arXiv subjects

Itai Cohen

Publications and source records attributed to Itai Cohen.

At least 37 records · Page 2Linked to original sources

arXiv2021

Forecasting racial dynamics at the neighborhood scale using Density-functional Fluctuation Theory

Racial residential segregation is a defining and enduring feature of U.S. society, shaping inter-group relations, racial disparities in income and health, and access to high-quality public goods and services. The design of policies aimed at addressing these inequities would be better informed by descriptive models of segregation that are able to predict neighborhood scale racial sorting dynamics. While coarse regional population projections are widely accessible, small area population changes remain challenging to predict because granular data on migration is limited and mobility behaviors are driven by complex social and idiosyncratic dynamics. Consequently, to account for such drivers, it is necessary to develop methods that can extract effective descriptions of their impacts on population dynamics based solely on statistical analysis of available data. Here, we develop and validate a Density-Functional Fluctuation Theory (DFFT) that quantifies segregation using density-dependent functions extracted from population counts and uses these functions to accurately forecast how the racial/ethnic compositions of neighborhoods across the US are likely to change. Importantly, DFFT makes minimal assumptions about the nature of the underlying causes of segregation and is designed to quantify segregation for neighborhoods with different total populations in regions with different compositions. This quantification can be used to accurately forecast both average changes in neighborhood compositions and the likelihood of more drastic changes such as those associated with gentrification and neighborhood tipping. As such, DFFT provides a powerful framework for researchers and policy makers alike to better quantify and forecast neighborhood-scale segregation and its associated dynamics.

physics.soc-ph↗

arXiv2021

Structural Origins of Cartilage Shear Mechanics

Healthy cartilage is a remarkable tissue, able to withstand tens of millions of loading cycles with minimal damage. While much is known about the structural origins of its compressive mechanics, how composition determines its mechanical behavior in shear, a major mode of failure, is poorly understood. Through a combination of microscale structure-function experiments and modeling, we develop a rigidity percolation framework to explain the structural origins of cartilage shear mechanics. This framework provides a new quantitative understanding for how the known degradative events in osteoarthritis determine the mechanical changes that are a hallmark of this disease. As such, this work provides a road map for understanding disease progression and in combination with non-invasive techniques such as MRI will enable more effective diagnosis and treatment.

physics.bio-ph↗

arXiv2021

Multi-valued inverse design: multiple surface geometries from one flat sheet

Designing flat sheets that can be made to deform into 3D shapes is an area of intense research with applications in micromachines, soft robotics, and medical implants. Thus far, such sheets were designed to adopt a single target shape. Here, we show that through anisotropic deformation applied inhomogenously throughout a sheet, it is possible to design a single sheet that can deform into multiple surface geometries upon different actuations. The key to our approach is development of an analytical method for solving this multi-valued inverse problem. Such sheets open the door to fabricating machines that can perform complex tasks through cyclic transitions between multiple shapes. As a proof of concept we design a simple swimmer capable of moving through a fluid at low Reynolds numbers.

cond-mat.soft↗

arXiv2020

Rigidity and fracture of fibrous double networks

Tunable mechanics and fracture resistance are hallmarks of biological tissues and highly desired in engineered materials. To elucidate the underlying mechanisms, we study a rigidly percolating double network (DN) made of a stiff and a flexible network. The DN shows remarkable tunability in mechanical response when the stiff network is just above its rigidity percolation threshold and minimal changes far from this threshold. Further, the DN can be modulated to either be extensible, breaking gradually, or stronger, breaking in a more brittle fashion by varying the flexible network's concentration.

cond-mat.soft↗

arXiv2020

Bidirectional Self-Folding with Atomic Layer Deposition Nanofilms for Microscale Origami

Origami design principles are scale invariant and enable direct miniaturization of origami structures provided the sheets used for folding have equal thickness to length ratios. Recently, seminal steps have been taken to fabricate microscale origami using unidirectionally actuated sheets with nanoscale thickness. Here, we extend the full power of origami-inspired fabrication to nanoscale sheets by engineering bidirectional folding with 4 nm thick atomic layer deposition (ALD) SiNx-SiO2 bilayer films. Strain differentials within these bilayers result in bending, producing microscopic radii of curvature. We lithographically pattern these bilayers and localize the bending using rigid panels to fabricate a variety of complex micro-origami devices. Upon release, these devices self-fold according to prescribed patterns. Our approach combines planar semiconductor microfabrication methods with computerized origami design, making it easy to fabricate and deploy such microstructures en masse. These devices represent an important step forward in the fabrication and assembly of deployable micromechanical systems that can interact with and manipulate micro- and nanoscale environments.

physics.app-ph↗

arXiv2019

Embedding Orthogonal Memories in a Colloidal Gel through Oscillatory Shear

It has recently been shown that in a broad class of disordered systems oscillatory shear training can embed memories of specific shear protocols in relevant physical parameters such as the yield strain. These shear protocols can be used to change the physical properties of the system and memories of the protocol can later be "read" out. Here we investigate shear training memories in colloidal gels, which include an attractive interaction and network structure, and discover that such systems can support memories both along and orthogonal to the training flow direction. We use oscillatory shear protocols to set and read out the yield strain memories and confocal microscopy to analyze the rearranging gel structure throughout the shear training. We find that the gel bonds remain largely isotropic in the shear-vorticity plane throughout the training suggesting that structures formed to support shear along the training shear plane are also able to support shear along the orthogonal plane. Orthogonal memory extends the usefulness of shear memories to more applications and should apply to many other disordered systems as well.

cond-mat.soft↗

arXiv2019

Using Acoustic Perturbations to Dynamically Tune Shear Thickening in Colloidal Suspensions

Colloidal suspensions in industrial processes often exhibit shear thickening that is difficult to control actively. Here, we use piezoelectric transducers to apply acoustic perturbations to dynamically tune the suspension viscosity in the shear-thickening regime. We attribute the mechanism of dethickening to the disruption of shear-induced force chains via perturbations that are large relative to the particle roughness scale. The ease with which this technique can be adapted to various flow geometries makes it a powerful tool for actively controlling suspension flow properties and investigating system dynamics.

cond-mat.soft↗

arXiv2018

Nonlinear mechanics of thin frames

The dramatic effect kirigami, such as hole cutting, has on the elastic properties of thin sheets invites a study of the mechanics of thin elastic frames under an external load. Such frames can be thought of as modular elements needed to build any kirigami pattern. Here we develop the technique of elastic charges to address a variety of elastic problems involving thin sheets with perforations, focusing on frames with sharp corners. We find that holes generate elastic defects (partial disclinations) which act as sources of geometric incompatibility. Numerical and analytic studies are made of three different aspects of loaded frames - the deformed configuration itself, the effective mechanical properties in the form of force-extension curves and the buckling transition triggered by defects. This allows us to understand generic kirigami mechanics in terms of a set of force-dependent elastic charges with long-range interactions.

cond-mat.soft↗

arXiv2018

Kirigami mechanics as stress relief by elastic charges

We develop a geometric approach to understand the mechanics of perforated thin elastic sheets, using the method of strain-dependent image elastic charges. This technique recognizes the buckling response of a hole under external load as a geometrically tuned mechanism of stress relief. We use a diagonally pulled square paper frame as a model system to quantitatively test and validate our approach. Specifically, we compare nonlinear force-extension curves and global displacement fields in theory and experiment. We find a strong softening of the force response accompanied by curvature localization at the inner corners of the buckled frame. Counterintuitively, though in complete agreement with our theory, for a range of intermediate hole sizes, wider frames are found to buckle more easily than narrower ones. Upon extending these ideas to many holes, we demonstrate that interacting elastic image charges can provide a useful kirigami design principle to selectively relax stresses in elastic materials.

cond-mat.soft↗

arXiv2018

Biases in particle localization algorithms

Automated particle locating algorithms have revolutionized microscopy image analysis, enabling researchers to rapidly locate many particles to within a few pixels in a microscope image. The vast majority of these algorithms operate through heuristic approaches inspired by computer vision, such as identifying particles with a blob detection. While rapid, these algorithms are plagued by biases [4, 15, 24], and many researchers still frequently ignore or understate these biases. In this paper, we examine sources of biases in particle localization. Rather than exhaustively examine all possible sources of bias, we illustrate their scale, the large number of sources, and the difficulty of correcting the biases with a heuristic method. We do this by generating a series of simple images, introducing sources of bias one at a time. Using these images, we examine the performance of two heuristic algorithms throughout the process: a centroid algorithm and a Gaussian fitting algorithm. We contrast the two heuristic methods with a new approach based on reconstructing an image with a generative model to fit the data (Parameter Extraction from Reconstructing Images, or PERI). While the heuristic approaches produce considerable biases even on unrealistically simple images, the reconstruction-based approach accurately measures particle positions even in complex, highly realistic images. We close by reiterating the fundamental reason that a reconstruction-based approach accurately extracts particle positions -- any imperfections in the fit both demonstrate which sources of systematic error are still present and provide a roadmap to incorporating them.

cond-mat.soft↗

arXiv2017

How Confinement-Induced Structures Alter the Contribution of Hydrodynamic and Short-Ranged Repulsion Forces to the Viscosity of Colloidal Suspensions

Understanding the correlation between structure and rheology in colloidal suspensions is important as these suspensions are crucial in industrial applications. Moreover, colloids exhibit a wide range of structures under confinement that could considerably alter the viscosity. Here, we use a combination of experiments and simulations to elucidate how confinement induced structures alter the relative contributions of hydrodynamic and repulsive forces to produce up to a ten fold change in the viscosity. We use a custom built confocal rheoscope to image the particle configurations of a colloidal suspension while simultaneously measuring the viscosity. We find a non-monotonic trend to the viscosity under confinement that is strongly correlated with the microstructure. As the gap decreases below 15 particle diameters, the viscosity first decreases from its bulk value, shows fluctuations with the gap, and then sharply increases for gaps below three particle diameters. Further, we compare our experimental results to two simulations techniques that enables us to determine the relative contributions of hydrodynamic and short range repulsive stresses. The first method uses the lubrication approximation to find the hydrodynamic stress and includes a short range repulsive force between the particles and the second is a Stokesian dynamics simulation that calculates the full hydrodynamic stress in the suspension. We find that the decrease in the viscosity at moderate confinements has a significant contribution from both the hydrodynamic and repulsive forces whereas the increase in viscosity at gaps less than three particle diameters arises primarily from short range repulsive forces. These results provide new insights to the unique rheological behavior of confined suspensions and further enable us to tune the viscosity by changing properties such as the gap, polydispersity, and the volume fraction.

cond-mat.soft↗

arXiv2017

Sculpting the Vertex: Manipulating the Configuration Space Topography and Topology of Origami Vertices to Design Mechanical Robustness

The geometric, aesthetic, and mathematical elegance of origami is being recognized as a powerful pathway to self-assembly of micro and nano-scale machines with programmable mechanical properties. The typical approach to designing the mechanical response of an ideal origami machine is to include mechanisms where mechanical constraints transform applied forces into a desired motion along a narrow set of degrees of freedom. In fact, to date, most design approaches focus on building up complex mechanisms from simple ones in ways that preserve each individual mechanism's degree of freedom (DOF), with examples ranging from simple robotic arms to homogenous arrays of identical vertices, such as the well-known Miura-ori. However, such approaches typically require tight fabrication tolerances, and often suffer from parasitic compliance. In this work, we demonstrate a technique in which high-degree-of-freedom mechanisms associated with single vertices are heterogeneously combined so that the coupled phase spaces of neighboring vertices are pared down to a controlled range of motions. This approach has the advantage that it produces mechanisms that retain the DOF at each vertex, are robust against fabrication tolerances and parasitic compliance, but nevertheless effectively constrain the range of motion of the entire machine. We demonstrate the utility of this approach by mapping out the configuration space for the modified Miura-ori vertex of degree 6, and show that when strung together, their combined configuration spaces create mechanisms that isolate deformations, constrain the configuration topology of neighboring vertices, or lead to sequential bistable folding throughout the entire origami sheet.

cond-mat.soft↗

arXiv2017

A watershed-based algorithm to segment and classify cells in fluorescence microscopy images

Imaging assays of cellular function, especially those using fluorescent stains, are ubiquitous in the biological and medical sciences. Despite advances in computer vision, such images are often analyzed using only manual or rudimentary automated processes. Watershed-based segmentation is an effective technique for identifying objects in images; it outperforms commonly used image analysis methods, but requires familiarity with computer-vision techniques to be applied successfully. In this report, we present and implement a watershed-based image analysis and classification algorithm in a GUI, enabling a broad set of users to easily understand the algorithm and adjust the parameters to their specific needs. As an example, we implement this algorithm to find and classify cells in a complex imaging assay for mitochondrial function. In a second example, we demonstrate a workflow using manual comparisons and receiver operator characteristics to optimize the algorithm parameters for finding live and dead cells in a standard viability assay. Overall, this watershed-based algorithm is more advanced than traditional thresholding and can produce optimized, automated results. By incorporating associated pre-processing steps in the GUI, the algorithm is also easily adjusted, rendering it user-friendly.

cs.CV↗

arXiv2017

Experimental Realization of the Green-Kubo Relation in Colloidal Suspensions Enabled by Image-based Stress Measurements

By combining confocal microscopy and Stress Assessment from Local Structural Anisotropy (SALSA), we directly measure stresses in 3D quiescent colloidal liquids. Our non-invasive and non-perturbative method allows us to measure forces $\lesssim$ 50 fN with a small and tunable probing volume, enabling us to resolve the stress fluctuations arising from particle thermal motions. We use the Green-Kubo relation to relate these measured stress fluctuations to the bulk Brownian viscosity at different volume fractions and comparing against simulations and conventional rheometry measurements. We demonstrate that the Green-Kubo analysis gives excellent agreement with these prior results. This agreement provides a strong demonstration of the applicability of the Green-Kubo relation in nearly hard-sphere suspensions and opens the door to investigations of local flow properties in many poorly understood far-from-equilibrium systems, including suspensions that are glassy, strongly-sheared, or highly-confined.

cond-mat.soft↗

arXiv2017

Light Microscopy at Maximal Precision

Microscopy is the workhorse of the physical and life sciences, producing crisp images of everything from atoms to cells well beyond the capabilities of the human eye. However, the analysis of these images is frequently little better than automated manual marking. Here, we revolutionize the analysis of microscopy images, extracting all the information theoretically contained in a complex microscope image. Using a generic, methodological approach, we extract the information by fitting experimental images with a detailed optical model of the microscope, a method we call Parameter Extraction from Reconstructing Images (PERI). As a proof of principle, we demonstrate this approach with a confocal image of colloidal spheres, improving measurements of particle positions and radii by 100x over current methods and attaining the maximum possible accuracy. With this unprecedented resolution, we measure nanometer-scale colloidal interactions in dense suspensions solely with light microscopy, a previously impossible feat. Our approach is generic and applicable to imaging methods from brightfield to electron microscopy, where we expect accuracies of 1 nm and 0.1 pm, respectively.

cond-mat.soft↗

arXiv2016

Relating Microstructure and Particle-level Stress in Colloidal Crystals Under Increased Confinement

The mechanical properties of crystalline materials can be substantially modified under confinement. Such modified macroscopic properties are usually governed by the altered microstructures and internal stress fields. Here, we use a parallel plate geometry to apply a quasi-static squeeze flow crushing a colloidal polycrystal while simultaneously imaging it with confocal microscopy. The confocal images are used to quantify the local structure order and, in conjunction with Stress Assessment from Local Structural Anisotropy (SALSA), determine the stress at the single-particle scale. We find that during compression, the crystalline regions break into small domains with different geometric packing. These domains are characterized by a pressure and deviatoric stress that are highly localized with correlation lengths that are half those found in bulk. Furthermore, the mean deviatoric stress almost doubles, suggesting a higher brittleness in the highly-confined samples.

cond-mat.soft↗

arXiv2016

Measuring nonlinear stresses generated by defects in 3D colloidal crystals

The mechanical, structural and functional properties of crystals are determined by their defects and the distribution of stresses surrounding these defects has broad implications for the understanding of transport phenomena. When the defect density rises to levels routinely found in real-world materials, transport is governed by local stresses that are predominantly nonlinear. Such stress fields however, cannot be measured using conventional bulk and local measurement techniques. Here, we report direct and spatially resolved experimental measurements of the nonlinear stresses surrounding colloidal crystalline defect cores, and show that the stresses at vacancy cores generate attractive interactions between them. We also directly visualize the softening of crystalline regions surrounding dislocation cores, and find that stress fluctuations in quiescent polycrystals are uniformly distributed rather than localized at grain boundaries, as is the case in strained atomic polycrystals. Nonlinear stress measurements have important implications for strain hardening, yield, and fatigue.

cond-mat.mtrl-sci↗

arXiv2016

Tunable Shear Thickening in Suspensions

Shear thickening, an increase of viscosity with shear rate, is a ubiquitous phenomena in suspended materials that has implications for broad technological applications. Controlling this thickening behavior remains a major challenge and has led to empirical strategies ranging from altering the particle surfaces and shape to modifying the solvent properties. However, none of these methods allow for tuning of flow properties during shear itself. Here, we demonstrate that by strategic imposition of a high-frequency and low-amplitude shear perturbation orthogonal to the primary shearing flow, we can largely eradicate shear thickening. The orthogonal shear effectively becomes a regulator for controlling thickening in the suspension, allowing the viscosity to be reduced by up to two decades on demand. In a separate setup, we show that such effects can be induced by simply agitating the sample transversely to the primary shear direction. Overall, the ability of in situ manipulation of shear thickening paves a route towards creating materials whose mechanical properties can be controlled.

cond-mat.soft↗