Cutting soft materials on the microscale has emerging applications in single-cell studies, tissue microdissection for organoid culture, drug screens, and other analyses. However, the cutting process is complex and remains incompletely understood. Furthermore, precise control over blade geometries, such as the blade tip radius, has been difficult to achieve. In this work, we use the Nanoscribe 3D printer to precisely fabricate micro-blades (i.e., blades <1 mm in length) and blade grid geometries. This fabrication method enables a systematic study of the effect of blade geometry on the indentation cutting of paraffin wax, a common tissue-embedding material. First, we print straight micro-blades with tip radius ranging from ~100 nm to 10 μm. The micro-blades are mounted in a custom nanoindentation setup to measure the cutting energy during indentation cutting of paraffin. Cutting energy, measured as the difference in dissipated energy between the first and second loading cycles, decreases as blade tip radius decreases, until ~357 nm when the cutting energy plateaus despite further decrease in tip radius. Second, we expand our method to blades printed in unconventional configurations, including parallel blade structures and blades arranged in a square grid. Under the conditions tested, the cutting energy scales approximately linearly with the total length of the blades comprising the blade structure. The experimental platform described can be extended to investigate other blade geometries and guide the design of microscale cutting of soft materials.

Soft materials such as rubbers and soft tissues often undergo large deformations and experience damage degradation that impairs their function. This energy dissipation mechanism can be described in a thermodynamically consistent framework known as continuum damage mechanics. Recently, data-driven methods have been developed to capture complex material behaviors with unmatched accuracy due to the high flexibility of deep learning architectures. Initial efforts focused on hyperelastic materials, and recent advances now offer the ability to satisfy physics constraints such as polyconvexity of the strain energy density function by default. However, modeling inelastic behavior with deep learning architectures and built-in physics has remained challenging. Here we show that neural ordinary differential equations (NODEs), which we used previously to model arbitrary hyperelastic materials with automatic polyconvexity, can be extended to model energy dissipation in a thermodynamically consistent way by introducing an inelastic potential: a monotonic yield function. We demonstrate the inherent flexibility of our network architecture in terms of different damage models proposed in the literature. Our results suggest that our NODEs re-discover the true damage function from synthetic stress-deformation history data. In addition, they can accurately characterize experimental skin and subcutaneous tissue data.

Subcutaneous (SQ) injection is an effective delivery route for various biologics, including proteins, antibodies, and vaccines. However, pain and discomfort induced during SQ injection pose a notable challenge for the broader and routine use of biologics. Understanding the underlying mechanism and quantification of injection-induced pain and discomfort (IPD) are urgently needed. A crucial knowledge gap is what changes in the skin tissue microenvironment are induced by the SQ injection, which may ultimately cause the IPD. In this study, thus, a hypothesis is postulated that the injection of biologics solution through the skin tissue microenvironment induces spatiotemporal mechanical changes. Specifically, the injection leads to tissue swelling and subsequent increases in the interstitial fluid pressure (IFP) and matrix stress around the injection site, which ultimately causes the IPD. To test this hypothesis, an engineered SQ injection model is developed capable of measuring tissue swelling during SQ injection. The injection model consists of a skin equivalent with quantum dot-labeled fibroblasts, which enables the measurement of injection-induced spatiotemporal deformation. The IFP and matrix stress are further estimated by computational analysis approximating the skin equivalent as a nonlinear poroelastic material. The result confirms significant injection-induced tissue swelling and increases in IFP and matrix stress. The extent of deformation is correlated to the injection rate. The results also suggest that the size of biologics particulates significantly affects the pattern and extent of the deformation. The results are further discussed to propose a quantitative understanding of the injection-induced changes in the skin microenvironment.

The biomechanical phenotype of soft tissues - i.e., the sum of spatially- and directionally-varying mechanical properties - is a critical marker of tissue health and disease. While biomechanical phenotyping is always challenging, it is particularly difficult with miniscule tissues. For example, tissues from small animal models are often only millimeters in size, which prevents the use of traditional test methods, such as uniaxial tensile testing. To overcome this challenge, our current work describes and tests a novel experimental and numerical pipeline. First, we introduce a micro-bulge test device with which we pressurize and inflate miniscule soft tissues. We combine this microbulge device with an optical coherence tomography device to also image the samples during inflation. Based on pressure data and images we then perform inverse finite element simulations to identify our tissues' unknown material parameters. For validation, we identify the material parameters of a thin sheet of latex rubber via both uniaxial tensile testing and via our novel pipeline. Next, we demonstrate our pipeline against anterior tricuspid valve leaflets from rats. The resulting material parameters for these tissues compare excellently with data collected in sheep via standard planar biaxial testing. Additionally, we show that our device is compatible with other imaging modalities such as 2-Photon microscopy. To this end, we image the in-situ microstructural changes of the leaflets during inflation using second harmonic generation imaging. In summary, we introduce a novel pipeline to biomechanically phenotype miniscule soft tissues and demonstrate its value by phenotyping the biomechanics of the anterior tricuspid valve leaflets from rats.

A stabilized FSI method is presented for coupling shear-rate dependent model of blood with finitely deforming anisotropic hyperelastic model of arteries. The blood-artery coupling conditions are weakly enforced to accommodate non-matching blood-artery meshes which provides great flexibility in independent discretization of fluid and solid subdomains in the patient-specific geometric models. The variationally derived interface coupling terms play an important role in the concurrent solution of the nonlinear mixed-field problem across non-matching discretizations. Two test cases are presented that investigate the effect of growth of aortic aneurysm on local changes in blood flow and stress concentrations in calcified arteries under pulsating flows to highlight the clinical relevance of the proposed method for cardiovascular applications.

A number of biological tissues and synthetic gels consist of a fibrous network infused with liquid. There have been a few experimental studies of the rheological properties of such gels under applied compressive strain. Their results suggest that a plot of rheological moduli as a function of applied compressive strain has a long plateau flanked by a steeply increasing curve for large compressive strains and a slowly decreasing curve for small strains. In this paper we explain these trends in rheological properties using a chemo-elastic model characterized by a double-well strain energy function for the underlying fibrous network. The wells correspond to rarefied and densified phases of the fibrous network at low and high strains, respectively. These phases can co-exist across a movable transition front in the gel in order to accommodate overall applied compression. We find that the rheological properties of fibrous gels share similarities with a Kelvin-Voigt visco-elastic solid. The storage modulus has its origins in the elasticity of the fibrous network, while the loss modulus is determined by the dissipation caused by liquid flow through pores. The rheological properties can depend on the number of phase transition fronts present in a compressed sample. Our analysis may explain the dependence of storage and loss moduli of fibrin gels on the loading history. We also point to the need for combining rheological measurements on gels with a microstructural analysis that could shed light on various dissipation mechanisms.

Urinalysis is a simple and non-invasive approach for the diagnosis and monitoring of organ health and also is often used as a facile technique in assessment of substance abuse. However, quantitative urinalysis is predominantly limited to clinical laboratories. Here, we present an electrical sensing based, reusable, cellular microfluidic device that offers a fast urinalysis through quantitative reading of the electrical signals. The spatial soft porous scaffolds decorated with electrically conductive multiwalled carbon nanotubes that are capable of physically interacting with biomarkers in urine are developed through a cyclic swelling/absorption process of soft materials and are utilized to manufacture the cellular microfluidic device. The sensing capability, sensitivity and reusability (via sunlight exposure) of the device to monitor red blood cells, , and albumin are systemically demonstrated by programming mechanical deformation of porous scaffolds. experiments in disease mouse models confirm the diagnosis robustness of the device in comparable results with existing biochemical tests. The full integration of electrically conductive nanomaterials into soft scaffolds provides a foundation for devising bioelectronic devices with mechanically programmable microfluidic features in a low-cost manner, with broad applications for rapid disease diagnoses through body fluid.

Ultrasound neuromodulation (UNM), where a region in the brain is targeted by focused ultrasound (FUS), which, in turn, causes excitation or inhibition of neural activity, has recently received considerable attention as a promising tool for neuroscience. Despite its great potential, several aspects of UNM are still unknown. An important question pertains to the off-target sensory effects of UNM and their dependence on stimulation frequency. To understand these effects, we have developed a finite-element model of a mouse, including elasticity and viscoelasticity, and used it to interrogate the response of mouse models to focused ultrasound (FUS). We find that, while some degree of focusing and magnification of the signal is achieved within the brain, the induced pressure-wave pattern is complex and delocalized. In addition, we find that the brain is largely insulated, or 'cloaked', from shear waves by the cranium and that the shear waves are largely carried away from the skull by the vertebral column, which acts as a waveguide. We find that, as expected, this waveguide mechanism is strongly frequency dependent, which may contribute to the frequency dependence of UNM effects. Our calculations further suggest that off-target skin locations experience displacements and stresses at levels that, while greatly attenuated from the source, could nevertheless induce sensory responses in the subject.

Nacre, a natural nanocomposite with a brick-and-mortar structure existing in the inner layer of mollusk shells, has been shown to optimize strength and toughness along the laminae (in-plane) direction. However, such natural materials more often experience impact load in the direction perpendicular to the layers (i.e., out-of-plane direction) from predators. The dynamic responses and deformation mechanisms of layered structures under impact load in the out-of-plane direction have been much less analyzed. This study investigates the dynamic mechanical behaviors of nacre-inspired layered nanocomposite films using a model system that comprises alternating multi-layer graphene (MLG) and polymethyl methacrylate (PMMA) phases. With a validated coarse-grained molecular dynamics simulation approach, we systematically study the mechanical properties and impact resistance of the MLG-PMMA nanocomposite films with different internal nanostructures, which are characterized by the layer thickness and number of repetitions while keeping the total volume constant. We find that as the layer thickness decreases, the effective modulus of the polymer phase confined by the adjacent MLG phases increases. Using ballistic impact simulations to explore the dynamic responses of nanocomposite films in the out-of-plane direction, we find that the impact resistance and dynamic failure mechanisms of the films depend on the internal nanostructures. Specifically, when each layer is relatively thick, the nanocomposite is more prone to spalling-like failure induced by compressive stress waves from the projectile impact. Whereas, when there are more repetitions, and each layer becomes relatively thin, a high-velocity projectile sequentially penetrates the nanocomposite film. In the low projectile velocity regime, the film develops crazing-like deformation zones in PMMA phases. We also show that the position of the soft PMMA phase relative to the stiff graphene sheets plays a significant role in the ballistic impact performance of the investigated films. Our study provides insights into the effect of nanostructures on the dynamic mechanical behaviors of layered nanocomposites, which can lead to effective design strategies for impact-resistant films.

Intelligence of physical agents, such as human-made (e.g., robots, autonomous cars) and biological (e.g., animals, plants) ones, is not only enabled by their computational intelligence (CI) in their brain, but also by their physical intelligence (PI) encoded in their body. Therefore, it is essential to advance the PI of human-made agents as much as possible, in addition to their CI, to operate them in unstructured and complex real-world environments like the biological agents. This article gives a perspective on what PI paradigm is, when PI can be more significant and dominant in physical and biological agents at different length scales and how bioinspired and abstract PI methods can be created in agent bodies. PI paradigm aims to synergize and merge many research fields, such as mechanics, materials science, robotics, mechanical design, fluidics, active matter, biology, self-assembly and collective systems, to enable advanced PI capabilities in human-made agent bodies, comparable to the ones observed in biological organisms. Such capabilities would progress the future robots and other machines beyond what can be realized using the current frameworks.

Fluorescent microscopy employs monochromatic light for excitation, which can adversely affect the cells being observed. We reported earlier that fibroblasts relax their contractile force in response to green light of typical intensity. Here we show that such effects are independent of extracellular matrix and cell lines. In addition, we establish a threshold intensity that elicits minimal or no adverse effect on cell contractility even for long-time exposure. This threshold intensity is wavelength dependent. We cultured fibroblasts on soft 2D elastic hydrogels embedded with fluorescent beads to trace substrate deformation and cell forces. The beads move towards cell center when cells contract, but they move away when cells relax. We use relaxation/contraction ratio ( ), in addition to traction force, as measures of cell response to red (wavelength, =635-650 nm), green (=545-580 nm) and blue (=455-490 nm) lights with varying intensities. Our results suggest that intensities below 57, 31 and 3.5 W/m for red, green and blue lights, respectively, do not perturb force homeostasis. To our knowledge, these intensities are the lowest reported safe thresholds, implying that cell traction is a highly sensitive readout of the effect of light on cells. Most importantly, we find these threshold intensities to be i.e., safe regardless of the energy dosage or time of exposure. Conversely, higher intensities result in widespread force-relaxation in cells with > 1. Furthermore, we present a photo-reaction based model that simulates photo-toxicity and predicts threshold intensity for different wavelengths within the visible spectra. In conclusion, we recommend employing illumination intensities below aforementioned wavelength-specific thresholds for time-lapse imaging of cells and tissues in order to avoid light-induced artifacts in experimental observations.

Due to the lack of therapeutics and vaccines, diagnostics of COVID-19 emerges as one of the primary tools for controlling the spread of SARS-COV-2. Here we aim to develop a theoretical model to study the detection process of SARS-COV-2 in lateral flow device (LFD), which can achieve rapid antigen diagnostic tests. The LFD is modeled as the adhesion of a spherical nanoparticle (NP) coated with ligands on the surface, mimicking the SARS-COV-2, on an infinite substrate distributed with receptors under a simple shear flow. The adhesive behaviors of NPs in the LFD are governed by the ligand-receptor binding (LRB) and local hydrodynamics. Through energy balance analysis, three types of motion are predicted: (i) firm-adhesion (FA); (ii) adhesive-rolling (AR); and (iii) free-rolling (FR), which correspond to LRB-dominated, LRB-hydrodynamics-competed, and hydrodynamics-dominated regimes, respectively. The transitions of FA-to-AR and AR-to-FR are found to be triggered by overcoming LRB barrier and saturation of LRB torque, respectively. Most importantly, in the AR regime, the smaller NPs can move faster than their larger counterparts, induced by the LRB effect that depends on the radius of NPs. In addition, a scaling law is found in the AR regime that (rolling velocity and shear rate ), with an approximate scaling factor identified through fitting both theoretical and numerical results. The scaling factor emerges from the energy-based stochastic LRB model, and is confirmed to be universal by examining selections of different LRB model parameters. This size-dependent rolling behavior under the control of flow strength may provide the theoretical guidance for designing efficient LFD in detecting infectious disease.

Grasping is one of the key tasks for robots. Gripping fragile and complex three-dimensional (3D) objects without applying excessive contact forces has been a challenge for traditional rigid robot grippers. To solve this challenge, soft robotic grippers have been recently proposed for applying small forces and for conforming to complex 3D object shapes passively and easily. However, rigid grippers are still able to exert larger forces, necessary for picking heavy objects. Therefore, in this study, we propose a magnetically switchable soft suction gripper (diameter: 20 mm) to be able to apply both small and large forces. The suction gripper is in its soft state during approach and attachment while it is switched to its rigid state during picking. Such stiffness switching is enabled by filling the soft suction cup with a magnetorheological fluid (MR fluid), which is switched between low-viscosity (soft) and high-viscosity (rigid) states using a strong magnetic field. We characterized the gripper by measuring the force required to pull the gripper from a smooth glass surface. The force was up to 90% larger when the magnetic field was applied (7.1 N vs. 3.8 N). We also demonstrated picking of curved, rough, and wet 3D objects, and thin and delicate films. The proposed stiffness-switchable gripper can also carry heavy objects and still be delicate while handling fragile objects, which is very beneficial for future potential industrial part pick-and-place applications.

The physico-mechanical properties of nanoscale lipid vesicles (e.g., natural nano-vesicles and artificial nano-liposomes) dictate their interaction with biological systems. Understanding the interplay between vesicle size and stiffness is critical to both the understanding of the biological functions of natural nano-vesicles and the optimization of nano-vesicle-based diagnostics and therapeutics. It has been predicted that, when vesicle size is comparable to its membrane thickness, the effective bending stiffness of the vesicle increases dramatically due to both the entropic effect as a result of reduced thermal undulation and the nonlinear curvature elasticity effect. Through systematic molecular dynamics simulations, we show that the vesicle membrane thins and softens with the decrease in vesicle size, which effectively counteracts the stiffening effects as already mentioned. Our simulations indicate that the softening of nano-vesicles results from a change in the bilayer's interior structure - a decrease in lipid packing order - as the membrane curvature increases. Our work thus leads to a more complete physical framework to understand the physico-mechanical properties of nanoscale lipid vesicles, paving the way to further advances in the biophysics of nano-vesicles and their biomedical applications.

Peroxidation of plasma membranes, characterized by oxidative attack of lipidic carbon-carbon double-bonds in unsaturated fatty acids, has been identified as an important biochemical event in multiple pathological conditions, including neurodegenerative diseases, atherosclerosis, diabetes, preeclampsia, aging, cancer, etc. Changes to the lipid bilayer structure as a result of lipid peroxidation may lead to lipid membrane malfunction, and consequently initiate further downstream biochemical cascades. However, how lipid peroxidation modulates the mechanical properties of lipid membranes remains largely controversial. In this study, we investigate the peroxidation of lipids with polyunsaturated fatty acid tails using molecular dynamics simulations. By systematically varying the oxidation site, we find that lipid peroxidation alters the biophysical properties of bilayer membrane in a peroxidation site-specific manner. Specifically, our results suggest that peroxidation at sites in the bilayer interior disturbs and softens the membrane, whereas peroxidation at sites near the membrane-water interface results in a more ordered and stiffer membrane. Such a peroxidation site-specific modulation of lipid membrane mechanics provides an explanation for the contradictory results obtained in previous experiments. Our study paves the way for an improved understanding of the initiation of the downstream cellular dysfunction caused by lipid peroxidation.

Coronavirus Disease 2019 (COVID-19) may spread through respiratory droplets released by infected individuals during coughing, sneezing, or speaking. Given the limited supply of professional respirators and face masks, the U.S. Centers for Disease Control and Prevention (CDC) has recommended home-made cloth face coverings for use by the general public. While there have been several studies on aerosol filtration performance of household fabrics, their effectiveness at blocking larger droplets has not been investigated. Here, we ascertained the performance of 11 common household fabrics at blocking large, high-velocity droplets, using a commercial medical mask as a benchmark. We also assessed the breathability (air permeability), texture, fiber composition, and water absorption properties of the fabrics. We found that most fabrics have substantial blocking efficiency (median values >70%). In particular, two layers of highly permeable fabric, such as T-shirt cloth, blocks droplets with an efficiency (>94%) similar to that of medical masks, while being approximately twice as breathable. The first layer allows about 17% of the droplet volume to transmit, but it significantly reduces their velocity. This allows the second layer to trap the transmitted droplets resulting in high blocking efficacy. Overall, our study suggests that cloth face coverings, especially with multiple layers, may help reduce droplet transmission of respiratory infections. Furthermore, face coverings made from materials such as cotton fabrics allow washing and reusing, and can help reduce the adverse environmental effects of widespread use of commercial disposable and non-biodegradable facemasks.

Understanding the outbreak dynamics of COVID-19 through the lens of mathematical models is an elusive but significant goal. Within only half a year, the COVID-19 pandemic has resulted in more than 19 million reported cases across 188 countries with more than 700,000 deaths worldwide. Unlike any other disease in history, COVID-19 has generated an unprecedented volume of data, well documented, continuously updated, and broadly available to the general public. Yet, the precise role of mathematical modeling in providing quantitative insight into the COVID-19 pandemic remains a topic of ongoing debate. Here we discuss the lessons learned from six month of modeling COVID-19. We highlight the early success of classical models for infectious diseases and show why these models fail to predict the current outbreak dynamics of COVID-19. We illustrate how data-driven modeling can integrate classical epidemiology modeling and machine learning to infer critical disease parameters-in real time-from reported case data to make informed predictions and guide political decision making. We critically discuss questions that these models can and cannot answer and showcase controversial decisions around the early outbreak dynamics, outbreak control, and exit strategies. We anticipate that this summary will stimulate discussion within the modeling community and help provide guidelines for robust mathematical models to understand and manage the COVID-19 pandemic. EML webinar speakers, videos, and overviews are updated at https://imechanica.org/node/24098.

The COVID-19 pandemic has brought infectious diseases again to the forefront of global public health concerns. In this EML webinar (Gao, 2020), we discuss some recent work on simulation-assisted discovery of membrane targeting nanomedicine to counter increasing antimicrobial resistance and potential application of similar ideas to the current pandemic. A recent report led by the world health organization (WHO) warned that 10 million people worldwide could die of bacterial infections each year by 2050. To avert the crisis, membrane targeting antibiotics are drawing increasing attention due to their intrinsic advantage of low resistance development. In collaboration with a number of experimental groups, we show examples of simulation-assisted discovery of molecular agents capable of selectively penetrating and aggregating in bacterial lipid membranes, causing membrane permeability/rupture. Through systematic all-atom molecular dynamics simulations and free energy analysis, we demonstrate that the membrane activity of the molecular agents correlates with their ability to enter, perturb and permeabilize the lipid bilayers. Further study on different cell membranes demonstrates that the selectivity results from the presence of cholesterol in mammalian but not in bacterial membranes, as the cholesterol can condense the hydrophobic region of membrane, preventing the penetration of the molecular agents. Following the molecular penetration, we establish a continuum theory and derive the energetic driving force for the domain aggregation and pore growth on lipid membrane. We show that the energy barrier to membrane pore formation can be significantly lowered through molecular aggregation on a large domain with intrinsic curvature and a sharp interface. The theory is consistent with experimental observations and validated with coarse-grained molecular dynamics simulations of molecular domain aggregation leading to pore formation in a lipid membrane. The mechanistic modelling and simulation provide some fundamental principles on how molecular antimicrobials interact with bacterial membranes and damage them through domain aggregation and pore formation. For treating viral infections and cancer therapy, we discuss potential size- and lipid-type-based selectivity principles for developing membrane active nanomedicine. These studies suggest a general simulation-assisted platform to accelerate discovery and innovation in nanomedicine against infectious diseases. EML Webinar speakers are updated at https://imechanica.org/node/24132.

We report a high-throughput method that enables us to automatically compute the vibrational spectra of more than 100,000 proteins available in the Protein Data Bank to date, in a consistent manner. Using this new algorithm we report a comprehensive database of the normal mode frequencies of all known protein structures, which has not been available before. We then use the resulting frequency spectra of the proteins to generate audible sound by overlaying the molecular vibrations and translating them to the audible frequency range using the music theoretic concept of transpositional equivalence. The method, implemented as a Max audio device for use in a digital audio workstation (DAW), provides unparalleled insights into the rich vibrational signatures of protein structures, and offers a new way for creative expression by using it as a new type of musical instrument. This musical instrument is fully defined by the vibrational feature of almost all known protein structures, making it fundamentally different from all the traditional instruments that are limited by the material properties of a few types of conventional engineering materials, such as wood, metals or polymers.

Noninvasive methods for precise characterization of the thermal properties of soft biological tissues such as the skin can yield vital details about physiological health status including at critical intervals during recovery following skin injury. Here, we introduce quantitative measurement and characterization methods that allow rapid, accurate determination of the thermal conductivity of soft materials using thin, skin-like resistive sensor platforms. Systematic evaluations of skin at eight different locations and of six different synthetic skin-mimicking materials across sensor sizes, measurement times, and surface geometries (planar, highly curvilinear) validate simple scaling laws for data interpretation and parameter extraction. As an example of the possibilities, changes in the thermal properties of skin (volar forearm) can be monitored during recovery from exposure to ultraviolet radiation (sunburn) and to stressors associated with localized heating and cooling. More generally, the results described here facilitate rapid, non-invasive thermal measurements on broad classes of biological and non-biological soft materials.

Cell-cell adhesions maintain the mechanical integrity of multicellular tissues and have recently been found to act as mechanotransducers, translating mechanical cues into biochemical signals. Mechanotransduction studies have primarily focused on focal adhesions, sites of cell-substrate attachment. These studies leverage technical advances in devices and systems interfacing with living cells through cell-extracellular matrix adhesions. As reports of aberrant signal transduction originating from mutations in cell-cell adhesion molecules are being increasingly associated with disease states, growing attention is being paid to this intercellular signaling hub. Along with this renewed focus, new requirements arise for the interrogation and stimulation of cell-cell adhesive junctions. This review covers established experimental techniques for stimulation and interrogation of cell-cell adhesion from cell pairs to monolayers.