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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.

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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Image computing techniques to extrapolate data for dust tracking in case of an experimental accident simulation in a nuclear fusion plant

In this paper, a preliminary shadowgraph-based analysis of dust particles re-suspension due to loss of vacuum accident (LOVA) in ITER-like nuclear fusion reactors has been presented. Dust particles are produced through different mechanisms in nuclear fusion devices, one of the main issues is that dust particles are capable of being re-suspended in case of events such as LOVA. Shadowgraph is based on an expanded collimated beam of light emitted by a laser or a lamp that emits light transversely compared to the flow field direction. In the STARDUST facility, the dust moves in the flow, and it causes variations of refractive index that can be detected by using a CCD camera. The STARDUST fast camera setup allows to detect and to track dust particles moving in the vessel and then to obtain information about the velocity field of dust mobilized. In particular, the acquired images are processed such that per each frame the moving dust particles are detected by applying a background subtraction technique based on the mixture of Gaussian algorithm. The obtained foreground masks are eventually filtered with morphological operations. Finally, a multi-object tracking algorithm is used to track the detected particles along the experiment. For each particle, a Kalman filter-based tracker is applied; the particles dynamic is described by taking into account position, velocity, and acceleration as state variable. The results demonstrate that it is possible to obtain dust particles’ velocity field during LOVA by automatically processing the data obtained with the shadowgraph approach.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Energetics of the nucleation and glide of disconnection modes in symmetric tilt grain boundaries

Grain boundaries (GBs) evolve by the nucleation and glide of disconnections, which are dislocations with a step character. In this work, motivated by recent success in predicting GB properties such as the shear coupling factor and mobility from the intrinsic properties of disconnections, we develop a systematic method to calculate the energy barriers for the nucleation and glide of individual disconnection modes under arbitrary driving forces and a quasi-2D setting. This method combines tools from bicrystallography to enumerate disconnection modes and the Nudged elastic band (NEB) method to calculate their energetics, yielding minimum energy paths and atomistic mechanisms for the nucleation and glide of each disconnection mode. We apply the method to accurately predict shear coupling factors of $[001]$ symmetric tilt grain boundaries in Cu. Particular attention is paid to the boundaries where the dislocation-based disconnection nucleation model produces incorrect nucleation barriers. We demonstrate that the method can accurately compute energy barriers and predict shear-coupling factors in the low-temperature regime. For certain disconnection modes in which the assumptions underlying our method do not hold, we report upper bounds on the energy barriers for disconnection nucleation and glide. In addition, the NEB trajectories reveal interesting phenomena such as the dissociation of a higher energy mode into lower energy modes, and in some cases, shear coupling being mediated by partial disconnections, wherein the GB structure temporarily changes to a metastable state before reverting back to its original structure. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2024

Elucidating Abnormal Grain Growth in Thermomagnetic Processed Materials with Transfer Learning and Reinforcement Learning

The goal of this research program is to establish the mechanism governing local grain boundary motion, which is needed to design and process desirable microstructures for better performance, by identifying the relative contributions of grain boundary (GB) energy and mobility to grain growth. Classical models for grain growth assume that the primary mechanism for reducing the total interfacial energy is area reduction and that GB restructuring is not significant. This assumption implies that grain growth is locally driven by curvature. However, recent experimental observations using new non-destructive 3D x-ray diffraction microscopy techniques (3D-XRM) reveal that classic descriptors (i.e., curvature, number of neighbors, grain size) do not predict real grain growth. Instead, local GB motion appears to be governed by its energy relative to its neighbors such that low-energy boundaries replace those of higher energy. However, simulations that incorporate GB energy anisotropy still fail to reproduce these observations. These discrepancies suggest that the common assumption for grain growth theory must be re-examined to predict and, thus, control microstructure evolution in real polycrystals. A significant challenge to testing this assumption is due to anisotropic GB mobility. Mobility may cause abnormal grain growth or affect the final grain shapes or growth rate but its true contributions are unknown because it is difficult to measure. For example, observations in Fe have found that grains associated with high energy and high mobility boundaries tend to experience abnormal grain growth, whereas abnormal grain growth is associated with low energy and high mobility boundaries in alumina. As mobility and energy both control GB motion, it is challenging to isolate the local driving forces necessary to test the common assumption that the primary mechanism is area reduction. The novelty of this work is the use of machine learning tools to capture GB mobility and energy from 3D-XRM measurements in polycrystals to test the common assumption used in grain growth models. Machine learning can capture high-order correlations in dynamic systems like those found in the evolving GB topology. The PIs have developed a physics-regularized interpretable machine learning microstructure evolution (PRIMME) model that accurately replicates the grain growth behavior of its trained data set.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Molecular Vision - Multimodal, multitask retrieval of molecular structure from measured signatures for reference-free compound identification

We are currently at risk of generating false conclusions based on limited methods to identify small molecules in biological systems and in chemical forensics. By definition, the chemical structures of novel small molecules have not been determined, let alone measured or synthesized. Currently, unambiguous structure determination of small molecules is constrained by the time and effort needed to isolate compounds and perform de novo structure elucidation using laboratory-based methods, significantly extending the time to inform mitigation strategies. To address this gap, we have developed a deep learning approach to directly map molecular structure to experimental signatures. We aim to unify measurement technologies employed in untargeted small molecule identification studies—such as infrared (IR) spectrometry, tandem mass spectrometry (MS/MS), ion mobility spectrometry-derived collision cross section (CCS)—through use of a multimodal, multitask deep learning architecture. Where existing methods require direct generation of information-rich spectra and/or properties, an inherently difficult task, we will simplify molecular signature-based identification by posing the problem as a recognition or retrieval task. The model is thus presented with relevant endpoints – structure and one or more molecular signatures – and need only determine whether they are semantically related. Thus, our approach offers the following advantages over existing techniques: (i) circumvents difficulties associated with direct generation of molecular signatures from structure and structure from signatures; (ii) incorporates multiple molecular signatures simultaneously, as available, to support identification; and (iii) enables rapid computation of structural embeddings toward broad coverage of known chemical space. Taken together, the approach removes the need to explicitly obtain or compute reference spectra, representing a powerful method for compound identification that requires only experimentally observed signatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Computational strategies for design and discovery of nanostructured thermoelectrics

Abstract The contribution of theoretical calculations and predictions in the development of advanced high-performance thermoelectrics has been increasingly significant and has successfully guided experiments to understand as well as achieve record-breaking results. In this review, recent developments in high-performance nanostructured bulk thermoelectric materials are discussed from the viewpoint of theoretical calculations. An effective emerging strategy for boosting thermoelectric performance involves minimizing electron scattering while maximizing heat-carrying phonon scattering on many length scales. We present several important strategies and key examples that highlight the contributions of first-principles-based calculations in revealing the intricate but tractable relationships for this synergistic optimization of thermoelectric performance. The integrated optimization approach results in a fourfold design strategy for improved materials: (1) a significant reduction of the lattice thermal conductivity through multiscale hierarchical architecturing, (2) a large enhancement of the Seebeck coefficient through intramatrix electronic band convergence engineering, (3) control of the carrier mobility through band alignment between the host and second phases, and (4) design of intrinsically low-thermal-conductivity materials by maximizing vibrational anharmonicity and acoustic-mode Gruneisen parameters. These combined effects serve to enhance the power factor while reducing the lattice thermal conductivity. This review provides an improved understanding of how theory is impacting the current state of this field and helps to guide the future search for high-performance thermoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Anisotropic Hydrolysis Susceptibility in Deformed Polydimethylsiloxanes

Chemical reactions involving the polydimethylsiloxane (PDMS) backbone can induce significant network rearrangements and ultimately degrade macro-scale mechanical properties of silicone components. Using two levels of quantum chemical theory, we identify a possible electronic driver for chemical susceptibility in strained PDMS chains and explore the complicated interplay between hydrolytic chain scissioning reactions, mechanical deformations of the backbone, water attack vector, and chain mobility. Density functional theory (DFT) calculations reveal that susceptibility to hydrolysis varies significantly with the vector for water attacks on silicon backbone atoms, which matches strain-induced anisotropic changes in the backbone electronic structure. Efficient semiempirical density functional tight binding (DFTB) calculations are shown to reproduce DFT predictions for select reaction pathways and facilitate more exhaustive explorations of configuration space. We show that concerted strains of the backbone must occur over at least a few monomer units to significantly increase hydrolysis susceptibility. In addition, we observe that sustaining tension across multiple monomer lengths by constraining molecular degrees of freedom further enhances hydrolysis susceptibility, leading to barrierless scission reactions for less substantial backbone deformations than otherwise. We then compute chain scission probabilities as functions of the backbone degrees of freedom, revealing complicated configurational interdependencies that impact the likelihood for hydrolytic degradation. As a result, the trends identified in our study suggest simple physical descriptions for the synergistic coupling between local mechanical deformation and environmental moisture in hydrolytic degradation of silicones.

36 MATERIALS SCIENCE↗
DOE OSTI2018

ULAMA: A Utilization-Aware Matching Approach for robust on-demand spatial service brokers

This article investigates an on-demand spatial service broker for suggesting service provider propositions and the corresponding time of service to mobile consumers while meeting the consumer’s maximum travel time and wait time constraints. The goal of the broker is to maximize the number of matched requests while also keeping the “eco-system” functioning and robust against variations in the supply–demand ratio by engaging as many service providers as possible and maintaining providers’ participation to ensure that providers do not drop out. This problem is important because of its many related societal applications in the on-demand and sharing economy (e.g., on-demand ride-hailing services, on-demand food delivery, etc.). Challenges of this problem include the need to satisfy many conflicting requirements of the broker, consumers and service providers and the high computational complexity since solving the problem for a number of available consumers at any time instant is NP-hard. Related work in spatial crowdsourcing and ridesharing has mainly focused on maximizing the number of matched requests and minimizing travel cost, but did not consider the importance of maintaining provider engagement and balancing provider utilization, which could become a priority when the available supply exceeds the demand. In this paper, we propose a Utilization-Aware Matching Approach (ULAMA) which employs novel provider-centric heuristics for balancing the utilization of providers, and a consumer-priority-based greedy matching algorithm that prioritizes consumers for maximizing the number of matched requests. Experimental results show that our proposed approach outperforms related work by achieving the lowest variance in provider utilization while matching all available providers even when supply greatly exceeds demand. Our approach also achieved a larger number of matched requests, particularly when supply exceeds demand and also when both supply and demand are balanced.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Emergence of superconductivity in doped multiorbital Hubbard chains

Abstract We introduce a variational state for one-dimensional two-orbital Hubbard models that intuitively explains the recent computational discovery of pairing in these systems when hole doped. Our ansatz is an optimized linear superposition of Affleck–Kennedy–Lieb–Tasaki valence-bond states, rendering the combination a valence-bond liquid dubbed orbital resonant valence bond. We show that the undoped (one-electron/orbital) quantum state of two sites coupled into a global spin singlet is exactly written employing only spin-1/2 singlets linking orbitals at nearest-neighbor sites. Generalizing to longer chains defines our variational state visualized geometrically expressing our chain as a two-leg ladder, with one orbital per leg. As in Anderson’s resonating valence-bond state, our undoped variational state contains preformed singlet pairs that via doping become mobile, leading to superconductivity. Doped real materials with one-dimensional substructures, two near-degenerate orbitals, and intermediate Hubbard U / W strengths— W the carrier’s bandwidth—could realize spin-singlet pairing if on-site anisotropies are small. If these anisotropies are robust, spin-triplet pairing emerges.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Platform for Integrated Land use And Transportation Experiments and Simulation (PILATES) v1.0

PILATES allows for flexibly and at-scale coupling of multiple models to allow for multi-scale and multi-resolution simulation of regional-scale transport networks. In particular, it couples the MATSim-derived transportation modeling framework for Behavior, Energy, Autonomy and Mobility (BEAM) with other models operating at different time scales. Rather than tightly coupling supply and demand models using shared agents and memory within the same software process, PILATES orchestrates different model runs in a containerized framework. This structure requires passing information from the demand models to BEAM in the format of a synthetic population and agent plans, and from BEAM to the demand models in terms or origin/destination tables (also known as "skims"). This allows it to take advantage of the behavioral sophistication of existing activity-based models as well as the reinforcement learning structure of MATSim replanning and adopted by BEAM, in a way that requires minimal changes to existing models. It also takes advantage of the computational performance of BEAM, which allows for simulations with millions of agents to complete in reasonable time as well as allowing for detailed mechanistic simulation of the operation of on-demand modes.

Needell, Zachary↗
DOE OSTI2019

MPF X-Ray Module Setup for 2018

The Mobile Plutonium Facility (MPF) is a portable module design facility that can be deployed anywhere in the world for the safe recovery, packaging, and shipment of a nations legacy, or excess plutonium stockpile. All of the modules of MPF have specific purposes for operating in a mission if one were to ever come up. For my past four internships here at SRNL I have been working on the support of the X-Ray module for MPF. This year I installed new equipment I had purchased last year for the X-Ray Module and made sure the module was mission ready for the chance that the team deploys in the future for plutonium recovery. Using digital radiography (DR) and computed tomography (CT) provides the MPF team the ability to verify the contents of containers that would be holding material. This also ensures the safety of the team members in the glovebox facilities by verifying that there are no sharps, liquids, or other items that may harm the workers or glovebox operations when working with radioactive material. X-Ray imaging can also pick up low density objects like plastic bags and tape. Note how the DR image hides the puncture in the inner can while the CT image shows the damage. New DR and CT computers with upgraded software, New imaging panel, NOVO Portable X-Ray System. Because of the new upgrades to the X-Ray module system containers can be scanned and evaluated quicker than before. With the last system the 3D model reconstruction alone would take anywhere from 10-12 minutes, now with the new system that time has been brought down to just shy of 1 minute 30 seconds. The new portable system also can 1,2 or 4 images of an object and stitch them together into a mosaic for scanning of larger containers.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Efficient and Robust p-Type Transistor Based on Ultrawide-Bandgap Semiconductor

The p-type transistor is an indispensable component of semiconductor technology, enabling a complementary operation with n-channel transistors for computation, storage, and communication. Achieving both high robustness and high efficiency is highly desirable but challenging for p-type transistors due to the limited semiconductors with reliable hole transport and their high activation energies. Here, in this study, we achieved a robust yet efficient p-type transistor by heterogeneously integrating an ultrawide-bandgap semiconductor and a high-κ dielectric layer through van der Waals integration. The p-type transistor employs a two-dimensional hole channel on hydrogenated diamond (bandgap 5.6 eV) combined with a high-κ (30.5) SrTiO 3 perovskite membrane. At room temperature, the transistor exhibits stable operation with a high on-current (∼200 mA/mm), low subthreshold swing (70 mV/dec), high hole mobility (566 cm 2 /(V·s) to 572 cm 2 /(V·s)), and high on–off ratio (∼10 9 ). Furthermore, tuning the annealing temperature allows operation in either enhancement or depletion mode. The robust p-type transistor with high efficiency holds great potential for future power electronics, ultraviolet (UV) optoelectronics, and harsh-environment electronic applications.

high-κ membrane↗
DOE OSTI2024

300_underground robot (final research report)

Recent advancements in mobile robotics have displayed impressive capabilities in traversing and accessing areas that are inaccessible to humans either due to the characteristics of the environment or potential hazards. Furthermore, these advancements within the field of mobile robotics, more specifically uncrewed ground vehicles (UGVs), give the ability to potentially survey, observe, and map these inaccessible areas for humans. However, one of the most challenging areas to implement this technology is underground environments. The main challenge with implementing this technology in underground environments is the dependence on either GPS or RF communication for UGVs to navigate properly. Therefore, in order to properly demonstrate the mapping capabilities of the UGV this challenge must be resolved. The overall goal of this study is to demonstrate the mapping capabilities of a UGV while addressing this challenge and documenting the implementation and testing phase of the robot. The proposed solution to this challenge is to implement a SLAM algorithm onto the main computational device of the UGV utilizing the Robot Operating System (ROS). The algorithm is the open-source software package Slam Toolbox developed by Steve Macenski. Furthermore, the sllidar_ros2 package from Slamtec will be used to gather the lidar data from an A3M1 2D lidar. A separate program will be created to gather odometry information for our UGV robot. All of these software packages will run together in a Docker environment. Through working on this project I have developed a better understanding of the world of robotics/autonomous systems, especially with applications such as navigation and mapping. Furthermore, through this project, I have been given exposure to how research is conducted within a DOE lab setting. As robotics/autonomous systems become more advanced it's important to pursue more avenues of research such as this project as it will ensure the development of our capabilities.

42 ENGINEERING↗
DOE OSTI2018

Optimization of collision-free paths in a differential-drive robot by a smoothing piecewise-linear approach

This paper presents a methodology for optimizing pre-calculated collision-free paths of differential-drive wheeled robots. The main advantage of this methodology is that optimization is done by considering the kinematics and mechanical constraints of the mobile robot. In accordance to this proposal, the optimized path is achieved by applying recursively a local smoothing on an initial path which is originally modeled as a one-dimensional piecewise linear function. By this recursive smoothing, it can be ensured that the original piecewise linear function can be transformed into a smooth one that fulfill the constraints established by the kinematic equations of the wheeled mobile in terms of a minimum radius of curvature. As a result of this, a trajectory which guarantees lower power consumption and lower mechanical wear, is obtained. To show the better performance of the proposed approach, numerical simulation results are contrasted to those obtained from other reported methods with regards to path length, minimum radius of curvature, cross track error, continuity and resulting acceleration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The impact of anisotropic thermal expansion on the isothermal annealing of polycrystalline α -uranium

Although grain growth impacts microstructural evolution in a wide variety of materials systems, the effect of anisotropic thermal expansion on grain boundary mobility and texture evolution has not been widely studied. Anisotropic thermal expansion occurs in multiple non-cubic metals, and the thermomechanical processing behavior of these materials can be better understood with further study into the impact of thermal expansion on grain boundary mobility and texture evolution. In this work, we develop a mesoscale phase field model of grain growth that includes the effect of anisotropic thermal expansion, which is applied to study polycrystalline α-uranium, a highly anisotropic metal. Three-dimensional simulations on polycrystalline a-uranium with and without thermal expansion eigenstrains are performed to study the grain boundary mobility and texture evolution as a function of temperature. A strain-free temperature of 933 K is selected, and the system is studied at within the range of 873 – 933 K at intervals of ten degrees, resulting in increasing thermal eigenstrain with decreasing temperature. We also estimate a grain boundary mobility prefactor and activation energy based on existing experimental data of isothermal annealing of α-uranium. The grain boundary mobility is found to display significant deviation from Arrhenius behavior with the inclusion of thermal expansion eigenstrain as the amount of thermal eigenstrain (and thus elastic strain energy within the system) increases. This result explains an experimentally observed grain boundary mobility deviation from Arrhenius behavior. Furthermore, the texture evolution is affected, such that the grain orientations become less random with increasing thermal eigenstrain, which could explain experimentally observed texture behavior. These results indicate that the effect of thermal expansion should be considered when predicting the thermomechanical processing behavior of α-uranium and other materials with anisotropic thermal expansion.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Cryo-EM structures of engineered active bc 1- cbb 3 type CIII 2 CIV super-complexes and electronic communication between the complexes

Respiratory electron transport complexes are organized as individual entities or combined as large supercomplexes (SC). Gram-negative bacteria deploy a mitochondrial-like cytochrome (cyt) bc 1 (Complex III, CIII 2 ), and may have specific cbb 3 -type cyt c oxidases (Complex IV, CIV) instead of the canonical aa 3 -type CIV. Electron transfer between these complexes is mediated by soluble ( c 2 ) and membrane-anchored ( c y ) cyts. Here, we report the structure of an engineered bc 1 - cbb 3 type SC (CIII 2 CIV, 5.2Å resolution) and three conformers of native CIII 2 (3.3 Å resolution). The SC is active in vivo and in vitro, contains all catalytic subunits and cofactors, and two extra transmembrane helices attributed to cyt c y and the assembly factor CcoH. The cyt c y is integral to SC, its cyt domain is mobile and it conveys electrons to CIV differently than cyt c 2 . The successful production of a native-like functional SC and determination of its structure illustrate the characteristics of membrane-confined and membrane-external respiratory electron transport pathways in Gram-negative bacteria.

42 ENGINEERING↗
DOE OSTI2017

A mesoporous cationic thorium-organic framework that rapidly traps anionic persistent organic pollutants

Many environmental pollutants inherently exist in their anionic forms and are therefore highly mobile in natural water systems. Cationic framework materials that can capture those pollutants are highly desirable but scarcely reported. Here we present a mesoporous cationic thorium-based MOF (SCU-8) containing channels with a large inner diameter of 2.2 nm and possessing a high surface area of 1360 m 2 g –1 . The anion-exchange properties of SCU-8 were explored with many anions including small oxo anions like ReO 4 – and Cr 2 O 7 2– as well as anionic organic dyes like methyl blue and the persistent organic pollutant, perfluorooctane sulfonate (PFOS). Both fast uptake kinetics and great sorption selectivity toward PFOS are observed. The underlying sorption mechanism was probed using quantum mechanical and molecular dynamics simulations. These computational results reveal that PFOS anions are immobilized in SCU-8 by driving forces including electrostatic interactions, hydrogen bonds, hydrophobic interactions, and van der Waals interactions at different adsorption stages.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

A phase-field study of stainless-steel oxidation from high-temperature carbon dioxide exposure

An electrochemical phase-field model has been developed to investigate the oxidation mechanisms of the 21-2N valve stainless steel alloy exposed to carbon dioxide at 973 K. Three oxide phases observed in oxidation experiments are included in the model: Mn 3 O 4 , Cr 2 O 3 , and MnCr 2 O 4 . Local charge neutrality and the conserved current condition are assumed to include the impact of the electric potential on oxidation, though it is found to be negligible when the system is electrically isolated. The sensitivity of the oxidation processes to the values of the diffusion mobilities is examined, and the oxidation rate is calibrated against experimental data by modifying the sensitive mobilities. We find that both inward oxygen and outward metal diffusion are important for oxidation. Furthermore, by investigating the impact of the order of the initial oxide layers, we find that Cr 2 O 3 serves as a barrier to outward Mn diffusion. Moreover, controlling the initial oxide layer order can be used to limit oxidation.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-E-I-12: Flexible Geometry Computed Tomography

Purpose: The concept separates the mechanical connection between the radiation source and detector. This design allows the trajectory and orientation of the radiation source/detector to be customized to the object that is being imaged. This is in contrast to the formulaic rotation-translation image acquisition of conventional computed tomography(CT).Background/significance:CT devices that image a full range of: anatomy, patient populations, and imaging procedures are large. The root cause of the expanding size of comprehensive CT is due to the commitment to helical geometry that is hardwired into the image reconstruction. FGCT extends the application of alternative reconstruction techniques, i.e. tomosynthesis, by separating the two main components— radiation source and detector— and allow for 6 degrees of freedom motion for radiation source, detector, or both. The image acquisition geometry is then tailored to how the patient/object is positioned. This provides greater flexibility on the position and location that the patient/object is being imaged. Additionally, removing the need of a rotating gantry reduces the footprint so that CT is more mobile and more available to move to where the patient/object is at, instead of the other way around. Methods: As proof-of-principle, a reconstruction algorithm is designed to produce FGCT images. Using simulated detector data, voxels intersecting a line drawn between the radiation source and an individual detector are traced and modified using the detector signal. The detector signal is modified to compensate for changes in the source to detector distance. Adjacent voxels are modified in proportion to the detector signal, providing a simple image filter. Results: Image-quality from the proposed FGCT reconstruction technique is proving to be a challenge, producing hardily recognizable images from limited projections angles. Conclusion: Preliminary assessment of the reconstruction technique demonstrates the inevitable tradeoff of producing meaningful image-quality verses the practicality of how to obtain sufficient image data.

60 APPLIED LIFE SCIENCES↗