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1. Regulating Tissue-Mimetic Mechanical Properties of Bottlebrush Elastomers by Magnetic Field

   https://doi.org/10.1021/acsami.1c12860  

   Kostrov, Sergei A.; Dashtimoghadam, Erfan; Keith, Andrew N.; Sheiko, Sergei S.; Kramarenko, Elena Yu. (August 2021, ACS Applied Materials & Interfaces)

   null (Ed.)

   We report on a new class of magnetoactive elastomers (MAEs) based on bottlebrush polymer networks filled with carbonyl iron microparticles. By synergistically combining solvent-free, yet supersoft polymer matrices, with magnetic microparticles, we enable the design of composites that not only mimic the mechanical behavior of various biological tissues but also permit contactless regulation of this behavior by external magnetic fields. While the bottlebrush architecture allows to finely tune the matrix elastic modulus and strain-stiffening, the magnetically aligned microparticles generate a 3-order increase in shear modulus accompanied by a switch from a viscoelastic to elastic regime as evidenced by a ca. 10-fold drop of the damping factor. The developed method for MAE preparation through solvent-free coinjection of bottlebrush melts and magnetic particles provides additional advantages such as injection molding of various shapes and uniform particle distribution within MAE composites. The synergistic combination of bottlebrush network architecture and magnetically responsive microparticles empowers new opportunities in the design of actuators and active vibration insulation systems. 

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2. Characterization and Quantification of Hierarchical Particle Microstructures in External Field-Processed Composites

   https://doi.org/10.1115/SMASIS2021-68127  

   Papula, Dashiell; Ounaies, Zoubeida; von Lockette, Paris; Widdowson, Denise; Erol, Anil; Masud, Abdulla (September 2021, ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems)

   Abstract In this study, we discuss the characterization and quantification of composite microstructures formed by the external field manipulation of high aspect ratio magnetic particles in an elastomeric matrix. In our prior work, we have demonstrated that the simultaneous application of electric and magnetic fields on hard magnetic particles with geometric anisotropy can create a hierarchy of structures at different length scales, which can be used to achieve a wide range of properties. We aim to characterize these hierarchical structures and relate them to final composite properties so we can achieve our ultimate goal of designing a material for a prescribed performance. The complex particle structures are formed during processing by using electric and magnetic fields, and they are then locked-in by curing the polymer matrix around the particles. The model materials used in the study are barium hexaferrite (BHF) particles and polydimethylsiloxane (PDMS) elastomer. BHF was selected for its hard magnetic properties and high geometric anisotropy. PDMS was selected for its good mechanical properties and its tunable curing kinetics. The resulting BHF-PDMS composites are magnetoactive, i.e., they will deform and actuate in response to magnetic fields. In order to investigate the resulting particle orientation, distribution and alignment and to predict the composite’s macro scale properties, we developed techniques to quantify the particle structures. The general framework we developed allows us to quantify and directly compare the microstructures created within the composites. To identify structures at the different length scales, images of the composite are taken using both optical microscopy and scanning electron microscopy. We then use ImageJ to analyze them and gather data on particle size, location, and orientation angle. The data is then exported to MATLAB, and is used to run a Minimum Spanning Tree Algorithm to classify the particle structures, and von Mises Distributions to quantify the alignment of said structures. Important findings show 1) the ability to control structure using a combination of external electric, magnetic and thermal fields; 2) that electric fields promote long range order while magnetic fields promote short-range order; and 3) the resulting hierarchical structure greatly influence bulk material properties. Manipulating particles in a composite material is technologically important because changes in microstructure can alter the properties of the bulk material. The multifield processing we investigate here can form the basis for next generation additive manufacturing platforms where desired properties are tailored locally through in-situ hierarchical control of particle arrangements. 

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3. Data‐Driven Intelligent Manipulation of Particles in Microfluidics

   https://doi.org/10.1002/advs.202205382  

   Fang, Wen‐Zhen; Xiong, Tongzhao; Pak, On_Shun; Zhu, Lailai (December 2022, Advanced Science)

   Abstract Automated manipulation of small particles using external (e.g., magnetic, electric and acoustic) fields has been an emerging technique widely used in different areas. The manipulation typically necessitates a reduced‐order physical model characterizing the field‐driven motion of particles in a complex environment. Such models are available only for highly idealized settings but are absent for a general scenario of particle manipulation typically involving complex nonlinear processes, which has limited its application. In this work, the authors present a data‐driven architecture for controlling particles in microfluidics based on hydrodynamic manipulation. The architecture replaces the difficult‐to‐derive model by a generally trainable artificial neural network to describe the kinematics of particles, and subsequently identifies the optimal operations to manipulate particles. The authors successfully demonstrate a diverse set of particle manipulations in a numerically emulated microfluidic chamber, including targeted assembly of particles and subsequent navigation of the assembled cluster, simultaneous path planning for multiple particles, and steering one particle through obstacles. The approach achieves both spatial and temporal controllability of high precision for these settings. This achievement revolutionizes automated particle manipulation, showing the potential of data‐driven approaches and machine learning in improving microfluidic technologies for enhanced flexibility and intelligence. 

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4. Characterizing the spatiotemporal evolution of paramagnetic colloids in time-varying magnetic fields with Minkowski functionals

   https://doi.org/10.1039/D0SM01100B  

   Hilou, Elaa; Joshi, Kedar; Biswal, Sibani Lisa (January 2020, Soft Matter)

   Phase separation processes are widely utilized to assemble complex fluids into novel materials. These separation processes can be thermodynamically driven due to changes in concentration, pressure, or temperature. Phase separation can also be induced with external stimuli, such as magnetic fields, resulting in novel nonequilibrium systems. However, how external stimuli influence the transition pathways between phases has not been explored in detail. Here, we describe the phase separation dynamics of superparamagnetic colloids in time-varying magnetic fields. An initially homogeneous colloidal suspension can transition from a continuous colloidal phase with voids to discrete colloidal clusters, through a bicontinuous phase formed via spinodal decomposition. The type of transition depends on the particle concentration and magnitude of the applied magnetic field. The spatiotemporal evolution of the microstructure during the nucleation and growth period is quantified by analyzing the morphology using Minkowski functionals. The characteristic length of the colloidal systems was determined to correlate with system variables such as magnetic field strength, particle concentration, and time in a power-law scaling relationship. Understanding the interplay between particle concentration and applied magnetic field allows for better control of the phases observed in these magnetically tunable colloidal systems. 

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5. Label-free microfluidic sorting of microparticles

   https://doi.org/doi: 10.1063/1.5120501  

   Zhou, J.; Mukherjee, P.; Gao, H.; Luan, Q.; Papautsky, I. (December 2019, APL bioengineering)

   Massive growth of the microfluidics field has triggered numerous advances in focusing, separating, ordering, concentrating, and mixing of microparticles. Microfluidic systems capable of performing these functions are rapidly finding applications in industrial, environmental, and biomedical fields. Passive and label-free methods are one of the major categories of such systems that have received enormous attention owing to device operational simplicity and low costs. With new platforms continuously being proposed, our aim here is to provide an updated overview of the state of the art for passive label-free microparticle separation, with emphasis on performance and operational conditions. In addition to the now common separation approaches using Newtonian flows, such as deterministic lateral displacement, pinched flow fractionation, cross-flow filtration, hydrodynamic filtration, and inertial microfluidics, we also discuss separation approaches using non-Newtonian, viscoelastic flow. We then highlight the newly emerging approach based on shear-induced diffusion, which enables direct processing of complex samples such as untreated whole blood. Finally, we hope that an improved understanding of label-free passive sorting approaches can lead to sophisticated and useful platforms toward automation in industrial, environmental, and biomedical fields. 

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