A Low-Voltage, High-Force Capacity Electroadhesive Clutch Based on Ionoelastomer Heterojunctions.

Electroadhesive devices with dielectric films can electrically program changes in stiffness and adhesion, but require hundreds of volts and are subject to failure by dielectric breakdown. Recent work on ionoelastomer heterojunctions has enabled reversible electroadhesion with low voltages, but these materials exhibit limited force capacities and high detachment forces. It is a grand challenge to engineer electroadhesives with large force capacities and programmable detachment at low voltages (<10 V). In this work, tough ionoelastomer/metal mesh composites with low surface energies are synthesized and surface roughness is controlled to realize sub-ten-volt clutches that are small, strong, and easily detachable. Models based on fracture and contact mechanics explain how clutch compliance and surface texture affect force capacity and contact area, which is validated over different geometries and voltages. These ionoelastomer clutches outperform the best existing electroadhesive clutches by fivefold in force capacity per unit area (102 N cm-2 ), with a 40-fold reduction in operating voltage (± 7.5 V). Finally, the ability of the ionoelastomer clutches to resist bending moments in a finger wearable and as a reversible adhesive in an adjustable phone mount is demonstrated.

Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics.

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions. However, material identification is a challenging task, especially when the characteristic of the material is highly nonlinear in nature, as is common in biological tissue. In this work, we identify unknown material properties in continuum solid mechanics via physics-informed neural networks (PINNs). To improve the accuracy and efficiency of PINNs, we develop efficient strategies to nonuniformly sample observational data. We also investigate different approaches to enforce Dirichlet-type boundary conditions (BCs) as soft or hard constraints. Finally, we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space. The estimated material parameters achieve relative errors of less than 1%. As such, this work is relevant to diverse applications, including optimizing structural integrity and developing novel materials.

Structure-property relationships from universal signatures of plasticity in disordered solids.

When deformed beyond their elastic limits, crystalline solids flow plastically via particle rearrangements localized around structural defects. Disordered solids also flow, but without obvious structural defects. We link structure to plasticity in disordered solids via a microscopic structural quantity, "softness," designed by machine learning to be maximally predictive of rearrangements. Experimental results and computations enabled us to measure the spatial correlations and strain response of softness, as well as two measures of plasticity: the size of rearrangements and the yield strain. All four quantities maintained remarkable commonality in their values for disordered packings of objects ranging from atoms to grains, spanning seven orders of magnitude in diameter and 13 orders of magnitude in elastic modulus. These commonalities link the spatial correlations and strain response of softness to rearrangement size and yield strain, respectively.

Preventing nanoscale wear of atomic force microscopy tips through the use of monolithic ultrananocrystalline diamond probes.

Nanoscale wear is a key limitation of conventional atomic force microscopy (AFM) probes that results in decreased resolution, accuracy, and reproducibility in probe-based imaging, writing, measurement, and nanomanufacturing applications. Diamond is potentially an ideal probe material due to its unrivaled hardness and stiffness, its low friction and wear, and its chemical inertness. However, the manufacture of monolithic diamond probes with consistently shaped small-radius tips has not been previously achieved. The first wafer-level fabrication of monolithic ultrananocrystalline diamond (UNCD) probes with <5-nm grain sizes and smooth tips with radii of 30-40 nm is reported, which are obtained through a combination of microfabrication and hot-filament chemical vapor deposition. Their nanoscale wear resistance under contact-mode scanning conditions is compared with that of conventional silicon nitride (SiN(x)) probes of similar geometry at two different relative humidity levels (approximately 15 and approximately 70%). While SiN(x) probes exhibit significant wear that further increases with humidity, UNCD probes show little measurable wear. The only significant degradation of the UNCD probes observed in one case is associated with removal of the initial seed layer of the UNCD film. The results show the potential of a new material for AFM probes and demonstrate a systematic approach to studying wear at the nanoscale.

Control of three-dimensional island growth with mechanically responsive single-crystal nanomembrane substrates.

Freestanding, ultracompliant crystalline-sheet substrates provide a new opportunity to control the growth of strained epitaxial films. Three-dimensional SiGe islands grown on thin silicon nanomembranes self-order as the strain field induced by initial island growth guides nucleation of subsequent islands on the opposite surface. A mechanics analysis explains this unique growth mode, possible only on ultracompliant substrates. The ordering can be tailored by manipulating the thickness and elastic properties of the membrane.