Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “partitioned methods”

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.

At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ultrafast Correlation Energy Estimator

A virtually no-cost method is proposed that can compute the correlation energies of general, covalently bonded, organic, and inorganic molecules (including conjugated π-electron systems) with a well-defined dominant Lewis structure at the accuracy of 99.5% of the near-exact values determined by the coupled-cluster singles, doubles, and perturbative triples [CCSD(T)] in the complete-basis-set (CBS) limit. This Correlation Energy Per Bond (CEPB) method assigns a partial correlation energy to each bond type (characterized by the identities of the two atoms forming the bond and its integer bond order) and to a lone pair, regardless of the bond length, bond angle, sp-hybridization, π-electron conjugation, ionicity, noncovalent interactions, etc. At its current stage, the method is mainly suitable for near-equilibrium geometries. The correlation energies per bond are determined by a fit to the CCSD(T)/CBS benchmarks. It can neither improve the equilibrium structures nor discern conformers or positional isomers, yet its accuracy for reaction energies rivals that of the second-order Møller–Plesset perturbation theory, which is far more expensive. Its promising performance underscores the possibility that surprisingly compact, chemically intuitive molecular fragments exist into which correlation energies can be partitioned, leading to various ultrafast correlation-energy estimators tailored to different purposes.

Binding energy↗
NASA NTRS1992

Unsupervised segmentation of polarimetric SAR data using the covariance matrix

A method for unsupervised segmentation of polarimetric synthetic aperture radar (SAR) data into classes of homogeneous microwave polarimetric backscatter characteristics is presented. Classes of polarimetric backscatter are selected on the basis of a multidimensional fuzzy clustering of the logarithm of the parameters composing the polarimetric covariance matrix. The clustering procedure uses both polarimetric amplitude and phase information, is adapted to the presence of image speckle, and does not require an arbitrary weighting of the different polarimetric channels; it also provides a partitioning of each data sample used for clustering into multiple clusters. Given the classes of polarimetric backscatter, the entire image is classified using a maximum a posteriori polarimetric classifier. Four-look polarimetric SAR complex data of lava flows and of sea ice acquired by the NASA/JPL airborne polarimetric radar (AIRSAR) are segmented using this technique. The results are discussed and compared with those obtained using supervised techniques.

Rignot, Eric J. M.↗
DOE OSTI2024

Performance Results on CPU/GPU Exascale Architectures for OMEGA: The Ocean Model for E3SM Global Applications

The US Department of Energy (DOE) conducts climate simulations on some of the world’s largest supercomputers. These exascale machines use heterogeneous architectures with both CPUs and GPUs, and scientific codes must adapt to make full use of this computing power. Los Alamos National Lab is developing Omega: The Ocean Model for E3SM Global Applications, which is specifically designed for modern exascale computers. It uses external libraries that have been optimized for a variety of architectures to run on different supercomputers. Omega is an unstructured-mesh ocean model based on TRiSK numerical methods. It will be the new ocean component of the DOE’s Energy Exascale Earth System Model (E3SM). The algorithms in Omega follow those of the current ocean component, MPAS-Ocean, but it will be written in C++ rather than Fortran to take advantage of the Kokkos performance portability library. Omega spatial operators are written as Kokkos kernels to run efficiently on both CPUs and GPUs. Work on Omega began in 2023 with a new C++ framework for unstructured mesh partitioning, halo exchanges, parallel IO, and Kokkos interfaces. The current version, Omega-0, is being developed to solve the shallow water equations and at present includes all of the tendency terms but not time stepping. Here we share the results of Omega-0 verification and performance testing. Verification includes unit tests implemented with CTest as well as convergence tests in Polaris, an in-house python package with a large suite of test problems. Performance tests compare simulations conducted on CPUs versus GPUs and across different architectures: tests are run on Frontier, which has AMD “Optimized 3rd Gen EPYC” CPUs and AMD MI250X GPUs, as well as Perlmutter, which is composed of AMD EPYC 7763 CPUs and NVIDIA A100 GPUs.

58 GEOSCIENCES↗
NASA NTRS1994

Ability of the total strain version of strainrange partitioning to characterize thermomechanical fatigue behavior

Strainrange partitioning (SRP) was originally developed on an inelastic strain basis for isothermal fatigue in the high-strain regime where the inelastic strainrange could be determined accurately. However, most power-generating equipment operates in the regime where the inelastic strains are small and difficult to determine with any degree of accuracy. This shortcoming led to the development of the total strain version of SRP (TS-SRP). Power-generating equipment seldom operates under isothermal conditions, and isothermal life prediction methods cannot be depended on to predict the lives of anisothermal cycles. To overcome this shortcoming, a method was proposed for extending TS-SRP to characterize anisothermal fatigue behavior and to predict the lives of thermomechanical fatigue (TMF) cycles using apppropriate anisothermal data. The viability of this method, referred to as TMF/TS-SRP, was demonstrated using TMF data for two high-temperature aerospace alloys. In this report, data from the literature are used to examine the ability of TMF/TS-SRP to characterize the failure and flow behavior of three low-strength, high-ductility alloys widely used for ground-based power-generating equipment. The three alloys are type 304 stainless steel, 1Cr-1Mo-0.25V steel, and 2.25Cr-1Mo steel. Because of the limited nature of the data, it was possible to evaluate the characterization, but not the predictive capability of TMF/TS-SRP.

Saltsman, James F.↗
DOE OSTI2021

A Focused Ion Beam-Scanning Transmission Electron Microscopy with Energy-Dispersive X-ray Spectroscopy Study on Technetium Incorporation within Iron Oxides through Fe(OH) 2 (s) Mineral Transformation

Incorporation of Tc(IV) into iron oxide/hydroxide minerals has been explored and proposed as a promising pathway to preventing Tc(IV) reoxidation to environmentally mobile pertechnetate (TcO 4 - ) and improving long-term immobilization of radioactive technetium-99 (Tc). However, visual evidence evaluating the distribution of Tc(IV) incorporated within iron oxide/hydroxide phases has not been available, until now, despite potential implications on Tc(IV) stability within the host phase. For the purpose of this study Tc(IV) incorporation into iron oxide/hydroxide phases was facilitated via Fe(OH) 2 (s) oxidation and mineral transformation to magnetite (Fe 3 O 4 ). Focused ion beam - scanning transmission electron microscopy equipped with energy-dispersive X-ray spectroscopy (FIB/STEM-EDS) methods were then combined with X-ray diffraction and absorption spectroscopy techniques to characterize and visually demonstrate that, for the first time, Tc(IV) is heterogeneously incorporated into different iron oxide/hydroxide phases as Tc(IV)-incorporated magnetite and/or TcO 2 ·2H 2 O(s) via different incorporation mechanisms. Heterogeneous distribution of Tc(IV) in magnetite suggests either (i) TcO4- is reduced quickly at the magnetite surface and then encapsulated into magnetite during continued octahedral crystal growth, or (ii) Tc(IV) alternatively partitioned into multiple layers of a blocky, plate-like morphological magnetite structure showing stratified Tc. With limited Tc-hematite (Fe 2 O 3 ) incorporation, the results suggest that TcO 2 ·2H 2 O(s) is formed and mainly associated/embedded in fibrous nanometer-sized polycrystalline hematite. This work highlights the power of modern state-of-the-art FIB/STEM-EDS approach to provide essential visual insights of the Tc-iron oxide/hydroxide incorporation and generate reliable mechanism-informed designs for waste forms relying on Tc mineral incorporation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1984

Fidelity of Satellite Soundings

Two pattern recognition procedures to provide improvements to first guess fields for satellite temperature retrievals were developed. The first technique where a radiometer measurement is used to select one or more historical radiosonde temperature profiles as analog estimates of ambient thermal structure. Radiosondes are the vertical scales of the analogs. The vertical resolving power of the satellite radiometer is relevant only to a decision process. The analog selection process is much more effective if implemented in an orthogonalized space of measurement information. The second procedure partitions a priori dependent data into shape coherent pattern libraries using structure information inherent in the data itself. It is shown that the pattern recognition techniques are able to reduce first guess profile errors by nearly 50%. The impact of pattern recognition on temperature retrieval error is assessed by regression and physical iterative retrieval algorithms. The influence of improved first guess fields is markedly different on these two types of algorithm. Pattern recognition was a strong positive impact on the physical iterative method.

Thompson, O.↗
NASA NTRSDate not supplied

A Partitioned -Task Parallel Implementation of the NASA Multiscale Analysis Tool for High Performance Computing

The NASA Multiscale Analysis Tool (NASMAT) is a “plug and play” software package that allows users to conduct massively multiscale modeling of hierarchical and nonlinear materials. This work extends the scalability and improves the High Performance Computing friendliness of NASMAT by adopting a Partitioned Task-Parallel approach. Interoperability of NASMAT with external software is enhanced through preCICE, a open source library for multiphysics coupling in a partitioned manner. Enhancement through preCICE allows for easy integration of NASMAT to other macro solvers and dissociates the parallelization strategy adopted within NASMAT from the macro solver. The task-parallel framework based on Master-Worker approach is implemented as the parallelization scheme. The scheme accounts for hierarchy of multiple scales (task-dependence) and heterogeneous nature (dynamic load balancing) of computations. The applicability and scalability of the framework will be evaluated by analyzing large scale engineering problems through massively multiscale methods.

NASMAT↗
DOE OSTI2022

Design optimization of an additively manufactured prototype recuperator for supercritical CO 2 power cycles

Supercritical CO 2 (sCO 2 ) power cycles are being developed due to their potential for high efficiency and reduced capital cost. It is necessary that these recuperators operate at high pressures and temperatures, up to 30 MPa and 900 K, with effectiveness values > 95% and pressure drops <1% to achieve high cycle efficiencies. Moreover, it is also necessary to have reasonable cost recuperators to control the capital costs of the sCO 2 power cycles. In this study, a Plate Pin-Fin (PPF) heat exchanger has been proposed as an sCO 2 recuperator. This preliminary recuperator design leverages capabilities enabled by additive manufacturing. Although the PPF design has characteristics similar to those of a plate heat exchanger, small diameter and relatively long fins are used to increase surface area, enhance heat transfer, and provide structural support for the partition plates that separate the fluid streams. Existing correlations for heat transfer and pressure drop were adapted for the PPF heat exchanger. These correlations were implemented in a 1D analytical model and used for the optimization of a 5-kW th high temperature recuperator for an indirect sCO 2 cycle by varying the design parameters to minimize the quantity of material required. A 3D conjugate heat transfer numerical simulations were conducted to validate the heat transfer and pressure loss correlations. A steepest descent method was used to minimize heat exchanger mass for a 5-kW prototype recuperator subject to a maximum specified pressure drop. Additionally, the design analysis indicated that an optimum PPF recuperator would be attained for the minimum allowable pin transverse spacing, minimum pin width, minimum pin height and near maximum cell aspect ratio. Moreover, at a low material requirement of 0.216 kg/kW and a pressure drop, which is almost five times lower than the allowable pressure drop design target, the optimized PPF heat exchanger has the high potential to be an alternative to a printed circuit heat exchanger, which is a conservative design basis for the current state-of-the-art sCO 2 recuperators.

42 ENGINEERING↗
DOE OSTI2020

Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning

Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydrophobic cellular membrane to reach their intracellular targets, and hydrophobicity is an important driving force for drug–protein binding. Atomistic molecular dynamics (MD) simulations are routinely used to calculate free energies of small molecules binding to proteins, crossing lipid membranes, and solvation but are computationally expensive. Machine learning (ML) and empirical methods are also used throughout drug discovery but rely on experimental data, limiting the domain of applicability. We present atomistic MD simulations calculating 15,000 small molecule free energies of transfer from water to cyclohexane. This large data set is used to train ML models that predict the free energies of transfer. We show that a spatial graph neural network model achieves the highest accuracy, followed closely by a 3D-convolutional neural network, and shallow learning based on the chemical fingerprint is significantly less accurate. A mean absolute error of ~4 kJ/mol compared to the MD calculations was achieved for our best ML model. We also show that including data from the MD simulation improves the predictions, tests the transferability of each model to a diverse set of molecules, and show multitask learning improves the predictions. This work provides insight into the hydrophobicity of small molecules and ML cheminformatics modeling, and our data set will be useful for designing and testing future ML cheminformatics methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Cloud-atmosphere impacts on the central Arctic surface energy budget (Final Report)

This project investigated how clouds and atmosphere-sea ice interactions shape the central Arctic surface energy budget using observations from the MOSAiC expedition and coordinated model experiments. Analyses focused on three themes. First, a new method applied to thermistor string data revealed that snow thermal conductivity is systematically higher than assumed in most models, strongly influencing conductive heat flux and sea ice growth, especially under clear skies. Second, a full annual cycle of surface energy budget observations, combined with detailed cloud microphysical retrievals, demonstrated how cloud regimes regulate radiative, turbulent, and conductive fluxes, driving seasonal contrasts in sea ice energy balance: in winter, radiative forcing elicits strong turbulent and conductive responses, while in summer excess energy is primarily partitioned into surface melt. Third, these observations were used to evaluate and improve regional and global forecast models. While some models captured key Arctic cloud-radiation states, most exhibited persistent deficiencies in representing liquid-containing clouds, snow-on-sea-ice processes, and flux responses to radiative forcing. Together, these results provide unprecedented benchmarks for understanding coupled Arctic processes, improve parameterizations of snow, sea ice, and cloud interactions, and highlight critical pathways by which atmospheric variability drives sea ice change in a rapidly evolving Arctic system. Lastly, the project was a resounding success, resulting in 38 peer-reviewed publications and dozens of presentations, while also supporting an early career scientist.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Dominant Wave Energy Systems and Conditional Wave Resource Characterization for Coastal Waters of the United States

Opportunities and constraints for wave energy conversion technologies and projects are evaluated by identifying and characterizing the dominant wave energy systems for United States (US) coastal waters using marginal and joint distributions of the wave energy in terms of the peak period, wave direction, and month. These distributions are computed using partitioned wave parameters generated from a 30 year WaveWatch III model hindcast, and regionally averaged to identify the dominant wave systems contributing to the total annual available energy ( A A E ) for eleven distinct US wave energy climate regions. These dominant wave systems are linked to the wind systems driving their generation and propagation. In addition, conditional resource parameters characterizing peak period spread, directional spread, and seasonal variability, which consider dependencies of the peak period, direction, and month, are introduced to augment characterization methods recommended by international standards. These conditional resource parameters reveal information that supports project planning, conceptual design, and operation and maintenance. The present study shows that wave energy resources for the United States are dominated by long-period North Pacific swells (Alaska, West Coast, Hawaii), short-period trade winds and nor’easter swells (East Coast, Puerto Rico), and wind seas (Gulf of Mexico). Seasonality, peak period spread, and directional spread of these dominant wave systems are characterized to assess regional opportunities and constraints for wave energy conversion technologies targeting the dominant wave systems.

16 TIDAL AND WAVE POWER↗
NASA NTRS1973

NASTRAN multipartitioning and one-shot substructuring

For intermediate size problems where all the data is accessible, the present method of substructuring in three separate phases (for static analysis) is unneccessarily cumbersome. The versatility of NASTRAN's DMAP and internal logic lends itself to finding a practical alternative to these procedures whereby self-contained special-purpose ALTER packages can be written to be run in one pass. Two examples are presented here under the titles of multipartitioning and one-shot substructuring. The flow of multipartitioning resembles that of the present three-phase substructuring. The basic effect is to partition the structure into substructures and operate on each substructure separately. This can be used to reduce the bandwidth of a given problem as well as to store information which will allow a change to be made in one of the substructures in a later run. This latter procedure is carried out in a second program titled one-shot substructuring.

Levy, A.↗
NASA NTRS2003

Robot Acquisition of Active Maps Through Teleoperation and Vector Space Analysis

The work performed under this contract was in the area of intelligent robotics. The problem being studied was the acquisition of intelligent behaviors by a robot. The method was to acquire action maps that describe tasks as sequences of reflexive behaviors. Action maps (a.k.a. topological maps) are graphs whose nodes represent sensorimotor states and whose edges represent the motor actions that cause the robot to proceed from one state to the next. The maps were acquired by the robot after being teleoperated or otherwise guided by a person through a task several times. During a guided task, the robot records all its sensorimotor signals. The signals from several task trials are partitioned into episodes of static behavior. The corresponding episodes from each trial are averaged to produce a task description as a sequence of characteristic episodes. The sensorimotor states that indicate episode boundaries become the nodes, and the static behaviors, the edges. It was demonstrated that if compound maps are constructed from a set of tasks then the robot can perform new tasks in which it was never explicitly trained.

Peters, Richard Alan, II↗
NASA NTRS2005

Structures Self-Assembled Through Directional Solidification

Nanotechnology has created a demand for new fabrication methods with an emphasis on simple, low-cost techniques. Directional solidification of eutectics (DSE) is an unconventional approach in comparison to low-temperature biomimetic approaches. A technical challenge for DSE is producing microstructural architectures on the nanometer scale. In both processes, the driving force is the minimization of Gibb's free energy. Selfassembly by biomimetic approaches depends on weak interaction forces between organic molecules to define the architectural structure. The architectural structure for solidification depends on strong chemical bonding between atoms. Constituents partition into atomic-level arrangements at the liquid-solid interface to form polyphase structures, and this atomic-level arrangement at the liquid-solid interface is controlled by atomic diffusion and total undercooling due to composition (diffusion), kinetics, and curvature of the boundary phases. Judicious selection of the materials system and control of the total undercooling are the keys to producing structures on the nanometer scale. The silicon-titanium silicide (Si-TiSi2) eutectic forms a rod structure under isothermal cooling conditions. At the NASA Glenn Research Center, directional solidification was employed along with a thermal gradient to promote uniform rods oriented with the thermal gradient. The preceding photomicrograph shows the typical transverse microstructure of a solidified Si-TiSi2 eutectic composition. The dark and light gray regions are Si and TiSi2, respectively. Preferred rod orientation along the thermal gradient was poor. The ordered TiSi2 rods have a narrow distribution in diameter of 2 to 3 m, as shown. The rod diameter showed a weak dependence on process conditions. Anisotropic etch behavior between different phases provides the opportunity to fabricate structures with high aspect ratios. The photomicrographs show the resulting microstructure after a wet chemical etch and a dry plasma etch. The wet chemical etches the silicon away, exposing the TiSi2 rods, whereas plasma etching preferentially etches the Si-TiSi2 interface to form a crater. The porous architectures are applicable to fabricating microdevices or creating templates for part fabrication. The porous rod structure can serve as a platform for fabricating microplasma devices for propulsion or microheat exchangers and for fabricating microfilters for miniatured chemical reactors. Although more work is required, self-assembly from DSE can have a role in microdevice fabrication.

Dynys, Frederick W.↗
NASA NTRS2011

Imaging the Moon's Core with Seismology

Constraining the structure of the lunar core is necessary to improve our understanding of the present-day thermal structure of the interior and the history of a lunar dynamo, as well as the origin and thermal and compositional evolution of the Moon. We analyze Apollo deep moonquake seismograms using terrestrial array processing methods to search for the presence of reflected and converted energy from the lunar core. Although moonquake fault parameters are not constrained, we first explore a suite of theoretical focal spheres to verify that fault planes exist that can produce favorable core reflection amplitudes relative to direct up-going energy at the Apollo stations. Beginning with stacks of event seismograms from the known distribution of deep moonquake clusters, we apply a polarization filter to account for the effects of seismic scattering that (a) partitions energy away from expected components of ground motion, and (b) obscures all but the main P- and S-wave arrivals. The filtered traces are then shifted to the predicted arrival time of a core phase (e.g. PcP) and stacked to enhance subtle arrivals associated with the Moon s core. This combination of filtering and array processing is well suited for detecting deep lunar seismic reflections, since we do not expect scattered wave energy from near surface (or deeper) structure recorded at varying epicentral distances and stations from varying moonquakes at varying depths to stack coherently. Our results indicate the presence of a solid inner and fluid outer core, overlain by a partial-melt-containing boundary layer (Table 1). These layers are consistently observed among stacks from four classes of reflections: P-to-P, S-to-P, P-to-S, and S-to-S, and are consistent with current indirect geophysical estimates of core and deep mantle properties, including mass, moment of inertia, lunar laser ranging, and electromagnetic induction. Future refinements are expected following the successful launch of the GRAIL lunar orbiter and SELENE 2 lunar lander missions.

Weber, Renee C.↗
DOE OSTI2020

Metabolite, Protein, and Lipid Extraction (MPLEx): a method that simultaneously inactivates Middle East respiratory syndrome coronavirus and allows analysis of multiple host cell components following infection

Mass spectrometry (MS)-based, integrated proteomics, metabolomics, and lipidomics (collectively, multi-omics) studies provide a very detailed snap shot of virus-induced changes to the host following infection and can lead to the identification of novel prophylactic and therapeutic targets for preventing or lessening disease severity. Multi-omics studies with Middle East respiratory syndrome coronavirus (MERS-CoV) are challenging as the requirements of Biosafety Level 3 containment limit the numbers of samples that can be safely managed. To address these issues, the multi-omics sample preparation technique MPLEx (metabolite, protein, and lipid extraction) was developed to partition a single sample into three distinct parts (metabolites, proteins and lipids) for multi-omics analysis, while simultaneously inactivating MERS-CoV by solubilizing and disrupting the viral envelope and denaturing viral proteins. Here we describe the MPLEx protocol, highlight the step of inactivation, and describe the details of downstream processing, instrumental analysis of studies with Middle East respiratory syndrome coronavirus (MERS-CoV) are challenging as the requirements of Biosafety Level 3 containment limit the numbers of samples that can be safely managed. To address these issues, the multi-omics sample preparation technique MPLEx (metabolite, protein, and lipid extraction) was developed to partition a single sample into three distinct parts (metabolites, proteins and lipids) for multi-omics analysis, while simultaneously inactivating MERS-CoV by solubilizing and disrupting the viral envelope and denaturing viral proteins. Here we describe the MPLEx protocol, highlight the step of inactivation, and describe the details of downstream processing, instrumental analysis of the three separate analytes and their subsequent informatics pipelines.

Nicora, Carrie D.↗
DOE OSTI2020

AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials

Intermolecular interactions are critical to many chemical phenomena, but their accurate computation using ab initio methods is often limited by computational cost. The recent emergence of machine learning (ML) potentials may be a promising alternative. Useful ML models should not only estimate accurate interaction energies but also predict smooth and asymptotically correct potential energy surfaces. However, existing ML models are not guaranteed to obey these constraints. Indeed, systemic deficiencies are apparent in the predictions of our previous hydrogen-bond model as well as the popular ANI-1X model, which we attribute to the use of an atomic energy partition. As a solution, we propose an alternative atomic-pairwise framework specifically for intermolecular ML potentials, and we introduce AP-Net—a neural network model for interaction energies. The AP-Net model is developed using this physically motivated atomic-pairwise paradigm and also exploits the interpretability of symmetry adapted perturbation theory (SAPT). We show that in contrast to other models, AP-Net produces smooth, physically meaningful intermolecular potentials exhibiting correct asymptotic behavior. Initially trained on only a limited number of mostly hydrogen-bonded dimers, AP-Net makes accurate predictions across the chemically diverse S66x8 dataset, demonstrating significant transferability. On a test set including experimental hydrogen-bonded dimers, AP-Net predicts total interaction energies with a mean absolute error of 0.37 kcal mol−1, reducing errors by a factor of 2–5 across SAPT components from previous neural network potentials. The pairwise interaction energies of the model are physically interpretable, and an investigation of predicted electrostatic energies suggests that the model “learns” the physics of hydrogen-bonded interactions.

Glick, Zachary L. (ORCID:0000000309002849)↗
DOE OSTI2025

The Zintl pnictides Yb10CdSb9 and Yb14CdSb11: New candidate thermoelectric materials

The synthesis of new materials is the lifeline of solid-state science, and it continues to offer us unique opportunities for testing various theoretical formulations and models on a practical material. Such an avenue, therefore, provides a breeding ground for technological innovations and advancements that can completely revolutionize our world. Here, we report the results of our exploratory syntheses in the Yb–Cd–Sb compositional space that lead to the identification of two new Zintl antimonides, namely, Yb10CdSb9 and Yb14CdSb11. Their crystal structures were established via single-crystal X-ray diffraction methods; the basic electronic and transport properties of the new materials were also characterized. Yb10CdSb9 crystallizes in a disordered variant of the tetragonal Ca10LiMgSb9 structure type with unit cell parameters a = 11.8473(8) Å and c = 17.1302(12) Å (space group P42/mnm). Yb14CdSb11 crystallizes in the tetragonal Ca14AlSb11 structure type with unit cell parameters: a = 16.605(3) Å and c = 12.144(7) Å (space group I41/acd). Although the structures of both compounds can be rationalized within the framework of the Zintl formalism, based on the partitioning of the valence electrons in the much disordered Yb10CdSb9 phase, the charge is indicative of a slightly electron-rich composition. Electronic structure calculations in both cases support the notion of intrinsic semiconductor behavior, as expected for a Zintl phase. The temperature dependence of the electrical resistivity of a single crystal of Yb10CdSb9 is in line with that, and the evolution of the Seebeck coefficient indicates an electron-dominated transport mechanism, and a respectable power factor of 0.71 μW/cm K2 at 460 K can be calculated for Yb10CdSb9. The electrical resistivity of Yb14CdSb11, however, evolves in a semimetallic manner, which could suggest an overdoped sample or degenerate semiconducting behavior.

Ogunbunmi, Michael O. (ORCID:0000000253409198)↗