• Extends Eshelby-based micromechanics to non-ellipsoidal inclusion shapes. • Integrates into multiscale continuum micromechanics homogenization. • Retains its efficiency while improving geometric fidelity. • Demonstrated for potentially misaligned orthotropic superspherical inclusions. Continuum micromechanics homogenization provides an efficient framework to relate the microstructural features of heterogeneous materials to their macroscopic mechanical response. The microstructure is idealized as an assembly of interacting matrix–inclusion problems, each governed by Eshelby’s analytical solution for ellipsoidal inclusion shapes. This assumption severely simplifies the often complex morphology of real materials—and, owing to the uniformity of strains inside Eshelby’s inclusion, provides access to average strains rather than the underlying field fluctuations in the heterogeneities of the material. To address these limitations, we propose the Deep Eshelby Network (DEshN), a machine-learning framework that generalizes the Eshelby problem to non-ellipsoidal inclusion geometries. The network consists of a Deep Material Network (DMN) that incorporates physical constraints through laminate building blocks into a tree-like architecture and a single linear layer that modulates the weights and orientations of the DMN. Trained on finite element solutions of inclusion problems with diverse shapes and stiffness ratios, the DEshN provides rapid and accurate predictions that can be seamlessly integrated into classical homogenization schemes. In this way, DEshN-based homogenization retains the efficiency of continuum micromechanical approaches while extending their applicability to materials with heterogeneities of arbitrary shape, volume fraction, orientation distribution, and even hierarchical multiscale organization. To unveil its potential, a DEshN is trained on superspherical inclusions to predict the homogenized stiffness for orthotropic matrix-inclusion-type materials and polycrystalline materials with aligned or randomly oriented superspheres, as a function of the supersphere shape parameter. This task could not have been solved with the approaches developed so far.

Redirected Walking (RDW) enables navigation in large virtual worlds by manipulating a user's viewpoint, yet the perceptual thresholds governing these manipulations are often treated as fixed sensory limits. In this work, we propose the concept of Semantic Anchors: objects that stabilise self-motion perception through high-level cognitive priors. In a psychophysical experiment $(N=22)$, participants judged virtual rotations while viewing familiar or arbitrary objects of varying sizes and rotational reference frames. Results reveal that scene semantics do not merely modulate thresholds but fundamentally alter the integration of spatial cues. When a familiar object's canonical size was violated to create a specific size-congruency conflict, the window for imperceptible redirection expanded significantly, allowing rotation gains of up to 30% to remain imperceptible. Crucially, familiarity inverted the geometric dependence observed with arbitrary shapes: while geometric frames dictated sensitivity for abstract objects, semantic stability dominated for familiar ones. These findings demonstrate that RDW thresholds are dynamically co-constructed by sensory input and scene plausibility, offering a new principle for designing semantically informed locomotion systems that leverage cognitive expectations to expand explorable space.

Currently, 3D interfacial evaporators have attracted significant attention due to their superior evaporation performance. However, the shape of carbon-based 3D evaporators is often constrained by the original form of biomass materials, which limits their practical applications. Herein, we report a novel strategy for fabricating 3D solar-driven interfacial evaporators with arbitrary shapes (hemisphere, cone, flake, and Z-type) by integrating carbon powder derived from corn shuck (CS) with binders. The silver-doped corn-based carbon (Ag-CCS) material exhibits exceptional photothermal conversion efficiency, achieving surface temperatures of 153.3°C (dry) and 95.7°C (wet) under 1 sun illumination. Among the 3D evaporators, the Z-type design demonstrates the highest evaporation rate of 4.42 kg·m-2 h-1, attributed to its porous structure, hydrophilicity, low evaporation enthalpy of adsorbed water (1286.13 J g-1), and efficient ambient energy absorption and thermal management. Outdoor experiments further validate the Z-type evaporator's superior performance, with a maximum daily water production of 25.1kg m-2 and automatic salt-cleaning capability over 20 days. This work paves the way for the scalable fabrication of 3D carbon-based evaporators, offering a viable solution for seawater desalination.

VLM-integrated 3D perception model for robust robotic grasping adapted to deformable sacks with arbitrary shapes

This study presents a data-informed convolutional neural network (CNN) for identifying fluid-filled (e.g., water) inclusions in two-dimensional (2D) solids. To generate training data, we simulate the propagation of elastic waves in structures containing fluid inclusions using Ansys Mechanical. To provide structural details, we introduce a gridwise classification method that reconstructs the targeted domain containing arbitrary fluid-filled inclusions by creating a 2D grid-structured image. This work produces training data comprising input-layer features (i.e., measured wave responses) and output-layer features (i.e., gridwise maps labeling each grid element as solid or fluid). The CNN is trained on these simulated data sets to classify grid elements based on wave responses recorded at surface sensors, and clusters of predicted fluid grid elements are used to reconstruct fluid-filled inclusions. Numerical results show that the proposed CNN can effectively detect fluid inclusions of various locations, sizes, and shapes. We also present parametric studies of factors such as the number of sensors, the size of the training data sets, and excitation frequency, providing a comprehensive analysis of their effect on detection performance.

Motivated by additive manufacturing (AM), we study surrogate modeling of transient heat conduction on complex geometries with a moving spot heat source. Existing surrogates are often limited to rectangular domains, require retraining for each new geometry, or rely on graph representations that add significant overhead and hinder scalability. To address these gaps, we introduce the Harmonic-Mapping Operator (HMO), a scalable, graph-free, and geometry-agnostic surrogate that learns thermo-physical responses without retraining for new part geometries. HMO maps arbitrary shapes onto a canonical square domain via harmonic maps, solves the transient heat equation with a single neural operator trained once, and projects results back through the inverse map. Geometry influences the model only through a metric tensor, enabling consistent generalization to unseen shapes. To scale to industrial components, a domain-decomposition inference (DDI) scheme applies the operator in parallel across subdomains while maintaining flux continuity. Compared with GeoFNO and MeshGraphNet baselines, HMO achieves lower errors, superior long-horizon stability, and faster inference, providing a reusable and real-time surrogate.

A navigation-guided 3D breast ultrasound scanning and reconstruction system for automated multi-lesion spatial localization and diagnosis.

Natural filaments, such as proteins, plant tendrils, octopus tentacles, and elephant trunks, can transform into arbitrary three-dimensional shapes that carry out vital functions. Their shape-morphing behavior arises from intricate patterning of active and passive regions, which are difficult to replicate in synthetic matter. Here, we introduce a filament-centric strategy for programmable shape morphing in which intrinsic curvature and twist are directly encoded within multimaterial elastomeric filaments during fabrication. By harnessing rotational multimaterial 3D printing, we directly prescribe the filament's natural curvature-twist field κ(s) through controlled material distribution and helical liquid crystal mesogen alignment. When heated above their nematic-to-isotropic transition temperature (TNI), the helically aligned liquid crystal elastomer regions contract along their local director field, while passive regions remain essentially unchanged. This approach enables independent control of bending and torsion at every cross-section along the filament centerline: the principal natural curvatures of the filament along two orthogonal axes as well as the local twist. Next, we printed architected lattices composed of unit cells formed by sinusoidal filaments that either reversibly contract, expand, or exhibit out-of-plane deformations. Discrete elastic rod simulations of Janus filaments with different natural curvatures and twist, which are interconnected within the printed lattices, allow accurate prediction of their observed shape-morphing behavior. By integrating active-passive elastomers, additive manufacturing, and computational modeling, we have created shape-morphing matter with complex programmable responses for applications that rely on adaptive, robotic, or deployable architectures.

The spatiotemporal dynamics and density of actin networks are key determinants of actin cytoskeleton-mediated cellular functions. In vitro reconstitution systems have been widely used to study actin cytoskeletal dynamics; however, many existing approaches offer limited flexibility in controlling the geometry, thickness, and density of the assembled actin networks. Here, we present an in vitro optogenetic protocol that enables precise control of actin network assembly on supported lipid bilayers using an improved light-induced dimer (iLID)-SspB-based light-inducible dimerization system. In this system, His-mEGFP-iLID is anchored to a Ni-NTA-containing lipid bilayer, while SspB-mScarlet-I-VCA, a nucleation-promoting factor fused with SspB, together with other actin cytoskeletal proteins, is supplied in bulk solution. Upon blue light illumination, SspB-mScarlet-I-VCA is recruited to the membrane in a spatially and temporally defined manner, inducing localized actin polymerization. By tuning illumination patterns and duration, actin networks with defined density, thickness, and geometry can be generated, and polymerization can be rapidly halted by stopping illumination. This protocol provides a versatile platform for reconstructing actin networks with controlled spatial organization and density, enabling quantitative analysis of density-dependent interactions between actin networks and actin-binding proteins. Key features • Actin networks with varying densities and arbitrary shapes can be formed on the same supported lipid bilayer by controlling blue light illumination through the objective lens. • Actin polymerization can be stopped simply by turning off blue light illumination, enabling the formation of actin networks with defined thicknesses. • This protocol requires purified actin and actin-binding proteins.

The drag force on planar structures of arbitrary shape is derived in free molecular flow using gas kinetic theory. The theory is formulated by considering the anisotropic intermolecular potential between the particle and gas molecules, in the limits of specular and diffuse scatterings. The drag force theory formulated by Dahneke [J. Aerosol Sci. 4, 147 (1973)10.1016/0021-8502(73)90066-9] for disklike bodies in free-molecular flow is shown to be a special case of the theory derived herein. The Einstein-Smoluchowski relationship is generalized with anisotropic drag to structures of arbitrary shape. Binary diffusion coefficients of planar molecules (benzene, naphthalene, phenanthrene, pyrene, coronene, and ovalene) in nitrogen are calculated using gas kinetic theory. The diffusion coefficients are in close agreement with available experimental data and with molecular dynamics simulations.

ABSTRACT In this proceeding, a phase‐field model to describe the evolution of size, shape, and composition of volcanic crystals is introduced. It is built on top of an existing model for idealized spherical crystals but uses the phase‐field approach to investigate arbitrary initial shapes and considers the diffusion of diffusion elements as well as heat. Furthermore, it also takes the development of the dislocation density into account, which makes it possible to make predictions about the stability of the crystals. In this proceeding, the effects of different initial configurations and the behavior of the model under instability are investigated.

This work establishes a generalized framework for predicting effectiveness factors (E) of catalysts with any types of kinetics using a modified Thiele modulus λp for catalysts of arbitrary geometries and assemblages with mixed properties. It is verified that the most appropriate definition of the Thiele modulus is given by λ p = V p A p R ( C AS ) 2 D e ∫ C Ae C AS R ( C A ) d C A which unifies diffusion-reaction phenomena for catalyst pellets of arbitrary shapes and property distributions. Numerical validations for various catalyst geometries (spheres, finite cylinder, hollow cylinders, cones, parallelepipeds) confirmed that E = 1 1 + λ p 2 closely predicts the effectiveness factor for different values of λp . The model resolves the limitations of classical approaches that are difficult and complex for pellets of non-basic shapes. For catalyst pellet assemblages of mixed properties, the overall modulus λb defined as follows was proved to be appropriate for the above equation for E: λ b = ( ∑ i = 1 N c v c i 1 + λ p c i 2 ) − 2 − 1 Validated for catalyst assemblages and fixed-bed reactor descriptions, this general definition of the Thiele modulus for any kinetics expression, together with the simple relation E = 1 1 + λ p 2 , enables optimization of industrial catalysts with complex geometries or property distributions. It also provides unified criteria for the asymptotic regimes of effectiveness factor; one for the regime in which pore diffusion does not affect the overall rate (λp < 0.1 within ∼ 1% error and λp < 0.3 within ∼ 10% error) so that E ≈ 1 and the other in which pore diffusion is strong enough to make the reaction occur mainly near the external surface (λp > 10 within ∼ 1% error and λp > 3 within ∼ 10% error) so that E ≈ 1 / λ p .

ABSTRACT Liquid crystal elastomers (LCEs) with integrated functionalities are critical for developing advanced soft actuators. However, challenges remain in simultaneously achieving reprogrammability, chemical recyclability, and intrinsic luminescence within existing exchangeable LCE systems. Herein, we develop a new class of LCE based on dynamic vinylogous urethane (VU) bonds to address these challenges, where VU moieties serve as both crosslinking points and luminescent clusters. The optimal polydomain vinylogous urethane‐based LCE (VULCE) material exhibits excellent mechanical properties, with a Young's modulus of 1.45 ± 0.08 MPa and a toughness of 148.26 ± 0.11 KJ m −3 . Thanks to the catalyst‐free transamination of VU bonds, the VULCE enables reprogramming into diverse three dimensional (3D) actuators with reversible actuation at 110°C for 10 min. Furthermore, the VULCE network can be depolymerized into LC oligomers and small molecules in an organic solvent under excess bifunctional amines at 80°C for 6 h. The recycled solution can be repolymerized into new VULCE with arbitrary shapes by reintroducing chain extenders, mesogenic monomers, and crosslinkers, achieving polymer‐oligomer/small molecule‐polymer recycling. More importantly, the VULCE emits intrinsic blue fluorescence owing to the clustering‐triggered emission (CTE) effect. This work demonstrates unprecedented multifunctionality, providing new insights for the development of multifunctional integrated LCE materials.

Liquid crystal elastomers (LCEs) with integrated functionalities are critical for developing advanced soft actuators. However, challenges remain in simultaneously achieving reprogrammability, chemical recyclability, and intrinsic luminescence within existing exchangeable LCE systems. Herein, we develop a new class of LCE based on dynamic vinylogous urethane (VU) bonds to address these challenges, where VU moieties serve as both crosslinking points and luminescent clusters. The optimal polydomain vinylogous urethane-based LCE (VULCE) material exhibits excellent mechanical properties, with a Young's modulus of 1.45 ± 0.08MPa and a toughness of 148.26 ± 0.11 KJ m-3. Thanks to the catalyst-free transamination of VU bonds, the VULCE enables reprogramming into diverse three dimensional (3D) actuators with reversible actuation at 110°C for 10min. Furthermore, the VULCE network can be depolymerized into LC oligomers and small molecules in an organic solvent under excess bifunctional amines at 80°C for 6h. The recycled solution can be repolymerized into new VULCE with arbitrary shapes by reintroducing chain extenders, mesogenic monomers, and crosslinkers, achieving polymer-oligomer/small molecule-polymer recycling. More importantly, the VULCE emits intrinsic blue fluorescence owing to the clustering-triggered emission (CTE) effect. This work demonstrates unprecedented multifunctionality, providing new insights for the development of multifunctional integrated LCE materials.

The manipulation of mass diffusion is crucial for applications spanning biotechnology to chemical engineering. However, established methods for designing diffusion metadevices are often constrained by high background diffusivity, inherent anisotropy, or implementation complexity. Here we present a unified theoretical framework for isotropic mass diffusion control that bridges transformation optics and convective transport. By applying homogenization theory to established convection-enhanced diffusion mechanisms, we demonstrate that a rotating fluid core effectively emulates the behavior of a near-zero-index medium in optics and thermotics. By combining this effective description with pseudoconformal mapping, we design an isotropic concentration cloak that remains applicable even in high-diffusivity environments. Numerical simulations validate that the cloak maintains robust performance for arbitrary target shapes in high-diffusivity regimes. Notably, the cloaked region remains sensitive to the external environment, allowing the internal concentration to track background changes in real time. A feasible experimental suggestion using perforated structures is also proposed. This work provides a diffusion-based reinterpretation of near-zero-index-inspired concepts and a synthesis of fluid mechanics and pseudoconformal mapping, offering a unified and practically accessible framework for engineering diffusive fields.

Comparison of rate models for gradient elution chromatography and experimental evaluation for IEC, HIC, and RPC systems.

• A novel metal additive manufacturing method is presented in which molten metal droplets are ejected from an array of nozzles to form the 3D part geometry. • High material deposition rates are possible without any compromise in feature size due to the small droplet sizes. • Spacing between concurrently printed tracks of material can be varied by changing the printing skew angle. • Preliminary results with the proposed method are demonstrated with 2D and 3D shapes. Molten metal droplet jetting (MMJ) is an emerging metal additive manufacturing (AM) technology that can use low-cost wire, rod, or even ingot feedstock material. This paper describes the architecture and preliminary implementation of what is believed to be among the first demonstrations of MMJ with a multi-nozzle array that is akin to inkjet printing using molten metal as the ink. A multi-nozzle printhead with a communal reservoir and three piezoelectric actuator pistons is presented. The drive waveforms for each nozzle are independently addressable, thus enabling precise control over drop placement for raster printing of arbitrary layer shapes. A jetting strategy is described in which variable track spacing is achieved by altering the yaw angle of the printhead. This yaw angle method allows printed row pitches that are less than or equal to the nozzle pitch. The printhead and build strategy are applied to demonstrate feasibility of the method with single and multi-layer test sample geometries. The influence of these initial results on future multi-nozzle systems is discussed.

BullFish: Software for an automated stepwise analysis of positional and postural kinematics of zebrafish locomotion.

Level-Set Particle And Geometry GEnerator (LS-PAGGE): Accurate representations of arbitrary particle shapes

Generic polychromatic light scattering theories for particles of arbitrary shapes and morphologies illuminated by laser pulses