjected to a bias voltage, only the site where Co ions bind with N atoms from four BTA molecules becomes activated, while others remain inert. This remarkable phenomenon resembles the selective in situ activation of LGICs on the postsynaptic membrane for neural signal regulation. Consequently, the bionic organic memristor network exhibits outstanding reliability (200 000 cycles), exceptional integration level (210 pixels), ultra-low energy consumption (4.05 pJ), and fast switching speed (94 ns). Moreover, the built near-sensor system based on it achieves emotion recognition with an accuracy exceeding 95%. This research substantively adds to the ambition of realizing empathetic interaction and presents an appealing bionic approach for the development of novel electronic devices.

Biomechanical comparison of the therapeutic effect of a novel proximal femoral bionic intramedullary nail and traditional inverted triangle hollow screw on femoral neck fracture.

OBJECTIVE: A novel Proximal Femoral Bionic Nail (PFBN) has been developed by a research team for the treatment of femoral neck fractures. This study aims to compare the biomechanical properties of the innovative PFBN with those of the conventional Inverted Triangular Cannulated Screw (ITCS) fixation method through biomechanical testing. METHODS: Sixteen male femoral specimens preserved in formalin were selected, with the donors' age at death averaging 56.1 ± 6.3 years (range 47-64 years), and a mean age of 51.4 years. The femurs showed no visible damage and were examined by X-rays to exclude diseases affecting bone quality such as tumors, severe osteoporosis, and deformities. The 16 femoral specimens were randomly divided into an experimental group (n = 8) and a control group (n = 8). All femurs were prepared with Pauwels type III femoral neck fractures, fixed with PFBN in the experimental group and ITCS in the control group. Displacement and stress limits of each specimen were measured through cyclic compression tests and failure experiments, and vertical displacement and strain values under a 600 N vertical load were measured in all specimens through vertical compression tests. RESULTS: In the vertical compression test, the average displacement at the anterior head region of the femur was 0.362 mm for the PFBN group, significantly less than the 0.480 mm for the ITCS group (p < 0.001). At the fracture line area, the average displacement for the PFBN group was also lower than that of the ITCS group (0.196 mm vs. 0.324 mm, p < 0.001). The difference in displacement in the shaft area was smaller, but the average displacement for the PFBN group (0.049 mm) was still significantly less than that for the ITCS group (0.062 mm, p = 0.016). The situation was similar on the posterior side of the femur. The average displacements in the head area, fracture line area, and shaft area for the PFBN group were 0.300 mm, 0.168 mm, and 0.081 mm, respectively, while those for the ITCS group were 0.558 mm, 0.274 mm, and 0.041 mm, with significant differences in all areas (p < 0.001). The average strain in the anterior head area for the PFBN group was 4947 µm/m, significantly less than the 1540 µm/m for the ITCS group (p < 0.001). Likewise, in the fracture line and shaft areas, the average strains for the PFBN group were significantly less than those for the ITCS group (p < 0.05). In the posterior head area, the average strain for the PFBN group was 4861 µm/m, significantly less than the 1442 µm/m for the ITCS group (p < 0.001). The strain conditions in the fracture line and shaft areas also showed the PFBN group was superior to the ITCS group (p < 0.001). In cyclic loading experiments, the PFBN fixation showed smaller maximum displacement (1.269 mm vs. 1.808 mm, p < 0.001), indicating better stability. In the failure experiments, the maximum failure load that the PFBN-fixated fracture block could withstand was significantly higher than that for the ITCS fixation (1817 N vs. 1116 N, p < 0.001). CONCLUSION: The PFBN can meet the biomechanical requirements for internal fixation of femoral neck fractures. PFBN is superior in biomechanical stability compared to ITCS, particularly showing less displacement and higher failure resistance in cyclic load and failure experiments. While there are differences in strain performance in different regions between the two fixation methods, overall, PFBN provides superior stability.

A Flexible Skin Bionic Thermally Comfortable Wearable for Machine Learning-Facilitated Ultrasensitive Sensing.

Tremendous popularity is observed for multifunctional flexible electronics with appealing applications in intelligent electronic skins, human-machine interfaces, and healthcare sensing. However, the reported sensing electronics, mostly can hardly provide ultrasensitive sensing sensitivity, wider sensing range, and robust cycling stability simultaneously, and are limited of efficient heat conduction out from the contacted skin interface after wearing flexible electronics on human skin to satisfy thermal comfort of human skin. Inspired from the ultrasensitive tactile perception microstructure (epidermis/spinosum/signal transmission) of human skin, a flexible comfortably wearable ultrasensitive electronics is hereby prepared from thermal conductive boron nitride nanosheets-incorporated polyurethane elastomer matrix with MXene nanosheets-coated surface microdomes as epidermis/spinosum layers assembled with interdigitated electrode as sensing signal transmission layer. It demonstrates appealing sensing performance with ultrasensitive sensitivity (≈288.95 kPa-1), up to 300 kPa sensing range, and up to 20 000 sensing cycles from obvious contact area variation between microdome microstructures and the contact electrode under external compression. Furthermore, the bioinspired electronics present advanced thermal management by timely efficient thermal dissipation out from the contacted skin surface to meet human skin thermal comfort with the incorporated thermal conductive boron nitride nanosheets. Thus, it is vitally promising in wearable artificial electronic skins, intelligent human-interactive sensing, and personal health management.

Highly Promising 2D/1D BP-C/CNT Bionic Opto-Olfactory Co-Sensory Artificial Synapses for Multisensory Integration.

The development of high-performance artificial synaptic neuromorphic devices poses a significant challenge in the creation of biomimetic sensing neural systems that seamlessly integrate both sensory and computational functionalities. In pursuit of this objective, promising bionic opto-olfactory co-sensory artificial synapse devices are constructed utilizing the BP-C/CNT (2D/1D) hybrid filter membrane as the resistive layer. Experimental results demonstrated that the devices seamlessly integrated the light modulation, gas detection, and biological synaptic functions into a single device while addressing the challenge with separating artificial synaptic devices from sensors. These devices offered the following advantages: 1) Simulating visual synapses, they can effectively replicate fundamental synaptic functions under both electrical and optical stimulation. 2) By emulating olfactory synapse responses to specific gases, they can achieve ultra-low detection limits and rapid identification of ethanol and acetone gases. 3) They enable photo-olfactory co-sensing simulations that mimic synaptic function under light-modulated pulse conditions in distinct gas environments, facilitating the study of synaptic learning rules and Pavlovian responses. This work provides a pioneering approach for exploring highly stable 2D BP-based optoelectronics and advancing the development of biomimetic neural systems.

Visualized in-sensor computing.

In artificial nervous systems, conductivity changes indicate synaptic weight updates, but they provide limited information compared to living organisms. We present the pioneering design and production of an electrochromic neuromorphic transistor employing color updates to represent synaptic weight for in-sensor computing. Here, we engineer a specialized mechanism for adaptively regulating ion doping through an ion-exchange membrane, enabling precise control over color-coded synaptic weight, an unprecedented achievement. The electrochromic neuromorphic transistor not only enhances electrochromatic capabilities for hardware coding but also establishes a visualized pattern-recognition network. Integrating the electrochromic neuromorphic transistor with an artificial whisker, we simulate a bionic reflex system inspired by the longicorn beetle, achieving real-time visualization of signal flow within the reflex arc in response to environmental stimuli. This research holds promise in extending the biomimetic coding paradigm and advancing the development of bio-hybrid interfaces, particularly in incorporating color-based expressions.

Bioprinting of Stable Bionic Interfaces Using Piezoresistive Hydrogel Organoelectronics.

Bionic tissues offer an exciting frontier in biomedical research by integrating biological cells with artificial electronics, such as sensors. One critical hurdle is the development of artificial electronics that can mechanically harmonize with biological tissues, ensuring a robust interface for effective strain transfer and local deformation sensing. In this study, a highly tissue-integrative, soft mechanical sensor fabricated from a composite piezoresistive hydrogel. The composite not only exhibits exceptional mechanical properties, with elongation at the point of fracture reaching up to 680%, but also maintains excellent biocompatibility across multiple cell types. Furthermore, the material exhibits bioadhesive qualities, facilitating stable cell adhesion to its surface. A unique advantage of the formulation is the compatibility with 3D bioprinting, an essential technique for fabricating stable interfaces. A multimaterial sensorized 3D bionic construct is successfully bioprinted, and it is compared to structures produced via hydrogel casting. In contrast to cast constructs, the bioprinted ones display a high (87%) cell viability, preserve differentiation ability, and structural integrity of the sensor-tissue interface throughout the tissue development duration of 10 d. With easy fabrication and effective soft tissue integration, this composite holds significant promise for various biomedical applications, including implantable electronics and organ-on-a-chip technologies.

Bioinspired Engineering of Fillable Gradient Structure into Flexible Capacitive Pressure Sensor Toward Ultra-High Sensitivity and Wide Working Range.

Tactile sensing is required for electronic skin and intelligent robots to function properly. However, the dielectric layer's poor structural compressibility in conventional pressure sensors results in a limited pressure sensing range and low sensitivity. To solve this issue, a flexible pressure sensor with a crocodile-inspired fillable gradient structure is provided. The fillable gradient structure and grooves in the pressure sensor accommodate the deformed microstructure that permits the enhancement of the media layer compressibility via COMSOL finite element simulation and optimization. The pressure sensor exhibits a high sensitivity of up to 0.97 k Pa-1 (0-4 kPa), a wide pressure detection range (7 Pa-380 kPa), and outstanding repeatability. The sensor can detect Morse code, robotic grabbing, and human motion monitoring. As a result, flexible sensors with a bionic fillable gradient structure pave the way for wearable devices and offer a novel method for achieving highly precise tactile perception.

Photoactive "Bionic Virus" Robustly Elicits the Synergy Anticancer Activity of Immunophotodynamic Therapy.

Coronavirus represents an inspiring model for designing drug delivery systems due to its unique infection machinery mechanism. Herein, we have developed a biomimetic viruslike nanocomplex, termed SDN, for improving cancer theranostics. SDN has a unique core-shell structure consisting of photosensitizer chlorin e6 (Ce6)-loaded nanostructured lipid carrier (CeNLC) (virus core)@poly(allylamine hydrochloride)-functionalized MnO2 nanoparticles (virus spike), generating a virus-mimicking nanocomplex. SDN not only prompted cellular uptake through rough-surface-mediated endocytosis but also achieved mitochondrial accumulation by the interaction of cationic spikes and the anionic mitochondrial surface, leading to mitochondria-specific photodynamic therapy. Meanwhile, SDN could even mediate oxygen generation to relieve tumor hypoxia and, consequently, improve macrophage-associated anticancer immune response. Importantly, SDN served as a robust magnetic resonance imaging (MRI) contrast agent due to the fast release of Mn2+ in the presence of intracellular redox components. We identified that SDN selectively accumulated in tumors and released Mn2+ to generate a 5.71-fold higher T1-MRI signal, allowing for effectively detecting suspected tumors. Particularly, SDN induced synergistic immunophotodynamic effects to eliminate malignant tumors with minimal adverse effects. Therefore, we present a novel biomimetic strategy for improving targeted theranostics, which has a wide range of potential biomedical applications.

Esophagus-Inspired Actuator for Solid Transportation via the Synergy of Lubrication and Contractile Deformation.

Directional transportation of objects has important applications from energy transfer and intelligent robots to biomedical devices. Although breakthroughs in liquid migration on 2D surfaces or 3D tubular devices have been achieved, realizing smooth/on-demand transportation of constrained solids within a 3D cavity environment under harsh pressurized environment still remains a daunting challenge, where strong interface friction force becomes the main obstacle restricting the movement of solids. Inspired by typical feeding mechanism in natural esophagus system which synergistically couples a lubricating mucosa surface with the peristaltic contraction deformation of the cavity, herein, this challenge is addressed by constructing an esophagus-inspired layered tubular actuator with a slippery inner surface and responsive hydrogel matrix to realize spherical solid propulsion by photo(thermo)-induced cavity deformation. The as-constructed tubular actuator containing Fe3 O4 nanoparticles exhibits local volumetric shrinkage upon NIR-irradiation, which can generate large hydrodynamic pressure and considerable mechanical extrusion force (Fdriving force ≈ 0.18 N) to overcome low interface friction force (ffriction force ≈ 0.03 N), enabling on-demand transportation of constrained (pressure: 0.103 MPa) spherical solids over a long distance in an arbitrary direction. This actuator is anticipated to be used as bionic medicine transportation devices or artificial in vitro esophagus simulation systems, for example, to help formula eating-related physiotherapy plans for patients and astronauts.

3D bioprinting in cardiac tissue engineering.

Heart disease is the main cause of death worldwide. Because death of the myocardium is irreversible, it remains a significant clinical challenge to rescue myocardial deficiency. Cardiac tissue engineering (CTE) is a promising strategy for repairing heart defects and offers platforms for studying cardiac tissue. Numerous achievements have been made in CTE in the past decades based on various advanced engineering approaches. 3D bioprinting has attracted much attention due to its ability to integrate multiple cells within printed scaffolds with complex 3D structures, and many advancements in bioprinted CTE have been reported recently. Herein, we review the recent progress in 3D bioprinting for CTE. After a brief overview of CTE with conventional methods, the current 3D printing strategies are discussed. Bioink formulations based on various biomaterials are introduced, and strategies utilizing composite bioinks are further discussed. Moreover, several applications including heart patches, tissue-engineered cardiac muscle, and other bionic structures created via 3D bioprinting are summarized. Finally, we discuss several crucial challenges and present our perspective on 3D bioprinting techniques in the field of CTE.

All in One, Self-Powered Bionic Artificial Nerve Based on a Triboelectric Nanogenerator.

Sensory and nerve systems play important role in mediating the interactions with the world. The pursuit of neuromorphic computing has inspired innovations in artificial sensory and nervous systems. Here, an all-in-one, tailorable artificial perception, and transmission nerve (APTN) was developed for mimicking the biological sensory and nervous ability to detect and transmit the location information of mechanical stimulation. The APTN shows excellent reliability with a single triboelectric electrode for the detection of multiple pixels, by employing a gradient thickness dielectric layer and a grid surface structure. The sliding mode is used on the APTN to eliminate the amplitude influence of output signal, such as force, interlayer distance. By tailoring the geometry, an L-shaped APTN is demonstrated for the application of single-electrode bionic artificial nerve for 2D detection. In addition, an APTN based prosthetic arm is also fabricated to biomimetically identify and transmit the stimuli location signal to pattern the feedback. With features of low-cost, easy installation, and good flexibility, the APTN renders as a promising artificial sensory and nervous system for artificial intelligence, human-machine interface, and robotics applications.

Stem Cells as a Source of Pancreatic Cells for Production of 3D Bioprinted Bionic Pancreas in the Treatment of Type 1 Diabetes.

Type 1 diabetes (T1D) is the third most common autoimmune disease which develops due to genetic and environmental risk factors. Often, intensive insulin therapy is insufficient, and patients require a pancreas or pancreatic islets transplant. However, both solutions are associated with many possible complications, including graft rejection. The best approach seems to be a donor-independent T1D treatment strategy based on human stem cells cultured in vitro and differentiated into insulin and glucagon-producing cells (ß and α cells, respectively). Both types of cells can then be incorporated into the bio-ink used for 3D printing of the bionic pancreas, which can be transplanted into T1D patients to restore glucose homeostasis. The aim of this review is to summarize current knowledge about stem cells sources and their transformation into key pancreatic cells. Last, but not least, we comment on possible solutions of post-transplant immune response triggered stem cell-derived pancreatic cells and their potential control mechanisms.

Conception of a Smart Artificial Retina Based on a Dual-Mode Organic Sensing Inverter.

The human visual system enables perceiving, learning, remembering, and recognizing elementary visual information (light, colors, and images), which has inspired the development of biomimicry visual system-based electronic devices. Photosensing and synaptic devices are integrated into these systems to realize elementary information storage and recognition to imitate image processing. However, the severe restrictions of the monotonic light response and complicated circuitry design remain challenges for the development of artificial visual devices. Here, the concept of a smart artificial retina based on an organic optical sensing inverter device that can be operated as a multiwavelength photodetector and recorder is reported first. The device exhibits a light-triggered broadband (red/green/blue) response, a low energy consumption as low as ±5 V, and an ultrafast response speed (<300 ms). Moreover, the multifunctional component is also combined within a single cell for health monitoring of the artificial retina during light surveillance to avoid retinopathy. Proof-of-concept devices, by simplifying the circuitry and providing dual-mode functions, can contribute significantly to the development of bionics design and broaden the horizon for smart artificial retinas in the human visual system.

Bionic Ultra-Sensitive Self-Powered Electromechanical Sensor for Muscle-Triggered Communication Application.

The past few decades have witnessed the tremendous progress of human-machine interface (HMI) in communication, education, and manufacturing fields. However, due to signal acquisition devices' limitations, the research on HMI related to communication aid applications for the disabled is progressing slowly. Here, inspired by frogs' croaking behavior, a bionic triboelectric nanogenerator (TENG)-based ultra-sensitive self-powered electromechanical sensor for muscle-triggered communication HMI application is developed. The sensor possesses a high sensitivity (54.6 mV mm-1 ), a high-intensity signal (± 700 mV), and a wide sensing range (0-5 mm). The signal intensity is 206 times higher than that of traditional biopotential electromyography methods. By leveraging machine learning algorithms and Morse code, the safe, accurate (96.3%), and stable communication aid HMI applications are achieved. The authors' bionic TENG-based electromechanical sensor provides a valuable toolkit for HMI applications of the disabled, and it brings new insights into the interdisciplinary cross-integration between TENG technology and bionics.

From cockroaches to tanks: The same power-mass-speed relation describes both biological and artificial ground-mobile systems.

This paper explores whether artificial ground-mobile systems exhibit a consistent regularity of relation among mass, power, and speed, similar to that which exists for biological organisms. To this end, we investigate an empirical allometric formula proposed in the 1980s for estimating the mechanical power expended by an organism of a given mass to move at a given speed, applicable over several orders of magnitude of mass, for a broad range of species, to determine if a comparable regularity applies to a range of vehicles. We show empirically that not only does a similar regularity apply to a wide variety of mobile systems; moreover, the formula is essentially the same, describing organisms and systems ranging from a roach (1 g) to a battle tank (35,000 kg). We also show that for very heavy vehicles (35,000-100,000,000 kg), the formula takes a qualitatively different form. These findings point to a fundamental similarity between biological and artificial locomotion that transcends great differences in morphology, mechanisms, materials, and behaviors. To illustrate the utility of this allometric relation, we investigate the significant extent to which ground robotic systems exhibit a higher cost of transport than either organisms or conventional vehicles, and discuss ways to overcome inefficiencies.

Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison.

BACKGROUND: Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm's output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions. METHODS: We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures. RESULTS: Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms. CONCLUSIONS: These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.

A functional assembly framework based on implementable neurobionic material.

Neurobionic material is an emerging field in material and translational science. For material design, much focus has already been transferred from von Neumann architecture to the neuromorphic framework. As it is impractical to reconstruct the real neural tissue solely from materials, it is necessary to develop a feasible neurobionics framework to realize advanced brain function. In this study, we proposed a mathematical neurobionic material model, and attempted to explore advanced function only by simple and feasible structures. Here an equivalent simplified framework was used to describe the dynamics expressed in an equation set, while in vivo study was performed to verify simulation results. In neural tissue, the output of neurobionic material was characterized by spike frequency, and the stability is based on the excitatory/inhibitory proportion. Spike frequency in mathematical neurobionic material model can spontaneously meet the solution of a nonlinear equation set. Assembly can also evolve into a certain distribution under different stimulations, closely related to decision making. Short-term memory can be formed by coupling neurobionic material assemblies. In vivo experiments further confirmed predictions in our mathematical neurobionic material model. The property of this neural biomimetic material model demonstrates its intrinsic neuromorphic computational ability, which should offer promises for implementable neurobionic device design.