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Hierarchical thermal-conductive polymer nanocomposites for thermal management
Managing heat in electrical conductors is a major challenge to meet the demands for sustainable energy use and electrical reliability, most notably power electronics and energy-critical electrical machines. Achieving such disparate functionalities, such as high temperature thermal and electrical reliability, require rational design and manufacturing of thermal conductor material and its hierarchical structures. Here we present hierarchical thermal-conductive nanocomposites, consisting of nanostructured ceramic conformal coating and aligned ultrahigh molecular weight polyethylene fiber, to tailor heat dissipation in electric conductors. The hybrid aligned thermal interface exhibits a highly desirable temperature dependent anisotropic high thermal conductivity with 0.98W m –1 K –1 and dielectric strength with 3.4. In addition, electrically insulating thermal interfaces demonstrate high-performing and reliable electrical systems under the dynamic load conditions. The surface temperature of heterogeneous ceramic-polymer encapsulated conductor is 17.8 °C lower than that of polymer-encapsulated conductor at the same electrical load. Consequently, the findings shown here hold great promises for directing heat extraction in electrical machine systems, advancing thermal management for emerging electronic applications.
Hierarchical microstructures: a potential route to enhanced stability in structural materials for advanced nuclear reactors
The drive to increase efficiency in nuclear energy systems is leading to the need for materials that operate at higher temperatures and stress levels for extended periods, while maintaining stable microstructures to ensure their performance is not compromised. A novel route to producing materials that can perform well in such environments is the creation of hierarchical microstructures. A hierarchical microstructure is a microstructure in which features are present at multiple length scales simultaneously. In this work, a hierarchical microstructure is fabricated in a nickel-base superalloy, featuring nanometer-size gamma precipitates inside larger gamma-prime particles, which are in turn embedded in the gamma matrix phase. The hierarchical features of the microstructure lead to enhanced stability of the gamma-prime precipitates during annealing; the particle size does not follow the expected t^(1/3) growth law predicted by the classic LSW theory. Phase-field simulations are used to understand the unexpected stability of the gamma-prime precipitates.
Hierarchical nickel valence gradient stabilizes high-nickel content layered cathode materials
High-nickel content cathode materials offer high energy density. However, the structural and surface instability may cause poor capacity retention and thermal stability of them. To circumvent this problem, nickel concentration-gradient materials have been developed to enhance high-nickel content cathode materials’ thermal and cycling stability. Even though promising, the fundamental mechanism of the nickel concentration gradient’s stabilization effect remains elusive because it is inseparable from nickel’s valence gradient effect. To isolate nickel’s valence gradient effect and understand its fundamental stabilization mechanism, we design and synthesize a LiNi 0.8 Mn 0.1 Co 0.1 O 2 material that is compositionally uniform and has a hierarchical valence gradient. The nickel valence gradient material shows superior cycling and thermal stability than the conventional one. The result suggests creating an oxidation state gradient that hides the more capacitive but less stable Ni 3+ away from the secondary particle surfaces is a viable principle towards the optimization of high-nickel content cathode materials.
A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models
Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.
Hierarchical microstructures: a potential route to enhanced stability in structural materials for advanced nuclear reactors
The drive to increase efficiency in nuclear energy systems is leading to the need for materials that operate at higher temperatures and stress levels for extended periods, while maintaining stable microstructures to ensure their performance is not compromised. A novel route to producing materials that can perform well in such environments is the creation of hierarchical microstructures. A hierarchical microstructure is a microstructure in which features are present at multiple length scales simultaneously. In this work, a hierarchical microstructure is fabricated in a nickel-base superalloy, featuring nanometer-size gamma precipitates inside larger gamma-prime particles, which are in turn embedded in the gamma matrix phase. The hierarchical features of the microstructure lead to enhanced stability of the gamma-prime precipitates during annealing; the particle size does not follow the expected t^(1/3) growth law predicted by the classic LSW theory. Phase-field simulations are used to understand the unexpected stability of the gamma-prime precipitates.
Nanoporous Materials Genome Center Final Technical Report
Nanoporous materials (NPMs), including zeolites/zeotypes, metal-organic frameworks (MOFs), covalent organic frameworks, polymers with intrinsic microporosity, and molecular cages, possess enormous potential in diverse areas relevant to the DOE Office of Science Basic Energy Sciences (BES) mission and objectives. The Nanoporous Materials Genome Center (NMGC) has developed exascale-ready software, computational/theoretical chemistry methods, and data-driven science approaches that enable (i) the de-novo design of functional NPMs for chemical separation and catalysis tasks of increasing complexity, (ii) the discovery of the most promising functional NPMs from databases of synthesized and hypothetical adsorbent structures and the optimization of process conditions for specific applications, and (iii) the microscopic-level understanding of the fundamental interactions underlying the function of NPMs including hierarchical architectures, composite materials, responsive frameworks that may undergo phase transitions or post-synthetic modifications, and materials containing defects, partial disorder, or interfaces. A pivotal part of the NMGC project has been a tight collaboration between leading experimental groups for synthesis and characterization of NPMs and of computational groups that allowed for iterative feedback. The NMGC project has resulted in the publication of more than 290 research and review articles including more than 60 publications in high-impact journals and more than 15 journal covers. NMGC publications have already received more than 20,000 citations (with more than 3,000 citations per year in 2021, 2022, and 2023) and contribute to an h-index of more than 72. The NMGC award has supported collaborative research involving 28 research groups and contributed to the training of more than 40 postdocs, more than 60 graduate students, and more than 20 undergraduate students with broad expertise in data-driven science approaches, computational chemistry methods, and high-performance computing, in addition to the skills to thrive in an integrated experimental and computational research environment.
Engineering hierarchical nanochannels in graphene oxide membranes by etching and polydopamine intercalation for highly efficient dye recovery
Graphene oxide (GO) membranes have been extensively investigated for dye removal from wastewater, and they are often reduced or cross-linked to improve their stability, which, however, decreases water permeance. This study demonstrates an effective approach to enhance both durability and water permeance and retain dye rejection by synergistically integrating chemical etching using H 2 O 2 and intercalation with polydopamine (PDA) to create hierarchical nanochannels. The nanopores on the holey GO (HGO) shorten the diffusion path, while the PDA intercalation increases the channel sizes and stabilizes the GO nanosheets. Optimized membranes exhibit water permeance of 70–120 LMH/bar (much higher than state-of-the-art commercial polyamide membranes) and a Congo Red rejection of 98.5%, better than GO membranes reported in the literature. Moreover, a membrane with water permeance of 70 LMH/bar exhibits stable performance in a 3-day continuous crossflow filtration test. The versatile approach reported here may be applied to other two-dimensional materials to create hierarchical nanochannels for desired separations.
Biomineral mesostructure
Abstract Biominerals formed by animals are most frequently calcium carbonate or phosphate polycrystalline materials with complex hierarchical structures. This article will focus on the 10-nm–10-µm scale, termed “mesoscale,” at which the “mesostructure” differs greatly across biominerals, is relevant to their mechanical properties, and reveals formation mechanisms in sea urchin teeth, mollusk shell prisms and nacre, human enamel, and coral skeletons. This article will conclude by focusing on important unanswered questions to inspire future research. Graphical abstract
MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning
Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.
Designing Multiscale Porous Metal by Simple Dealloying with 3D Morphological Evolution Mechanism Revealed via X-ray Nano-tomography
Designing materials with multiscale, hierarchical structure is critical to drive the advancement of new technology. Specifically, porous metals with multiscale porosity from nanometer to micrometer sizes would lead to enhanced physical and chemical properties—the micron-sized pores can increase the effective diffusivity of ion transport within the porous media, and the nano-sized pores provide high specific surface area, enabling functionalities that are unique to nanoporous metals. A new ternary precursor alloy selection concept utilizing the different mixing enthalpies is demonstrated in this work for the design of multiscale, bimodal porous copper from a simple, one-step dealloying of Cu–Fe–Al ternary alloy. The nanoporosity in the bimodal porous structure is formed from dealloying of the Cu-rich phase, whereas the microporosity is controlled by dissolving the Fe-rich phase, determined by both the initial Fe particle size and sintering profile. In addition to advancing the materials design method, the multiscale pore formation during dealloying was directly visualized and quantified via an interrupted in situ synchrotron X-ray nano-tomography. The 3D morphological analysis on tortuosity showed that the presence of the microporosity can compensate the increase of the diffusion path length due to nanoporosity, which facilitates diffusion within the porous structure. Altogether the focus of the work is to introduce a new strategy to design multiscale porous metals with enhanced transport properties, and sheds light on the fundamental mechanisms on the 3D morphological evolution of the system using advanced synchrotron X-ray nano-tomography for future materials development and applications.
A Multiscale Inelastic Internal State Variable Corrosion Model
We present a corrosion internal state variable (ISV) damage model based upon the integrated computational materials engineering (ICME) hierarchical multiscale paradigm. Structure–property experiments for magnesium alloys were used where the only inputs were the volume fractions of each element of the periodic table. This macroscale ISV corrosion model finds its basis in Horstemeyer’s mechanical damage model, which includes three separate ISVs for damage nucleation, growth, and coalescence, as well as Walton’s inclusion of corrosion, which introduces five new ISVs for pit nucleation, growth, and coalescence, along with general corrosion and intergranular corrosion. While Walton’s corrosion ISVs are phenomenological in nature, herein we develop a multiscale physical basis for the corrosion ISVs. The parameters for the macroscale corrosion ISVs were garnered from the mesoscale Butler–Volmer equations. Pure magnesium with differing amounts of aluminum were used in corrosion tests to exemplify the different pitting, general corrosion, and intergranular corrosion rates, and the macroscale ISV model was calibrated with said data, in which the only inputs to the model are the volume percentages of the elements magnesium and aluminum. Although magnesium alloys were used to motivate and calibrate the model, the model is abstract enough to possibly capture other material systems as well.
Mesocrystal growth through oriented sliding and attachment of nanoplates
Oriented attachment is a critical, yet poorly understood, crystal growth pathway based on the self-assembly of nanocrystals. During oriented attachment, solvent-separated particles align and coalesce through forces that enable precise rotation and translation. While prior studies emphasized intragap forces driving crystallographic alignment, the forces enabling uniform stacking and superlattice formation remain unclear. Here, we demonstrate how macroscopic gibbsite mesocrystals emerge from nanoplates guided into staggered positions by directional sliding. Electron microscopy and X-ray scattering reveal the monoclinic superlattice structure, based on nanoplate stacking with a uniform ≈50° stagger along the gibbsite [010] direction. In situ liquid-cell TEM captures preferential sliding along the gibbsite [010] direction, decelerating with increasing particle overlap. Molecular dynamics simulations reveal that this staggered arrangement corresponds to a global free-energy minimum, rather than full alignment. The simulations also confirm that sliding along the [010] direction is energetically favored and provide insight into the role of interfacial water in achieving long-range ordered assemblies. These insights highlight the energy landscape’s role in oriented attachment, with implications for material synthesis and hierarchical structures in nature.
Post-Modification of Crystalline Peptoid Nanomembranes with Active Nanoparticles for Efficient Photooxidation of a Mustard Gas Simulant
Peptoids (or poly-N-substituted glycines) hold immense potential for assembling into hierarchically structured functional materials via controlled molecular interactions. To create self-assembled materials with tailored functionalities, peptoid sequences are often conjugated with reactive or recognition motifs to enable applications including specific binding, biomimetic catalysis, and fluorescence imaging. However, the direct integration of bulky functional motifs into peptoid sequences can disrupt assembly processes and structural outcomes. Herein, we present a post-modification strategy for functionalizing pre-formed 2D crystalline assemblies. Through introducing clickable active sites, such as azide, alkyne, or thiol groups into a peptoid sequence, site-specific conjugation is achieved post-assembly via efficient “click”-type reactions. This strategy enables the ordered alignment of functional groups and gold nanoparticles (Au NPs) on the surface of 2D peptoid nanomaterials with controlled density, while preserving their high crystallinity and structural integrity. Furthermore, we demonstrated that nanomembranes functionalized with both Au NPs and porphyrins enhance the efficiency and selectivity of the photooxidation of 2-chloroethyl ethyl sulfide, a simulant of sulfur mustard. This innovative strategy lays the groundwork for advancing peptoid-based functional materials across diverse applications, from catalysis to biomedicine.
Self-assembly of aramid amphiphiles into ultra-stable nanoribbons and aligned nanoribbon threads
Small molecule self-assembly is an established route for producing high surface area nanostructures with readily customizable chemistries and precise molecular organization. However, these structures are fragile, exhibiting molecular exchange, migration, and rearrangement - among other dynamic instabilities - and ultimately disassociate upon drying. Here we show a small molecule platform, the aramid amphiphile (AA), that overcomes these dynamic instabilities by incorporating a Kevlar-inspired domain into the molecular structure. Strong, anisotropic interactions between AAs suppress molecular exchange and elicit spontaneous self-assembly in water to form nanoribbons with lengths of up to 20 microns. The nanoribbons have a Young’s modulus of 1.7 GPa and tensile strength of 1.9 GPa. Here, we exploit this stability to extend small molecule self-assembly to hierarchically ordered macroscopic materials outside of solvated environments. Through an aqueous shear alignment process, we organize AA nanoribbons into arbitrarily long flexible threads that support 200 times their weight when dried. Tensile tests of the threads provide a benchmark for Young’s moduli (between ~400 and 600 MPa) and extensibilities (between ~0.6 and 1.1%) that depend on the counterion chemistry in solution. This bottom-up approach to macroscopic materials could benefit solid-state applications, historically inaccessible by self-assembled nanomaterials.
Spatially varying colloidal phase behavior on multi-dimensional energy landscapes
A method is reported to determine equilibrium concentration profiles and local phase behavior for colloids on multi-dimensional energy landscapes. A general expression is derived based on local particle concentration and osmotic pressure differences that are balanced by forces on colloids due to energy landscape gradients. This analysis is applied to colloidal particles in high frequency AC electric fields within octupolar electrodes, where the energy landscape can be shaped in two dimensions. These results are also directly applicable to any particles having induced dipoles in spatially non-uniform electromagnetic fields. Predictions based on modelling colloids with an effective hard disk equation of state indicate inhomogeneous solid and fluid states coexisting on different shaped energy landscapes including multiple minima. Model predictions show excellent agreement with time-averaged Brownian Dynamic simulations at equilibrium. Findings demonstrate a general approach to understand colloidal phase behavior on energy landscapes due to external fields, which could enable control of colloidal microstructure on morphing energy landscapes and the inverse design of fields to assemble hierarchically structured colloidal materials.
Leveraging Additive Manufacturing to Fabricate High Temperature Alloys With Co-Designed Mechanical Properties and Environmental Resistance
A paradigm shift in the traditional sequential design approaches is critically essential to create application-specific hierarchical and multifunctional materials with superior long-term performance for next-generation energy technologies involving extreme environments. In the current work, we aim to leverage the flexibility and geometric/compositional complexity offered by additive manufacturing to demonstrate this new approach by codesigning a compositionally graded Ni-based alloy for molten salts\sCO 2 heat exchangers to enable mitigation of environmental degradation of surfaces exposed to molten halide salts, while simultaneously suppressing the consequent deterioration in mechanical stability. Further, thermokinetic modeling describing the underlying physics of thermally- and environmentally induced spatiotemporal compositional and microstructural evolution will be employed to predict the parameter space of material deposition processes and precisely identify the required composition gradient. Preliminary corrosion and mechanical testing of the dual material demonstrated the potential of the material to replace existing solid solution strengthened materials for this application.
System and method for using ultramicroporous carbon for the selective removal of nitrate with capacitive deionization
The present disclosure relates to a flow through electrode, capacitive deionization (FTE-CDI) system which is able to adsorb nitrates from water being treated using the system. The system makes use of a pair of electrodes arranged generally parallel to one another, with a water permeable dielectric sandwiched between the electrodes. The electrodes receive a direct current voltage from an electrical circuit. At least one of the electrodes is formed from a carbon material having a hierarchical pore size distribution which includes a first plurality of pores having a width of no more than about 1 nm, and a second plurality of micro-sized pores. The micron-sized pores enable a flow of water to be pushed through the electrodes while the first plurality of pores form adsorption sites for nitrate molecules carried in the water flowing through the electrodes.