Silica-based distributed fiber-optic sensor (DFOS) systems have been a powerful tool for sensing strain, pressure, vibration, acceleration, temperature, and humidity in inextensible structures. DFOS systems, however, are incompatible with the large strains associated with soft robotics and stretchable electronics. We develop a sensor composed of parallel assemblies of elastomeric lightguides that incorporate continuum or discrete chromatic patterns. By exploiting a combination of frustrated total internal reflection and absorption, stretchable DFOSs can distinguish and measure the locations, magnitudes, and modes (stretch, bend, or press) of mechanical deformation. We further demonstrate multilocation decoupling and multimodal deformation decoupling through a stretchable DFOS–integrated wireless glove that can reconfigure all types of finger joint movements and external presses simultaneously, with only a single sensor in real time.

No abstract

Flesh encodes a variety of haptic information including deformation, temperature, vibration, and damage stimuli using a multisensory array of mechanoreceptors distributed on the surface of the human body. Currently, soft sensors are capable of detecting some haptic stimuli, but whole-body multimodal perception at scales similar to a human adult (surface area ~17,000 square centimeters) is still a challenge in artificially intelligent agents due to the lack of encoding. This encoding is needed to reduce the wiring required to send the vast amount of information transmitted to the processor. We created a robotic flesh that could be further developed for use in these agents. This engineered flesh is an optical, elastomeric matrix “innervated” with stretchable lightguides that encodes haptic stimuli into light: temperature into wavelength due to thermochromic dyes and forces into intensity due to mechanical deformation. By exploiting the optical properties of the constitutive materials and using machine learning, we infer spatiotemporal, haptic information from light that is read by an image sensor. We demonstrate the capabilities of our system in various assemblies to estimate temperature, contact location, normal and shear force, gestures, and damage from temporal snapshots of light coming from the entire haptic sensor with errors <5%.

An acoustic liquefaction approach to enhance the flow of yield stress fluids during Digital Light Processing (DLP)‐based 3D printing is reported. This enhanced flow enables processing of ultrahigh‐viscosity resins (μapp > 3700 Pa s at shear rates  = 0.01 s–1) based on silica particles in a silicone photopolymer. Numerical simulations of the acousto–mechanical coupling in the DLP resin feed system at different agitation frequencies predict local resin flow velocities exceeding 100 mm s–1 at acoustic transduction frequencies of 110 s–1. Under these conditions, highly loaded particle suspensions (weight fractions, ϕ = 0.23) can be printed successfully in complex geometries. Such mechanically reinforced composites possess a tensile toughness 2000% greater than the neat photopolymer. Beyond an increase in processible viscosities, acoustophoretic liquefaction DLP (AL‐DLP) creates a transient reduction in apparent viscosity that promotes resin recirculation and decreases viscous adhesion. As a result, acoustophoretic liquefaction Digital Light Processing (AL‐DLP) improves the printed feature resolution by more than 25%, increases printable object sizes by over 50 times, and can build parts >3 × faster when compared to conventional methodologies.