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At least 307 records · Page 17

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DOE OSTI2026

Tailored Silicone Network Architecture for Ultimate Mechanical Reinforcement

Hydrosilylation cured silicone elastomers are subject to reaction inefficiency, leading to incomplete and non-uniform crosslink networks, restricting the potential of mechanical reinforcement. This work investigates pre-synthesized, functional PDMS architectures as additives to improve ultimate mechanical performance relative to conventional single-step curing. Three custom, functional structures were prepared: a partially crosslinked PDMS scaffold (Structure A), a bottle-brush PDMS (Structure B), and a star-shaped PDMS derived from an MQ resin (Structure C). Rheological characterization was used to identify the ultimate design space and proper stoichiometric ratio for Structure A, and confirm successful formation of all structures for suitable incorporation into a base silicone formulation at 30wt%. Mechanical tests indicated that all three structures increased in ultimate tensile strength relative to their single-step counterparts, with Structure A providing additional improvements to toughness (432 vs. 258 kJ/m3) and ultimate elongation (158 vs. 115%). Furthermore, Structure B remained very soft in the unfilled state, while Structure C provided hardness (23 vs. 18 Shore A) and stiffness (780 vs. 420 kPa Young’s modulus) increases. In silica filled systems, Structure A retained increased strength but reduced elongation, while Structure B indicated strong reinforcement in terms of strength, toughness, and stiffness. Thermal analysis on the cure profiles of these materials suggested that pre-formation of network architectures enable a more complete reaction than a single-step process (15.9 vs. 15.1 J/g). Ultimately, these results indicate that tailoring PDMS architecture before the final cure can improve ultimate mechanical properties via improved network development in silicone elastomers. Furthermore, this work offers a promising strategy for designing higher-performance, more tunable silicone formulations.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Identifying Topological Defects in Lamellar Phases through Contour Analysis of Complex Wave Fields

Lamellar phases frequently contain structural imperfections that significantly affect their behaviors and properties. Our previous research successfully reconstructed real-space configurations of defective lamellar phases from diffuse scattering patterns, indicating the presence of phase vortices as a potential method for identifying topological defects disrupting the smectic ordering. Here, this report presents a mathematical framework using regularized wave fields to represent defective lamellar structures in real space. Phase singularities, resulting from the interference of random waves and indicating lamellar order disruption, are identified through a contour integral. These wave fields, derived from coherent scattering in reciprocal space, were validated via computational benchmarks analyzing small-angle neutron scattering data from AOT surfactant solutions, facilitating further statistical analysis of the defects. Our study highlights the potential to extract meaningful information about topological defects in lyotropic phases by inversely analyzing experimentally measured two-point static correlations. Our method allows for detailed structural analysis of various lyotropic phases, both particulate and nonparticulate, in their quiescent states and facilitates quantitative investigation of defects’ role in phase transitions. By integrating small-angle scattering, deep learning, and vortex tangle analysis, our comprehensive approach shows promise in addressing complex challenges in the structural analysis of soft matter systems.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Emerging iongel materials towards applications in energy and bioelectronics

In the past two decades, ionic liquids (ILs) have blossomed as versatile task-specific materials with a unique combination of properties, which can be beneficial for a plethora of different applications. The additional need of incorporating ILs into solid devices led to the development of a new class of ionic soft-solid materials, named here iongels. Nowadays, iongels cover a wide range of materials mostly composed of an IL component immobilized within different matrices such as polymers, inorganic networks, biopolymers or inorganic nanoparticles. Here this review aims at presenting an integrated perspective on the recent progress and advances in this emerging type of material. We provide an analysis of the main families of iongels and highlight the emerging types of these ionic soft materials offering additional properties, such as thermoresponsiveness, self-healing, mixed ionic/electronic properties, and (photo)luminescence, among others. Next, recent trends in additive manufacturing (3D printing) of iongels are presented. Finally, their new applications in the areas of energy, gas separation and (bio)electronics are detailed and discussed in terms of performance, underpinning it to the structural features and processing of iongel materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Scientific machine learning for modeling and simulating complex fluids

The formulation of rheological constitutive equations—models that relate internal stresses and deformations in complex fluids—is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these “digital fluid twins” to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation—a task that is not achievable using any previously developed data-driven rheological equation of state.

Science & Technology - Other Topics↗

DOE OSTI2021

Autonomous self-repair in piezoelectric molecular crystals

Living tissue uses stress-accumulated electrical charge to close wounds. Self-repairing synthetic materials, which are typically soft and amorphous, usually require external stimuli, prolonged physical contact, and long healing times. We overcome many of these limitations in piezoelectric bipyrazole organic crystals, which recombine following mechanical fracture without any external direction, autonomously self-healing in milliseconds with crystallographic precision. Kelvin probe force microscopy, birefringence experiments, and atomic-resolution structural studies reveal that these noncentrosymmetric crystals, with a combination of hydrogen bonds and dispersive interactions, develop large stress-induced opposite electrical charges on fracture surfaces, prompting an electrostatically driven precise recombination of the pieces via diffusionless self-healing.

Bhunia, Surojit↗

DOE OSTI2024

Design and Synthesis of PtPdNiCoMn High‐Entropy Alloy Electrocatalyst for Enhanced Alkaline Hydrogen Evolution Reaction: A Theoretically Supported Predictive Design Approach

Electrocatalytic hydrogen generation requires a multifunctional electrocatalyst with abundant active sites to drive multielectron transfer reactions. High entropy alloys (HEA) are five or more-elements with high configurational entropy are considered unique materials for next-generation electrocatalysts. Here, in this work, based on new screening guidelines for catalyst selections that combine density-functional theory calculated Gibbs formation-enthalpy with bond length and electronegativity variance, a novel HEA electrocatalyst consisting of five elements, namely, Pt, Pd, Ni, Co, and Mn has been designed. By simple room temperature electrodeposition, the designed catalyst is prepared and its hydrogen evolution reaction (HER) is explored and validated through experimental and theoretical approaches. The HEA demonstrated a superior HER activity with an overpotential of 22.6 mV at -10 mA cm -2 which outperforms Pt/C commercial catalyst. No evident degradation of the material is detected even after 100 hours of continuous operation under high current density. Moreover, the HEA has shown exceptional performance in harsh electrolyte conditions such as in simulated seawater and actual seawater. Remarkably, the density-functional theory calculated Gibbs formation-enthalpy is small (≈0 eV) compared to Pt/C placing the new HEA near the apex of Trasatti's model of Volcano plot, which is also suggestive of superior HER activity.

36 MATERIALS SCIENCE↗

NASA NTRS1994

Formation and chemical reactivity of carbon fibers prepared by defluorination of graphite fluoride

Defluorination of graphite fluoride (CFX) by heating to temperatures of 250 to 450 C in chemically reactive environments was studied. This is a new and possibly inexpensive process to produce new carbon-based materials. For example, CF 0.68 fibers, made from P-100 carbon fibers, can be defluorinated in BrH2C-CH = CH-CH2Br (1,4-dibromo-2butene) heated to 370 C, and graphitized to produce fibers with an unusually high modulus and a graphite layer structure that is healed and cross-linked. Conversely, a sulfur-doped, visibly soft carbon fiber was produced by defluorinating CF 0.9 fibers, made from P-25, in sulfur (S) vapor at 370 C and then heating to 660 C in nitrogen (N2). Furthermore, defluorination of the CF 0.68 fibers in bromine (Br2) produced fragile, structurally damaged carbon fibers. Heating these fragile fibers to 1100 C in N2 caused further structural damage, whereas heating to 150 C in bromoform (CHBr3) and then to 1100 C in N2 healed the structural defects. The defluorination product of CFX, tentatively called activated graphite, has the composition and molecular structure of graphite, but is chemically more reactive. Activated graphite is a scavenger of manganese (Mn), and can be intercalated with magnesium (Mg). Also, it can easily collect large amounts of an alloy made from copper (Cu) and type 304 stainless steel to form a composite. Finally, there are indications that activated graphite can wet metals or ceramics, thereby forming stronger composites with them than the pristine carbon fibers can form.

Hung, Ching-Cheh↗

DOE OSTI2021

Nanolatticed Architecture Mitigates Damage in Shark Egg Cases

Structural versatility and multifunctionality of biological materials have resulted in countless bioinspired strategies seeking to emulate the properties of nature. The nanostructured egg case of swell sharks is one of the toughest permeable membranes known and, thus, presents itself as a model system for materials where the conflicting properties, strength and porosity, are desirable. The egg case possesses an intricately ordered structure that is designed to protect delicate embryos from the external environment while enabling respiratory and metabolic exchange, achieving a tactical balance between conflicting properties. Herein, structural analyses revealed an enabling nanolattice architecture that constitutes a Bouligand-like nanoribbon hierarchical assembly. Three distinct hierarchical architectural adaptations enhance egg case survival: Bouligand-like organization for in-plane isotropic reinforcement, noncylindrical nanoribbons maximizing interfacial stress distribution, and highly ordered nanolattices enabling permeability and lattice-governed toughening mechanisms. Furthermore, these discoveries provide fundamental insights for the improvement of multifunctional membranes, fiber-reinforced soft composites, and mechanical metamaterials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

Inferring effective electrostatic interaction of charge-stabilized colloids from scattering using deep learning

In this article, an innovative strategy is presented that incorporates deep auto-encoder networks into a least-squares fitting framework to address the potential inversion problem in small-angle scattering. To evaluate the performance of the proposed approach, a detailed case study focusing on charged colloidal suspensions was carried out. The results clearly indicate that a deep learning solution offers a reliable and quantitative method for studying molecular interactions. The approach surpasses existing deterministic approaches with respect to both numerical accuracy and computational efficiency. Overall, this work demonstrates the potential of deep learning techniques in tackling complex problems in soft-matter structures and beyond.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Tailoring the Properties of Chemically Recyclable Polyethylene‐Like Multiblock Polymers by Modulating the Branch Structure

Abstract Developing plastics that fill the need of polyolefins yet are more easily recyclable is a critical need to address the plastic waste crisis. However, most efforts in this vein have focused on high‐density polyethylene (PE), while many different types of PE exist. To create broadly sustainable PE with modular properties, we present the synthesis, characterization, and demonstration of materials applications for chemically recyclable PE‐like multiblock polymers prepared from distinct hard and soft blocks using ruthenium‐catalyzed dehydrogenative polymerization. By altering the branching pattern within the soft blocks, a series of PE‐like multiblock polymers were synthesized with tunable glass transition temperatures ( T g ) while maintaining consistent high melting temperatures ( T m ). A clear U‐shape trend between T g and mechanical properties was found, showcasing their potential as sustainable materials with tailored properties spanning commercial linear low‐density polyethylene (LLDPE) and low‐density polyethylene (LDPE). These materials offer adjustable adhesive strength to metal and demonstrate chemical recyclability and selective depolymerization in mixed plastic streams, promoting circularity and separation.

Zhao, Yucheng↗

DOE OSTI2024

Tailoring the Properties of Chemically Recyclable Polyethylene‐Like Multiblock Polymers by Modulating the Branch Structure

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Prediction of superconductivity and topological aspects in single-layer β -Bi 2 Pd

Topological superconductors, characterized by topologically nontrivial states residing in a superconducting gap, are a recently discovered class of materials having Majorana fermions. The interplay of superconductivity and topological states gives rise to opportunities for achieving such topological superconductors in condensed matter systems. Up to now, several single-material topological superconductors in this form have been theoretically predicted and experimentally confirmed. Here, using the first-principles calculations, we study the superconducting single-layer β-Bi 2 Pd. In this work, the electronic density of states near Fermi level of this monolayer are dominated by the Bi-p and Pd-d orbitals, forming a two-band Fermi surface with multiclass sheets. The presence of soft phonon bands, in cooperation with the electron susceptibility, accounts for electron-phonon superconductivity of single-layer β-Bi 2 Pd. With the centrosymmetric structure, single-layer β-Bi 2 Pd possesses a continuous gap over the whole Brillouin zone and topological Dirac-like states at its one-dimensional boundary. The present findings would lead to the expectation of one-dimensional topological superconductivity and Majorana bound states in a monolayer candidate of β-Bi 2 Pd with intrinsic full-gap superconductivity.

36 MATERIALS SCIENCE↗

DOE OSTI2026

On the Prospect of Chemically Transferable Coarse-Grained Electronic Models for Soft Materials

Electronic coarse-graining (ECG) methods predict quantum-mechanical electronic properties directly from coarse-grained (CG) molecular configurations, enabling electronic predictions at mesoscale length scales. Here, we present a diagnostic assessment of the feasibility of chemically transferable ECG models across a broad polymer-relevant chemical space using all-atom, united-atom, and Martini-scale representations. While high-resolution ECG models achieve near-quantitative accuracy, we show that chemically transferable ECG at the Martini resolution fails because the CG force field does not sample the same configurational distribution of local molecular structure as that underlying the DFT-parameterized ECG model. We demonstrate that our proposed Element-Count-Label (ECL) representation, which augments Martini beads with explicit stoichiometric data, significantly improves chemical generalization across diverse polymer chemistries. However, we find that even with improved chemical resolution, the model cannot recover electronic property distributions that are absent from the configurational space sampled by the CG force field. These results demonstrate that chemically transferable ECG requires future Martini-like force fields to explicitly preserve quantum chemistry–compatible local molecular structure in addition to thermodynamic and structural fidelity.

Kidder, Katherine M [Department of Chemistry; Univ↗

DOE OSTI2020

Imaging broadband soft X-ray transmission-grating spectrograph for a wavelength range λ > 111 Å

A family of aperiodic Mo/Be multilayer mirrors was designed for maximum uniform reflectivity in the ranges 111 – 138, 111 – 150, 111 – 200 and 111 – 222 Å at normal radiation incidence. The simulations took into account the existence of Mo-on-Be and Be-on-Mo transition layers. It was shown that the reflectivity may be as high as 10% in an octave wavelength range of 111 – 222 Å. We demonstrate the operation of an imaging transmission-grating spectrograph with a broadband Mo/Be multilayer mirror with a uniform reflectivity in the wavelength range 111 – 138 Å synthesised at the Institute for the Physics of Microstructures, RAS. The spectrograph was used to record the line spectra of multiply charged fluorine and magnesium ions from a laser-produced plasma. The role of the grating support structure in the formation of spectral source images is discussed. The spatial resolution of 50 μm is demonstrated. (laser applications and other topics in quantum electronics)

36 MATERIALS SCIENCE↗

DOE OSTI2024

Improving Charge Transport and Environmental Stability of Carbohydrate-Bearing Semiconducting Polymers in Organic Field-Effect Transistors

Semiconducting polymers offer synthetic tunability, good mechanical properties, and biocompatibility, enabling the development of soft technologies previously inaccessible. Side-chain engineering is a versatile approach for optimizing these semiconducting materials, but minor modifications can significantly impact material properties and device performance. Carbohydrate side chains have been previously introduced to improve the solubility of semiconducting polymers in greener solvents. Despite this achievement, these materials exhibit suboptimal performance and stability in field-effect transistors. In this work, structure–property relationships are explored to enhance the device performance of carbohydrate-bearing semiconducting polymers. Toward this objective, a series of isoindigo-based polymers with carbohydrate side chains of varied carbon-spacer lengths is developed. Material and device characterizations reveal the effects of side chain composition on solid-state packing and device performance. With this new design, charge mobility is improved by up to three orders of magnitude compared to the previous studies. Processing–property relationships are also established by modulating annealing conditions and evaluating device stability upon air exposure. Notably, incidental oxygen-doping effects lead to increased charge mobility after 10 days of exposure to ambient air, correlated with decreased contact resistance. Bias stress stability is also evaluated. This work highlights the importance of understanding structure–property relationships toward the optimization of device performance

36 MATERIALS SCIENCE↗

DOE OSTI2022

Reversible motions and disordered structure of soft particles in amorphous solids

In amorphous solids, soft vibrational modes derived from normal mode analysis can be utilized to identify the soft particles that are prone to irreversible rearrangements. However, the normal mode analysis approach cannot explain why the spatial distributions of clustered soft particles do not change over time. We define a softness parameter based on the vibrational density of states calculated directly from molecular dynamics simulations with both the harmonic vibrations and anharmonic relaxations being properly captured at finite temperatures. This parameter spontaneously correlates with the real space atomic motions and the dynamics heterogeneity. Using the softness parameter, we show that the softest particles are confined within rigid cages. These particles keep rearranging reversibly without long-range diffusion. The moderately soft particles rearrange irreversibly, and the hard particles mainly participate in vibrations without rearrangement. Here, we also show that the soft particles form locally disordered structures, while the hard particles present strong ordering. These findings confirm the defective nature of soft particles, and provide insights on the nature of softness as the ability to rearrange, but not necessarily irreversibly.

36 MATERIALS SCIENCE↗

NASA NTRS2009

Thermal Performance Testing of Order Dependancy of Aerogels Multilayered Insulation

Robust multilayer insulation systems have long been a goal of many research projects. Such insulation systems must provide some degree of structural support and also mechanical integrity during loss of vacuum scenarios while continuing to provide insulative value to the vessel. Aerogel composite blankets can be the best insulation materials in ambient pressure environments; in high vacuum, the thermal performance of aerogel improves by about one order of magnitude. Standard multilayer insulation (MU) is typically 50% worse at ambient pressure and at soft vacuum, but as much as two or three orders of magnitude better at high vacuum. Different combinations of aerogel and multilayer insulation systems have been tested at Cryogenics Test Laboratory of NASA Kennedy Space Center. Analysis performed at Oak Ridge National Laboratory showed an importance to the relative location of the MU and aerogel blankets. Apparent thermal conductivity testing under cryogenic-vacuum conditions was performed to verify the analytical conclusion. Tests results are shown to be in agreement with the analysis which indicated that the best performance is obtained with aerogel layers located in the middle of the blanket insulation system.

Johnson, Wesley L.↗

DOE OSTI2026

Explainable machine learning reveals that local structural motifs encode the thermodynamic state across the CuZr metallic glass-forming range

Metallic glasses derive their properties from the statistics of local atomic motifs rather than from long-range order, yet a quantitative, chemistry-specific link between motif populations and the underlying glassy state has remained elusive. In this work we combine large-scale molecular dynamics, Voronoi tessellation, deep neural networks, and SHapley Additive exPlanations (SHAP) to identify which local structural motifs define the glassy state of Cu—Zr metallic glasses. A dataset of 17,180 atomistic configurations spanning ten compositions (Cu 20 Zr 80 –Cu 80 Zr 20 ) and four quench rates (10 9 –10 12 K/s) is used to train a feed-forward neural network that regresses temperature across the 50–2000 K liquid–supercooled–glass range, achieving a mean absolute error of 19.89 K and R 2 = 0.9974, confirming that the local structural state is faithfully encoded in motif-level structure. SHAP analysis then reveals that a tightly coupled near-icosahedral family of motifs (coordination numbers (CN) 11–13, including the full icosahedron 001200 and its single-atom-perturbation sibling 10930) collectively encodes the thermodynamic state of the system across the full glass-forming range. The CN = 11–13 ordered members carry negative SHAP values at high populations, tracking the most deeply-quenched configurations, while 10930 shows the reversed signature consistent with its role as a soft-spot host whose population shrinks as the icosahedral network deepens. The analysis demonstrates that explainable machine learning can isolate the minimal motif vocabulary defining the glassy state and recovers the near-icosahedral building blocks previously identified by data-driven analyses of Cu—Zr. The approach provides a general, chemistry-specific route for characterizing the structural state of disordered materials.

36 MATERIALS SCIENCE↗