arXiv subjects

Carlos M. Portela

Publications and source records attributed to Carlos M. Portela.

12 recordsLinked to original sources

arXiv2026

Hierarchical Granular Metamaterials

Granular materials dissipate energy efficiently through intergranular interactions, yet their disordered, dense nature precludes precise control and integration into lightweight systems. Architected materials offer tunable mechanical responses at low densities but tend to localize stress, limiting dissipation efficiency. Here, we introduce hierarchical granular metamaterials that reconcile these trade-offs through three levels of design: lightweight architected grains engineered with hollow elliptical inclusions, crystal-inspired grain packings, and functional gradients and defects within grain tessellations. These metamaterials exhibit simultaneous increases in impact energy absorption per unit mass and reductions in transmitted peak force at low densities, outperforming conventional architected materials. In situ nanomechanical experiments and nonlinear computational models reveal that enhanced lateral grain expansion drives recruitment of neighboring grains, amplifying plastic and frictional dissipation. Multiscale impact experiments confirm that these mechanisms persist across length scales, constituent materials, and dimensionalities. Beyond mechanical performance, we demonstrate that spatially programmable inter-grain contact networks enable deterministic routing of deformation, which extends to electrical transport pathways independently of packing geometry. By combining granular principles with architected material design, this work establishes a paradigm for multifunctional metamaterials whose contact topology, mechanical response, and transport properties can be programmed independently.

cond-mat.soft↗

arXiv2026

Enhanced Impact Mitigation via 3D-Multilayered Material Architectures

Materials designed by nature commonly exhibit functional grading and laminated structures, particularly when intended for enhanced impact protection. Synthetic materials have also found success in exploiting this concept with fully dense but spatially varying architectures, as is the case with advanced fiber-based composites. In the lightweight materials space, porous architected materials have shown benefits for extreme impact mitigation, proving to be advantageous in dissipating large amounts of energy per unit mass, but rarely harness the benefits of layering or functional grading in designs. Here, a design paradigm for lightweight multilayered materials towards high impact-mitigation efficacy is demonstrated, showing that the use of alternating monolithic and beam-based architectures leads to enhanced and predictable responses under extreme conditions. These layered, mass-equivalent `heterostructures' with different ordering and proportions of octet and monolithic layers outperform single-architecture lattices on a mass-normalized energy dissipation basis by >50% when subjected to supersonic microparticle impact. Through analysis that combines wave-propagation analysis, nonlinear finite element simulations, and post-impact crater reconstruction, layer-by-layer mechanical properties are mapped to crater formation and energy dissipation behaviors. This heterostructure design framework offers a simple approach towards tuning failure and impact resistance of materials for protective applications from Whipple shields to sports equipment.

cond-mat.mtrl-sci↗

arXiv2026

Magnetically Responsive Microprintable Soft Nanocomposites with Tunable Nanoparticle Loading

Magnetic remote actuation of soft materials is attractive for applications such as transforming materials and medical robots. However, due to manufacturing limitations, microscale magnetoactive devices are scarce -- light-based additive manufacturing methods, despite achieving microscale resolution, struggle with particle-induced light scattering. Moreover, large hard-magnetic microparticles restrict ultimate feature sizes, and deformation of soft-magnetic nanoparticle composites requires impractically high loading and field gradients. Among successfully fabricated microscale soft-magnetic composites, limited control over particle loading, distribution, and matrix-phase stiffness has hindered their functionality. Here, we combine two-photon polymerization with iron oxide nanoparticle coprecipitation to fabricate 3D-printed microscale nanocomposites with spatially tunable nanoparticle distribution. We control nanoparticle content by locally modulating the two-photon dose, imbuing parts with varied magnetic functionality and achieving millimeter-scale elastic deformations, demonstrated by a soft robotic gripper and a bistable bit register and sensor. Our approach enables precise control of mechanical and magnetic properties towards microscale metamaterial and robotics applications.

cond-mat.soft↗

arXiv2026

Dynamic Mechanical Response of Spinodal Architectures Across Length and Time Scales

High-throughput characterization of architected materials across a wide range of length scales enables rapid screening of topologies for engineering applications. Scaled-down specimens manufactured and evaluated in laboratory environments enable this iteration, but application scenarios may involve differing length scales and loading conditions that complicate direct comparisons. Here, we use a spinodal architected morphology to determine the interplay among the constituent material's strain-rate sensitivity, the topological length scale, and the imposed deformation rates. We report characterization spanning strain rates from $10^{-3}$ s$^{-1}$ to $10^{4}$ s$^{-1}$ on spinodal architected specimens with length scales of 100 $μ$m (microscale) and 30 mm (macroscale). The experiments show that while microscale specimens exhibit moderate increase in strength at high strain rates, macroscale specimens exhibit a nearly tenfold increase in strength at equivalent strain rates. Finite element calculations reveal that this increase is linked to a transition from a response governed by constituent material strain-rate sensitivity to inertia-dominated behavior in macroscale specimens, a transition not observed in microscale specimens at the strain rates investigated here. Using extensive finite element calculations, we develop maps to establish the parameters governing the regimes of behavior, illustrating that the transition from behavior governed by constituent material rate sensitivity to inertia-dominated behavior has analogies to fluids in that it depends on a structural length scale. Our findings provide insights into the physical parameters that govern responses across length and time scales, towards the development and design of new laboratory experiments that extract relevant dynamic properties for structural applications.

cond-mat.mtrl-sci↗

arXiv2025

Scale-Rich Network-Based Metamaterials

Materials, at their essence, are networks defined by homogeneity: uniform bonds, fixed thicknesses, and discrete length scales. Mechanical metamaterials, while representing structurally more diverse microstructures, remain defined by the homogeneity of their unit cells, pore sizes, or repeating features. In contrast, as network science has revealed, real-world and biological systems -- from the Internet to the brain -- derive their function from broad, multiscale variability in connectivity and link length. Here, we introduce Scale-Rich (SR) metamaterials, a design framework that embeds network heterogeneity into mechanical metamaterials, achieving order-of-magnitude heterogeneity in ligament lengths, thicknesses, and connectivity. Governed by only two parameters, SR networks span orders of magnitude in structural features, overcoming prior constraints in metamaterial design. Translating these network models into physically realizable materials, we use simulations and experiments to show that SR metamaterials exhibit properties inaccessible to traditional single-scale systems, including highly tunable elastic anisotropy, delocalized nonlinear deformation with high energy absorption, and programmable acoustic wave control. This network-science-based paradigm establishes a minimal yet universal framework for engineering multifunctional materials whose mechanical and acoustic behavior emerge directly from scale diversity itself.

cond-mat.soft↗

arXiv2025

Entanglement-driven responses through multiscale 3D-printed knits

For their resilience and toughness, filamentous entanglements are ubiquitous in both natural and engineered systems across length scales, from polymer-chain- to collagen-networks and from cable-net structures to forest canopies. Textiles are an everyday manifestation of filamentous entanglement: the remarkable resilience and toughness in knitted fabrics arise predominately from the topology of interlooped yarns. Yet most architected materials do not exploit entanglement as a design primitive, and industrial knitting fixes a narrow set of patterns for manufacturability. Additive manufacturing has recently enabled interlocking structures such as chainmail, knot and woven assemblies, hinting at broader possibilities for entangled architectures. The general challenge is to treat knitting itself as a three-dimensional architected material with predictable and tunable mechanics across scales. Here, we show that knitted architectures fabricated additively can be recast as periodic entangled solids whose responses are both fabric-like and programmable. We reproduce the characteristic behavior of conventional planar knits and extend knitting into the third dimension by interlooping along three orthogonal directions, yielding volumetric knits whose stiffness and dissipation are tuned by prescribed pre-strain. We propose a simple scaling that unifies the responses across stitch geometries and constituent materials. Further, we realize the same topology from centimeter to micrometer scales, culminating in the fabrication of what is, to our knowledge, the smallest knitted structure ever made. By demonstrating 3D-printed knits can be interpreted both as a traditional fabric, as well as a novel architected material with defined periodicity, this work establishes the dual nature of entangled filaments and paves the way towards a new form of material architectures with high degrees of entanglement.

physics.app-ph↗

arXiv2025

Design framework for programmable three-dimensional woven metamaterials

Mechanical metamaterials have continued to offer unprecedented tunability in mechanical properties, but most designs to date have prioritized attaining high stiffness and strength while sacrificing deformability. The emergence of woven lattices-three-dimensional networks of entangled fibers-has introduced a pathway to the largely overlooked compliant and stretchable regime of metamaterials. However, the design and implementation of these complex architectures has remained a primarily manual process, restricting identification and validation of their full achievable design and property space. Here, we present a geometric design framework that encodes woven topology using a graph structure, enabling the creation of woven lattices with tunable architectures, functional gradients, and arbitrary heterogeneity. Through use of microscale in situ tension experiments and computational mechanics models, we reveal highly tunable anisotropic stiffness (varying by over an order of magnitude) and extreme stretchability (up to a stretch of four) within the design space produced by the framework. Moreover, we demonstrate the ability of woven metamaterials to exhibit programmable failure patterns by leveraging tunability in the design process. This framework provides a design and modeling toolbox to access this previously unattainable high-compliance regime of mechanical metamaterials, enabling programmable large-deformation, nonlinear responses.

cond-mat.soft↗

arXiv2025

Mechanics of three-dimensional micro-architected interpenetrating phase composites

Composite materials are used across engineering applications for their superior mechanical performance, a result of efficient load transfer between the structure and matrix phases. However, the inherently two-dimensional structure of laminated composites reduces their robustness to shear and out-of-plane loads, while unpredictable interlaminar failure and fiber pull-out can cause a catastrophic loss of load capacity. Meanwhile, advances toward uncovering structure-property relations in architected materials have led to highly tunable mechanical properties, deformation, and even failure. Some of these architected materials have reached near-theoretical limits; however, the majority of current work focuses on describing the response of a single-material network in air, and the effect of adding a load-bearing second phase to a three-dimensional architecture is not well understood. Here, we develop facile fabrication methods for realizing centimeter-scale polymer- and carbon-based architected interpenetrating phase composite (IPC) materials, i.e., two-phase materials consisting of a continuous 3D architecture surrounded by a load-bearing matrix across length scales, and determine the effect of geometry and constituent material properties on the mechanics of these architected IPCs. Using these experiments together with computational models, we show that the matrix phase distributes stress effectively, resulting in a high-strength, stable response. Notably, failure delocalization enhances energy dissipation of the composite, achieving specific energy absorption (SEA) values comparable to those of wound fiber tubes. Finally, we demonstrate that the stress state in an IPC can be tuned using geometric design and introduce an example in an architected composite. Altogether, this work bridges the gap between mechanically efficient composites and tunable architected materials.

cond-mat.soft↗

arXiv2025

Curvature-Guided Mechanics and Design of Spinodal and Shell-Based Architected Materials

Additively manufactured (AM) architected materials have enabled unprecedented control over mechanical properties of engineered materials. While lattice architectures have played a key role in these advances, they suffer from stress concentrations at sharp joints and bending-dominated behavior at high relative densities, limiting their mechanical efficiency. Additionally, high-resolution AM techniques often result in low-throughput or costly fabrication, restricting manufacturing scalability of these materials. Aperiodic spinodal architected materials offer a promising alternative by leveraging low-curvature architectures that can be fabricated through techniques beyond AM. Enabled by phase separation processes, these architectures exhibit tunable mechanical properties and enhanced defect tolerance by tailoring their curvature distributions. However, the relation between curvature and their anisotropic mechanical behavior remains poorly understood. In this work, we develop a theoretical framework to quantify the role of curvature in governing the anisotropic stiffness and strength of shell-based spinodal architected materials. We introduce geometric metrics that predict the distribution of stretching and bending energies under different loading conditions, bridging the gap between curvature in doubly curved shell-based morphologies and their mechanical anisotropy. We validate our framework through finite element simulations and microscale experiments, demonstrating its utility in designing mechanically robust spinodal architectures. This study provides fundamental insights into curvature-driven mechanics, guiding the optimization of next-generation architected materials for engineering applications.

cond-mat.mtrl-sci↗

arXiv2025

Experiment-informed finite-strain inverse design of spinodal metamaterials

Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when designing mechanical metamaterials with extreme performance. While their elastic mechanical properties have been systematically determined, their large-deformation, nonlinear responses have been challenging to predict and design, in part due to limited data sets and the need for complex nonlinear simulations. This work presents a novel physics-enhanced machine learning (ML) and optimization framework tailored to address the challenges of designing intricate spinodal metamaterials with customized mechanical properties in large-deformation scenarios where computational modeling is restrictive and experimental data is sparse. By utilizing large-deformation experimental data directly, this approach facilitates the inverse design of spinodal structures with precise finite-strain mechanical responses. The framework sheds light on instability-induced pattern formation in spinodal metamaterials -- observed experimentally and in selected nonlinear simulations -- leveraging physics-based inductive biases in the form of nonconvex energetic potentials. Altogether, this combined ML, experimental, and computational effort provides a route for efficient and accurate design of complex spinodal metamaterials for large-deformation scenarios where energy absorption and prediction of nonlinear failure mechanisms is essential.

cs.CE↗

arXiv2024

Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

Metamaterials with functional responses can exhibit varying properties under different conditions (e.g., wave-based responses or deformation-induced property variation). This work addresses the rapid inverse design of such metamaterials to meet target qualitative functional behaviors, a challenge due to its intractability and non-unique solutions. Unlike data-intensive and non-interpretable deep-learning-based methods, we propose the Random-forest-based Interpretable Generative Inverse Design (RIGID), a single-shot inverse design method for fast generation of metamaterial designs with on-demand functional behaviors. RIGID leverages the interpretability of a random forest-based "design$\rightarrow$response" forward model, eliminating the need for a more complex "response$\rightarrow$design" inverse model. Based on the likelihood of target satisfaction derived from the trained random forest, one can sample a desired number of design solutions using Markov chain Monte Carlo methods. We validate RIGID on acoustic and optical metamaterial design problems, each with fewer than 250 training samples. Compared to the genetic algorithm-based design generation approach, RIGID generates satisfactory solutions that cover a broader range of the design space, allowing for better consideration of additional figures of merit beyond target satisfaction. This work offers a new perspective on solving on-demand inverse design problems, showcasing the potential for incorporating interpretable machine learning into generative design under small data constraints.

physics.optics↗

arXiv2024

Double-network-inspired mechanical metamaterials

Mechanical metamaterials are renowned for their ability to achieve high stiffness and strength at low densities, often at the expense of low ductility and stretchability-a persistent trade-off in materials. In contrast, materials such as double-network hydrogels feature interpenetrating compliant and stiff polymer networks, and exhibit unprecedented combinations of high stiffness and stretchability, resulting in exceptional toughness. Here, we present double-network-inspired (DNI) metamaterials by integrating monolithic truss (stiff) and woven (compliant) components into a metamaterial architecture, which achieve a tenfold increase in stiffness and stretchability compared to their pure woven and truss counterparts, respectively. Nonlinear computational mechanics models elucidate that enhanced energy dissipation in these DNI metamaterials stems from increased frictional dissipation due to entanglements between the two networks. Through introduction of internal defects, which typically degrade mechanical properties, we demonstrate an opposite effect of a threefold increase in energy dissipation for these metamaterials via failure delocalization. This work opens avenues for developing new classes of metamaterials in a high-compliance regime inspired by polymer network topologies.

cond-mat.soft↗