Soft Matter Science Research Articles

Renaissance Goo: Senses and Materials in Early Modern Apothecary Taxonomies and Soft Matter Science

This essay brings together research in the history of science and soft matter physics to consider how early modern Italian apothecaries organised and communicated their knowledge from the fourteenth to the seventeenth century through an “apothecary taxonomy.” This was based on a what we call a “hylocentric” classification scheme (from the Greek hyle = matter, material, stuff) founded on a tactile understanding of materials. We will investigate how the behaviour of medicines under deformation and flow – their “rheology” – is a previously underestimated organisational principle, and consider the specialist vocabulary these author-practitioners used to describe different liquid and liquid-like formulations. We will also suggest that the rheology of these formulations – which today falls under the domain of “soft matter science” – affected the material culture of apothecary shops, in the arrangement and selection of drug bottles and jars, which presented this knowledge visually to visitors and clients. That soft matter scientists organise the substances they study in similar ways to early modern apothecaries suggests the agency of materials in affecting human categorisations.

Photochemical Patterning and Characterization of Mechanical Properties on Soft Materials

AbstractAlthough different chemistries for the spatio‐temporal localization of molecules and gradients of chemical signals within soft materials are now available, the achievement of spatio‐temporal patterns of mechanical properties in such materials and their characterization remain considerable challenges. This study presents the syntheses of two novel photo‐sensitive and thermoresponsive hydrogel systems, a photo‐stiffening and a photo‐softening hydrogel. Their potential for fabricating soft materials with patterned mechanical properties is then demonstrated by fabricating an actuator whose higher‐order bending properties can be switched on with light, and by encoding mechanical properties for digital information encryption and storage. Microindentation and a custom‐made data analysis software are essential for the characterization of all the materials. From a general perspective, this work opens a route to the fabrication of soft materials with patterned mechanical properties, addressing an important emerging challenge in soft materials science with applications in soft robotics and information encryption and storage.

Morphological Influence on a Nonionic Bilayer Bending Rigidity and Compression Modulus.

The mechanical properties of multilamellar vesicles and their relevance to soft matter physics and material science are of significant interest. The bending rigidity, κ, and compression modulus, B, of three-dimensional (3D) finite nonspontaneous multilamellar vesicles, formed by a nonionic surfactant, are linked to nanoscale bilayer thickness, δ, estimated via small-angle X-ray scattering, and macroscopic elastic modulus measured through small-amplitude oscillatory shear experiments. κ and B significantly differ from the same system in the two-dimensional (2D) infinite nanostructured planar lamellar phase. Particularly, κ3D was found to be much smaller than κ2D, while an opposite behavior was seen for B. The 2D-to-3D morphology transition occurs under a transient mechanical field, resulting in rheopectic behavior. κ scales quadratically with δ, consistent with bilayer membrane theories, and linearly with vesicle radius in the densely packed state. These findings have implications for understanding and designing soft interfaces due to the influence of bending rigidity on transport properties.

Voxelated bioprinting of modular double-network bio-ink droplets

Analogous of pixels to two-dimensional pictures, voxels—in the form of either small cubes or spheres—are the basic building blocks of three-dimensional objects. However, precise manipulation of viscoelastic bio-ink voxels in three-dimensional space represents a grand challenge in both soft matter science and biomanufacturing. Here, we present a voxelated bioprinting technology that enables the digital assembly of interpenetrating double-network hydrogel droplets made of polyacrylamide/alginate-based or hyaluronic acid/alginate-based polymers. The hydrogels are crosslinked via additive-free and biofriendly click reaction between a pair of stoichiometrically matched polymers carrying norbornene and tetrazine groups, respectively. We develop theoretical frameworks to describe the crosslinking kinetics and stiffness of the hydrogels, and construct a diagram-of-state to delineate their mechanical properties. Multi-channel print nozzles are developed to allow on-demand mixing of highly viscoelastic bio-inks without significantly impairing cell viability. Further, we showcase the distinctive capability of voxelated bioprinting by creating highly complex three-dimensional structures such as a hollow sphere composed of interconnected yet distinguishable hydrogel particles. Finally, we validate the cytocompatibility and in vivo stability of the printed double-network scaffolds through cell encapsulation and animal transplantation.

Spearheading a new era in complex colloid synthesis with TPM and other silanes.

Colloid science has recently grown substantially owing to the innovative use of silane coupling agents (SCAs), especially 3-trimethoxysilylpropyl methacrylate(TPM). SCAs were previously used mainly as modifying agents, but their ability to form droplets and condense onto pre-existing structures has enabled their use as a versatile and powerful tool to create novel anisotropic colloids with increasing complexity. In this Review, we highlight the advances in complex colloid synthesis facilitated by the use of TPM and show how this has driven remarkable new applications. The focus is on TPM as the current state-of-the-art in colloid science, but we also discuss other silanes and their potential to make an impact. We outline the remarkable properties of TPM colloids and their synthesis strategies, and discuss areas of soft matter science that have benefited from TPM and other SCAs.

Development of a novel fractional magneto-viscoelastic dynamic model for an adaptive beam featuring functional composite magnetoactive elastomers: Simulations and experimental studies

Recently, the rapid emergence of functional composite magnetoactive elastomers with integrated hard magnetic particles (known as hard magnetoactive elastomers) has attracted significant interest in fields such as soft robotics and material science. It is of paramount importance to develop an efficient model that can accurately predict the nonlinear dynamic behavior of adaptive structures for their practical application and the synthesis of effective control strategies. In this study, a novel nonlinear fractional magneto-viscoelastic model of an adaptive cantilevered beam featuring hard magnetoactive elastomers has been developed. This model effectively characterizes the beam’s nonlinear and rate-dependent response behavior under magnetic stimuli of varying frequencies and magnitudes. Considering the fractional Kelvin-Voigt energy dissipation model and large deformation nonlinearity, the governing equations for the cantilevered hard-magnetic soft beam were derived using the Hamilton principle. The finite difference method, combined with the Galerkin modal decomposition scheme, was subsequently utilized to discretize the time and space domains, respectively. A hardware-in-the-loop experimental framework was finally designed to experimentally investigate the beam’s nonlinear response behavior and validate the simulation results. The simulated nonlinear quasi-static responses of the beam, subjected to magnetic loading up to 30 mT, exhibited excellent agreement with the experimental data collected. Moreover, the simulated dynamic responses of the beam, subjected to various amplitudes of the applied magnetic field and magnetic frequencies up to 2 Hz, were thoroughly investigated. These included time-histories, phase-plane, and hysteretic responses, all demonstrating strong alignment with the experimental observations.

Shear-Induced Nonequilibrium Patterns in Lipid Bilayer Membranes Exhibiting Phase Separation.

The nonequilibrium dynamics of a fluid lipid membrane under external stimuli is an important issue that spans disciplines such as soft matter, biophysical chemistry, and interface science. This study investigated the dynamic response of lipid vesicles with order-disorder phase separation, which mimics a plasma membrane heterogeneity, to shear flow. Lipid vesicles were immobilized in a microfluidic chamber, and shear-induced nonequilibrium patterns on the membrane surface were observed by an optical microscope. We found that phase-separated membranes exhibit a dissipative structure of stripe patterns along the vortex flow on the membrane surface, and the number of stripes increased with the flow rate. At a high flow rate, the membrane exhibited a stripe-to-wave transition, where striped domains often migrated and the replacement of two different phases happened at vortex centers with time. We obtained a dynamic phase diagram of the shear-induced wave pattern by changing the flow rate, membrane components, and temperature. These findings could provide insight into the dissipative structures of lipid membranes out of equilibrium and flow-mediated mechanotransduction of biological membranes.

The fluidic memristor as a collective phenomenon in elastohydrodynamic networks

Fluid flow networks are ubiquitous and can be found in a broad range of contexts, from human-made systems such as water supply networks to living systems like animal and plant vasculature. In many cases, the elements forming these networks exhibit a highly non-linear pressure-flow relationship. Although we understand how these elements work individually, their collective behavior remains poorly understood. In this work, we combine experiments, theory, and numerical simulations to understand the main mechanisms underlying the collective behavior of soft flow networks with elements that exhibit negative differential resistance. Strikingly, our theoretical analysis and experiments reveal that a minimal network of nonlinear resistors, which we have termed a ‘fluidic memristor’, displays history-dependent resistance. This new class of element can be understood as a collection of hysteresis loops that allows this fluidic system to store information, and it can be directly used as a tunable resistor in fluidic setups. Our results provide insights that can inform other applications of fluid flow networks in soft materials science, biomedical settings, and soft robotics, and may also motivate new understanding of the flow networks involved in animal and plant physiology.

Hierarchical assembly is more robust than egalitarian assembly in synthetic capsids

Self-assembly of complex and functional materials remains a grand challenge in soft material science. Efficient assembly depends on a delicate balance between thermodynamic and kinetic effects, requiring fine-tuning affinities and concentrations of subunits. By contrast, we introduce an assembly paradigm that allows large error-tolerance in the subunit affinity and helps avoid kinetic traps. Our combined experimental and computational approach uses a model system of triangular subunits programmed to assemble into T = 3 icosahedral capsids comprising 60 units. The experimental platform uses DNA origami to create monodisperse colloids whose three-dimensional geometry is controlled to nanometer precision, with two distinct bonds whose affinities are controlled to kBT precision, quantified in situ by static light scattering. The computational model uses a coarse-grained representation of subunits, short-ranged potentials, and Langevin dynamics. Experimental observations and modeling reveal that when the bond affinities are unequal, two distinct hierarchical assembly pathways occur, in which the subunits first form dimers in one case and pentamers in another. These hierarchical pathways produce complete capsids faster and are more robust against affinity variation than egalitarian pathways, in which all binding sites have equal strengths. This finding suggests that hierarchical assembly may be a general engineering principle for optimizing self-assembly of complex target structures.

Ordered hierarchical superlattice amplifies coated-CeO2 nanoparticles luminescence

Achieving a controlled preparation of nanoparticle superstructures with spatially periodic arrangement, also called superlattices, is one of the most intriguing and open questions in soft matter science. The interest in such regular superlattices originates from the potentialities in tailoring the physicochemical properties of the individual constituent nanoparticles, eventually leading to emerging behaviors and/or functionalities that are not exhibited by the initial building blocks. Despite progress, it is currently difficult to obtain such ordered structures; the influence of parameters, such as size, softness, interaction potentials, and entropy, are neither fully understood yet and not sufficiently studied for 3D systems. In this work, we describe the synthesis and characterization of spatially ordered hierarchical structures of coated cerium oxide nanoparticles in water suspension prepared by a bottom-up approach. Covering the CeO2 surface with amphiphilic molecules having chains of appropriate length makes it possible to form ordered structures in which the particles occupy well-defined positions. In the present case superlattice arrangement is accompanied by an improvement in photoluminescence (PL) efficiency, as an increase in PL intensity of the superlattice structure of up to 400 % compared with that of randomly dispersed nanoparticles was observed. To the best of our knowledge, this is one of the first works in the literature in which the coexistence of 3D structures in solution, such as face-centered cubic (FCC) and Frank-Kasper (FK) phases, of semiconductor nanoparticles have been related to their optical properties.

Hydrogen-bonded liquid crystal complexes: A comprehensive study of structure, behaviour and potential applications

Intermolecular hydrogen bond (HB) interaction becomes an intensively studied topic in supramolecular chemistry and soft matter science. Herein, a new series of hydrogen-bonded liquid crystals (HBLCs) composed of 3-flurobenzoic acid derivatives (nOBAF) and 1,4-diazabicyclo [2.2.2] octane (DABCO) has been prepared and characterized by Fourier-transform infrared spectroscopy. The thermal behavior was determined by differential scanning calorimetry and observed by polarized optical microscopy. All complexes were found to exhibit smectic G (SmG) and smectic B (SmB) phases over a wide temperature range. Furthermore, it was found that the thermal stability of these mesophases increases with increasing the alkoxy chain length. The complex dielectric permittivity was reported as a function of frequency in the range 1 Hz–10 MHz. Lower frequency process attributed to the collective fluctuations of azimuthal angle and higher frequencies, associated to the soft mode were observed in SmB. In the SmG, additional to the soft mode (106 Hz) a higher frequency mode (105 Hz) was detected for the first time. The experimental results obtained for these complexes were correlated with density functional theory (DFT) calculations. Furthermore, these results suggest that nOBAF/DABCO has the potential to be switchable dielectric materials.

Recovery of the response function of a flexible membrane to a volume oscillation using double digital fringe projection technique and frequency filtering

This study presents a method to recover the response function of a flexible latex membrane undergoing a dynamic event by volume oscillation using Double Digital Fringe Projection to measure the profile of the surface in a sequence of frames. By applying a frequency filter to the interference pattern, a moiré pattern was obtained. Optical phase retrieval from individual frames was performed using the Isotropic Quadrature Transform Algorithm. This fringe projection technique enables full-field measurements, including edges where other techniques could not perform due to shadows generated by the tilt angle. The procedure was devised as a non-contact system to retrieve the deformation function of a flexible surface, enhancing a valuable aspect of the methodology. The results demonstrate successful recovery of the membrane response function, highlighting the relevance of the method for accurate deformation measurements using a straightforward system. These outcomes provide significant value for the development of precise measurement techniques for flexible structures in engineering applications, such as soft robotics, biomedical devices, and material science.

What Do Different Modes of AFM-IR Mean for Measuring Soft Matter Surfaces?

In the past decade, rapidly emerging atomic force microscopy-based photothermal infrared microscopy (AFM-IR) techniques have routinely delivered surface chemical imaging with tens of nanometers spatial resolution. The commercial availability of AFM-IR instruments has accelerated their popularity among soft matter and surface science communities. Various AFM-IR modes exist with different characteristics. In this Perspective, we discuss the challenges and opportunities associated with many AFM-IR modes, clarifying the possible confusion arising from terminologies and describing the possible benefits of using multiple AFM-IR modes for a better understanding of the nanoscale composition organization of the interface.

Delivery of probiotics and enzymes in self-assemblies of lipids and biopolymers based on colloidal principles

Food is a complex soft matter, because various components, such as proteins, lipids, and carbohydrates, are self-assembled via non-covalent, colloidal interactions and form hierarchical structures at multiple length scales. Soft matter scientists have shown an increasing interest in understanding the general principles governing the food structure formation. During the last several decades, an increasing number of studies have shown that the maintenance of healthy gastrointestinal tract and its microbiome is essential for human health and wellbeing. The realization of the importance of the gastrointestinal microbiome has led to the development of probiotics, which are defined as living bacteria that confer a health benefit on the host. Probiotic bacteria and enzymes can be delivered to the intestinal system by formulating appropriate carriers and including these into food ingested by humans. Despite this simple statement, it involves many challenges in the field of soft matter science. This review aims to highlight how the key concepts in soft matter science can be used to design, characterize, and evaluate self-assembled formulations of probiotics and enzymes based on lipids and biopolymers. The topics covered in this review includes the emulsification of oil-water mixtures, the self-assembly of lipids and polymers at interfaces, the electrostatics and viscoelasticity of interfaces, and the wetting/adhesion of colloidal particles.

Modulating the trapping and manipulation of semiconductor particles using Bessel beam optical tweezers

We show that the optical trapping and manipulation of spherical-shaped semiconductor (germanium) particles can be strongly modulated and controlled using optical tweezers based on a highly focused Bessel beam. Depending on the setup parameters, the germanium beads can be stably trapped outside the optical axis, present a characteristic oscillatory dynamics near the focal plane or even be repelled from the focus. This intricate behavior was studied by varying relevant parameters such as the laser power, the radius of the Bessel beam and the focal height of the trap. The results show that Bessel beam optical tweezers using germanium beads are very versatile tools for applying pico- to femto-Newton constant or oscillatory forces on systems, opening perspectives for new applications in soft matter science and correlated fields, as well as in the construction of microdevices.

Modulating the Viscoelastic Properties of Covalently Crosslinked Protein Hydrogels.

Protein engineering allows for the programming of specific building blocks to form functional and novel materials with customisable physical properties suitable for tailored engineering applications. We have successfully designed and programmed engineered proteins to form covalent molecular networks with defined physical characteristics. Our hydrogel design incorporates the SpyTag (ST) peptide and SpyCatcher (SC) protein that spontaneously form covalent crosslinks upon mixing. This genetically encodable chemistry allowed us to easily incorporate two stiff and rod-like recombinant proteins in the hydrogels and modulate the resulting viscoelastic properties. We demonstrated how differences in the composition of the microscopic building blocks change the macroscopic viscoelastic properties of the hydrogels. We specifically investigated how the identity of the protein pairs, the molar ratio of ST:SC, and the concentration of the proteins influence the viscoelastic response of the hydrogels. By showing tuneable changes in protein hydrogel rheology, we increased the capabilities of synthetic biology to create novel materials, allowing engineering biology to interface with soft matter, tissue engineering, and material science.

Excess shear force exerted on an oscillating plate due to a nearby particle

Quartz crystal microbalance with dissipation monitoring (QCM-D) is a standard analytical tool in soft matter and life sciences; its resonant frequency and bandwidth are very sensitive to a tiny amount of substance (e.g., molecular assemblies, proteins, cells) adhering to the quartz resonator surface. Since the resonator is often immersed in aqueous solution set in motion by surface oscillations, viscous forces also impact measurements. Thus, reliable interpretation of QCM-D results requires quantitative study of the shear stress due to the complex flow affected by particles. We examine theoretically the hydrodynamic stress due to suspended and adsorbed particles and their impact on QCM-D response.

Introduction to viscoelasticity and plasticity, and their relation to the underlying microscopic dynamics in soft matter systems

Understanding the rheological behavior of complex fluids, and in particular its relation to the dynamics of the elementary microscopic constituents, is one of the open challenges in soft matter science. In this contribution, we give a brief introduction to linear shear rheology and passive microrheology of soft amorphous materials, and we then focus on the transition to non-linearity during oscillatory rheology tests performed on soft glassy materials. These materials exhibit solid-like properties at rest, but they display characteristics of fluids when a sufficiently large shear is applied. Significant progress in understanding such ‘fluidization’ has been recently achieved by combining rheometry with techniques capable to capture the microscopic dynamics induced by shear, and in particular by oscillatory shear. We here summarize some of these achievements, as an introduction to the field for a non-expert reader.

A Time of Change

A Time of Change

A data-driven method for automated data superposition with applications in soft matter science

Abstract The superposition of data sets with internal parametric self-similarity is a longstanding and widespread technique for the analysis of many types of experimental data across the physical sciences. Typically, this superposition is performed manually, or recently through the application of one of a few automated algorithms. However, these methods are often heuristic in nature, are prone to user bias via manual data shifting or parameterization, and lack a native framework for handling uncertainty in both the data and the resulting model of the superposed data. In this work, we develop a data-driven, nonparametric method for superposing experimental data with arbitrary coordinate transformations, which employs Gaussian process regression to learn statistical models that describe the data, and then uses maximum a posteriori estimation to optimally superpose the data sets. This statistical framework is robust to experimental noise and automatically produces uncertainty estimates for the learned coordinate transformations. Moreover, it is distinguished from black-box machine learning in its interpretability—specifically, it produces a model that may itself be interrogated to gain insight into the system under study. We demonstrate these salient features of our method through its application to four representative data sets characterizing the mechanics of soft materials. In every case, our method replicates results obtained using other approaches, but with reduced bias and the addition of uncertainty estimates. This method enables a standardized, statistical treatment of self-similar data across many fields, producing interpretable data-driven models that may inform applications such as materials classification, design, and discovery.