This paper presents a novel over-the-air (OTA) gesture recognition system using a single antenna, 3D-printed using liquid metals infills, and utilizing magnitude-only RF transmission data, unlike previous approaches that rely on phase or complex-valued data. The antenna is fabricated via stereolitho-graphic additive manufacturing with liquid metal microchannels, enabling a wearable and deformable sensing platform. Gesture-induced variations in antenna gain are detected in the far-field through received power measurements, eliminating the need for multiple antennas or phase calibration. The antenna's |S-21| response was measured at -45 dB at 3.64 m and a frequency bandwidth in the MHz range, with gesture capture performed using a vector network analyzer (VNA) over a bandwidth of 150-300 MHz. A dataset of 10 British Sign Language gestures was collected at a distance exceeding 3.5 m and classified using machine learning (ML) techniques. The results show a high accuracy of 92%, validating magnitude-based OTA sensing relying on uncalibrated S-parameter magnitudes.

Electrical impedance tomography (EIT) enables non-invasive, spatially continuous reconstruction of internal conductivity distributions, providing full field sensing beyond conventional point measurements. Here, we report the first in situ implementation of EIT within a tunable architected lattice materials framework, enabling systematic exploration across a broad lattice design space while achieving real time monitoring of damage evolution, including early stage, prefracture events, in 3D printed multifunctional lattice composites. Lattices are designed via Voronoi based branch trunk branch motifs inspired by 2D wallpaper symmetries and fabricated using CNT infused photocurable resins, with nanoscale filler dispersion confirmed by field emission scanning electron microscopy. Sixteen electrodes distributed along the lattice periphery enable EIT measurements during quasi static tensile loading. Conductivity maps reconstructed using adjacent and across current injection schemes resolve sequential ligament fracture with high temporal resolution, with localised conductivity loss quantitatively coinciding with fracture sites, including regions remote from electrodes. Architectural tunability allows systematic control of EIT imaging sensitivity to early stage damage, while pronounced resistance discontinuities at failure further corroborate spatial localisation; global end to end resistance measurements complement macroscopic stress strain responses. Collectively, these results establish in situ EIT as a scalable, full field sensing modality for architected multifunctional materials, providing an experimentally validated pathway toward autonomous, intelligent materials and data rich material states that can inform digital twin frameworks for structural, biomedical, and energy related applications.

The axial crushing behaviour of fused deposition modelling (FDM) fabricated acrylonitrile butadiene styrene (ABS) tubes with triply periodic minimal surface (TPMS) diamond lattice architectures is investigated under quasi-static compression and drop-weight impact loading, as there is high need for lightweight, additively manufactured energy absorbers in automotive crashworthiness, aerospace impact protection, and personalised protective systems. Two configurations are examined which are monolithic pure lattice (PL) tubes and sandwich tubes (ST) comprising of 1 mm solid ABS face skins enclosing a TPMS diamond lattice core. Specimens of different lengths (40 mm and 55 mm) are fabricated at three relative densities (ρ*) of 25%, 30%, and 35%, with a fixed unit cell size of 5 mm. Drop-weight impact tests are conducted for heights of 0.5, 1.0, and 1.5 m; quasi-static compression at 2 mm/min provides reference for the same. Transient full-field eyy strain evolution is captured via high-speed digital image correlation (DIC) at 3,000 fps. ST configurations achieve 39% higher mean peak crushing force (PCF), 127% higher mean crushing force (MCF), and 38% higher crush force efficiency (CFE) than PL for equivalent ρ*, as geometry selection is important for impact critical structural applications. A type-conditional ensemble combining a convolutional neural network (CNN) for ST and a gradient boosting machine (GBM) for PL, trained on per specimen DIC eyy strain-field images, predicts PCF with LOSO cross-validated R² of 0.752 and mean absolute error (MAE) of 2.40 kN, enabling instrumentation free PCF estimation applicable to field impact assessment and rapid prototype screening.

This study explores the impact of incorporating corrugated stiffeners into hexagonal (Hex) honeycombs on their quasi-static compressive behaviour and energy absorption. The effectiveness of this novel design is validated through a comprehensive set of experiments and finite element simulations performed under in-plane and out-of-plane compression. It is demonstrated that the architected hexagonal honeycombs with corrugated stiffeners (AHex) outperform traditional Hex honeycombs of equal weight when subject to in-plane loading along the stiffener direction, reporting maximum enhancements in the elastic modulus, collapse strength and energy absorption of 348 %, 187 % and 112 %. When loaded transverse to the stiffeners or along the out-of-plane direction, the AHex and Hex honeycombs show comparable compressive performance. A finite element-based parametric study further shows that the compressive performance of AHex honeycombs is maximized when the stiffeners are 1.2 times thicker than the hexagonal cell walls and form an internal angle of 155°. The results highlight the potential of corrugated networks in honeycombs to improve compressive performance in preferred directions, expanding their applicability.

This study investigates the quasi-static and dynamic compression performance of a newly designed stacked pyramidal lattice (SPL) structure composed of struts that resemble I-beams. These novel lattice structures are 3D-printed considering three different stacking sequences, and their stiffness, strength, and energy absorption properties are experimentally assessed through low-velocity impact (1.54 m/s) and quasi-static compression tests. Additionally, dynamic finite element (FE) simulations are carried out to delve deeper into the collapse mechanisms and failure processes. The findings indicate that the SPLs with I-beam struts outperform conventional SPLs with square struts of same mass showcasing superior rigidity, durability, and energy absorption. Specifically, we report enhancements in strength and energy absorption of 26% and 109% under quasi-static compression and 34% and 74% under low-velocity impact, respectively. The latter enhancements are attributed to the improved transverse bending stiffness of the I-shaped cross-section, resulting in lateral (sideward) buckling of the lattice struts. Both experimental and numerical findings demonstrate that altering the stacking sequence of the SPL can lead to significant improvements in the dynamic compression performance, with enhancements of up to 84% in collapse strength.

Next-generation protective systems require adaptive materials capable of reconfiguring their response to impact type and severity, thereby offering multiple force-displacement pathways. Here, the study introduces twisting metamaterials, a subclass of architected lattices whose mechanics are captured by micropolar elasticity. Derived from twisting operations on primitive lattices, these structures exhibit geometry-induced torsional actuation and nonlinear responses, enabling adaptive crashworthiness. A multiscale predictive framework-combining Cosserat continuum mechanics, finite element modeling, and experiments-demonstrates its viability. Twisting sheet-based gyroid structures (10% relative density) are additively manufactured in FE7131 steel and tested under quasi-static and dynamic compression with varied torsional constraints, revealing adaptive energy absorption. When rotation is constrained, the structures achieve high axial stiffness (4.8 GPa), collapse stress (21 MPa), and specific energy absorption (15.36 J g-1), while free-to-twist and over-rotation conditions reduce these values by up to 25%, 24%, and 33%, respectively. A macroscale model captures both axial and torsional responses, while SEM and µCT analyses of process-induced defects inform a parametric finite element study extended to 5% and 15% relative densities. Mapping their performance onto an Ashby chart highlights twisting metamaterials as a promising class of mechanically adaptive, crashworthy materials for advanced protection systems in automotive, rail, aerospace, and defence applications.

This study investigates the tunable auxetic and crushing characteristics of a set of novel, multilayered tetra- chiral (TC) lattices through a combination of experimental testing and finite element (FE) modeling, aiming to uncover how the mechanical properties are affected by layer stacking. Utilizing Digital Light Processing (DLP) to fabricate multilayered lattices from PlasGray photoresin across two distinct length scales, we observe length scale-dependent material properties, which prompt amore in-depth examination of the deformation and failure characteristics of multilayered lattices. The experimental results form the basis for subsequent FE analysis, employing a Drucker-Prager material model combined with ductile damage criteria to investigate the effects of layering on stiffness, strength, and energy absorption characteristics. By tuning the architectural parameters within the FE simulations, we expand the design space, aiming to uncover configurations that exhibit superior mechanical performance. The results indicate that, fora constant mass, bi-layered and multi- layered TC structures exhibit enhanced energy absorption, with increases of 114% and 149%, respectively. This study advances the understanding of layered tetra-chiral lattices and provides a foundation for future efforts to optimize the design and functionality of multilayered structures in various engineering applications.

This research paper investigates the shear response of pyramidal lattice (PL) sandwich cores, where square-shaped strut cross-sections are geometrically modified into I-beam-like configurations. These PL sandwich cores are 3D printed via Digital Light Processing (DLP), and their shear performance is experimentally and numerically evaluated for various I-beam-like strut cross-sections. The measurements reveal that PL structures with I-beam struts outperform conventional square-beam structures in terms of shear modulus (+ 13%), shear strength (+ 11%) and gravimetric energy (+ 24%). These improvements are attributed to the larger bending stiffness of the I-beam struts, enhancing their capacity to resist shear loads. A numerical parametric study further examines how various architectural parameters of the tailored PL structure affect the shear performance, showing significant enhancements in shear modulus (6-23%), shear strength (3-16%), and gravimetric energy (8-25%) compared to square-strut PL structures of equal weight. Additionally, a simple analytical model is developed to estimate the strength enhancements, demonstrating a reasonable agreement with the numerical predictions and measurements. Notably, a reduction of the internal strut angle to 30° is found to enhance the shear strength of the PL structure in both shearing directions, making this arrangement an excellent choice for sandwich designs where core shear failure is a limiting factor.

This study demonstrates the multifunctional performance of innovative 2D auxetic lattices through a combination of multiscale experiments, finite element modeling and data-driven prediction. A geometric modeling approach utilizing Voronoi partitioning and a unique branch-stem-branch (BSB) structure, patterned according to 2D wallpaper symmetries, enables precise concurrent geometric and topological tuning of lattices across a continuous parameter space. Selected architectures are physically realized via material extrusion of polylactic acid (PLA) infused with carbon black (CB). Experimental characterizations, supported by Finite Element modeling, reveal the significant influence of BSB structure's design parameters on mechanical and piezoresistive performance under tensile loading, with a remarkable Poisson’s ratio of -0.74, accompanied by a 15-fold increase in elastic stiffness and a 34-fold increase in strain sensitivity. Additionally, architecturally, and topologically tailored lattice structures exhibit tunable damage sensitivity, reflecting the rate of conductive network destruction within the lattice. This offers insights into the rapidity of cell wall failure, with a steeper slope of the piezoresistance curve in the inelastic regime indicating a faster breakdown and quicker onset of mechanical failure. Integration of Gaussian Process Regression enables accurate exploration of the design space beyond realized structures, highlighting the potential of these intelligent lattice structures for applications such as sensors and in situ health monitoring, marking a significant advancement in multifunctional materials.

Temperature sensors are one of the most fundamental sensors and are found in industrial, environmental, and biomedical applications. The traditional approach of reading the resistive response of Positive Temperature Coefficient thermistors at DC hindered their adoption as wide-range temperature sensors. Here, we present a large-area thermistor, based on a flexible and stretchable short carbon fibre incorporated Polydimethylsiloxane composite, enabled by a radio frequency sensing interface. The radio frequency readout overcomes the decades-old sensing range limit of thermistors. The composite exhibits a resistance sensitivity over 1000 °C −1 , while maintaining stability against bending (20,000 cycles) and stretching (1000 cycles). Leveraging its large-area processing, the anisotropic composite is used as a substrate for sub-6 GHz radio frequency components, where the thermistor-based microwave resonators achieve a wide temperature sensing range (30 to 205 °C) compared to reported flexible temperature sensors, and high sensitivity (3.2 MHz/°C) compared to radio frequency temperature sensors. Wireless sensing is demonstrated using a microstrip patch antenna based on a thermistor substrate, and a battery-less radio frequency identification tag. This radio frequency-based sensor readout technique could enable functional materials to be directly integrated in wireless sensing applications.

In this study, we describe the development of composites comprising ultra-high molecular weight polyethylene (UHMWPE) reinforced with graphene nanoplatelets (GNP), specifically designed for additive manufacturing (AM) of self-sensing structures through selective laser sintering (SLS). We employed ball-milled GNP/UHMWPE powder feedstocks to fabricate standard test specimens and 2D cellular structures with varying GNP content. A comprehensive assessment of their mechanical and piezoresistive properties was carried out under uniaxial tensile loading. The incorporation of 1.5 wt% GNPs into UHMWPE demonstrated a notable increase in crystallinity by ∼28 % and a significant reduction in porosity by about 98 %. These enhancements contributed to a substantial improvement in both strength (∼21 %) and elastic modulus (∼40 %). Moreover, the introduction of 1.5 wt% GNPs resulted in the formation of electrically percolated composites characterized by prominent piezoresistive behavior. These composites exhibited gauge factors ranging from 9.6 to 18 under uniaxial tensile loading. During cyclic tensile loading, the GNP/UHMWPE composite displayed hysteresis in its piezoresistive response due to viscoelasticity, impeding an immediate return to its original state. Additionally, the gauge factors of the 2D cellular structures generally demonstrated lower values compared to those of the parent composite, scaling proportionally with the effective elastic modulus.

Integrating autonomous sensing materials into future applications necessitates developing advanced multiscale multiphysics predictive models. This study introduces an experimentally informed predictive framework for autonomous sensing architected materials, combining theoretical and computational methodologies. By incorporating stress-dependent electrical resistivity through anisotropic piezoresistive constitutive effects, alongside considering material, geometric, and contact nonlinearities, the proposed multiscale model captures the architecture-dependent piezoresistive responses of lattice composites produced via additive manufacturing of polyetherimide (PEI)/carbon nanotube (CNT) nanoengineered feedstock. The PEI/CNT composite exhibits exceptional strength (105 MPa), stiffness (3368 MPa), and strain sensitivity (gauge factor approximate to 13), translating into remarkable piezoresistive characteristics for the PEI/CNT lattice composites, surpassing existing works (gauge factor approximate to 3 to 11). This multiscale finite element model accurately predicts both macroscopic piezoresistive responses and the influence of architectural and topological variations on electric current paths, validated via infrared thermography analysis. Additionally, an Ashby chart for the gauge factor of PEI/CNT lattice composites suggests their prediction through a scaling law similar to mechanical properties, underscoring the tunable strain and damage sensitivity of these materials. The combined experimental, theoretical, and numerical findings offer critical insights into optimizing piezoresistive composites through architected design, with profound implications for smart orthopedics, structural health monitoring, sensors, batteries, and other multifunctional applications. This study introduces an experimentally informed predictive framework for autonomous sensing architected materials enabled by additive manufacturing, integrating theoretical and computational methods. By incorporating stress-dependent electrical resistivity and accounting for material, geometric, and contact nonlinearities, the framework accurately captures architecture-dependent responses and electric current paths, validated through thermography. This integration advances the development of novel multifunctional materials across diverse applications. image

This study investigates a novel self-sensing honeycomb composite structure composed of two distinct cellular layers with differing unit cell architectures, specifically hexagonal and re-entrant designs. Short carbon fiber (CF)/polyamide 12 (PA12) composite filaments with 0, 5 or 15 wt.% CF content were utilized to additively manufacture the honeycomb structures via Fused Filament Fabrication (FFF), and their mechanical and piezoresistive self-sensing characteristics were experimentally investigated under quasi-static in-plane and out-ofplane compression at both room temperature and elevated temperatures. The results reveal that the hybrid hexagonal/re-entrant (HR) honeycombs mechanically outperform their non-hybrid double-layer and single-layer counterparts under in-plane loading, reporting an increase in collapse strength and energy absorption by factors of 1.64 and 2.25, respectively. These improvements are attributed to the mechanical interactions occurring at the interface between the auxetic and non-auxetic layers within the hybrid structure, effectively enhancing its structural attributes. Furthermore, the double-layer honeycombs display excellent strain-sensing capabilities within the elastic regime, with gauge factors reaching values as high as 146. Mechanical tests conducted at elevated temperatures reveal that the CF/PA12 honeycombs retain a significant portion of their elastic modulus, strength and energy absorption even at 125 degrees C, while maintaining high gauge factors of up to 72.4. These honeycombs also exhibit pronounced thermoresistive behavior, evidenced by a decrease in electrical resistance of up to 41.3 % with increasing temperatures from 25 to 125 degrees C. Considering their exceptional combination of thermo-mechanical, thermoresistive and piezoresistive characteristics, these hybrid double-layered CF/PA12 honeycombs hold promise for potential applications in multifunctional lightweight structures, offering integrated temperature and strain-sensing capabilities.

This study demonstrates the mechanical, self‐sensing, and biological characteristics of carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs)‐engineered 3D‐printed polyetheretherketone (PEEK) composite scaffolds, utilizing custom‐made feedstocks. Microstructural analysis and macroscale testing reveal that the PEEK/CNT scaffolds with 6 wt% CNT content and 46% relative density achieve a gauge factor of up to 75, a modulus of 0.64 GPa, and a compressive strength of 64 MPa. The PEEK/CNT2.5/GNP2.5 scaffolds evince still better performance, at a relative density of 73%, reporting a modulus of up to 1.1 GPa and a compressive strength of 122 MPa. Importantly, stability in mechanical and piezoresistive performance up to 500 cycles is noted, indicating a durable and reliable performance under cyclic loading. Murine preosteoblast cells (MC3T3‐E1) are used to biologically characterize sulfonated scaffolds over 14 days. Cytotoxicity, DNA, and alkaline phosphatase (ALP) levels are quantified through in vitro assays, evaluating cell viability, proliferation, and osteogenic properties. Notably, PEEK/CNT 6 wt% scaffolds exhibit nearly 80% cytocompatibility, while PEEK/CNT2.5/GNP2.5 scaffolds reach nearly 100%. Both types of scaffolds support cell differentiation, as evidenced by elevated ALP levels. These findings carry significant promise in bone tissue engineering, paving the way for the development of adaptive, intelligent structural implants boasting enhanced biocompatibility and self‐sensing capabilities.

Pyramidal lattice structures have frequently been employed as the core material in the design of sandwich panels due to their impressive weight-specific strength. However, the struts in pyramidal lattice structures bend when subjected to axial, shear, or bending loads, leading to non-uniform stress distributions, especially at low relative densities. The current work introduces a geometrical tailoring scheme that provides the designer with additional parameters that can be adjusted to tune the cross-sectional properties of the lattice struts with the goal of obtaining more uniform stress distributions across their thickness. Specifically, the conventional square and circular pyramidal lattice struts are reshaped into I-beam-like cross-sections, forming a tailored pyramidal lattice. These geometrically tailored pyramidal lattices are 3D printed via the Digital Light Processing (DLP) technique. The quasi-static compressive responses of the lattices are experimentally evaluated in terms of elastic modulus, collapse strength, and energy absorption capacity. Additionally, the collapse mechanisms of the geometrically tailored structures were assessed via a non-linear finite element analysis which was validated against the experimental evidence. The results substantiate the validity of the geometrical tailoring strategy as the reported energy absorption capacity of the tailored pyramidal lattice structure exhibits a significant enhancement up to 64% and 15% respectively. The latter enhancements were attributed to the lateral buckling of struts, prompting the tailored struts to bend sideways during the collapse phase.

Recent decades have witnessed significant advancements in additive manufacturing (AM) of polymer composites, leading to the development of material systems with intricate architecture and composition. Selective laser sintering (SLS), among various AM methods, offers numerous advantages such as high mechanical strength in printed parts, recyclability of unused powders, and the ability to print large batches without support structures. Moreover, SLS has remarkably succeeded in fabricating electrically conductive polymer composites (ECPCs) with exceptional functional performance which is attributed primarily to the formation of a segregated filler network along the powder particle boundaries. This review aims to delve into SLS for processing polymer-based materials, examining consolidation mechanisms and process parameters, specifically highlighting advancements in electrically conductive polymer composites with a focus on piezoresistive strain-sensing materials and self-sensing structures. Furthermore, the review seeks to elucidate the complex process-structure-property relationships in SLS 3D printed polymer composites, providing an exhaustive overview of the current state-of-the-art in piezoresistive polymer composites.