Tuning shear thickening behavior is a longstanding problem in the field of dense suspensions. Acoustic perturbations offer a convenient way to control shear thickening in real time, opening the door to a new class of smart materials. However, complete control over shear thickening requires a quantitative description for how suspension viscosity varies under acoustic perturbation. Here, we achieve this goal by experimentally probing suspensions with acoustic perturbations and incorporating their effect on the suspension viscosity into a universal scaling framework where the viscosity is described by a scaling function, which captures a crossover from the frictionless jamming critical point to a frictional shear jamming critical point. Our analysis reveals that the effect of acoustic perturbations may be explained by the introduction of an effective interparticle repulsion whose magnitude is roughly equal to the acoustic energy density. Furthermore, we show how this scaling framework may be leveraged to produce explicit predictions for the viscosity of a dense suspension under acoustic perturbation. Our results demonstrate the utility of the scaling framework for experimentally manipulating shear thickening systems.

The past few years have seen great strides in our ability to build synthetic microscopic machines. However, the function of such machines is often controlled directly by externally applied fields that deterministically specify the instantaneous machine dynamics. A crucial step towards machines that can respond adaptively to changes in their environment is the ability to program multiple functions that actuate under the same external driving field, so that their internal state dictates which function is executed. Here, we demonstrate that energy landscapes with designed multistability enable the same externally applied field to drive multiple configurations and dynamic responses in microscopic machines, enabling increasing levels of autonomy. We show three examples. First, we write a bistable energy landscape into a microscopic device, enabling the device to exhibit two stable mechanical configurations under the same external magnetic field. Next, adding a second degree of freedom enables differing dynamic responses to the same external magnetic field, which we direct into net displacement of the environment. Finally, we demonstrate how a microscopic machine with a continuous symmetry autonomously channels a single degree-of-freedom magnetic actuation into locomotion and adaptively responds to forces induced by other machines.

Autonomous robotic-assisted surgery (RAS) has emerged as a promising objective in biomedical technology, further enhanced by miniaturization toward microrobotic-assisted surgery (μ-RAS). This reduction in scale promises minimally invasive, partially or fully automated surgical procedures, with the potential to reduce patient recovery times, lower medical costs, and enable previously unavailable procedural options. This perspective highlights the specific advances in RAS that potentially map to the microscale (μ-RAS), organized across five surgical domains: endovascular, endoluminal, laparoscopic, ophthalmic, and orthopedic. We examine both clinical demands and technological advances in surgical robotics and identify the key innovations required for progress across these surgical fields. Our contribution is distinct in combining the perspectives of both surgical experts and bioengineering innovators, outlining a roadmap for the advancement and eventual integration of autonomous RAS and μ-RAS into mainstream surgical practice.

Objective: Osteoarthritis (OA) is a chronic disease that affects more than 500 million individuals worldwide1. With aging populations, rising rates of obesity, genetics, and joint injury, the prevalence of OA is expected to continue growing globally1. Therefore, understanding and defining tissue-level changes between healthy and OA cartilage will be a critical step in slowing disease progression. The most abundant components in cartilage are collagen, especially type II, and aggrecan; any depletion of either or both can impair tissue mechanics. During OA development, ECM depletion occurs gradually, beginning at the surface and progressing to deeper zones, eventually reaching the bone2. However, little is known about how matrix depletion affects the depth-dependent shear mechanics of OA human cartilage. We hypothesize that OA cartilage will exhibit a lower shear modulus due to ECM loss compared to healthy cartilage. This study aims to: 1) determine how changes in collagen and aggrecan composition influence the depth-dependent microscale mechanics of OA human cartilage, and 2) evaluate how effectively the grading scale predicts shear mechanics. Methods: Tissue Collection: Cartilage samples were obtained as operating room discard after approval by an IRB (both healthy and OA tissue) and patient consent. The age range for healthy cartilage (from osteochondral allograft) was 14 to 28 years, while OA samples (from total knee replacement) ranged from 69 to 73 years old. Articular cartilage plugs (6 mm diameter and 2 mm height) were harvested from the femoral condyle (n=7 donors, k=13 samples) for healthy tissue and OA (n=2, k=6), then bisected into halves. Outerbridge scoring was performed prior to tissue dissection, and OARSI grading was applied to histological slides. Mechanical Testing: Depth-dependent shear mechanics were tested following an established protocol and custom MATLAB code3. Samples were compressed to 15% of tissue thickness and subjected to 1% peak shear strain. Strain data were fitted to a sinusoidal function, and stress was calculated based on the force exerted by the stationary plate on the tissue cross-section. The shear modulus (G*) was derived by dividing the depth-dependent shear strain by a constant shear stress. G* min represents the minimum modulus, while G* plateau was determined once the modulus ceased increasing at depths greater than 1000 µm. Composition Analysis: FT-IR spectroscopy was utilized to study the depth-dependent composition of the samples4. Polarized light and second-harmonic generation imaging were conducted throughout the tissue depth. Concentrations of aggrecan and collagen were measured using dimethyl methylene blue and hydroxyproline assays, respectively. Statistical Analysis: Welch's t-test compared the shear G* min and plateau between healthy and OA samples, while Pearson correlation assessed relationships between grading, mechanics, and biochemical composition using R Studio (α = 0.05). Results: The shear strain of OA samples showed higher than healthy cartilage in the first 400 μm (Fig. 1A). The shear modulus of OA samples was lower throughout the tissue depth compared to healthy, both of which increase as the depth goes towards the bone (Fig. 1B). We highlighted the first 200 μm to show where the difference of shear modulus is approximately two orders of magnitude (Fig. 1B). The healthy cartilage showed higher stiffness compared to OA cartilage in both the shear min ( p =0.0071) and plateau ( p =0.0074) (Fig. 1C-D). The collagen content for the OA tissue was decreased throughout the depth of the tissue compared against the healthy, while aggrecan level followed a similar pattern but the difference was not the same level as the collagen (Fig. 1E). Pearson correlation was performed only on the OA samples to better understand the relationship between grading scales (OARSI and Outerbridge), shear mechanics (shear min, shear and plateau), and bulk biochemical composition, and showed that shear min, collagen (volume), and aggrecan (volume) are negatively correlated with Outerbridge. However, there was a strong positive correlation with shear min and collagen (volume) (Fig. 1F). Conclusions: This study examined depth-dependent shear mechanics in healthy and OA human cartilage. The results showed that OA cartilage experiences shear strain levels that are one order of magnitude higher than healthy tissue, with most of the strain concentrated within the first 400 μm. The shear modulus of OA samples exhibited a decrease of more than 20-fold around the surface, while the deeper zone did not match the healthy shear modulus level but still showed approximately a 5-fold decrease. Other studies have reported similar findings, with very low shear modulus around the first 100-400 μm when tissues were treated with trypsin or collagenase, although shear modulus tends to return to healthy levels at the deeper zone5,6. However, our data indicate that the modulus in the deeper zone does not recover, which is characteristic of total ECM breakdown. The compositional analysis supported these mechanical findings, showing that both collagen and aggrecan were present at low levels at the surface and did not increase as they do in healthy tissue. The collagen content decreased 4-fold around the first 200 μm but stayed around 3-fold throughout the rest of the tissue depth compared to healthy samples. Our data also demonstrated that the change of collagen concentration is higher than the aggrecan level throughout the depth of the tissue. Meanwhile, the correlation analysis revealed that the grading system may be more predictive of overall biochemical properties than shear mechanics. The shear mechanics of OA cartilage are governed by compositional changes, and are more sensitive to collagen than aggrecan, while most of the change occurs at the articular surface.

Traumatic injury to synovial joints results in focal defects and proinflammatory changes in cartilage tissue, eventually leading to post-traumatic osteoarthritis (PTOA). Additionally, disparate incidence rates of PTOA occur between joints, particularly the knee and ankle. We hypothesize that differences in PTOA rates between knee and ankle cartilage are related to inherent differences in mechanical properties and chondrocyte sensitivity to mechanical loads. Our objective was to compare the effect of injury on the spatial patterns of cellular response between knee and ankle cartilage and relate these responses to changes in tissue mechanical properties. Cartilage explants of neonatal bovids from the talar dome of the ankle and femoral condyle of the knee were subjected to our ex vivo combined injury model which combines rapid impact injury and repetitive cartilage articulation. Explants were bisected and fluorescently stained to assess global and depth-dependent cell death, caspase activity, and mitochondrial depolarization. Explants were tested via confocal elastography to determine the local shear strain and shear modulus profile. Results showed that chondrocyte response differed between joints, with knee cartilage showing greater damage within the surface region. Additionally, ankle cartilage experienced a greater decrease in shear modulus post-injury compared to knee, causing depth-dependent shear strain to significantly increase (p < 0.0287). Furthermore, regression analyses relating cellular response to local shear strain revealed joint-specific differences in chondrocyte sensitivity, defined as the slope of cellular response versus shear strain, for cell death (knee: 873 vs ankle: 551), apoptosis (knee: 7779 vs ankle: 3577), and MT depolarization (knee: 848 vs ankle: 535).

Through programmable self-assembly, simple building blocks can be made to form highly complex structures following local rules of interaction. However, materials systems that are most commonly utilized for programmable assembly often lack interactions that exhibit the strength, specificity, and long ranges, which would, as a result, allow for robust and rapid hierarchical self-assembly processes. "Magnetic handshake" building blocks resolve many of these challenges at once, incorporating strong, long-range, and specific magnetic interactions through patterning of magnetic dipoles onto rigid panels. When appropriately designed, the panels organize hierarchically: first into chains, and subsequently those chains combine to form dense stacks. Here, we examine differences in phase behavior and morphology for four panel types. We delineate how perpendicular chaining and stacking interactions between panels compete and how they can be manipulated to reverse the sequence of the hierarchical assembly pathway. Collectively, our work shows the enormous potential for using magnetic handshake materials for self-assembly of hierarchically organized complex structures.

Life thrives due to its remarkable ability to create complex structures through the self-assembly of proteins, nucleic acids, and other biomolecules. Achieving such complex assemblies with the same level of fidelity, reproducibility, and advanced functionality in synthetic systems, however, has remained a grand challenge. One outstanding problem is the presence of parasitic products and long-lived intermediate states that slow the reaction process and limit the yield of the final product. Biology overcomes this challenge by proofreading to recognize and disassemble parasitic products. Such local checks, however, are currently difficult to implement in available self-assembly platforms. Here, we overcome this challenge by implementing a proofreading mechanism in a self-assembly platform. Specifically, we design intermediate states that strongly couple to an external force but a final product that is decoupled and thus highly stable to external driving, such that application of external forces selectively dissociates parasitic products. To implement this idea, we introduce lithographically patterned magnetic dipoles and an applied magnetic field to drive an assembly process similar to thermal self-assembly, but with additional controls. By applying patterns of magnetic driving that selectively destabilize parasitic states, we effectively implement a proofreading strategy to enable high-yield, time-efficient self-assembly. This realization of a general proofreading mechanism bridges the gap between artificial and biological self-assembly, paving the way for advanced self-assembled materials, with applications in next generation responsive materials, biomimetic devices, and microscale machines.

Rigidity transitions induced by the formation of system-spanning disordered rigid clusters, like the jamming transition, can be well described in most physically relevant dimensions by mean-field theories. A dynamical mean-field theory commonly used to study these transitions, the coherent potential approximation (CPA), shows logarithmic corrections in two dimensions. By solving the theory in arbitrary dimensions and extracting the universal scaling predictions, we show that these logarithmic corrections are a symptom of an upper critical dimension d_{upper}=2, below which the critical exponents are modified. We recapitulate Ken Wilson's phenomenology of the (4-ε)-dimensional Ising model, but with the upper critical dimension reduced to 2. We interpret this using normal form theory as a transcritical bifurcation in the RG flows and extract the universal nonlinear coefficients to make explicit predictions for the behavior near two dimensions. This bifurcation is driven by a variable that is dangerously irrelevant in all dimensions d>2 which incorporates the physics of long-wavelength phonons and low-frequency elastic dissipation. We derive universal scaling functions from the CPA sufficient to predict all linear response in randomly diluted isotropic elastic systems in all dimensions.

Recently, we proposed a universal scaling framework that shows shear thickening in dense suspensions is governed by the crossover between two critical points: one associated with frictionless isotropic jamming and a second corresponding to frictional shear jamming. Here, we show that orthogonal perturbations to the flows, an effective method for tuning shear thickening, can also be folded into this universal scaling framework. Specifically we show that the effect of adding orthogonal shear perturbations can be incorporated into the scaling variable via a multiplicative function, determined through our measurements, to achieve collapse of the entire thickening and dethickening dataset onto a single universal curve. We then show that this universal scaling framework can be used to control the phase behavior in thickening and jamming suspensions.

Articular cartilage is a load-bearing, hierarchically organized tissue composed of a network of type II collagen embedded in an aggrecan-rich polyelectrolyte gel. Its ability to resist deformation and dissipate energy arises from spatially varying matrix composition and architecture. Here, we review experimental and theoretical advances that elucidate the mechanistic basis of cartilage shear mechanics. Recent studies have shown that the tissue operates near a rigidity transition, in which small changes in collagen density, cross-linking, or osmotic stress can produce large, nonlinear changes in shear stiffness. We discuss how this behavior is captured by models rooted in rigidity percolation, continuum elasticity, and micromechanics, and how these frameworks connect depth-dependent composition to macroscale mechanical response. Throughout, we emphasize physical principles that describe observations across native, degraded, and engineered tissues, and we highlight emerging strategies for designing cartilage-inspired materials with tunable, anisotropic mechanics, with applications in soft robotics, synthetic gels, and load-bearing biomaterials.

Animals rely on rapid sensorimotor processing to detect and respond to visual stimuli in their environment, yet how sensorimotor networks are organized to generate appropriate behaviors remains unclear. Here, we identify a bilateral pair of descending neurons (DNs), DNp03, as a hub for collision avoidance in flying flies. DNp03 receives visual information related to looming objects approaching on a collision course and connects directly and indirectly to motor neurons of the wings and neck, enabling the coordinated banked turn and head stabilization maneuvers of a rapid saccade. Although DNp03 can drive saccade-like behavior when optogenetically activated, naturalistic looming-evoked saccade behavior relies on a network of interconnected DNs that can partially compensate for DNp03 in its absence. The connectivity of this hierarchical network suggests DNp03 operates in parallel with two additional DN hubs that directly recruit subservient DNs to reinforce and expand behavioral outputs. We also find competition between the saccade network and descending pathways for landing behavior, where direct inhibitory connections from DNp03 reduce the likelihood a fly decides to land on, rather than turn away from, a looming object. Altogether, we provide a detailed mapping of one key sensorimotor pathway from visual inputs to motor outputs to demonstrate how even rapid, innate sensorimotor transformations rely on complex networks. These findings reveal intricate interconnectivity and hierarchy in descending pathways, a strategy that may represent a general principle of motor control across species.

New Zealand white rabbits are a prevalent model species used to study preclinical articular cartilage repair therapies. The composition and structure of rabbit articular cartilage have been extensively characterized, yet the local shear properties of the tissue are unknown. Characterizing the local shear properties is essential for understanding the structure-function relationship in the tissue and relating the rabbit preclinical model to human disease. Therefore, the objectives of this study were to (1) characterize the local shear properties of articular cartilage from the femoral condyles of New Zealand white rabbits, (2) determine if local protein content or matrix structure correlated with local shear properties, and (3) compare microscale shear moduli values of rabbit cartilage to those previously reported for human, equine, and bovine tissues. Local shear strains and moduli varied with rabbit cartilage tissue depth; shear modulus was highest ∼ 50 µm below the tissue surface and decreased to plateau values around 150 µm, mirroring the trend with shear strains. Local shear strains showed significant correlations with local protein content but not matrix organization. Rabbit cartilage shear properties followed similar spatial trends as bovine, equine, and human tissue in the first ∼ 100 um of the tissue depth. However, rabbit tissue then differentiated from the larger animals as shear modulus values plateaued and did not increase by an order of magnitude like that seen in the larger species. Local shear properties of rabbit articular cartilage capture the surface properties of human, equine, and bovine cartilage but mechanically lack the deep zone region.

Whether recovering after a gust of wind, or rapidly saccading away from an oncoming predator, fruit flies show remarkable aerial dexterity about their body roll axis. Here, we investigated the detailed wing kinematic changes during free-flight roll motion and probed the neuromuscular basis for such changes. Consistent with previous work, we observed that flies manipulated the stroke amplitude difference between their wings to control their roll angle. Here, we show that flies are capable of achieving such changes by altering the stroke amplitude of either or both of their wings. Further we found that during corrections flies can also take advantage of an aerodynamically significant change in the angle of attack of their uppermost wing. Curiously, these corrective wing changes cannot be eliminated when motor neurons hypothesized to be used during roll maneuvers (i1, i2, b1, b2, and b3) are individually inhibited. However, free-flight optogenetic manipulations and quasi-steady aerodynamic calculations show that each of these motor neurons individually can effect kinematic changes consistent with a roll correction. Combining this evidence with an analysis of haltere inputs found in the BANC connectome, we propose that the observed robustness could be the result of two sets of muscular redundancies that receive shared inputs from haltere sensory afferents: one set, containing b1 and b2, is able to increase the stroke amplitude of the lower wing; while the other set, containing i1, i2, and b3, is able to decrease the stroke amplitude and wing pitch angle of the upper wing. Because of the redundancy in the input sensory information and output wing motion in the muscles in each cluster, the fly is able to perform roll stability maneuvers even when one of the constituent motor neurons is inhibited. This framework proposes new ways fast aerial maneuverability can be implemented when dealing with the fly’s most unstable rotational degree of freedom. ### Competing Interest Statement The authors have declared no competing interest. National Institute of Neurological Disorders and Stroke, https://ror.org/01s5ya894, R01NS116595, 1U01NS131438 U.S. National Science Foundation, https://ror.org/021nxhr62, DGE -- 1650441, IOS2221458 Searle Scholars Program McKnight Foundation, McKnight Scholar Award

Tuning shear thickening behavior is a longstanding problem in the field of dense suspensions. Acoustic perturbations offer a convenient way to control shear thickening in real time, opening the door to a new class of smart materials. However, complete control over shear thickening requires a quantitative description for how suspension viscosity varies under acoustic perturbation. Here, we achieve this goal by experimentally probing suspensions with acoustic perturbations and incorporating their effect on the suspension viscosity into a universal scaling framework where the viscosity is described by a scaling function, which captures a crossover from the frictionless jamming critical point to a frictional shear jamming critical point. Our analysis reveals that the effect of acoustic perturbations may be explained by the introduction of an effective interparticle repulsion whose magnitude is roughly equal to the acoustic energy density. Furthermore, we demonstrate how this scaling framework may be leveraged to produce explicit predictions for the viscosity of a dense suspension under acoustic perturbation. Our results demonstrate the utility of the scaling framework for experimentally manipulating shear thickening systems.

Abstract To perform most behaviors, animals must send commands from higher-order processing centers in the brain to premotor circuits that reside in ganglia distinct from the brain, such as the mammalian spinal cord or insect ventral nerve cord. How these circuits are functionally organized to generate the great diversity of animal behavior remains unclear. An important first step in unraveling the organization of premotor circuits is to identify their constituent cell types and create tools to monitor and manipulate these with high specificity to assess their functions. This is possible in the tractable ventral nerve cord of the fly. To generate such a toolkit, we used a combinatorial genetic technique (split-GAL4) to create 195 sparse transgenic driver lines targeting 196 individual cell types in the ventral nerve cord. These included wing and haltere motoneurons, modulatory neurons, and interneurons. Using a combination of behavioral, developmental, and anatomical analyses, we systematically characterized the cell types targeted in our collection. In addition, we identified correspondences between the cells in this collection and a recent connectomic data set of the ventral nerve cord. Taken together, the resources and results presented here form a powerful toolkit for future investigations of neuronal circuits and connectivity of premotor circuits while linking them to behavioral outputs.

Flapping flight is inherently unstable, requiring Drosophila to fine-tune their wing motions on a milliseconds timescale. Pioneering studies have shown that the direct steering muscles, which attach to the wing hinge, are important for generating these rapid flight reflexes. Recent connectome data, however, reveal that indirect steering muscles or tension muscles, which alter the mechanics of the thorax, receive some of the same synaptic inputs from the sensory apparatus as the direct steering muscles. This discovery suggests that the indirect steering muscles may also be important for flight control. Here, we show that the indirect tergopleural muscles indeed contribute substantially to stabilization, particularly for large pitch perturbations. We find that for small perturbations (less than 1000 deg/s), flies modulate their wing stroke amplitude to generate lift-based corrective torques, a strategy that has been previously documented. For larger perturbations, however, we observe that Drosophila engage an additional wing degree of freedom-the wing pitch angle-to leverage additional lift and drag forces during the corrective maneuver. Quasi-steady aerodynamic simulations reveal that this strategy minimizes power consumption, anaolgous to how some mammals (including humans) adjust their steady-state gaits in a near energy-optimal manner. Using optogenetics and a control theory framework we demonstrate that the tergopleural muscle is activated by a proportional gain component of a nonlinear PI controller responsible for determining the wing pitch angle during large perturbations. A simplified torsional-spring model for the wing hinge captures the changes in the wing pitch dynamics observed during correction maneuvers by using the tergopleural muscle to adjust the rest angle of the wing. These findings provide a striking example of reflex strategy selection in time-critical behaviors and underscores the vital role of indirect steering muscles in flight stabilization.

We study how the rigidity transition in a triangular lattice changes as a function of anisotropy by preferentially filling bonds on the lattice in one direction. We discover that the onset of rigidity in anisotropic spring networks on a regular triangular lattice arises in at least two steps, reminiscent of the two-step melting transition in two dimensional crystals. In particular, our simulations demonstrate that the percolation of stress-supporting bonds happens at different critical volume fractions along different directions. By examining each independent component of the elasticity tensor, we determine universal exponents and develop universal scaling functions to analyze isotropic rigidity percolation as a multicritical point. Our crossover scaling approach is applicable to anisotropic biological materials (e.g. cellular cytoskeletons, extracellular networks of tissues like tendons), and extensions to this analysis are important for the strain stiffening of these materials.

Increasingly functional microscopic machines are poised to have massive technical influence in areas including targeted drug delivery, precise surgical interventions, and environmental remediation. Such functionalities would increase markedly if collections of these microscopic machines were able to coordinate their function to achieve cooperative emergent behaviors. Implementing such coordination, however, requires a scalable strategy for synchronization—a key stumbling block for achieving collective behaviors of multiple autonomous microscopic units. Here, we show that pulse-coupled complementary metal-oxide semiconductor oscillators offer a tangible solution for such scalable synchronization. Specifically, we designed low-power oscillating modules with attached mechanical elements that exchange electronic pulses to advance their neighbor’s phase until the entire system is synchronized with the fastest oscillator or “leader.” We showed that this strategy is amenable to different oscillator connection topologies. The cooperative behaviors were robust to disturbances that scrambled the synchronization. In addition, when connections between oscillators were severed, the resulting subgroups synchronized on their own. This advance opens the door to functionalities in microscopic robot swarms that were once considered out of reach, ranging from autonomously induced fluidic transport to drive chemical reactions to cooperative building of physical structures at the microscale.