Macroscopic Material Properties Research Articles

Structural evolution and mechanical response of multiple Al Σ5(210) grain boundaries with segregation of solute atoms: First-principles study

Grain boundary (GB) segregation of solute atoms is a key factor affecting the microstructure and macroscopic properties of materials. In this study, first-principles calculations were carried out to investigate the effect of solute atoms (Mg and Cu) segregation on the structural evolution and mechanical response of Al ground-state Σ5(210) GB (GB-Ⅰ) and metastable Σ5(210) GBs (GB-Ⅱ and GB-Ⅲ). The GB energy, segregation energy, and the theoretical tensile strength of the multiple Σ5(210) GBs were calculated. The results show that both Mg and Cu atoms tend to segregate in the boundary plane, thus reducing GB energy and improving GB stability. The segregation of Cu atoms reduced the GB energy more significantly than that of Mg atoms. The segregation of solute atoms can change the symmetric GB structure into an asymmetric one or induce GB phase transformation. The theoretical strength of GB-I is weaker than that of the metastable GB-II with higher GB energy, suggesting that grain boundaries with higher energy are not necessarily unstable. In addition, the effect of solute atom segregation on the GB strength depends not only on the type of element but also on the specific GB structure. The weakening effect of Mg segregation in GB-I and GB-II is due to the weak MgAl bond which enlarges the low charge density region of GB and increases the free volume of GB. The strengthening of GB-I and GB-II by Cu segregation mainly depends on the stronger AlCu bond than AlAl bond along the GB fracture path. The enhanced strength of GB-III with Mg and Cu segregation is attributed to the GB structural phase transformation caused by the segregation of solute atoms. The calculation results further elucidate the relationship between element segregation and grain boundary characteristics.

Chemical environment effects on the size of mono-vacancy in CrFeCoNi alloy

Atomic studies of mono-vacancy are crucial for comprehending the mechanisms governing the related macroscopic properties of materials. High-entropy alloys (HEAs) manifest unique properties arising from a complex, localized, disordered chemical environment and distinctive site lattice distortions. In this study, the effects of chemical environment on the size of vacancy point defects in equiatomic CrFeCoNi alloy are investigated using Density Functional Theory through positron annihilation lifetime (PL) calculations. To better understand the nature of local lattice distortion, a similar atomic environment method is employed for supercell construction. Our findings reveal a significant inverse relationship between the PL of single-vacancy in CrFeCoNi HEA and the number of its first nearest neighbor Cr atoms. The underlying mechanisms in the perspective of local atomic mismatch and electronic structures are discussed.

The study on the micromechanical mechanism of PP/EPDM blends and PP/SEBS blends using atomic force microscopy-based nanomechanics

Using atomic force microscope-based nanomechanics to measure the micromechanical properties of different polymer blends, we found that the miscibility of the blend system affects the phase structure and micromechanical properties at the nanoscale, which further affects macroscopic mechanical properties of materials. In the immiscible polypropylene/ethylene propylene diene rubber blends, the microscopic phase structure is connected to the macroscopic mechanical properties of the material, since the microscopic modulus of elasticity of the phases remains constant even if the blend ratio is changed. On the other hand, in the partially miscible polypropylene/styrene-ethylene-butylene-styrene block copolymer blends, the macroscopic mechanical properties of the material are determined by the combination of the microscopic phase structure and the microscopic elastic modulus, because the microscopic elastic modulus of each phase changes with the blending ratio.

Crystallisation Dynamics in Large-Scale Extrusion Additive Manufacturing: An Analysis with and without Temperature Modification.

Large-Scale Material Extrusion (LS-MEX) is increasingly being used in small-scale production and prototyping due to its ability to create components in new temporal and spatial dimensions. However, the use of this manufacturing process poses microscopic and macroscopic challenges not encountered in previous small-scale production systems. These challenges arise primarily from the prolonged retention of heat in the material, which leads to insufficient strength in the extruded strands at the macrostructural level. As a result, the component can collapse, a phenomenon known as 'slumping'. Thermal energy also influences microstructural changes, such as crystallisation kinetics, which affect properties such as the strength and stiffness of the final product. The duration and dynamics of thermal energy are influenced by manufacturing parameters and the possible use of additional peripheral equipment, which affects component quality. In this study, the influence of thermal energy on structural processes through simulations of polyamide 6 with 40% carbon fibres (PA6 wt.%40 CF) is investigated. The results show that by adjusting the process parameters and using modification units, the thermal profile of the material can be accurately controlled, which allows the microstructural processes to be precisely controlled. This leads to the targeted modification of the macroscopic material properties. The focus of this work is on the combination of numerical simulations of the LS-MEX process with semi-empirical methods for the analysis of crystallisation processes. The application of the Nakamura model, which is used throughout similar investigations, allows a detailed description and prediction of the crystallisation kinetics during the manufacturing process. The study shows that the absolute degree of crystallisation can be determined with simplified assumptions using a combination of thermal simulations and semi-empirical approaches. It was found that the absolute degree of crystallisation increases from the outer interface of the strand to the print bed across the cross-section. This can be attributed to the specific thermal boundary conditions and the resulting temperature profiles at different points.

Adaptive residual convolutional neural network for compressive strength prediction of energetic materials using SEM images

The microstructure of materials has a great influence on the macroscopic mechanical properties of materials, and the relationship between them is of great significance to the design of materials. With the development of artificial intelligence and deep learning, many researchers have used convolutional neural networks (CNN) to correlate microscopic images with material properties, and achieved good results. However, for different types of material performance prediction tasks and datasets of different sizes, researchers need to design a special CNN network structure, and the adjustment steps of network structure parameters are cumbersome and time consuming. In this paper, an adaptive residual convolutional neural network with variable depth and width is proposed. We take the prediction error of the test dataset of the specific material performance prediction task as the evaluation index, and adaptively adjusts the network structure based on simulated annealing algorithm to find the best network structure. Finally, it is verified on the dataset composed of scanning electron microscopy (SEM) images and compressive strength of polymer explosive materials. Mean absolute percentage error (MAPE) of our model is 1.3% and the goodness of fit (R2 ) is 0.943. Further, we use Grad-CAM to explore the correlation between material microstructure and material properties.

Design and Preparation Technology of Green Multiple Solid Waste Cementitious Materials.

For solid waste-based cementitious materials, most scholars focus their research on the hydration reaction of cementitious materials, but there is still a lack of solid waste design that comprehensively considers mechanical properties and durability. Therefore, this article focuses on exploring the mix of design and the microscopic and macroscopic properties of multi solid waste cementitious materials (MSWCMs), namely steel slag (SS), slag powder (SP), desulfurization gypsum (DG), fly ash (FA), and ordinary Portland cement (OPC). According to the orthogonal experimental results, the compressive strength of MSWCMs is optimal when the OPC content is 50% and the SS, SP, DG, and FA contents are 10%, 20%, 5%, and 15%, respectively. The MSWCMs group with an OPC content of 50% and SS, SP, DG, and FA contents of 5%, 15%, 5%, and 25% was selected as the control group. The pure OPC group was used as the blank group, and the optimal MSWCMs ratio group had a 28-day compressive strength of 50.7 megapascals, which was 14% and 7.6% higher than the control group and blank group, respectively. The drying shrinkage rate and resistance to chloride ions were also significantly improved, with maximum increases of 22.9%, 22.6%, and 8.9%, 9.8%, respectively. According to XRD, TG-DTG, and NMR testing, the improvement in macroscopic performance can be attributed to the synergistic effect between various solid wastes. This synergistic effect produces more ettringite (AFt) and C-(A)-S-H gel. This study provides a good theoretical basis for improving the comprehensive performance of MSWCMs and is conducive to reducing the use of cement, with significant economic and environmental benefits.

R‐Vine Copulas for Data‐Driven Quantification of Descriptor Relationships in Porous Materials

AbstractLocal variations in the 3D microstructure can control the macroscopic behavior of heterogeneous porous materials. For example, the permittivity through porous sheets or membranes is governed by local high‐volume pathways or bottlenecks. Due to local variations, unfeasibly large amounts of microstructure data may be needed to reliably predict such material properties directly from image data. Here it is demonstrated that a vine copula approach provides parametric models for local microstructure descriptors that compactly capture the 3D microstructure including its local variations and efficiently probe it with respect to selected, measurable properties. In contrast to common methods of complexity reduction, the proposed approach creates parametric models for the multivariate probability distribution of high‐dimensional descriptor vectors that inherently contain the complex, nonlinear dependencies between these descriptors. Therein, material properties are offered in physically motivated distributions of microstructure descriptors rather than as normally distributed data. Applied to porous fiber networks (paper) before and after unidirectional compression, it is shown that the copula‐based models reveal material‐characteristic relationships between two or more microstructure descriptors. In this way, the presented modeling approach can provide deeper insight into the microscopic origin of effective macroscopic properties of heterogeneous porous materials.

Tuning grain boundary cation segregation with oxygen deficiency and atomic structure in a perovskite compositionally complex oxide thin film

Compositionally complex oxides (CCOs) are an emerging class of materials encompassing high entropy and entropy stabilized oxides. These promising advanced materials leverage tunable chemical bond structure, lattice distortion, and chemical disorder for unprecedented properties. Grain boundary (GB) and point defect segregation to GBs are relatively understudied in CCOs even though they can govern macroscopic material properties. For example, GB segregation can govern local chemical (dis)order and point defect distribution, playing a critical role in electrochemical reaction kinetics, and charge and mass transport in solid electrolytes. However, compared with conventional oxides, GBs in multi-cation CCO systems are expected to exhibit more complex segregation phenomena and, thus, prove more difficult to tune through GB design strategies. Here, GB segregation was studied in a model perovskite CCO LaFe0.7Ni0.1Co0.1Cu0.05Pd0.05O3−x textured thin film by (sub-)atomic-resolution scanning transmission electron microscopy imaging and spectroscopy. It is found that GB segregation is correlated with cation reducibility—predicted by an Ellingham diagram—as Pd and Cu segregate to GBs rich in oxygen vacancies (VO··). Furthermore, Pd and Cu segregation is highly sensitive to the concentration and spatial distribution of VO·· along the GB plane, as well as fluctuations in atomic structure and elastic strain induced by GB local disorder, such as dislocations. This work offers a perspective of controlling segregation concentration of CCO cations to GBs by tuning reducibility of CCO cations and oxygen deficiency, which is expected to guide GB design in CCOs.

An extended continuum-mechanics standard linear solid rheology for fluid-saturated porous rock

SUMMARY In a large body of rock-physics research, seismic wave velocity dispersion and attenuation in fluid-saturated porous rock are studied by constructing analytical or numerical models for time- or frequency-dependent dynamic (effective, or viscoelastic) moduli. A key and broadly used model of such kind is the Zener's, or the standard linear solid (SLS). This model is qualitatively successful in explaining many field and laboratory observations and serves as the key element of many generalizations such as the Burgers model for plastic deformations or the generalized SLS explaining band-limited or near-constant seismic attenuation. However, as a physical model of fluid-saturated porous rock, the SLS has several major limitations: disregard of inertial effects, absence of secondary wave modes and lack of key physical parameters such as porosity and Skempton coefficients. Grainy and porous rock is an unconsolidated material in which the effective density is frequency-dependent, and its effects on wave velocities may exceed those of the dynamic modulus. To overcome these limitations of the empirical SLS, we propose a rigorous rheologic model based on classical continuum mechanics and called the extended SLS, or eSLS. This rheology explains the available attenuation and dispersion observations equally well, but it is also close to Biot's model, honours all poroelastic relations, includes inertial effects, and reveals several new physical properties of the material. Detailed comparison of the eSLS and Biot's models gives a physical-mechanism-based classification of wave-induced fluid flow (WIFF) phenomena. In this classification, the so-called ‘global-scale’ flows occur in Biot's type structures within the material, whereas the ‘local-scale’ WIFF occurs in eSLS-type structures. Combining Biot's and eSLS models gives a broad class of rheologies for linear anelastic phenomena within rock with a single type of porosity. The model can be readily generalized to multiple porosities and different types of internal variables, such as describing squirt flows, wetting or thermoelastic effects. Modelling is conducted with relatively little effort, using a single matrix equation similar to a mechanical form of the standard SLS. By combining the eSLS and Biot's models, observations of dynamic-modulus dispersion and attenuation can be inverted for macroscopic mechanical properties of porous materials.

The Influence of Quartz Powder on the Mechanical–Thermal–Chemical–Durability Properties of Cement-Based Materials

Using industrial by-products to replace cement is an important way to reduce carbon emissions in the cement industry. The purpose of this article is to understand the effect of quartz powder on the properties of cement-based materials. Experimental studies were conducted on the macroscopic and microscopic properties of cement-based materials mixed with quartz powder to evaluate their feasibility as a replacement for cement. The substitution rates of quartz powder were 0% (Qu0), 7.5% (Qu7.5), and 15% (Qu15). The test time was from 1 day to 28 days, and the main results are as follows: In the early stage of the hydration reaction, as the amount of quartz powder substitution increases, the cumulative hydration heat increases. This is mainly because the nucleation effect of quartz powder accelerates the hydration reaction of cement. In the later stage of the hydration reaction, as the amount of quartz powder substitution increases, the cumulative heat of hydration decreases. This is mainly due to the diluting effect of quartz powder. For Qu0, Qu7.5, and Qu15, the decrease in compressive strength after 1 day is not obvious. The decrease in compressive strength at 28 days is more obvious. Overall, there are exponential relationships between the UPV measurement or surface resistivity results and the compressive strength measurement results at 1, 3, 7, and 28 days. The XRD test results show that the main products of the reaction are AFt, CH, Hc, and Mc. From Day 1 to Day 28, the content of Mc becomes evident. The test results for TG showed that, as the amount of quartz powder substitution increases, the mass loss decreases. For different specimens of Qu0, Qu7.5, and Qu15 at different test times (3 and 28 days), there is an exponential function relationship between chemically bound water and strength. A numerical hydration model is proposed for cement–quartz binary blends. The parameters of the hydration model are determined based on the hydration heat normalized by the cement mass. Moreover, the hydration heat at 28 days is calculated using the proposed model. The strength development of all specimens and all test ages can be expressed as an exponential function of hydration heat.

High-Precision 3D reconstruction and quantitative structure description: Linking microstructure to macroscopic heat transfer of aerogels

The intricate interplay between microstructure and macroscopic transfer processes in porous materials has long been recognized, yet achieving a definitive link between them remains a formidable challenge. Herein, we propose an effective approach to address this challenge by introducing a high-precision 3D reconstruction and structure descriptor database analysis method of nanoporous aerogels, which enables deeper understanding and accurate predictions of structure-directed properties. By employing focused ion beam-scanning electron microscope (FIB-SEM) technique supplemented with image preprocessing and image segmentation, we successfully built a 3D reconstruction model that exhibits high consistency with experimental results. Then, a structure descriptor database from the view of original porous structure, structure connectivity, and topological information is established, and 10 descriptors are preliminarily filtered relevance with thermal conductivity. Consequently, the quantitative relationship between descriptor and thermal conductivity is evaluated via a back propagation (BP) neural network, and three categories structure descriptors which have the most significant impact are determined as (i) the description of gaseous heat transfer pathways, (ii) the description of three-dimensional connectivity, and (iii) the description of interface complexity. With the profound understanding of structure descriptor, the present work offers a novel, structure-focused strategy that closely link the microstructure to macroscopic properties of porous materials.

Nano Revolution: Smart Therapeutics Unleash with Nanomaterials

Nanotechnology explores substances and technologies organized at the nanoscale level, measuring one billionth of a meter in at least one dimension. This field of study encompasses both science and engineering1. At these scales, the individual molecules and interacting groups of molecules play a crucial role in determining the bulk macroscopic properties of materials or devices. The ability to manage the fundamental molecular structure grants control over the macroscopic chemical and physical properties2. In the realms of physiology and medicine, there are intriguing applications involving substances and tools designed to precisely interact with the body at subcellular or molecular dimensions. These advancements hold promise for targeted interventions and therapies, ushering in a new era of precision medicine. This may result in tailored, tissue- and cell-specific clinical applications that aim to maximize therapeutic benefits while minimizing adverse consequences. The scientific and technological dimensions of nanotechnology, are accompanied by an exploration of potential therapeutic applications. It is essential to recognize that nanotechnology does not exist in isolation; rather, it emerges from the synergistic convergence of established disciplines such as biology, chemistry, physics, and materials science. This interdisciplinary approach provides the foundational knowledge necessary for advancing these cutting-edge technologies.

PEMFC sealing modeling relating to fractal characteristics of gasket-BPP interface based on micro-contact

The interface leaking of the gasket-BPP sealing structure affects the efficiency, safety, and reliability of PEMFCs. Aiming to reveal the basic cause of interface leaking and predict the leakage rate, the contact behavior at the micro-scale had been researched by FEM with the fractal multi-scale method, which could eliminate the negative influence of a single observation scale on the gasket-BPP rough interface modeling. Furthermore, in order to relate the contacting behavior at the micro-scale to the leakage rate at the macro-scale of PEMFCs, the mapping model had been established to analyze the sealing performance of the entire interface through the self-affine and self-similar characteristics of the fractal multi-scale contacting model of the gasket-BPP. Not only macroscopic properties of gasket rubber materials are considered in this model, but also microscopic morphologies are taken in to investigate the interface leakage rate, which had not been studied in traditional macroscopic research. By comparing the compression curves and interface leakage rates of the flange sealing device in testing and calculating approaches, the accuracy and reliability of the mapping model had been verified, leading to the analysis and the prediction of the PEMFC interface leakage rate rapidly.

Development of a genetic algorithm based interatomic potential and application in thermal conductivity study of ThO2 grain boundaries

ThO2 is a promising fuel for next-generation reactors due to its superior thermo-physical properties. In this study, we present a Coulomb-Buckingham-Morse interatomic potential for ThO2, developed using a homemade genetic algorithm (GA) method. The potential accurately captures fundamental properties, such as lattice constants, elastic constants, point defect energies, phonon spectra, and thermal conductivity. We apply this potential to perform a systematic study of thermal transport of twenty-one tilt grain boundaries (GBs) in ThO2, which have a significant effect on the macroscopic properties of polycrystalline materials. We demonstrate that the microscopic structure, GB excess energy, and GB disorder index are well correlated with the Kapitza conductance for a wide variety of GBs. Additionally, we discuss the effect of high temperature on the Kapitza conductance, where the GB structure undergoes disordering when approaching the melting temperature, resulting in an increase of thermal resistance. Finally, a detailed analysis of the phonon density of states is presented to reveal the mechanisms of thermal conductance in ThO2.

Recent application of porous materials in explosives: A mini-review

Nowadays, porous structures have exhibited many novel characteristics in the microstructure design of energetic materials, which possesses a unique impact on the macroscopic properties of energetic materials, and thus have exhibited a wide range of application prospects. In this review, several typical porous structures were introduced from the aspects of synthesis, performance and structure, and the main research progress of porous structures applied to the design of energetic materials in recent years was reviewed. Specifically, this article discussed the impact of nanopores, ordered structures and high specific surface areas on the safety and energy release of typical energetic materials, and focused on research area including the explosive adsorption and detection, new host-guest explosives, and new porous energetic materials. At the end of the review, an outlook and discussion were provided on the development trend of the combination of porous structure and explosives.

Periodic Composite Function-Based Approach for Designing Architected Materials With Programable Poisson’s Ratios

Abstract Advances in additive manufacturing enable fabrication of architected materials composed of microstructures with extreme mechanical properties. In the design of such architected materials, the parameterization of microstructures determines not just the computational cost but also connectivity between adjacent microstructures. In this paper, we propose a periodic composite function (PCF)-based approach for designing microstructures. The shape of the microstructures is characterized by the value of the periodic composite functions. The proposed method can program microstructures with both positive and negative Poisson’s ratios by a small number of parameters. Furthermore, due to its implicit representation, the proposed method allows for continuously tiling of microstructures with different mechanical properties. Explicit geometric features of the PCF-based microstructures are extracted, and the condition to maintain connectivity between adjacent microstructures is derived. Based on the proposed approach, multiple groups of 2D and 3D microstructures with Poisson’s ratios ranging from negative to positive are presented. Combining with a deep neural network (DNN)-based surrogate model to predict macroscopic material properties of the microstructures, the proposed method is applied to the design of architected materials for elastic deformation control. Numerical examples on both microstructure representation and architected materials design are presented to demonstrate the efficacy of the proposed approach.

DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing

Microstructure largely dictates macroscopic material properties and is strongly affected by processing. Therefore, the simulation of microstructure evolution in response to thermal fields during processing is of significant interest within the computational materials science community. Additive manufacturing (AM) has emerged as a technique for producing complex geometries and unique microstructures. Yet, complex and rapid thermal cycles in AM pose computational challenges for existing microstructure models. This work proposes a discrete event inspired cellular automata (CA) approach, titled DECA, to accelerate simulation of grain structure evolution in AM. In contrast to conventional time-stepped CA models, this model directly solves the times capture events would take place allowing for stepping in events rather than time (a technique also found in the field of discrete-event simulation). In comparison to purely serial discrete-event models, DECA allows for temporary violation of the causality constraint, but detects and corrects these violations, leading to an emergent phenomenon dubbed causality rippling, in which previously calculated capture events are overwritten. The amount of repeated calculations, defined by the capture ratio, is taken as a measure of computational inefficiency, and the model parameters that affect this ratio are evaluated. The new DECA approach was found to be more computationally efficient than conventional time-stepped CA models while guaranteeing an accurate solution, which can only be achieved in the conventional models for vanishingly small time steps. Finally, opportunities for parallelization and scaling of the new approach are discussed.

In-situ positron annihilation spectroscopy during tensile tests on Al alloys

We present a materials analysis method to enable in-situ Doppler broadening spectroscopy (DBS) of the 511 keV annihilation line in matter during tensile tests. This technique allows the correlation between the formation of lattice defects on an atomic scale and the macroscopic physical materials properties stress and strain. By implanting a monoenergetic positron beam into samples of AlMg3 (3.3535) and AlMgSi (3.3206) the onset of plastic deformation was clearly observed in the recorded annihilation spectra during the measurement of the corresponding stress–strain curves. The changes of the DBS spectra are attributed to positron annihilation in (open-volume) defects—predominantly vacancies elastically bound to dislocation lines—formed during plastic deformation. The elastic strain (Hook’s region), however, does not lead to changes in DBS spectra.

Azo-linked four-heterocyclic energetic molecule and its complexes: Exploring the important influence of conjugated planar structure on their crystal arrangement and stability

Using two routes, this study designed and synthesized a novel azo-linked four-heterocyclic compound, 1,2-bis(5-(1H-tetrazol-5-yl)-4H-1,2,4-triazol-3-yl) diazene (3, H4BTTD), with high yields. It corroborated that large conjugated planar energetic molecules in energetic compounds, exemplified by H4BTTD, contribute to the formation of layered crystal stacking based on abundant hydrogen bonds and interlayer π-π interactions. This markedly diminishes the mechanical sensitivities of energetic compounds. Single-crystal X-ray diffraction (XRD) experiments revealed the presence of layered structures in H4BTTD hydrate, as well as its magnesium-based complex [Mg2(BTTD)(H2O)8] (4) and calcium salt [Ca(H2O)7] (H3BTTD)2(5). Based on these structural data, this study analyzed the causes of these layered structures. Furthermore, this study systematically characterized the compounds’ physical and chemical properties, including mechanical sensitivities (IS ≥ 20 J, FS > 360 N), thermal stability (Td = 253.7–287.8 °C), and detonation performance (D = 6808–8253 m⋅s−1), confirming the influence of molecular structures on the macroscopic properties of energetic materials through crystal stacking. Additionally, pyrotechnic formulas based on compounds 3 and 5 exhibited the most intense light emission within a wavelength range of 658.6–689.8 nm, underscoring the potential application of both compounds as promising candidates in preparing high-purity red pyrotechnic formulation.

De novo design of modular protein hydrogels with programmable intra- and extracellular viscoelasticity

Relating the macroscopic properties of protein-based materials to their underlying component microstructure is an outstanding challenge. Here, we exploit computational design to specify the size, flexibility, and valency of de novo protein building blocks, as well as the interaction dynamics between them, to investigate how molecular parameters govern the macroscopic viscoelasticity of the resultant protein hydrogels. We construct gel systems from pairs of symmetric protein homo-oligomers, each comprising 2, 5, 24, or 120 individual protein components, that are crosslinked either physically or covalently into idealized step-growth biopolymer networks. Through rheological assessment, we find that the covalent linkage of multifunctional precursors yields hydrogels whose viscoelasticity depends on the crosslink length between the constituent building blocks. In contrast, reversibly crosslinking the homo-oligomeric components with a computationally designed heterodimer results in viscoelastic biomaterials exhibiting fluid-like properties under rest and low shear, but solid-like behavior at higher frequencies. Exploiting the unique genetic encodability of these materials, we demonstrate the assembly of protein networks within living mammalian cells and show via fluorescence recovery after photobleaching (FRAP) that mechanical properties can be tuned intracellularly in a manner similar to formulations formed extracellularly. We anticipate that the ability to modularly construct and systematically program the viscoelastic properties of designer protein-based materials could have broad utility in biomedicine, with applications in tissue engineering, therapeutic delivery, and synthetic biology.