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At least 307 records · Page 17

Graph neural networks for efficient learning of mechanical properties of polycrystals

Herein we present graph neural networks (GNNs) as an efficient and accurate machine learning approach to predict mechanical properties of polycrystalline materials. Here, a GNN was developed based on graph representation of polycrystals incorporating only fundamental features of grains including their crystallographic orientation, size, and grain neighbor connectivity information. We tested our method on modeling stiffness and yield strength of α -Ti microstructures, varying in their crystallographic texture. We find the GNN predicts both properties with high accuracy with mean relative errors of ~1% for unseen microstructures from a given set of textures and < 2% for microstructures of unseen texture, even when presented with limited training data. This accuracy is comparable to methods that require high-resolution three-dimensional (3D) microstructure data, such as 3D convolutional neural networks (3D-CNNs) and models that depend on the computation of spatial statistics. The present results show that graph-based deep learning is a promising framework for property prediction, especially considering the high cost associated with obtaining high-resolution 3D microstructure data and the general scarcity of experimental materials datasets.

36 MATERIALS SCIENCE↗

Bio-treatment of poplar via amino acid for interface control in biocomposites

Advanced biocomposites reinforced by abundant biomass-derived fillers can add a revenue stream to enhance the economic viability of biofuel production chains and the energy efficiency of the composite industry. However, the low stiffness of biopolymers limits their use in structural applications. Poplar fibers (mesh size: <180 μm, Populus spp.), an abundant waste from the wood industry, were used as bio-filler to fabricate high-performance biocomposites based on polylactic acid (PLA), in which the poplar fibers were modified by an amino acid (l-lysine). As a benefit of the amino acid treatment, the tensile Young's moduli of the lysine/poplar/PLA composites increased by up to 68% with the addition of a small amount of lysine, compared with neat PLA. At the same time, the tensile strength, failure strain, and Young's modulus of the poplar/PLA composites all increased after adding only 0.1 wt % of lysine. It has been observed that the lysine content has a significant effect on the decomposition temperature, complex viscosity, storage modulus, crystallization temperature, and crystallinity of composites. The fracture surfaces of the composites with an optimum lysine content had fewer voids and were more compact compared with composites without any lysine. The pores on the surfaces of poplar fibers became more available for the penetration of PLA molecules as a result of lysine addition. Finally, this study presents a facile method for reinforcing biocomposites with extremely low-cost and environmentally friendly biofillers.

59 BASIC BIOLOGICAL SCIENCES↗

Effects of Normal and Lateral Electric Fields on Membrane Mechanical Properties

As a core component of biological and synthetic membranes, lipid bilayers are key to compartmentalizing chemical processes. Bilayer morphology and mechanical properties are heavily influenced by electric fields such as those caused by biological ion concentration gradients. We present atomistic simulations exploring the effects of electric fields applied normally and laterally to lipid bilayers. We find that normal fields decrease membrane tension, while lateral fields increase it. Free energy perturbation calculations indicate the importance of dipole-dipole interactions to these tension changes, especially for lateral fields. We additionally show that membrane area compressibilities can be related to their cohesive energies, allowing us to estimate changes in membrane bending rigidity under applied fields. We find that normal fields decrease bending rigidity, while lateral fields increase it. Furthermore, these results point to the use of directed electric fields to locally control membrane stiffness, thereby modulating associated cellular processes.

Chemistry↗

Synthesis of High-Performance Lignin-Based Inverse Thermoplastic Vulcanizates with Tailored Morphology and Properties

In this work, we report synthesis of a high-strength renewable phenolic composition with linear large deformation strain without a thermoplastic-like yielding while retaining thermal processability. Small molecule carboxylic acid derivatives with varying molecular architectures act as esterifying crosslinkers in an equal mass mixture of lignin and acrylonitrile–butadiene copolymers in a highly scalable, solvent-free process. These “inverse thermoplastic vulcanizates” (iTPVs)—unique in their approach of crosslinking the rigid lignin phase rather than the soft phase—exhibit ordered self-assembly, tunable nanoscale morphology, and processability. The first of its kind iTPV compositions exhibit engineering stress–strain curves with two- to sixfold linear extensibility, a twofold rise in strength, and an order of magnitude enhanced modulus compared to a simple lignin-rubber blend. Viscoelastic properties correlate well with crosslinker architecture and the resulting morphology, allowing competing properties of toughness and stiffness to be tuned. This research finds a path for identifying the potential of lignin as a sustainable feedstock.

36 MATERIALS SCIENCE↗

Influence of Rigidity–Hydration Coupling on Size-Dependent Diffusion in Hydrated Polymer Membranes

Selective ion transport in polymer membranes depends critically on how penetrant motion couples to polymer dynamics and hydration. Yet, the mechanistic interplay between polymer rigidity, water content, and penetrant size remains poorly understood, especially in the regime where the penetrant diameter, polymer Kuhn length, and correlation length are comparable. Here, we employ coarse-grained molecular dynamics simulations to systematically investigate penetrant diffusion in hydrated polymer networks across a broad range of water volume fractions, chain rigidities, and penetrant sizes. The results reveal a transition from a decoupled regime, where small penetrants diffuse nearly independently of polymer relaxation, to a coupled regime in which large penetrants require cooperative polymer motion for transport. Increasing polymer rigidity amplifies the sensitivity of diffusivity to hydration, particularly at low water content, leading to pronounced deviations from Stokes−Einstein scaling. Comparison with scaling theories and free-volume models shows that classical nanoparticle-based frameworks fail to capture this intermediate regime. To address this gap, we extend the Yasuda model to incorporate polymer rigidity through a single parameter that quantifies the dynamic contribution of chain stiffness to free-volume fluctuations. The resulting model collapses diffusivity data across all sizes, water contents, and rigidities, providing a unified description of penetrant transport in hydrated polymer matrices. Furthermore, these findings establish polymer rigidity as a key, tunable determinant of diffusion and offer a framework for interpreting size-dependent transport in ion-selective membranes.

diffusion↗

Topology-Informed Design Rules for Deconstructable Thermoset Copolymer Networks

Existing models of thermoset deconstruction facilitated by incorporating cleavable comonomers rely on a mean-field reverse gel point paradigm, which predicts network dissolution once cleavable bonds reach a critical stoichiometric threshold, but does not account for where those bonds reside within the network architecture. Using reactive coarse-grained molecular dynamics simulations coupled with graph-theoretic analysis, we extend this stoichiometric picture to show that deconstructability is governed by the curing-imprinted network topology rather than stoichiometry alone. This topological organization is hierarchical: at the local scale, the elastic effectiveness of cross-link junctions determines which cross-links constitute the load-bearing scaffold; at the mesoscale, the cross-linking rate kinetically templates that scaffold into topologically modular communities─densely cross-linked clusters connected by sparse bridging strands that sustain network connectivity. Using betweenness centrality to identify nodes that disproportionately lie on intercommunity shortest paths, we demonstrate that effective deconstruction of the network into macromolecular fragments requires cleavable comonomers to intercept these high-centrality bridging strands. We further find that under uniform, disassortative comonomer incorporation, this topological requirement provides a mechanistic basis for extending the reverse gel point to incorporate network topology. We also show that modularity imposes a fundamental limit on fragment uniformity that persists even when the centrality requirement is met. Finally, we demonstrate that chain stiffness provides a nearly independent lever to suppress mechanically redundant cross-links and raise the glass transition temperature without significantly altering the deconstruction outcome. Together, these findings reframe the thermoset design space around network topology and provide actionable guidelines for engineering thermoset copolymers with predictable deconstructability and targeted thermomechanical performance.

coarse-grained molecular dynamics↗

Demonstrating resonant ultrasound spectroscopy as a viable technique to characterize thermally conditioned high explosive materials

We present results of resonant ultrasound spectroscopy (RUS) measurements applied to granular high explosive materials at different bulk pressing densities and degree of thermal conditioning. The material chosen in this study is a ubiquitously used explosive material known as pentaerythritol tetranitrate (PETN), which is used commercially in civil and defense applications both as a binderized plastic bonded explosive material and an unbinderized neat material. However, changes in granular PETN bulk elastic properties due to thermal conditioning, which could have implications for better understanding environmental aging-related effects, have not been well studied even though it is believed that elasticity may play an important role in explosive material initiation mechanisms. Furthermore, monitoring elastic property changes in granular explosive pressings has not yet been demonstrated using RUS, which is an appealing non-destructive characterization tool that requires only dry point contact with the explosive material. To this end, we report the first study using RUS to quantify the elastic properties of binderized and neat PETN pressings as well as to quantify changes in elastic properties as a function of both thermal conditioning and bulk pressing density. Elastic stiffness coefficients, sometimes more commonly referred to as elastic constants, calculated from the RUS measurements on the different PETN-based materials show a significant increase for the post-conditioned samples compared to the pre-conditioned samples. This trend of increasing elastic properties with thermal conditioning was consistent for different density pressings, different thermal exposure conditions, and even different neat PETN pressings of differing average crystal sizes and/or specific surface areas.

36 MATERIALS SCIENCE↗

Physics-Informed Neural Network Solution of Point Kinetics Equations for a Nuclear Reactor Digital Twin

A digital twin (DT) for nuclear reactor monitoring can be implemented using either a differential equations-based physics model or a data-driven machine learning model. The challenge of a physics-model-based DT consists of achieving sufficient model fidelity to represent a complex experimental system, whereas the challenge of a data-driven DT consists of extensive training requirements and a potential lack of predictive ability. We investigate the performance of a hybrid approach, which is based on physics-informed neural networks (PINNs) that encode fundamental physical laws into the loss function of the neural network. We develop a PINN model to solve the point kinetic equations (PKEs), which are time-dependent, stiff, nonlinear, ordinary differential equations that constitute a nuclear reactor reduced-order model under the approximation of ignoring spatial dependence of the neutron flux. The PINN model solution of PKEs is developed to monitor the start-up transient of Purdue University Reactor Number One (PUR-1) using experimental parameters for the reactivity feedback schedule and the neutron source. The results demonstrate strong agreement between the PINN solution and finite difference numerical solution of PKEs. We investigate PINNs performance in both data interpolation and extrapolation. For the test cases considered, the extrapolation errors are comparable to those of interpolation predictions. Extrapolation accuracy decreases with increasing time interval.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Dendrite Suppression by a Polymer Coating: A Coarse‐Grained Molecular Study

Abstract A major hurdle to the successful deployment of high‐energy‐density lithium metal based batteries is dendrite growth during battery cycling, which raises safety and cycle life concerns. Coating the Li metal anode with a soft polymer layer has been previously shown to be effective in suppressing dendrite growth, leading to uniform lithium deposition even at high current densities. A 3D coarse‐grained molecular model to study the mechanism of dendrite suppression is presented. It is found that the most effective coatings delay or even prevent dendrites from penetrating the polymer layer during deposition. The optimal deposition can be achieved by jointly tuning the polymer stiffness and relaxation time. Higher polymer dielectric permittivity and coating thickness are also effective, but the deposition rate and, therefore, the charging current density is reduced. These findings provide the basis for rational design of soft polymer coatings for stable lithium deposition.

Kong, Xian↗

Extreme Elasticity Anisotropy in Molecular Glasses

Abstract Glasses are generally assumed to be isotropic and there are no literature reports of elastic anisotropy for molecular glasses. However, as glasses formed by physical vapor deposition can be structurally anisotropic, it is of interest to investigate the elastic anisotropy in these materials. Micro‐Brillouin light spectroscopy is used in several experimental geometries to determine the elastic stiffness tensors of three glass films of itraconazole vapor‐deposited at substrate temperatures ( T sub ) of 330, 315, and 290 K, respectively. Significant elastic anisotropy is observed and, in these glasses, the elastic anisotropy shows a strong correlation with the molecular orientation. The out‐of‐plane and in‐plane Young's moduli of the high T sub (330 K) sample, which features a predominantly vertical molecular orientation, exhibit a high anisotropy ratio of 2.2. The observed elastic anisotropy is much larger than those previously observed in liquid crystals and even many crystalline solids.

Cang, Yu↗

Hydration‐Induced β‐Sheet Crosslinking of α‐Helical‐Rich Spider Prey‐Wrapping Silk

Abstract Due to its moderate strength (≈700 MPa) and impressive extensibility before breaking (≈60–80%), orb‐weaving spider aciniform (AC) prey‐wrapping silks are actually the toughest of the spider silks but are remarkably understudied. The previous results indicate that native AC silk fibers are an α‐helix rich coiled‐coil/β‐sheet hybrid nanofiber, and that conversion of disordered or helical domains to β‐sheet aggregates is surprisingly minimal and overall β‐sheet content is low (≈15%). In this work, it is demonstrated through scanning electron microscopy that native AC silk fibers undergo matted cross‐linking upon exposure to moisture that increases silk stiffness. The unique molecular mechanism of water‐induced cross‐linking is revealed with solid‐state NMR (SSNMR) methods; water‐induced morphological changes are correlated with an increase in AC silk protein β‐sheet content, and additionally a minor unfolding of coiled‐coil regions is observed. Continued and increased β‐sheet cross‐linking is observed upon application of mechanical shear. The size of these β‐sheet domains to be 4–6 nm using Wide‐Line Separation SSNMR is determined. The observation that merely water treatment can be used to convert a protein‐based material from a flexible/extensible α‐helix‐rich fiber to a rigid crossed‐linked β‐sheet mat is a novel observation that should provide new avenues in bioinspired materials design.

Stengel, Dillan↗

Mechanical, Morphological, and Charge Transport Properties of NDI Polymers with Variable Built‐in Π‐Conjugation Lengths Probed by Simulation and Experiment

Abstract Mechanically deformable polymeric semiconductors are a key material for fabricating flexible organic thin‐film transistors (FOTFTs)—the building block of electronic circuits and wearable electronic devices. However, for many π‐conjugated polymers achieving mechanical deformability and efficient charge transport remains challenging. Here the effects of polymer backbone bending stiffness and film microstructure on mechanical flexibility and charge transport are investigated via experimental and computational methods for a series of electron‐transporting naphthalene diimide (NDI) polymers having differing extents of π‐conjugation. The results show that replacing increasing amounts of the π‐conjugated comonomer dithienylvinylene (TVT) with the π‐nonconjugated comonomer dithienylethane (TET) in the backbone of the fully π‐conjugated polymeric semiconductor, PNDI‐TVT 100 (yielding polymeric series PNDI‐TVT x , 100 ≥x≥ 0), lowers backbone rigidity, degree of texturing, and π–π stacking interactions between NDI moieties. Importantly, this comonomer substitution increases the mechanical robustness of PNDI‐TVT x while retaining efficient charge transport. Thus, reducing the TVT content of PNDI‐TVT x suppresses film crack formation and dramatically stabilizes the field‐effect electron mobility upon bending (e.g., 2 mm over 2000 bending cycles). This work provides a route to tune π–π stacking in π‐conjugated polymers while simultaneously promoting mechanical flexibility and retaining good carrier mobility in FOTFTs.

Chemistry↗

Formation and Shape Changing of Conductive Helical Ribbons via Deposition of Highly Stressed Films on Mechanically Responsive Substrates

Abstract This work demonstrates that the electrodeposition of highly stressed films on compliant ribbons is a robust process to obtain helical structures with excellent mechanical stability and potentially high thermal and electrical conductance. Electrodeposition on end‐tethered ribbons alters their axial and bending stiffness while imparting mechanical stress to drive the formation of a helix with a microscale diameter and pitch in a controlled and scalable manner. The process generates helices with diameters and pitches between 80 and 200 µm and lengths as large as several millimeters. The approach is amenable to parallel processing a large number of 3D structures on any substrate, including large‐area semiconductor wafers. This phenomenon is explained in terms of the change of stress gradients as material is added. Applications of the fabricated helices include antennas, metamaterials, and slow‐wave structures in frequency ranges not previously attainable.

Chemistry↗

Charge‐Programmable Photopolymers for 3D Electronics via Additive Manufacturing

Charge‐programmed 3D printing enables the fabrication of 3D electronics with lightweight and high precision via selective patterning of metals. This selective metal deposition is catalyzed by Pd nanoparticles that are specifically immobilized onto the charged surface and promises to fabricate a myriad of complex electronic devices with self‐sensing, actuation, and structural elements assembled in a designed 3D layout. However, the achievable property space and the material‐performance correlation of the charge‐programmed printing remain unexplored. Herein, a series of photo‐curable resins are designed for unveiling how the charge and crosslink densities synergistically impact the nanocatalyst‐guided selective deposition in catalytic efficiency and properties of the 3D printed charge‐programmed architectures, leading to high‐quality 3D patterning of solid and liquid metals. The findings offer a wide tunability of the structural properties of the printed electronics, ranging from stiff to extreme flexibility. Capitalizing on these results, the printing and successful application of an ultralight‐weight and deployable 3D multi‐layer antenna system operating at an ultrahigh‐frequency of 19 GHz are demonstrated.

36 MATERIALS SCIENCE↗

Conductive Liquid Metal Vitrimer Composites for Reconfigurable and Recyclable Flexible Electronics

Liquid metal (LM) elastomer composites exhibit excellent functionality for stretchable electronics and wearables, but limited recycling and reuse pathways constrain their sustainable use. Here, to address these challenges amid growing concerns over electronic waste, a conductive LM–vitrimer composite is presented that enables recyclable and reconfigurable electronics. This soft and stretchable composite features uniformly distributed LM inclusions that enhance thermal conductivity by 6.53× and enable the formation of conductive traces with electrical self-healing, while the vitrimer provides structural restoration. The dynamic covalent bonds of the vitrimer matrix are leveraged for both reprocessing the composite and chemically recovering 94% of the LM. This liquid-state filler slightly reduces the vitrimer's stiffness to 2.63 MPa (≈20% lower), while maintaining its high stretchability (>135% strain) and thermal stability. It is further examined how ultrasonicated LM inclusions interact with the vitrimer matrix and demonstrate the composite's self-healing and recyclability through two distinct approaches: 1) thermomechanical reprocessing, which restores fragmented composites under heat and compression for circuit reconfiguration; and 2) chemical recycling, which recovers the embedded LM for reuse in fabricating new composites and redesigned circuitry. With the integration of recyclability and diverse functional capabilities, LM–vitrimer composites emerge as a promising material platform for sustainable, flexible electronics.

Han, Youngshang [Univ. of Washington, Seattle, WA ↗

Local Thermal Conductivity Patterning in Rotating Lattice Crystals of Anisotropic Sb 2 S 3

The ability to control material heat transport properties over space and time can drive advanced functionalities in thermal management for electronics and system-on-chip, and enable thermal circuits. Despite the technological relevance, there are limited demonstrations of local thermal property control. Rotating lattice single (RLS) crystals—formed via laser-induced crystallization of an amorphous substrate—offer a novel avenue for local crystal engineering, unlocking opportunities for microscale property patterning. Here, thermal conductivity (𝜅) imaging is applied to RLS crystals of Sb2S3 to resolve microscale 𝜅 variations across patterned regions. Amorphous areas exhibit 𝜅 as low as 0.6 Wm −1 K −1 , while crystalline regions display periodic 𝜅 variations from 0.7 to over 2.5 Wm −1 K −1 . These variations correspond to changes in crystal orientation, revealing marked 𝜅 anisotropy. The crystal out-of-plane direction (c axis)—featuring van der Waals bonds—shows amorphous-like transport, whereas in-plane directions (a, b axes) exhibit 3.5x and 1.7x larger 𝜅, respectively. First-principles calculations, in excellent agreement with experiments, suggest that the in-plane anisotropy originates from expressed Sb lone pairs, which impart a corrugation along the b axis affecting bond stiffness and 𝜅. These findings demonstrate microscale control of thermal properties via laser-processed metastructures, with significant implications for next-generation thermal management.

14 SOLAR ENERGY↗