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DOE OSTI2018

The mechanisms of ductile rupture

One of the most confounding controversies in the ductile fracture community is the large discrepancy between predicted and experimentally observed strain-to-failure values during shear-dominant loading. Currently presented solutions focus on better accounting for how the deviatoric stress state influences void growth or on measuring strain at the microscale rather than the macroscale. While these approaches are useful, they do not address a significant aspect of the problem: the only rupture micromechanisms that are generally considered are void nucleation, growth, and coalescence (for tensile-dominated loading), and shear-localization and void coalescence (for shear-dominated loading). Current phenomenological models have thus focused on predicting the competition between these mechanisms based on the stress state and the strain-hardening capacity of the material. However, in the present study, we demonstrate that there are at least five other failure mechanisms. Because these have long been ignored, little is known about how all seven mechanisms interact with one another or the factors that control their competition. These questions are addressed by characterizing the fracture process in three high-purity face-centered cubic (FCC) metals of medium-to-high stacking fault energy: copper, nickel, and aluminum. These data demonstrate that, for a given stress state and material, several mechanisms frequently work together in a sequential manner to cause fracture. The selection of a failure mechanism is significantly affected by the plasticity-induced microstructural evolution that occurs before tearing begins, which can create or eliminate sites for void nucleation. At the macroscale, failure mechanisms that do not involve cracking or pore growth were observed to facilitate subsequent void growth and coalescence processes. Even though the focus of this study is on damage accumulation in pure metals, these results are also applicable to understanding failure in engineering alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A deep learning upscaling framework: Reactive transport and mineral precipitation in fracture-matrix systems

Pore-scale modeling has limited applicability at large scales due to its high computational cost. One common approach to upscale pore-scale models is the use of effective medium theories, which homogenize small-scale features in a porous structure and characterize the medium by macroscale properties (e.g., permeability) and equations (e.g., Darcy’s law). However, there are classes of physical processes for which effective medium approximations may become inaccurate, e.g., mineral precipitation and clogging during reactive transport. We have developed a deep learning upscaling framework, in which pore-scale modeling is directly employed in macroscale systems, without relying on effective medium approximations. The upscaling framework is first developed for general multiscale systems and then applied to modeling reactive transport with mineral precipitation in the altered layer in fracture-matrix structures. Solute transport from the fractures to the matrix is modeled as a wall boundary condition for the fractures, which, in turn, is predicted by recurrent neural networks using the concentration histories at the fracture-matrix boundary. Specifically, we consider a meter-scale fracture network embedded in sandstones, where the smallest feature is at the micron scale. Here the proposed framework allows us to span five orders of magnitude in length scales by capturing mineral precipitation in the altered layer of the rock matrix at the pore scale across the entire meter-scale fracture network.

42 ENGINEERING↗

DOE OSTI2020

A multiscale cohesive law for carbon fiber networks

Better predictive models of mechanical failure in low-weight heat shield composites would aid material certification for missions with aggressive atmospheric entry conditions. In this study, we develop such a model for the rapid engineering analysis of the failure limits of phenolic impregnated carbon ablator (PICA) - a leading heat shield material whose structural component is a carbon fiber network. We hypothesize inelastic deformation failure mechanisms and model their behavior using molecular dynamics simulations to calculate the binding energy. We then upscale this binding energy to the macroscale using a renormalization argument. The approach delivers insightful and reasonably accurate macroscale predictions that compare favorably to experiments. In application, the model is validated for a particular variety of PICA by comparison to experiment and would then be used to study design scenarios in different entry conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A multiscale phase field fracture approach based on the non-affine microsphere model for rubber-like materials

Rubber-like materials have a broad scope of applications due to their unique properties like high stretchability and increased toughness. Hence, computational models for simulating their fracture behavior are paramount for designing them against failures. In this study, the phase field fracture approach is integrated with a multiscale polymer model for predicting the fracture behavior in elastomers. At the microscale, damaged polymer chains are modeled to be made up of a number of elastic chain segments pinned together. Using the phase field approach, the damage in the chains is represented using a continuous variable. Both the bond stretch internal energy and the entropic free energy of the chain are assumed to drive the damage, and the advantages of this assumption are expounded. A framework for utilizing the non-affine microsphere model for damaged systems is proposed here by considering the minimization of a hypothetical undamaged free energy, ultimately connecting the chain stretch to the macroscale deformation gradient. At the macroscale, a thermodynamically consistent formulation is derived in which the total dissipation is assumed to be mainly due to the rupture of molecular bonds. Using a monolithic scheme, the proposed model is numerically implemented and the resulting three-dimensional simulation predictions are compared with existing experimental data. The capability of the model to qualitatively predict the propagation of complex crack paths and quantitatively estimate the overall fracture behavior is verified. Additionally, the effect of the length scale parameter on the predicted fracture behavior is studied for an inhomogeneous system.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2011

Valuing National and Basin Level Geologic CO2 Storage Capacity Assessments in a Broader Context

The technical literature and much of the public policy dialogue about national or basin scale geologic carbon dioxide storage capacity estimates emphasizes the short comings in the methodologies employed in these studies because of their need to employ simplifying assumptions over large volumes of deep geologic structures. This short communication seeks to bring forward a better understanding of the role of these macroscale assessments and their value. In doing so, the author hopes that the there will be a more balanced discussion of macroscale geologic carbon dioxide storage assessments as compliments to the detailed site specific assessments needed to site any given carbon dioxide capture and storage facility.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Statistical evaluation of microscale stress conditions leading to void nucleation in the weak shock regime

Here, we investigate the heterogeneity of the stress state driven by anisotropic deformation response at the single crystal level through five statistical volume element (SVE) calculations of polycrystalline BCC tantalum. This work focuses on grain boundaries as a prominent material defect type prone to void nucleation based upon experimental observations of predominantly intergranular void nucleation in this material. The SVEs are constructed to be statistically representative of larger volumes of material and are meshed such that mean and standard deviation of grain size and orientation information is reconstructed. The computational meshes feature hexahedral (brick) elements and smooth conformal grain boundaries where significant stress concentration is known to occur, a tail effect of interest in the extreme events process of dynamic ductile damage. An existing micromechanical crystallographic plasticity model shown to capture the single crystal behavior of BCC tantalum well is used to perform the polycrystal calculations. The model includes representation of the non-Schmid effect of non-planar screw dislocation kinetics in tantalum. A three-dimensional stress state time profile predicted by damage modeling of a flyer plate impact experiment is applied as boundary conditions to each SVE. Resulting grain boundary stress state statistics are strongly non-Gaussian. Significant structural evolution is observed within the compressive hold before unloading into tension in the stress profile. Strong angular dependence of grain boundary traction magnitude with shock direction is observed. Non-Schmid effects continue to suggest their influence on propensity of microstructural defect types to nucleate voids. A general void nucleation criterion is proposed using probability theory. The general framework is specified to polycrystalline BCC tantalum in the weak shock regime to include the SVE calculations and literature molecular dynamics calculations of grain boundary void nucleation strength. Probability density functions (PDFs) are used to describe the interaction between the local stress state heterogeneity and the distributed grain boundary void nucleation strength state. A causation entropy maximization procedure removes the requirement for ad hoc selection of a PDF functional form and provides a rigorous procedure for data-based PDF determination. The resulting physically informed PDF describes the spatial appearance frequency of nucleated voids as a function of applied macroscale pressure. Lower length scale physics are thus packaged in a precise and computationally efficient way to provide computational plasticity insight to macroscale dynamic ductile damage models.

36 MATERIALS SCIENCE↗

DOE OSTI2013

A multiscale modeling technique for bridging molecular dynamics with finite element method

In computational mechanics, molecular dynamics (MD) and finite element (FE) analysis are well developed and most popular on nanoscale and macroscale analysis, respectively. MD can very well simulate the atomistic behavior, but cannot simulate macroscale length and time due to computational limits. FE can very well simulate continuum mechanics (CM) problems, but has the limitation of the lack of atomistic level degrees of freedom. Multiscale modeling is an expedient methodology with a potential to connect different levels of modeling such as quantum mechanics, molecular dynamics, and continuum mechanics. This study proposes a new multiscale modeling technique to couple MD with FE. The proposed method relies on weighted average momentum principle. A wave propagation example has been used to illustrate the challenges in coupling MD with FE and to verify the proposed technique. Furthermore, 2-Dimensional problem has also been used to demonstrate how this method would translate into real world applications. -- Highlights: •A weighted averaging momentum method is introduced for bridging molecular dynamics (MD) with finite element (FE) method. •The proposed method shows excellent coupling results in 1-D and 2-D examples. •The proposed method successfully reduces the spurious wave reflection at the border of MD and FE regions. •Big advantages of the proposed method are simplicity and inexpensive computational cost of multiscale analysis.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Multi-scale diffuse interface modeling of multi-component two-phase flow with partial miscibility

In this paper, we introduce a diffuse interface model to simulate multi-component two-phase flow with partial miscibility based on a realistic equation of state (e.g. Peng–Robinson equation of state). Because of partial miscibility, thermodynamic relations are used to model not only interfacial properties but also bulk properties, including density, composition, pressure, and realistic viscosity. As far as we know, this effort is the first time to use diffuse interface modeling based on equation of state for modeling of multi-component two-phase flow with partial miscibility. In numerical simulation, the key issue is to resolve the high contrast of scales from the microscopic interface composition to macroscale bulk fluid motion since the interface has a nanoscale thickness only. To efficiently solve this challenging problem, we develop a multi-scale simulation method. At the microscopic scale, we deduce a reduced interfacial equation under reasonable assumptions, and then we propose a formulation of capillary pressure, which is consistent with macroscale flow equations. Moreover, we show that Young–Laplace equation is an approximation of this capillarity formulation, and this formulation is also consistent with the concept of Tolman length, which is a correction of Young–Laplace equation. At the macroscopical scale, the interfaces are treated as discontinuous surfaces separating two phases of fluids. Our approach differs from conventional sharp-interface two-phase flow model in that we use the capillary pressure directly instead of a combination of surface tension and Young–Laplace equation because capillarity can be calculated from our proposed capillarity formulation. A compatible condition is also derived for the pressure in flow equations. Furthermore, based on the proposed capillarity formulation, we design an efficient numerical method for directly computing the capillary pressure between two fluids composed of multiple components. Finally, numerical tests are carried out to verify the effectiveness of the proposed multi-scale method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Characterizing major and trace element compositions in fallout melt glass from a near-surface nuclear test

We report the chemical and isotopic compositions of fallout melt glasses from nuclear tests contain a range of information constraining the physical conditions within the fireball and the mechanisms of fallout formation but historic studies tended to exclude the behavior of stable major and trace elements. Here, we present a large study specifically focused on major and trace element relationships within a population of macroscale fallout samples from a single event. We interpret these data to better constrain how fallout melt glass formation in near surface environments is influenced by that environment and demonstrate how major and trace element abundances can provide useful insights into chemical processes within the fireball. Data confirm that the uranium in the fallout glass population derives from two isotopically distinct endmembers: isotopically enriched uranium (presumably from the weapon), and natural composition uranium that may be a combination of anthropogenic and environmental materials from within the blast zone. The similarity between major and trace element concentrations in fallout and corresponding local soils from the event site confirm the local soils as the most probable source of entrained material into the fireball and the source of carrier material into which the bomb vapor was incorporated. The lack of correlation between major and trace element abundances with size indicates that volatility driven processes, such as condensation from the fireball, do not control the composition of macroscale fallout melt glass. Although the fallout has major and trace element chemical characteristics broadly similar to those of the local, associated soils, some systematic differences are observed between the two populations. Fallout melt glass is depleted in volatile elements such as K, Na, Tl and Pb, consistent with heating to temperatures above ~1000 °C for 3-10 s. This is supported by the results of laser heating experiments performed on rhyolitic soil at temperatures (1600-2200 °C) and timescales (1-120 s) that are broadly relevant to fallout formation conditions. Relative enrichments of metals such as Cu and Co do not correlate with the abundance of uranium, suggesting that fallout also records input of near field anthropogenic materials. Our observations suggest that major chemical features can be related to processing in the fireball and used to inform the thermal-chemical evolution of the system. Ultimately, these data are consistent with a fallout formation mechanism that involves rapid melting of surface materials to form carrier material melts with minor incorporation of bomb vapor and a degree of volumetric volatile loss due to heating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Optimal lattice-structured materials

This paper describes a method for optimizing the mesostructure of lattice-structured materials. These materials are periodic arrays of slender members resembling efficient, lightweight macroscale structures like bridges and frame buildings. Current additive manufacturing technologies can assemble lattice structures with length scales ranging from nanometers to millimeters. Previous work demonstrates that lattice materials have excellent stiffness- and strength-to-weight scaling, outperforming natural materials. However, there are currently no methods for producing optimal mesostructures that consider the full space of possible 3D lattice topologies. The inverse homogenization approach for optimizing the periodic structure of lattice materials requires a parameterized, homogenized material model describing the response of an arbitrary structure. This work develops such a model, starting with a method for describing the long-wavelength, macroscale deformation of an arbitrary lattice. The work combines the homogenized model with a parameterized description of the total design space to generate a parameterized model. Finally, the work describes an optimization method capable of producing optimal mesostructures. Several examples demonstrate the optimization method. One of these examples produces an elastically isotropic, maximally stiff structure, here called the isotruss, that arguably outperforms the anisotropic octet truss topology.

36 MATERIALS SCIENCE↗

DOE OSTI2021

The role of micro-inertia on the shock structure in porous metals

The behavior of porous materials under shock loading is a multi-scale problem bridging orders of magnitude across the macroscale geometry and the microscale pores. Under static loading, this problem is well understood, relating mechanisms of pore closure and crushing to the equivalent macroscale models. The dynamic response of porous solids under shock loading is related to the effects of viscoplasticity and micro-acceleration fields around the void boundaries. The significance of the micro-inertia effects in modeling the dynamic behavior of porous materials remains an open question. In this work, an experimental investigation on closed-cell porous aluminum with small porosity provides the evidence for the first time of micro-inertia’s fundamental role in describing the shock structure in these materials. Materials with different levels of porosity were manufactured using a modified process of additive manufacturing to achieve a mean pore size below 50$\mu$m. Plate impact experiments on porous aluminum samples were conducted at pressures in the range of 2 to 11 GPa. The structure of the steady shock was characterized as a function of porosity and shown to validate behavior revealed by an analytical approach (Czarnota et al. [J. Mech. Phys. Solids 107 (2017)]), highlighting the fundamental role of micro-inertia effects in such cases.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Learning macroscopic internal variables and history dependence from microscopic models

This paper concerns the study of history dependent phenomena in heterogeneous materials in a two-scale setting where the material is specified at a fine microscopic scale of heterogeneities that is much smaller than the coarse macroscopic scale of application. Here, we specifically study a polycrystalline medium where each grain is governed by crystal plasticity while the solid is subjected to macroscopic dynamic loads. The theory of homogenization allows us to solve the macroscale problem directly with a constitutive relation that is defined implicitly by the solution of the microscale problem. However, the homogenization leads to a highly complex history dependence at the macroscale, one that can be quite different from that at the microscale. In this paper, we examine the use of machine-learning, and especially deep neural networks, to harness data generated by repeatedly solving the finer scale model to: (i) gain insights into the history dependence and the macroscopic internal variables that govern the overall response; and (ii) to create a computationally efficient surrogate of its solution operator, that can directly be used at the coarser scale with no further modeling. We do so by introducing a recurrent neural operator (RNO), and show that: (i) the architecture and the learned internal variables can provide insight into the physics of the macroscopic problem; and (ii) that the RNO can provide multiscale, specifically FE 2 , accuracy at a cost comparable to a conventional empirical constitutive relation.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Multiscale characterization of phase change materials for building thermal energy storage applications

Phase change materials (PCMs) store and release large amounts of thermal energy because of their high latent energy storage capacity. However, long-term cyclic stability, supercooling and performance-scalability are some of the major challenges for their use in building thermal energy storage (TES) applications. Here, in this study, we present a comprehensive multiscale characterization of two commercially available organic PCMs, Puretemp 18 and Puretemp 23. At the microscale, differential scanning calorimetry (DSC) was used to characterize phase change temperature, specific heat, and latent heat. At the mesoscale, a heat flow meter apparatus (HFMA), following the ASTM C1784 standard, was employed to measure the phase change temperature, specific heat, and latent heat properties. A comparative analysis of latent heat as a function of temperature was conducted by integrating the DSC and HFMA results. At the macroscale, the thermal performance and cyclic stability of the TES system was evaluated using Puretemp 23. The TES system consisted of a finned tube heat exchanger with a storage volume of 0.0189 m 3 (5 gal), which represents a compact, real-world TES solution suitable for building energy storage. The results showed consistent thermal stability of the PCM over 200 cycles, and the supercooling temperature remained within 0.2 °C, which was not detected in smaller-scale characterization methods. Additionally, the macroscale testing methodology of the PCM revealed that the TES is able to charge and discharge stored latent energy within 2 h under a temperature differential of 16.67 °C measured between the inlet water temperature and the phase transition temperature of the PCM. The proposed multiscale PCM characterization method provides a systematic basis for comparing important thermal storage properties while also investigating the scalability, reliability and integration challenges in large scale TES applications.

Latent heat↗

DOE OSTI2025

Shock compression of additively manufactured high solids loaded polymer composites with aligned porosity

The effects of aligned porosity on the shock-compression response of additively manufactured (AM) composite structures with collinear and log-cabin geometries are investigated. The composites contain two inorganic and two organic particle populations and a UV-curable polymer binder. X-ray phase contrast imaging (X-PCI) is used as an in-material diagnostic in plate-impact experiments performed at the Dynamic Compression Sector of the Advanced Photon Source at the Argonne National Laboratory. Macroscale shock and particle velocities and microscale pore collapse velocities are measured from X-PCI images obtained from samples impacted in various orientations relative to the AM build directions. The linear shock and particle velocity relation for the structures with collinear build geometry shows directional differences, with the build direction exhibiting the highest slope and the filament print direction having the shallowest slope. Composites with log-cabin geometry, due to their slightly lower porosity and smaller sized pores, show higher shock velocities than the collinear structures at the same particle velocity. Microscale directionality effects are also observed, with the pore collapse velocity dependent on the pore configuration. Specifically, the pore collapse velocity is highest for pores elongated along the impact direction which is the case for the colinear structures impacted in the filament print direction, which is consistent with the directionality trend observed in the data for the shock and particle velocity equation of state. The pore collapse velocity approaches ∼80 % of the shock velocity and is several times the average bulk particle velocity, suggesting a highly hydrodynamic mechanism of pore collapse. Overall, both the macroscale response as measured by the shock and particle velocity relationship, and the microscale response as measured by the pore collapse velocity, are found to be anisotropic and dependent on the AM composite structure.

Wagner, K. B. [Georgia Institute of Technology, At↗

DOE OSTI2017

3D printing technologies for electrochemical energy storage

Fabrication of electrodes and electrolytes play an important role in promoting the performance of electrochemical energy storage (EES) devices such as batteries and supercapacitors. Traditional fabrication techniques have limited capability in controlling the geometry and architecture of the electrode and solid-state electrolytes, which would otherwise compromise the performance. 3D printing, a disruptive manufacturing technology, has emerged as an innovative approach to fabricating EES devices from nanoscale to macroscale and from nanowatt to megawatt, providing great opportunities to accurately control device geometry (e.g., dimension, porosity, morphology) and structure with enhanced specific energy and power densities. Moreover, the additive manufacturing nature of 3D printing provides excellent controllability of the electrode thickness with much simplified process in a cost effective manner. With the unique spatial and temporal material manipulation capability, 3D printing can integrate multiple nanomaterials in the same print, and multi-functional EES devices (including functional gradient devices) can be fabricated. Herein, we review recent advances in 3D printing of EES devices. We focused on two major 3D printing technologies including direct writing and inkjet printing. The direct material deposition characteristics of these two processes enable them to print on a variety of flat substrates, even a conformal one, well suiting them to applications such as wearable devices and on-chip integrations. Other potential 3D printing techniques such as freeze nano-printing, stereolithography, fused deposition modeling, binder jetting, laminated object manufacturing, and metal 3D printing are also introduced. The advantages and limitations of each 3D printing technology are extensively discussed. More importantly, we provide a perspective on how to integrate the emerging 3D printing with existing technologies to create structures over multiple length scale from macro to nano for EES applications.

3D printing↗

DOE OSTI2015

A Hybrid Multiscale Framework for Subsurface Flow and Transport Simulations

Extensive research efforts have been invested in reducing model errors to improve the predictive ability of biogeochemical earth and environmental system simulators, with applications ranging from contaminant transport and remediation to impacts of biogeochemical elemental cycling (e.g., carbon and nitrogen) on local ecosystems and regional to global climate. While the bulk of this research has focused on improving model parameterizations in the face of observational limitations, the more challenging type of model error/uncertainty to identify and quantify is model structural error which arises from incorrect mathematical representations of (or failure to consider) important physical, chemical, or biological processes, properties, or system states in model formulations. While improved process understanding can be achieved through scientific study, such understanding is usually developed at small scales. Process-based numerical models are typically designed for a particular characteristic length and time scale. For application-relevant scales, it is generally necessary to introduce approximations and empirical parameterizations to describe complex systems or processes. This single-scale approach has been the best available to date because of limited understanding of process coupling combined with practical limitations on system characterization and computation. While computational power is increasing significantly and our understanding of biological and environmental processes at fundamental scales is accelerating, using this information to advance our knowledge of the larger system behavior requires the development of multiscale simulators. Accordingly there has been much recent interest in novel multiscale methods in which microscale and macroscale models are explicitly coupled in a single hybrid multiscale simulation. A limited number of hybrid multiscale simulations have been developed for biogeochemical earth systems, but they mostly utilize application-specific and sometimes ad-hoc approaches for model coupling. We are developing a generalized approach to hierarchical model coupling designed for high-performance computational systems, based on the Swift computing workflow framework. In this presentation we will describe the generalized approach and provide two use cases: 1) simulation of a mixing-controlled biogeochemical reaction coupling pore- and continuum-scale models, and 2) simulation of biogeochemical impacts of groundwater – river water interactions coupling fine- and coarse-grid model representations. This generalized framework can be customized for use with any pair of linked models (microscale and macroscale) with minimal intrusiveness to the at-scale simulators. It combines a set of python scripts with the Swift workflow environment to execute a complex multiscale simulation utilizing an approach similar to the well-known Heterogeneous Multiscale Method. User customization is facilitated through user-provided input and output file templates and processing function scripts, and execution within a high-performance computing environment is handled by Swift, such that minimal to no user modification of at-scale codes is required.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Elucidating the Microscale Behavior and Phase Separation Kinetics of Thermally Responsive Ionic Liquid–Water Mixtures

Thermally responsive ionic liquids (ILs) exhibit liquid-liquid phase separation into a water-rich (WR) and ionic-liquid-rich (ILR) phase when heated above a lower critical solution temperature (LCST). This phase behavior has been leveraged for applications ranging from forward osmosis (FO) desalination, where the IL acts as a draw solute, to refrigeration and dehumidification cycles, where the IL acts as a liquid desiccant. While significant effort has been devoted to characterizing the thermodynamic and thermophysical properties of LCST ILs, their phase separation kinetics have not been investigated. In this work, we describe the macroscale phase separation kinetics (phase separation time) by gleaning insight into the microscale colloidal behavior of aqueous mixtures of four different materials, P 4444 TFA (tetrabutylphosphonium-2,4-trifluoroacetate), P 4444 DMBS (tetrabutylphosphonium-2,4-dimethyl-benzenesulfonate), N 4444 Sal (tetrabutylammonium salicylate), and P 4444 Sal (tetrabutylphosphonium salicylate) as a function of IL concentration at a separation temperature of 70 °C. We report the discontinuous microscale size distributions for each material and correlate their theoretical settling velocities to experimental phase separation times. The results indicate that a simple Stokes' law model can predict the phase separation time within reasonable accuracy. Overall, this work lays the foundation for understanding the micro- to macroscale phase separation behavior and kinetics of LCST ILs for various water-energy applications.

LCST↗

DOE OSTI2023

High-Resolution X-ray Spectromicroscopy Reveals Process-Structure Correlations in sub-5-μm Diameter Carbon Nanotube-Polymer Composite Dry-Spun Yarns

A persistent lack of detailed and quantitative structural analysis of these hierarchical carbon nanotube (CNT) ensembles precludes establishing processing-structure-property relationships that are essential to enhance macroscale performance (e.g., in mechanical, electrical, thermal applications). Here, in this work, we use scanning transmission X-ray microscopy (STXM) to analyze the hierarchical, twisted morphology of dry-spun CNT yarns and their composites, quantifying key structural characteristics such as density, porosity, alignment, and polymer loading. As the yarn twist density increases (15,000 to 150,000 turns per meter), the yarn diameter decreased (4.4–1.4 μm) and density increased (0.55–1.26 g·cm –3 ), as intuitively expected. Yarn density, ρ, ubiquitously scaled with diameter d according to ρ ~ d –2 for all parameters studied here. Spectromicroscopy probes with 30 nm resolution and elemental specificity were employed to analyze the radial and longitudinal distribution of the oxygen-containing polymer content (~30% weight fraction), demonstrating nearly perfect filling of the voids between CNTs with a vapor-phase polymer coating and cross-linking process. These quantitative correlations highlight the intimate connections between processing conditions and yarn structure with important implications for translating the nanoscale properties of CNTs to the macroscale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗