Soft Self-Healing Robot Driven by New Micro Two-Way Shape Memory Alloy Spring.

Soft robotic bodies are susceptible to mechanical fatigue, punctures, electrical breakdown, and aging, which can result in the degradation of performance or unexpected failure. To overcome these challenges, a soft self-healing robot is created using a thermoplastic methyl thioglycolate-modified styrene-butadiene-styrene (MG-SBS) elastomer tube fabricated by melt-extrusion, to allow the robot to self-heal autonomously at room temperature. After repeated damage and being separated into several parts, the robot is able to heal its stiffness and elongation to break to enable almost complete recovery of robot performance after being allowed to heal at room temperature for 24 h. The self-healing capability of the robot is examined across the material scale to robot scale by detailed investigations of the healing process, healing efficiency, mechanical characterization of the robot, and assessment of dynamic performance before and after healing. The self-healing robot is driven by a new micro two-way shape-memory alloy (TWSMA) spring actuator which achieved a crawling speed of 21.6 cm/min, equivalent to 1.57 body length per minute. An analytical model of the robot is created to understand the robot dynamics and to act as an efficient tool for self-healing robot design and optimization. This work therefore provides a new methodology to create efficient, robust, and damage-tolerant soft robots.

A Component Content Measurement Method Modified Using Indirect Hard Modeling for Polymer Blends Based on Raman Spectroscopy.

Raman spectroscopy has been proven to be useful for the component content measurement of polymer blends. However, the soft modeling methods commonly used in quantitative analysis of Raman spectroscopy require a large number of training samples, resulting in a waste of materials and time. This work adopted a modified indirect hard modeling (IHM) method to measure the component content of polymer blends based on Raman spectroscopy. The Raman spectra of polypropylene (PP)/polystyrene (PS) blends with different component content were collected and resolved into the sum of multiple Voigt peak functions. For a large number of peak parameters, the two-dimensional correlation spectroscopy was used to screen out the characteristic Voigt peaks highly correlated with component content to reduce the parameter dimensions and build the parameterized spectral models. The spectral model of the blend was expressed as the weighted sum of the pure component spectral models, during which the parameters of the pure component models were adjusted within a range. According to the relationship between the weight and content of the pure component, a linear regression model for component content prediction was established. The coefficient of determination (R2)/root mean squared error of the IHM component content prediction model was 0.9931/0.4367 wt%. Besides, two popular soft modeling methods, partial least squares and artificial neural network, were compared with the IHM method, which showed that the IHM model had higher prediction accuracy with fewer training samples.

Effectiveness of exercise interventions for children with cerebral palsy: A systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: The results of previous research into exercise interventions for children with cerebral palsy are inconsistent. The aim of this study is to assess the effectiveness of such exercise interventions. DESIGN: Systematic review and meta-analysis. METHODS: Systematic searches of the PubMed, Embase and Cochrane Library databases for randomized controlled trials involving exercise interventions for children with cerebral palsy, from inception to January 2020, were performed. Pooled weighted mean differences (WMDs) with 95% confidence intervals (95% CI) for gross motor function, gait speed, and muscle strength were calculated using random-effects models. RESULTS: A final total of 27 trials, including 834 children with cerebral palsy, were selected for quantitative analysis. Exercise interventions had no significant effect on the level of gross motor function (WMD 1.19; 95% CI -1.07 to 3.46; p = 0.302). However, exercise interventions were associated with higher levels of gait speed (WMD 0.05; 95% CI 0.00-0.10; p = 0.032) and muscle strength (WMD 0.92; 95% CI 0.19-1.64; p = 0.013). CONCLUSION: These results suggest that exercise interventions may have beneficial effects on gait speed and muscle strength, but no significant effect on gross motor function in children with cerebral palsy.

Triboelectric and Piezoelectric Nanogenerators for Future Soft Robots and Machines.

The triboelectric nanogenerator (TENG) and piezoelectric nanogenerator (PENG) are two recently developed technologies for effective harvesting of ambient mechanical energy for the creation of self-powered systems. The advantages of TENGs and PENGs which include large open-circuit output voltage, low cost, ease of fabrication, and high conversion efficiency enable their application as new flexible sensors, wearable devices, soft robotics, and machines. This perspective provides an overview of the current state of the art in triboelectric and piezoelectric devices that are used as self-powered sensors and energy harvesters for soft robots and machines; hybrid approaches that combine the advantages of both mechanisms are also discussed. To improve system performance and efficiency, the potential of providing self-powered soft systems with a degree of multifunctionality is investigated. This includes optical sensing, transparency, self-healing, water resistance, photo-luminescence, or an ability to operate in hostile environments such as low temperature, high humidity, or high strain/stretch. Finally, areas for future research directions are identified.