SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Correction for ‘Interfacial rheology of lanthanide binding peptide surfactants at the air–water interface’ by Stephen A. Crane et al. , Soft Matter , 2024, 20 , 9161–9173, https://doi.org/10.1039/D4SM00493K.
Polymers of intrinsic microporosity (PIMs) offer exceptional gas permeability but remain brittle and susceptible to physical aging, limiting their durability in separation applications. Here, we introduce a reconfigurable microporous polymer network that uniquely integrates permanent PIM microporosity with autonomous, intrinsic self-healing driven by imidazolium-based ionic motifs. Spirobisindane units generate the intrinsic free-volume architecture, while an imidazolium-containing polyamide ionene supplies dynamic ionic and hydrogen-bonding interactions that reorganize under mild activation. Incorporation of imidazolium-based ionic liquids further tunes cohesion, mobility, and densification, enabling the network to relax, re-associate, and retain microporosity without structural collapse. Through a comprehensive multiscale approach combining spectroscopy, scattering, thermal and mechanical characterization with all-atom molecular dynamics and density functional theory calculations, we elucidate how ionic content, as a single control parameter that reshapes free-volume distributions, modulates local coordination environments, and governs relaxation and healing kinetics. At intermediate ionic loadings, the networks achieve rapid, repeatable self-healing while maintaining CO$_2$ selectivity, demonstrating an optimal balance between segmental mobility and structural integrity. By establishing how hierarchical ionic interactions couple structure, dynamics, and transport in microporous ionene networks, this work provides generalizable design rules for adaptive soft-matter systems that require simultaneous mechanical resilience, reconfigurability, and selective gas transport.
Here, this work introduces a model-independent, dimensionless metric for predicting optimal measurement duration in time-resolved small-angle neutron scattering using early-time data. Built on a Gaussian process regression framework, the method reconstructs scattering profiles with quantified uncertainty, even from sparse or noisy measurements. Demonstrated on the EQ-SANS instrument at the Spallation Neutron Source, the approach generalizes to general SANS instruments with a two-dimensional detector. A key result is the discovery of a dimensionless convergence metric revealing a universal power-law scaling in profile evolution across soft matter systems. When time is normalized by a system-specific characteristic time t*, the variation in inferred profiles collapses onto a single curve with an exponent between −2 and −1. This trend emerges within the first ten time steps, enabling early prediction of measurement sufficiency. The method supports real-time experimental optimization and is especially valuable for maximizing efficiency in low-flux environments such as compact accelerator-based neutron sources.
Here, we present a Bayesian inference framework for reconstructing anisotropic two-dimensional small-angle scattering (2D SAS) patterns from sparse, noisy, or partially missing data. The method combines a symmetry-aware angular basis with radial Gaussian process priors to enable accurate, training-free interpolation and denoising. Computational benchmarks demonstrate reliable recovery of both isotropic and high-order anisotropic features under severe data reduction. Experimental validations on stretched polymers, sheared wormlike micelles, and carbon fibers show improved fidelity and resolution compared to raw measurements, achieving comparable accuracy with up to 50-fold fewer detected neutrons. This approach enables quantitative structural analysis under low-flux, time-limited, or single-shot conditions, extending the applicability of 2D SAS techniques to compact neutron sources and mechanically driven soft matter systems undergoing transient structural changes.
Electron transport in complex fluids, biology, and soft matter is a valuable characteristic in processes ranging from redox reactions to electrochemical energy storage. These processes often employ conductor–insulator composites in which electron transport properties are fundamentally linked to the microstructure and dynamics of the conductive phase. While microstructure and dynamics are well recognized as key determinants of the electrical properties, a unified description of their effect has yet to be determined, especially under flowing conditions. In this work, the conductivity and shear viscosity are measured for conductive colloidal suspensions to build a unified description by exploiting both recent quantification of the effect of flow-induced dynamics on electron transport and well-established relationships between electrical properties, microstructure, and flow. These model suspensions consist of conductive carbon black (CB) particles dispersed in fluids of varying viscosities and dielectric constants. In a stable, well-characterized shear rate regime where all suspensions undergo self-similar agglomerate breakup, competing relationships between conductivity and shear rate were observed. To account for the role of variable agglomerate size, equivalent microstructural states were identified using a dimensionless fluid Mason number, Mn f , which allowed for isolation of the role of dynamics on the flow-induced electron transport rate. At equivalent microstructural states, shear-enhanced particle–particle collisions are found to dominate the electron transport rate. This work rationalizes seemingly contradictory experimental observations in literature concerning the shear-dependent electrical properties of CB suspensions and can be extended to other flowing composite systems.
Multidimensional solitons are prevalent in numerous research fields. In orientationally ordered soft matter system, three-dimensional director solitons exemplify the localized distortion of molecular orientation. However, their precise manipulation remains challenging due to unpredictable and uncontrolled generation. Here, we utilize preimposed programmable photopatterning in nematics to control the kinetics of director solitons. This enables both unidirectional and bidirectional generation at specific locations and times, confinement within micron-scaled patterns of diverse shapes, and directed propagation along predefined trajectories. A focused dynamical model provides insight into the origins of these solitons and aligns closely with experimental observations, underscoring the pivotal role of anchoring conditions in soliton manipulation. Our findings pave the way for diverse fundamental research avenues and promising applications, including microcargo transportation and optical information processing.
The design of synthetic soft matter capable of emulating the complex behaviors of living organisms, such as sensing and adapting to their environment, remains an important challenge in developing biomimetic materials. Functionalized hydrogels are ideal candidates for such materials since they are highly responsive to their environment and can be operated in water. In this work, we investigate a hybrid bonding hydrogel composed of peptide amphiphile supramolecular nanofibers covalently attached to a photoresponsive network, in which high-aspect-ratio ferromagnetic nanowires are aligned along the length of the sample, designed to swim under oscillating magnetic fields. This hybrid hydrogel swimmer can autonomously swim toward a light source by utilizing photoinduced interactions between supramolecular and covalent networks reminiscent of phototactic swimming in living systems. Using a combination of experimental techniques and a continuum model incorporating photochemistry, magnetoelasticity, and hydrodynamics, we explain the swimming mechanism and predict phototactic behavior. Our work highlights the potential role of hybrid bonding polymers, which leverage the interplay between supramolecular assemblies and covalent networks. We demonstrate how these polymers can be tailored to react dynamically to their environment, paving the way for developing intelligent and autonomous robotic systems.
Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.
The dynamics of physical systems are typically modeled by partial differential equations (PDEs). Because of the complexity in physical systems, PDE models rely on approximations or limiting cases. To model the full complexity of physical systems, it is necessary to use multiscale approaches where appropriate models are used at each level. Alternatively, complexity can be directly tackled by fractional integrodifferential equations that must be derived for each system. Here, we argue that complexity can be restored in PDEs by describing them from a fractional calculus perspective. Rather than deriving a fractional integrodifferential equation, we reinterpret the dispersion relation of the system by use of the Riesz definition, which contains the required information relating the energy and momentum space of the system and thus fully describes their dynamics. The approach is demonstrated by two examples: the Landau–Lifshitz equation in a 1D ferromagnetic chain and a modified KdV equation supporting surface gravity waves or Euler dispersion. The presented approach is applicable to fluids, soft matter, and solid-state matter and can be readily generalized to higher dimensions and more complex systems. While numerical calculations are needed to determine the fractional operator, the approach is analytical and can be utilized to determine analytical solutions and investigate nonlinear problems.
Recent experimental realizations of liquid-liquid phase separation of active liquid crystals have offered an insight into the interaction between phase separation, ubiquitous in soft matter and biology, and chaotic active flows. Here, in this Letter, we use continuum theory to examine phase separation of an active liquid crystal and a passive fluid and report two new results. First, we provide an analytical derivation of the activity-induced suppression of the phase boundary of the coexistence region—a result first reported in simulations and experiments. We show that the shift in the critical point is a result of the balance between self-stirring active flows and phase-separating diffusive fluxes. Second, we show that this same balance is responsible for dramatically changing the morphology of the phase separated state, resulting in the emergence of a new mixed active phase consisting of a dynamical filamentous active network that invades the entire system area, trapping droplets of passive material. This structure exists even for very low volume fractions of active material. Our work provides an important step towards the goal of understanding how to use activity as a new handle for sculpting interfaces.
We show that various nonmonotonic ratchet effects can arise when mesophase pattern-forming systems, which exhibit anisotropic crystal, stripe, and bubble regimes, are coupled to one-dimensional asymmetric substrates under ac driving. The patterns emerge in the absence of a substrate when the ratio of attraction to repulsion is varied for particles with competing short-range attraction and long-range repulsion potentials. In the presence of the substrate, we find that the ratchet efficiency varies nonmonotonically with increasing attraction, depending upon how well the mesophase morphology matches the substrate spacing and periodicity. For strong repulsion, there is a weak but finite ratchet effect, while at intermediate attraction, there is a robust ratchet effect in which the system forms stripes aligned with the substrate symmetry direction. For strong attraction, large bubbles appear that have weak or no ratchet effects when the bubble width exceeds the substrate lattice spacing, causing the bubble to be only weakly coupled to the substrate. For very strong attraction, small bubbles form and undergo a strong ratchet effect with an efficiency that oscillates as a function of ac drive amplitude. Although the small bubble regime is a strongly correlated regime in which each bubble contains many particles, the system behaves as if it is in a single-particle regime since the bubble width is much smaller than the substrate lattice spacing. We map out the different rectification phases as a function of the pattern morphology, substrate strength, and ac drive amplitude. The pronounced ratchet effects that we observe in some regimes can be exploited for pattern sorting in hard and soft matter systems. Published by the American Physical Society 2024
We develop a hydrodynamic description of self-generated electrolyte flow in capillaries whose bounding walls feature nonuniform distributions of charge nonuniform active ionic fluxes. The hydrodynamic velocity arising in such a system has components that are forbidden by symmetry in the absence of charge and fluxes. However, when these two boundary mechanisms are simultaneously present, they can lead to a symmetry broken state where steady flows with both unidirectional and circulatory components emerge. We show that these flow states arise when modulated boundary patterns of charge and fluxes are offset by a flux-charge phase difference, which is associated with the separation between sites of their peak densities on the wall. Mismatch in diffusivity of cationic and anionic species can modify the flow states and becomes an enhancing factor when fluxes of both ion species are being produced together at the same site. We demonstrate that this mechanism can be realized with a microfluidic generator that is powered by enzyme-coated patches that catalyze reactants in the solution to produce fluxes of ions. The local ionic elevation or depletion, which disrupts a nonuniform double layer, promotes self-induced gradients yielding persistent body forces to generate bulk fluid motion. Our work quantifies a boundary-driven mechanism behind self-sustained electrolyte flow in confined environments that exists without any external bulk-imposed fields or gradients. It provides a theoretical framework for understanding the combined effect of active and charged boundaries that are relevant in biological or soft matter systems, and can be utilized in electrofluidic and iontronic applications.
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.
Lamellar phases are essential in various soft matter systems, with topological defects significantly influencing their mechanical properties. In this report, we present a machine-learning approach for quantitatively analyzing the structure and dynamics of distorted lamellar phases using scattering techniques. By leveraging the mathematical framework of Kolmogorov–Arnold networks, we demonstrate that the conformations of these distorted phases – expressed as superpositions of complex waves – can be reconstructed from small-angle scattering intensities. Through the contour analysis of wave field phase singularities, we obtain the statistics of the spatial distribution of topological defects. Furthermore, we establish that the temporal evolution of these defects can be derived from the time-dependent traveling wave field, informed by the dispersion relation of spectral components. This method opens new avenues for investigating the dynamics of distorted lamellar phases using various dynamic scattering techniques such as neutron spin echo and X-ray photon correlation spectroscopy. These findings enhance our microscopic understanding of how defects influence the physical properties of lamellar materials, with implications for both equilibrium and non-equilibrium states in general lamellar systems.
Self-organization of polymers in constrained geometries largely determines their applications in high-strength materials, photonics, and electronics. Chiral organization under confinement is well established for polymers with intrinsic molecular chirality; however, it has not been observed for achiral polymers. Here, we report the emergence of chirality in spatially confined solutions of achiral rigid-rod polymers. We show that kinetically arrested phase separation of polymer solutions confined to narrow capillaries resulted in alternating segments of isotropic and chiral nematic phases. The chiral structure of the nematic segments originated from the interplay between the constrained geometry, surface anchoring, orientational wetting, and elastic anisotropy of rigid-rod polymers. The catenoidal shape of the chiral structure recapitulated the morphology of biological chiral structures. These findings provide insight into the organization of soft matter under spatial confinement and offer a straightforward way to form chiral structures from achiral synthetic polymers.
Redox-active colloids (RACs) represent a novel class of energy carriers that exchange electrical energy upon contact. Understanding contact-mediated electron transfer dynamics in RACs offers insights into physical contact events in colloidal suspensions and enables quantification of electrical energy transport in nonconjugated polymers. Redox-based electron transport was directly observed in monolayers of micron-sized RACs containing ethyl-viologen side groups via fluorescence microscopy through an unexpected nonlinear electrofluorochromism that is quantitatively coupled to the redox state of the colloid. Via imaging studies, using this electrofluorochromism, the apparent charge transfer diffusion coefficient D CT of the RAC was easily determined. The visualization of energy transport within suspensions of redox-active colloids was also demonstrated. Our work elucidates fundamental mechanisms of energy transport in colloidal systems, informs the development of next-generation redox flow batteries, and may inspire new designs of smart active soft matter including conductive polymers for applications ranging from electrochemical sensors and organic electronics to colloidal robotics.
Understanding molecular motion in diffusion-driven complex environments is critical for designing sustainable materials and improving chemical processes. Here, we introduce a multidimensional nuclear magnetic resonance (NMR) method that captures how molecular populations exchange across different dynamic regimes. By extending the modulated gradient spin-echo technique to include frequency-frequency correlations, our approach reveals diffusion pathways that are otherwise obscured in heterogeneous systems. Implemented on a unilateral NMR magnet, the method eliminates gradient pulsing constraints and accesses dynamics in the kilohertz regime. We apply this technique to swelling and acid-catalyzed deconstruction of cross-linked and linear polymers to observe how structural heterogeneity evolves over time. By linking molecular motion to topology and chemical state, we extract physical metrics such as fractal surface dimensionality and reaction wavefront velocity, properties inaccessible with standard diffusion measurements. This work expands the capabilities of NMR for probing soft matter, with implications for polymer recycling and materials design.
Direct-ink writing (DIW) has rapidly become a versatile 3D fabrication method due to its ability to deposit a wide range of complex fluids into customizable 3D geometries. This review highlights key fundamental fluid mechanics and soft matter challenges across the different stages of the DIW printing process. The rheology of fluids and suspensions governs the flow behavior through narrow nozzles, posing questions about extrudability, confined flow dynamics, and clogging mechanisms. Downstream, the formation and deposition of extruded filaments involve extensional flows and potential instabilities, while postdeposition dynamics introduces complexities related to yield stress and structural stability. These stages are inherently interdependent, as optimizing material composition without considering filament stability risks compromising the final structure. As DIW applications expand through advanced ink formulations, developing fundamental fluid mechanics frameworks is essential to replace trial-and-error approaches with predictive design methodologies to enable more precise control and improved reliability of the printing process.