Robots typically interact with their environments via feedback loops consisting of electronic sensors, microcontrollers, and actuators, which can be bulky and complex. Researchers have sought new strategies for achieving autonomous sensing and control in next-generation soft robots. We describe here an electronics-free approach for autonomous control of soft robots, whose compositional and structural features embody the sensing, control, and actuation feedback loop of their soft bodies. Specifically, we design multiple modular control units that are regulated by responsive materials such as liquid crystal elastomers. These modules enable the robot to sense and respond to different external stimuli (light, heat, and solvents), causing autonomous changes to the robot’s trajectory. By combining multiple types of control modules, complex responses can be achieved, such as logical evaluations that require multiple events to occur in the environment before an action is performed. This framework for embodied control offers a new strategy toward autonomous soft robots that operate in uncertain or dynamic environments.
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
-
In this work, we report 3D printable soft composites that are simultaneously stretchable and tough. The matrix of the composite consists of polydimethylsiloxane containing octuple hydrogen bonding sites, resulting in a material significantly tougher than conventional polydimethylsiloxane. Short glass fibers are also added to the material. Prior to solvent evaporation, the material possesses a viscoelastic yield stress making it suitable for printing via direct ink writing. We mechanically characterize the printed composite, including fracture tests. We observe robust crack deflection and delay of catastrophic failure, leading to measured toughness values up to 2 00 000 J m −2 for specimens with 5 vol% glass fibers. The printed composites exhibit an unprecedented combination of stiffness, stretchability, and toughness.more » « less
-
A ubiquitous structural feature in biological systems is texture in extracellular matrix that gains functions when hardened, for example, cell walls, insect scales, and diatom tests. Here, we develop patterned liquid crystal elastomer (LCE) particles by recapitulating the biophysical patterning mechanism that forms pollen grain surfaces. In pollen grains, a phase separation of extracellular material into a pattern of condensed and fluid-like phases induces undulations in the underlying elastic cell membrane to form patterns on the cell surface. In this work, LCE particles with variable surface patterns were created through a phase separation of liquid crystal oligomers (LCOs) droplet coupled to homeotropic anchoring at the droplet interface, analogously to the pollen grain wall formation. Specifically, nematically ordered polydisperse LCOs and isotropic organic solvent (dichloromethane) phase-separate at the surface of oil-in-water droplets, while, different LCO chain lengths segregate to different surface curvatures simultaneously. This phase separation, which creates a distortion in the director field, is in competition with homeotropic anchoring induced by sodium dodecyl sulfate (SDS). By tuning the polymer chemistry of the system, we are able to influence this separation process and tune the types of surface patterns in these pollen-like microparticles. Our study reveals that the energetically favorable biological mechanism can be leveraged to offer simple yet versatile approaches to synthesize microparticles for mechanosensing, tissue engineering, drug delivery, energy storage, and displays.
-
Abstract We present the results for CAPRI Round 54, the 5th joint CASP‐CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo‐trimers, 13 heterodimers including 3 antibody–antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High‐quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2‐Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2‐Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.
Free, publicly-accessible full text available December 1, 2024 -
Abstract Direct ink writing of liquid crystal elastomers (LCEs) offers a new opportunity to program geometries for a wide variety of shape transformation modes toward applications such as soft robotics. So far, most 3D‐printed LCEs are thermally actuated. Herein, a 3D‐printable photoresponsive gold nanorod (AuNR)/LCE composite ink is developed, allowing for photothermal actuation of the 3D‐printed structures with AuNR as low as 0.1 wt.%. It is shown that the printed filament has a superior photothermal response with 27% actuation strain upon irradiation to near‐infrared (NIR) light (808 nm) at 1.4 W cm−2(corresponding to 160 °C) under optimal printing conditions. The 3D‐printed composite structures can be globally or locally actuated into different shapes by controlling the area exposed to the NIR laser. Taking advantage of the customized structures enabled by 3D printing and the ability to control locally exposed light, a light‐responsive soft robot is demonstrated that can climb on a ratchet surface with a maximum speed of 0.284 mm s−1(on a flat surface) and 0.216 mm s−1(on a 30° titled surface), respectively, corresponding to 0.428 and 0.324 body length per min, respectively, with a large body mass (0.23 g) and thickness (1 mm).
-
Abstract Liquid crystal elastomers (LCEs) are of interest for applications such as soft robotics and shape‐morphing devices. Among the different actuation mechanisms, light offers advantages such as spatial and local control of actuation via the photothermal effect. However, the unwanted aggregation of the light‐absorbing nanoparticles in the LCE matrix will limit the photothermal response speed, actuation performance, and repeatability. Herein, a near‐infrared‐responsive LCE composite consisting of up to 0.20 wt% poly(ethylene glycol)‐modified gold nanorods (AuNRs) without apparent aggregation is demonstrated. The high Young's modulus, 20.3 MPa, and excellent photothermal performance render repeated and fast actuation of the films (actuation within 5 s and recovery in 2 s) when exposed to 800 nm light at an average output power of ≈1.0 W cm−2, while maintaining a large actuation strain (56%). Further, it is shown that the same sheet of AuNR/LCE film (100 µm thick) can be morphed into different shapes simply by varying the motifs of the photomasks.