Efficient structural reliability including the effects of crystallographic texture on engineering-scale performance.
Abstract not provided.
Emery, John M↗
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Probing the subcellular nanostructure of engineered human cardiomyocytes in 3D tissue
Abstract The structural and functional maturation of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is essential for pharmaceutical testing, disease modeling, and ultimately therapeutic use. Multicellular 3D-tissue platforms have improved the functional maturation of hiPSC-CMs, but probing cardiac contractile properties in a 3D environment remains challenging, especially at depth and in live tissues. Using small-angle X-ray scattering (SAXS) imaging, we show that hiPSC-CMs matured and examined in a 3D environment exhibit a periodic spatial arrangement of the myofilament lattice, which has not been previously detected in hiPSC-CMs. The contractile force is found to correlate with both the scattering intensity ( R 2 = 0.44) and lattice spacing ( R 2 = 0.46). The scattering intensity also correlates with lattice spacing ( R 2 = 0.81), suggestive of lower noise in our structural measurement than in the functional measurement. Notably, we observed decreased myofilament ordering in tissues with a myofilament mutation known to lead to hypertrophic cardiomyopathy (HCM). Our results highlight the progress of human cardiac tissue engineering and enable unprecedented study of structural maturation in hiPSC-CMs.
36 MATERIALS SCIENCE↗
Molecular scale insight of pore morphology relation with mechanical properties of amorphous silica using ReaxFF
Porous materials are typically heterogeneous and they contain large variations of micro-/nano-pore structures, causing complicated behaviors. In continuum models, most mechanical properties of porous materials are estimated based on porosity, while the variations of micro/nano structures are ignored. That could be problematic as the microscopic heterogeneity may affect the mechanical response of porous materials. Thus, understanding micro/nano heterogeneity impact has been the focus in many scientific and engineering subjects. In the present study, we investigated the effect of nanopore structure (including pore shape and orientation) as well as porosity on mechanical properties of amorphous silica (a-SiO 2 ). The pore sizes in our simulations are comparable to the corresponding ones observed in a-SiO 2 based materials. We found that the existing of nanopores strongly influences Young’s modulus (E) and critical energy release rate (G IC ). These properties decrease with increasing porosity. Importantly, the impact of nanopores was characterized by structural parameters of porous materials. In addition to dependency on porosity, Young’s modulus also was found to vary as a function of potential energy per atom, which highly depends on nanopore shape. Furthermore, critical energy release rate was found to increase with increasing ligament length (also known as pore wall thickness). Finally, the results highlighted the importance of nanopore structures, which must be taken into account when studying fracture mechanisms in porous materials. Based on our findings, it was proposed that mechanical properties of porous materials can be controlled by nano-engineering pore structures.
36 MATERIALS SCIENCE↗
Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning
Abstract Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.
Bruefach, Alexandra (ORCID:0000000209323477)↗
Atomically dispersed metal–nitrogen–carbon catalysts for fuel cells: advances in catalyst design, electrode performance, and durability improvement
The urgent need to address the high-cost issue of proton-exchange membrane fuel cell (PEMFC) technologies, particularly for transportation applications, drives the development of simultaneously highly active and durable platinum group metal-free (PGM-free) catalysts and electrodes. The past decade has witnessed remarkable progress in exploring PGM-free cathode catalysts for the oxygen reduction reaction (ORR) to overcome sluggish kinetics and catalyst instability in acids. Among others, scientists have identified the newly emerging atomically dispersed transition metal (M: Fe, Co, or/and Mn) and nitrogen co-doped carbon (M–N–C) catalysts as the most promising alternative to PGM catalysts. Here, we provide a comprehensive review of significant breakthroughs, remaining challenges, and perspectives regarding the M–N–C catalysts in terms of catalyst activity, stability, and membrane electrode assembly (MEA) performance. A variety of novel synthetic strategies demonstrated effectiveness in improving intrinsic activity, increasing active site density, and attaining optimal porous structures of catalysts. Rationally designing and engineering the coordination environment of single metal MN x sites and their local structures are crucial for enhancing intrinsic activity. Increasing the site density relies on the innovative strategies of restricting the migration and agglomeration of single metal sites into metallic clusters. Relevant understandings provide the correlations among the nature of active sites, nanostructures, and catalytic activity of M–N–C catalysts at the atomic scale through a combination of experimentation and theory. Current knowledge of the transferring catalytic properties of M–N–C catalysts to MEA performance is limited. Rationally designing morphologic features of M–N–C catalysts play a vital role in boosting electrode performance through exposing more accessible active sites, realizing uniform ionomer distribution, and facilitating mass/proton transports. We outline future research directions concerning the comprehensive evaluation of M–N–C catalysts in MEAs. The most considerable challenge of current M–N–C catalysts is the unsatisfied stability and rapid performance degradation in MEAs. Therefore, we further discuss practical methods and strategies to mitigate catalyst and electrode degradation, which is fundamentally essential to make M–N–C catalysts viable in PEMFC technologies.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Constructing Polymorphic Nanodomains in BaTiO 3 Films via Epitaxial Symmetry Engineering
Ferroelectric materials owning a polymorphic nanodomain structure usually exhibit colossal susceptibilities to external mechanical, electrical, and thermal stimuli, thus holding huge potential for relevant applications. Despite the success of traditional strategies by means of complex composition design, alternative simple methods such as strain engineering have been intensively sought to achieve a polymorphic nanodomain state in lead-free, simple-composition ferroelectric oxides in recent years. Here, a nanodomain configuration with morphed structural phases is realized in an epitaxial BaTiO3 film grown on a (111)-oriented SrTiO3 substrate. Using a combination of experimental and theoretical approaches, it is revealed that a threefold rotational symmetry element enforced by the epitaxial constraint along the [111] direction of BaTiO3 introduces considerable instability among intrinsic tetragonal, orthorhombic, and rhombohedral phases. Such phase degeneracy induces ultrafine ferroelectric nanodomains (1-10 nm) with low-angle domain walls, which exhibit significantly enhanced dielectric and piezoelectric responses compared to the (001)-oriented BaTiO3 film with uniaxial ferroelectricity. Therefore, the finding highlights the important role of epitaxial symmetry in domain engineering of oxide ferroelectrics and facilitates the development of dielectric capacitors and piezoelectric devices.
36 MATERIALS SCIENCE↗
Development of self-sensing materials for extreme environments based on metamaterial concept and additive manufacturing
Structural health monitoring of an engineered component in a harsh environment is critical for multiple DOE missions including nuclear fuel cycle, subsurface energy production/storage, and energy conversion. Supported by a seeding Laboratory Directed Research & Development (LDRD) project, we have explored a new concept for structural health monitoring by introducing a self-sensing capability into structural components. The concept is based on two recent technological advances: metamaterials and additive manufacturing. A self-sensing capability can be engineered by embedding a metastructure, for example, a sheet of electromagnetic resonators, either metallic or dielectric, into a material component. This embedment can now be realized using 3-D printing. The precise geometry of the embedded metastructure determines how the material interacts with an incident electromagnetic wave. Any change in the structure of the material (e.g., straining, degradation, etc.) would inevitably perturbate the embedded metastructures or metasurface array and therefore alter the electromagnetic response of the material, thus resulting in a frequency shift of a reflection spectrum that can be detected passively and remotely. This new sensing approach eliminates complicated environmental shielding, in-situ power supply, and wire routing that are generally required by the existing active-circuit-based sensors. The work documented in this report has preliminarily demonstrated the feasibility of the proposed concept. The work has established the needed simulation tools and experimental capabilities for future studies.
36 MATERIALS SCIENCE↗
Gas flow path for a gas turbine engine
A duct arrangement in a can annular gas turbine engine. The gas turbine engine has a gas delivery structure for delivering gases from a plurality of combustors to an annular chamber that extends circumferentially and is oriented concentric to a gas turbine engine longitudinal axis for delivering the gas flow to a first row of blades A gas flow path is formed by the duct arrangement between a respective combustor and the annular chamber for conveying gases from each combustor to the first row of turbine blades The duct arrangement includes at least one straight section having a centerline that is misaligned with a centerline of the combustor.
33 ADVANCED PROPULSION SYSTEMS↗
Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications
Polymeric materials are ubiquitous in modern society and they play an instrumental role in almost all industries, undoubtedly including the energy and environment sectors. Increased demand of energy and awareness to sustainability both necessitates the development of novel polymers with enhanced properties. Unfortunately, their structural and behavioral complexity render such discovery challenging and impeded. To address this problem, scientists are developing various computational modeling techniques and leveraging their power to depict the relationship between structural characteristics of polymers and their properties (such as rheological behaviors), and use such prediction to guide the design and syntheses of novel polymeric materials with enhanced performances. Unfortunately, predicting the relationships between polymer structure and composition with rheological properties via atomistic modeling is still a major challenge because of the extended time and length scales involved. Studying dynamic shear viscosity and linear viscoelasticity using molecular models requires capabilities that have been elusive, including representation of large molecular weight chains with an effective internal scale capable of describing entanglement, shear-rates that are in the s-1 scale with accurate quantitative stresses, and chemically-realistic combinations of both homogeneous and heterogeneous systems. Motivated by these unmet challenges, the overall technical objective of this DOE-STTR Phase II project is to develop robust molecular predictive methods for advanced polymer discovery and applications and especially for designing and demonstrating the “smart” polymer-based waterflooding enhanced oil recovery (EOR) process. In particular, we apply state-of-the-art molecular modeling methods developed by our academic partner, Materials Stimulation Center (MSC) at California Institute of Technology (Caltech), to facilitate and accelerate the experimental discovery processes. During the Phase I of this project, we had focused on development and demonstration of the molecular modeling methods to describe rheological properties of non-Newtonian polymer fluids, and to improve our fundamental understandings of shear-thickening mechanism and kinetics. In Phase II, we further apply the theoretical models to guide our experimental programs to improve our design of smart rheology modifier (SRM) polymers and their optimization for EOR. Specifically, we have three objectives in the Phase II study: (1) to further improve out computational modeling methods, coupling with the advanced machine learning algorithms; (2) to develop cost-effective and efficient SRM-flooding process suitable for EOR applications under typical reservoir conditions; and (3) to further explore the application of our molecular predictive models for innovative material discovery in other industrial applications. The recent development of our multiscale predictive framework allows the successful prediction of rheological properties from the chemical structure for polymers of experimentally relevant molecular weights, and provides an in-silico machine learning engine for screening novel compositions and structures with optimized non-Newtonian response, required for both shear-thinning and shear-thickening applications. Our framework provides: (1) procedures and tools for systematic coarsening from atomistic models and reverse mapping of coarse-grain models to atomistic, (2) unique ab initio methods to characterize the atomistic origin of colloidal and interfacial interactions and phenomena, (3) systematic structure and composition builders based on practical descriptors that drive rheological changes in polymer melts and diluted polymer mixtures, (4) a rheological properties engine capable of predicting viscosity in the zero-shear limit and under realistic dynamic conditions (for shear-rates commensurate with experiments) for large heterogeneous systems, (5) coarse-grain force fields with improved non-bond descriptions based on accurate quantum mechanics, (6) an in-silico screening machine learning engine that feeds from the systematic model builders to cover the descriptors search space, computes the rheological properties from converged trajectories spanning sub-milliseconds and ranks them for each structure/composition using an automated viscosity-vs-shear rate fitness function that can be tuned for shear-thickening, shear-thinning and other rheological responses.
02 PETROLEUM↗
3D printed structures for modeling the Young’s modulus of bamboo parenchyma
Not Available
Dixon, P. G.↗
Thermal photonics with broken symmetries
Abstract Nanophotonic engineering provides an effective platform to manipulate thermal emission on-demand, enabling unprecedented heat management superior to conventional bulk materials. Amongst a plethora of nanophotonic structures, symmetries play an important role in controlling radiative heat transfer in both near-field and far-field. In physics, broken symmetries generally increase the degree of freedom in a system, enriching the understanding of physical mechanisms and bringing many exciting opportunities for novel applications. In this review, we discussed the underlying physics and functionalities of nanophotonic structures with broken geometrical symmetries, engineered mode symmetries, and broken reciprocity for the control of thermal emission. We overview a variety of physical phenomena and interesting applications, and provide the outlook for future development.
Liu, Tianji↗
Accounting for erroneous model structures in biokinetic process models
In engineering practice, model-based design requires not only a good process-based model, but also a good description of stochastic disturbances and measurement errors to learn credible parameter values from observations. However, typical methods use Gaussian error models, which often cannot describe the complex temporal patterns of residuals. Consequently, this results in overconfidence in the identified parameters and, in turn, optimistic reactor designs. Here, we assess the strengths and weaknesses of a method to statistically describe these patterns with autocorrelated error models. This method produces increased widths of the credible prediction intervals following the inclusion of the bias term, in turn leading to more conservative design choices. However, we also show that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design.
42 ENGINEERING↗
Layer-by-layer epitaxial growth of defect-engineered strontium cobaltites
Here, control over structure and composition of (ABO 3 ) perovskite oxides offers exciting opportunities since these materials possess unique, tunable properties. Perovskite oxides with cobalt B-site cations are particularly promising, as the range of the cation’s stable oxidation states leads to many possible structural frameworks. Here, we report growth of strontium cobalt oxide thin films by molecular beam epitaxy, and conditions necessary to stabilize different defect concentration phases. In situ X-ray scattering is used to monitor structural evolution during growth, while in situ X-ray absorption near-edge spectroscopy is used to probe oxidation state and measure changes to oxygen vacancy concentration as a function of film thickness. Experimental results are compared to kinetically-limited thermodynamic predictions, in particular, solute trapping, with semi-quantitative agreement. Agreement between observations of dependence of cobaltite phase on oxidation activity and deposition rate, and predictions indicates that a combined experimental/theoretical approach is key to understanding phase behavior in the strontium cobalt oxide system.
36 MATERIALS SCIENCE↗
Achieving high tensile strength and ductility in refractory alloys by tuning electronic structure
The energy efficiency of heat engines (gas and steam turbines) for electricity production and propulsion is determined by the Carnot cycle and scales with operating temperature. Commercial nickel- and cobalt-based superalloys melt near 1,500 °C and rapidly lose mechanical strength beyond 1,000 °C. Refractory metals melt well above 2,000 °C but have inherent manufacturability challenges that are barriers to adoption, such as high ductile-to-brittle transition temperatures. Using density functional theory-guided design, we demonstrate tailored local lattice distortions that promote phase-stable, non-equiatomic refractory concentrated solid solutions with both high ductility and strength. Here, we exemplify this for single-phase, body-centred cubic Nb 4 Ta 4 V 3 Ti that exhibits castability, excellent room-temperature tensile yield strength (∼1 GPa) and ductility (approaching 20% uniform strain), and exceptional high-temperature tensile strength (500 MPa at 1,000 °C). These findings illustrate a path for designing materials that hold great potential for advancing next-generation technologies such as Generation IV fission reactors, first-generation fusion-plasma reactors, and more efficient gas turbines for electricity generation and propulsion.
DFT↗
Developing a sustainability science approach for water systems
We convened a workshop to enable scientists who study water systems from both social science and physical science perspectives to develop a shared language. This shared language is necessary to bridge a divide between these disciplines’ different conceptual frameworks. As a result of this workshop, we argue that we should view socio-hydrological systems as structurally co-constituted of social, engineered, and natural elements and study the “characteristic management challenges” that emerge from this structure and reoccur across time, space, and socioeconomic contexts. This approach is in contrast to theories that view these systems as separately conceptualized natural and social domains connected by bi-directional feedbacks, as is prevalent in much of the water systems research arising from the physical sciences. A focus on emergent characteristic management challenges encourages us to go beyond searching for evidence of feedbacks and instead ask questions such as: What types of innovations have successfully been used to address these challenges? What structural components of the system affect its resilience to hydrological events and through what mechanisms? Are there differences between successful and unsuccessful strategies to solve one of the characteristic management challenges? If so, how are these differences affected by institutional structure and ecological and economic contexts? To answer these questions, social processes must now take center stage in the study and practice of water management. We also argue that water systems are an important class of coupled systems with relevance for sustainability science because they are particularly amenable to the kinds of systematic comparisons that allow knowledge to accumulate. Indeed, the characteristic management challenges we identify are few in number and recur over most of human history and in most geographical locations. This recurrence should allow us to accumulate knowledge to answer the above questions by studying the long historical record of institutional innovations to manage water systems.
54 ENVIRONMENTAL SCIENCES↗
Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods
Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allows numerical predictions, providing the bridge from microstructure to materials properties and performances. Several constitutive models have been proposed in the last few decades to capture the mechanics and plasticity behavior of materials. While some UQ studies have been performed, the robustness and uncertainty of these constitutive models have not been rigorously established. In this work, we apply a stochastic collocation (SC) method, which is mathematically rigorous and has been widely used in the field of UQ, to quantify the uncertainty of three most commonly used constitutive models in CPFEM, namely phenomenological models (with and without twinning), and dislocation-density-based constitutive models, for three different types of crystal structures, namely face-centered cubic (fcc) copper (Cu), body-centered cubic (bcc) tungsten (W), and hexagonal close packing (hcp) magnesium (Mg). Our numerical results not only quantify the uncertainty of these constitutive models in stress-strain curve, but also analyze the global sensitivity of the underlying constitutive parameters with respect to the initial yield behavior, which may be helpful for robust constitutive model calibration works in the future.
36 MATERIALS SCIENCE↗
Transforming a Simple Structure Model to Represent a Complex Dynamic System with Unknown Boundary Restraints
Imposing a boundary condition on a structure can significantly alter its dynamic properties. However, sometimes the specifics of the new boundary conditions are not known. When the effects of a boundary condition are uncertain or there is not enough information, engineers need to excite the complex structure to obtain these modified properties. In order to experimentally obtain the new properties, engineers need multiple experiments and many outputs for interpolation in order to sufficiently represent the entire structure. In this study, the researchers attached a stinger to a cantilever beam, acting as a new transverse restraint of unknown properties. This paper presents a conversion expression that predicts the dynamic behavior of any point in the system with the new boundary condition. This expression relies only on one impact hammer experiment with one output and the model of the stinger-free cantilever beam, referred to as the simple structure. Researchers estimated the Transfer Function (TRF) of the beam and compared it with an experimentally measured TRF to validate the method. The mean absolute error of the estimated TRF compared to the experimental TRF is 1.99 dB. This demonstrates the use of the proposed method for estimating unmeasured TRFs in a system with an uncertain boundary condition using a single input, single output (SISO) test and a model of the simple structure.
42 ENGINEERING↗
GIS Supported Optimal Site Selection for Coastal Structure Integrated Wave Energy Converters: Preprint
There is an urgent need for adaptative engineering towards more resilient coastal communities, and Coastal Structure Integrated Wave Energy Converters (CSI-WECs) are a promising solution. CSI-WECs are wave energy converters (WECs) that are built into coastal protection structures, such as breakwaters. These devices provide the dual benefits of coastal protection and local energy production, and unlike other WECs, maximizing energy production is not always the main objective. CSI-WECs are located near the shore, where the wave resource is lower, thus site selection for these devices differs from the typical offshore WECs. Other attributes of a site that may be more important than wave power include existing coastal structures, port proximity, electric transmission line proximity, and location of disadvantaged communities. Geospatial information systems (GIS) interfaces can be used to easily visualize geospatial data that represents these difference kinds of criteria important for the determination of optimal marine energy sites. Multi-Criteria Decision Analysis (MCDA) is a geospatial analysis method that allows for the evaluation of multiple, usually overlapping, criteria. This project applies GIS-based MCDA methods to two distinct case studies in Puerto Rico and California for CSI-WEC site selection. The two study sites contrast in terms of wave resource, coastal hazards, and local energy needs. This research demonstrates the utility of applying an MCDA framework within GIS to facilitate efficient site selection for devices with unique characteristics in different use cases.
coastal protection↗