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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

High rate sodium ion battery anodes from block copolymer templated mesoporous nickel–cobalt carbonates and oxides

In this work, micelle-templated ordered mesoporous nickel–cobalt carbonates and oxides are fabricated using a metal nitrate–citric acid strategy, which avoids the hydrolysis and aging requirements associated with sol–gel chemistry. A series of mesoporous Ni x Co (3– x ) (CO 3 ) y and Ni x Co (3– x ) O 4 films with varying Ni–Co compositions and 14 ± 4 nm mesopores are fabricated with the same block copolymer template. AFM and GISAXS analysis indicates that the mesostructure is maintained through the formation of the carbonate and oxide, while GIXD profiles confirm formation of pure spinel phases of semi-crystalline Ni x Co (3– x ) O 4 . The micelle templated mesopores are interconnected and provide transport paths for the electrolyte to minimize the solid-state diffusion requirements associated with battery electrodes. These materials exhibit good performance as sodium ion battery anodes even at high current densities of 4 A g –1 . Amongst the mixed-metal oxides, Ni 2 CoO 4 exhibits the highest specific capacity of 239 mA h g –1 after galvanostatic cycling at a current density of 1 A g –1 for 10 cycles. We attribute the superior performance of Ni 2 CoO 4 at high rates to the high surface area and short ion-diffusion paths of the nanoporous anode architecture, while the higher nickel content in the mixed metal oxide provides enhanced stability during oxide formation along with enhanced electronic conductivity, leading to improved cycling stability of the anode. This micelle template metal nitrate–citric acid method enables new possibilities for fabricating variety of ordered mesoporous mixed-metal carbonates and oxides that could be used in a wide range of applications.

25 ENERGY STORAGE↗
DOE OSTI2016

Oxygen influencing the photocarriers lifetime of CH{sub 3}NH{sub 3}PbI{sub 3−x}Cl{sub x} film grown by two-step interdiffusion method and its photovoltaic performance

During the growth of CH{sub 3}NH{sub 3}PbI{sub 3−x}Cl{sub x} (MAPbI{sub 3−x}Cl{sub x}) perovskite films by the two-step inter-diffusion method, the presence of a trace amount of oxygen gas is critical to their physical properties and photovoltaic performance. As the oxygen concentration increases, poor film morphologies and incomplete surface coverage are observed. Moreover, by XRD, Raman scattering, and photoluminescence measurements, we find that MAPbI{sub 3−x}Cl{sub x} grains become more distorted and the electron-hole recombination rate dramatically increases. Higher oxygen concentration triggers a sharp decrease in the current density and the fill factor of corresponding solar cells, which degrades device performance, on average, from 14.3% to 4.4%. This work proves the importance of controlling the oxygen atmosphere in the fabrication of high-performance perovskite solar cells.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Measurement of fluid rotation, dilation, and displacement in particle image velocimetry using a Fourier–Mellin cross-correlation

Traditional particle image velocimetry (PIV) uses discrete Cartesian cross correlations (CCs) to estimate the displacements of groups of tracer particles within small subregions of sequentially captured images. However, these CCs fail in regions with large velocity gradients or high rates of rotation. In this paper, we propose a new PIV correlation method based on the Fourier–Mellin transformation (FMT) that enables direct measurement of the rotation and dilation of particle image patterns. In previously unresolvable regions of large rotation, our algorithm significantly improves the velocity estimates compared to traditional correlations by aligning the rotated and stretched particle patterns prior to performing Cartesian correlations to estimate their displacements. Furthermore, our algorithm, which we term Fourier–Mellin correlation (FMC), reliably measures particle pattern displacement between pairs of interrogation regions with up to ±180° of angular misalignment, compared to 6–8° for traditional correlations, and dilation/compression factors of 0.5–2.0, compared to 0.9–1.1 for a single iteration of traditional correlations.

42 ENGINEERING↗
DOE OSTI2019

Computational prediction of nanostructured alloys with enhanced thermoelectric properties

The Materials Genome Initiative calls for a dramatic increase in the rate of materials discovery and development. High-throughput (HT) calculations can advance this goal by efficiently screening a large search space for candidate materials to study in more depth. Thermoelectric materials (TEs) are prime candidates for such HT calculations: The properties required to achieve good performance are known, but systematic ways of improving these properties are scarce. Furthermore, known HT methods for TEs only address bulk crystals—screening realistic multicomponent alloys for their TE properties has yet to be accomplished. In this paper, we use a density functional theory driven HT screening-and-sorting procedure to search for new multicomponent bulk-nanostructured thermoelectric materials. We make maximum use of minimal calculations to obtain eight descriptors of the thermodynamics and TE performance of five-element semiconductor alloy systems from combinations of ternary additions in binary compounds. We use these descriptors to reduce a search space of 29 700 five-element systems to a set of 130 candidates. Lastly, we screen these candidates using TE descriptors to identify several existing high-performance thermoelectrics as well as promising new material systems awaiting further experimental verification.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration

A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, here we propose a cost-effective, data-driven approach to assessing a power system's load margin probabilistically. Using actual wind data, a kernel density estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of traditional Monte-Carlo simulations that process a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian-learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate, which serves as a highly efficient, parametric Bayesian prior, is developed. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Results of simulations that are performed on several test systems reveal the impressive performance of the proposed method in the probabilistic load-margin assessment.

17 WIND ENERGY↗
DOE OSTI2014

SU-E-T-95: Delivery Time Estimator

Purpose: The development and testing of a tool for the inclusion of delivery time as a parameter in plan optimization. Methods: We developed an algorithm that estimates the time required for the machine and personnel movements required to deliver a treatment plan on a linear accelerator. We included dose rate, leaf motion, collimator motion, gantry motion, and couch motions (including time to enter the room to rotate the couch safely). Vault-specific parameters to account for time to enter and perform couch angle adjustments were also included. This algorithm works for static, step and shoot IMRT, and VMAT beams photon beams and for fixed electron beams. This was implemented as a script in our treatment planning system. We validated the estimator against actual recorded delivery time from our R and V system as well as recorded times from our IMRT QA delivery. Results: Data was collected (Figure 1) for 12 treatment plans by examining the R and V beam start times, and by manually timing the QA treatment for a reference, but the QA measurements were only significant to the nearest minute. The average difference between the estimated and R and V times was 15%, and 11% when excluding the major outliers. Outliers arose due to respiratory aides and gating techniques which could not be accounted for in the estimator. Conclusion: Non-mechanical factors such as the time a therapist needs to walk in and out of the room to adjust the couch needed to be fine-tuned and cycled back into the algorithm to improve the estimate. The algorithm has been demonstrated to provide reasonable and useful estimates for delivery time. This estimate has provided a useful additional input for clinical decision-making when comparing several potential radiation treatment options.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2012

REBCO coated conductor cables for fusion magnets - Final Report

The feasibility for fusion as a practical energy source needs to be enhanced significantly by removing some of the restrictions that low -- temperature superconductors (LTS) put on the fusion magnet systems. One method to simplify the magnet system is by using high -- temperature superconductors (HTS) that allow for much larger temperature margins, a higher magnet performance and much less mechanical degradation in the superconductor during operation. For use in fusion magnets, HTS need to be bundled into high -- current cables that can be operated at high magnetic fields and at high current ramp rates, while experiencing a homogenous current distribution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Detecting Dwarf Galaxies Around NGC 55

Lambda-CDM predicts the existence of faint dwarf galaxies orbiting both large host galaxies, like the Milky Way, and smaller hosts like the Magellanic Clouds. However, the satellite populations around these smaller host galaxies are poorly understood from both a theoretical and observational perspective. In order to improve our understanding of the satellite populations of low-mass galaxies, the DELVE-DEEP Survey is performing a search for satellites around the nearby, low-mass galaxy, NGC 55. As part of this search, we aim to characterize our detection sensitivity by injecting artificial dwarf galaxies into our data set and attempting to recover them. In this poster, I present our methods of injection and recovery, as well as our preliminary results showing our measured detection rates for artificial dwarf galaxies of varying size and luminosity. Additionally, we explore the blending of resolved stellar fluxes that results as a consequence of source recovery and how this affects the recovered magnitudes of injected dwarf galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Dark Energy Survey Year 6 Results: Synthetic-source Injection Across the Full Survey Using Balrog

Synthetic source injection (SSI), the insertion of sources into pixel-level on-sky images, is a powerful method for characterizing object detection and measurement in wide-field, astronomical imaging surveys. Within the Dark Energy Survey (DES), SSI plays a critical role in characterizing all necessary algorithms used in converting images to catalogs, and in deriving quantities needed for the cosmology analysis, such as object detection rates, galaxy redshift estimation, galaxy magnification, star-galaxy classification, and photometric performance. We present here a source injection catalog of 146 million injections spanning the entire 5000 deg 2 DES footprint, generated using the Balrog SSI pipeline. Through this SSI sample, we demonstrate that the DES Year 6 (Y6) image processing pipeline provides accurate estimates of the object properties, for both galaxies and stars, at the percent-level, and we highlight specific regimes where the accuracy is reduced. We then show the consistency between SSI and data catalogs, for all galaxy samples developed within the weak lensing and galaxy clustering analyses of DES Y6. The consistency between the two catalogs also extends to their correlations with survey observing properties (seeing, airmass, depth, extinction, etc.). Lastly, we highlight a number of applications of this catalog to the DES Y6 cosmology analysis, such as estimates of the redshift distribution and lens magnification. This dataset is the largest SSI catalog produced at this fidelity and will serve as a key testing ground for exploring the utility of SSI catalogs in upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Guide to Nondestructive Evaluation Techniques

Nondestructive evaluation (NDE), alternately known as nondestructive examination (NDE), (commonly used within the nuclear industry), nondestructive testing (NDT) (commonly used in the aerospace and petrochemical industry), and nondestructive inspection (NDI) (commonly used within the military), are as the name implies test and assessment technologies designed to detect and characterize components without damaging or compromising the component’s ability to perform its design function. The most common application is detection of flaws caused either by manufacturing anomalies, service or environmental stresses, or natural material aging. More generally, applications may include estimation of mechanical and material properties, stress/strain, and dynamic response behavior. Post-manufacturing inspections frequently enjoy unfettered access to the component or part that may allow more thorough examinations, while in-service examinations after components have been in use may impose severe access limitations for in-situ testing. If flaws are detected, they must then be evaluated relative to their size, location, growth rate, growth mechanism, and likelihood that they are or will become a liability to the component’s function or performance. NDE is comprised of a large family of specific test disciplines including visual inspection, dimensional metrology, ultrasound, radiography, penetrant test, magnetic particle test, leak test, eddy current tests, potential drop tests, flash and vibro thermography, shearography, acoustic emissions, and many other methods. This section provides discussion on general NDE science and considerations for specific technique selection.

nondestructive, examination, inspection, NDE, NDT↗
DOE OSTI2024

Recycled graphite enabled superior performance for lithium ion batteries

Recycling graphite attracts growing attention since cumulative amount of spent Li-ion batteries and the shortage of graphite supply chain. Although various recycling methods have been reported, the recycled graphite cannot reach the strict commercial standards of purity, scalability, efficiency, and capacity, preventing it from battery manufacturing. Herein, the important roles of defects and functional groups on the graphite surface are deeply studied, and a closed-loop graphite recycling process with the surface recovery and modification for the graphite from the end-of-life batteries is demonstrated. The recovered graphite delivers a purity of over 99.9 % and an average initial coulombic efficiency of 91.5 %. Compared with commercial graphite in industrial standard battery testing parameters, full cells with recovered graphite possess enhanced rate reversibility, doubled cycle life, over 10 % higher capacity along with half anode material cost. In conclusion, these impressive results not only underscore the transformative potential of surface reconstruction and modification in graphite recycling, but also present economic feasibility and sustainable pathway for significantly improving battery performance and addressing global resource challenges via integration with the hydrometallurgical recycling process.

25 ENERGY STORAGE↗
DOE OSTI2018

LADR: low-cost application-level detector for reducing silent output corruptions

Applications running on future high performance computing (HPC) systems are more likely to experience transient faults due to technology scaling trends with respect to higher circuit density, smaller transistor size and near-threshold voltage (NTV) operations. A transient fault could corrupt application state without warning, possibly leading to incorrect application output. Such errors are called silent data corruptions (SDCs). In this paper, we present LADR, a low-cost application-level SDC detector for scientific applications. LADR protects scientific applications from SDCs by watching for data anomalies in their state variables (those of scientific interest). It employs compile-time data-flow analysis to minimize the number of monitored variables, thereby reducing runtime and memory overheads while maintaining a high level of fault coverage with low false positive rates. We evaluated LADR with 4 scientific workloads and results show that LADR achieved < 80% fault coverage with only ∼ 3% runtime overheads and ∼ 1% memory overheads. As compared to prior state-of-the-art anomaly-based detection methods, SDC achieved comparable or improved fault coverage, but reduced runtime overheads by 21% ∼ 75%, and memory overheads by 35% ∼ 55% for the evaluated workloads. We believe that such an approach with low memory and runtime overheads coupled with attractive detection precision makes LADR a viable approach for assuring the correct output from large-scale high performance simulations.

Chen, Chao↗
DOE OSTI2024

Computational study on the impact of gasoline-ethanol blending on autoignition and soot/NO x emissions under low-load gasoline compression ignition conditions

Here, in the present work, computational fluid dynamics (CFD) simulations of a single-cylinder gasoline compression ignition (GCI) engine are performed to investigate the impact of gasoline-ethanol blending on autoignition, nitrogen oxide (NO x ), and soot emissions under low-load conditions. In order to represent the test gasoline (RD5-87), a four-component toluene primary reference fuel (TPRF)+ethanol (ETPRF) surrogate (with 10% ethanol by volume; E10) is employed. A three-dimensional (3D) engine CFD model employing finite-rate chemistry with a skeletal kinetic mechanism (including NO x sub-mechanism), adaptive mesh refinement (AMR), and hybrid method of moments (HMOM) is adopted to capture the in-cylinder combustion phenomena and soot/NO x emissions. The engine CFD model is validated against experimental data for three gasoline-ethanol blends: E10, E30 and E100, with varying ethanol content by volume. Model validation is carried out for a broad range of start-of-injection (SOI) timings (−21, −27, −36, and −45 crank angle degrees (°CA) after top-dead-center (aTDC)) with respect to in-cylinder pressure, heat release rate, combustion phasing, NO x and soot emissions. For relatively later injection timings (−21 and −27 °CA aTDC), E30 yields higher amount of soot than E10; while the trend reverses for early injection cases (−36 and −45 °CA aTDC ). On the other hand, E100 yields the lowest amount of soot among all fuels irrespective of SOI timing. Further, E10 shows a non-monotonic trend in soot emissions with SOI timing: SOI-36>SOI-45>SOI-21>SOI-27, while soot emissions from E30 exhibit monotonic decrease with advancing SOI timing. NO x emissions from various fuels follow a trend of E10>E30>E100. On the other hand, NO x emissions increase as SOI timing is advanced for all fuels, with an anomaly for E10 and E100 where NO x decreases when SOI is advanced beyond −36 °CA aTDC. Detailed analysis of the numerical results is performed to investigate the soot/NO x emission trends and elucidate the impact of chemical composition and physical properties on autoignition and emissions characteristics.

Computational fluid dynamics↗
DOE OSTI2020

Feedback on Forty-year Long Clean-up Operations of a Contaminated Soil for Environmental Purpose - 20026

One of the CEA facilities in France is the place of miscellaneous mapping and clean-up operations since the 1980's. The final purpose of the conducted work deals with the environmental remediation of the building. In France, the absence of a regulatory framework for the management of sites polluted by radioactive substances, especially as the lack of release thresholds, has led to develop different approaches of soil radiological characterization. The geostatistical approach is part of them. Preliminary investigations are an unavoidable step prior to clearance and remediation. First, historical analysis enables to define site perimeters to investigate as well as to precisely locate expected contaminated parts. For radioactive pollutants, the media (water, soil, air) and transfer routes participate in the definition of the investigation perimeter and thus also need to be identified through a geological study. At the same time, functional analysis leads to choose the best measurement means which could provide useful information when combined with visual inspection. An equipment well adapted to easy-to-measure radionuclides as gamma emitters, such as gamma probe or on-site gamma spectrometry, is usually selected as a non-destructive assay. Coupled with spatial positions, processed on-site radiological data can be mapped. Data processing consists in mapping and kriging before a geostatistical analysis in order to identify the zones of interest to be targeted. Selected zones are not only linked to high level count rates but also to low level ones so that a pollution-free reference is known in the area. This data processing also enables to categorize waste types according to their origin and contamination levels. An expected volume of different waste then follows as well as the waste characterization equipments. In-depth investigations, e.g. core drillings and samplings, naturally ensue to aim at remediation optimization through the assessment of environmental impact. After clean-up operations, non-destructive assays as well as destructive samplings enable to check the effective clearance. Following this methodology in the present remediation work, results of the first non-destructive assay campaign highlighted a long-lived transuranic radionuclides contamination in the concrete flagstone. Successive non-destructive assays were then performed in the defined zones of interest. Count rates measured with a surface probe were first mapped. The best localization of destructive assays, i.e. core drillings at a 1 meter depth, came from this surface mapping followed by a geostatistical analysis. The concrete flagstone was totally removed. Then, the depth of soil to remove under the flagstone was optimized from the results of radiological activities measured in core drillings samples. A new surface mapping was then drawn after this partial soil excavation. The sand and demolition rubble samples, homogeneously constituted from the flagstone fragments, were analyzed by gamma spectrometry. Meanwhile, waste drums and other containers were also measured by gamma spectrometry and passive neutron measurement devices. All these results have brought miscellaneous pieces of information. In this context, major issues appear in terms of physical constraints such as the premises tininess as well as of soil sampling techniques and in terms of measurement performances assessment and the representativeness of homogeneous samples. The choice of radiological soil and waste characterization devices is described in the light of enhanced performances by keeping in mind radiation protection requirements and a willing to always optimize waste categories (very low level waste) and volume. The improvement of the characterization methods appears through this forty-year long work while sharing the feedback of encountered difficulties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

A method for removing arm backscatter from EPID images

Purpose: To develop a method for removing the support arm backscatter from images acquired using current Varian electronic portal imaging devices (EPIDs).Methods: The effect of arm backscatter on EPID images was modeled using a kernel convolution method. The parameters of the model were optimized by comparing on-arm images to off-arm images. The model was used to develop a method to remove the effect of backscatter from measured EPID images. The performance of the backscatter removal method was tested by comparing backscatter corrected on-arm images to measured off-arm images for 17 rectangular fields of different sizes and locations on the imager. The method was also tested using on- and off-arm images from 42 intensity modulated radiotherapy (IMRT) fields.Results: Images generated by the backscatter removal method gave consistently better agreement with off-arm images than images without backscatter correction. For the 17 rectangular fields studied, the root mean square difference of in-plane profiles compared to off-arm profiles was reduced from 1.19% (standard deviation 0.59%) on average without backscatter removal to 0.38% (standard deviation 0.18%) when using the backscatter removal method. When comparing to the off-arm images from the 42 IMRT fields, the mean {gamma} and percentage of pixels with {gamma} < 1 were improved by the backscatter removal method in all but one of the images studied. The mean {gamma} value (1%, 1 mm) for the IMRT fields studied was reduced from 0.80 to 0.57 by using the backscatter removal method, while the mean {gamma} pass rate was increased from 72.2% to 84.6%.Conclusions: A backscatter removal method has been developed to estimate the image acquired by the EPID without any arm backscatter from an image acquired in the presence of arm backscatter. The method has been shown to produce consistently reliable results for a wide range of field sizes and jaw configurations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Graves' disease radioiodine-therapy: Choosing target absorbed doses for therapy planning

Purpose: The precise determination of organ mass (m{sub th}) and total number of disintegrations within the thyroid gland (A{sup ~}) are essential for thyroid absorbed-dose calculations for radioiodine therapy. Nevertheless, these parameters may vary according to the method employed for their estimation, thus introducing uncertainty in the estimated thyroid absorbed dose and in any dose–response relationship derived using such estimates. In consideration of these points, thyroid absorbed doses for Graves’ disease (GD) treatment planning were calculated using different approaches to estimating the m{sub th} and the A{sup ~}. Methods: Fifty patients were included in the study. Thyroid{sup 131}I uptake measurements were performed at 2, 6, 24, 48, 96, and 220 h postadministration of a tracer activity in order to estimate the effective half-time (T{sub eff}) of {sup 131}I in the thyroid; the thyroid cumulated activity was then estimated using the T{sub eff} thus determined or, alternatively, calculated by numeric integration of the measured time-activity data. Thyroid mass was estimated by ultrasonography (USG) and scintigraphy (SCTG). Absorbed doses were calculated with the OLINDA/EXM software. The relationships between thyroid absorbed dose and therapy response were evaluated at 3 months and 1 year after therapy. Results: The average ratio (±1 standard deviation) betweenm{sub th} estimated by SCTG and USG was 1.74 (±0.64) and that between A{sup ~} obtained by T{sub eff} and the integration of measured activity in the gland was 1.71 (±0.14). These differences affect the calculated absorbed dose. Overall, therapeutic success, corresponding to induction of durable hypothyroidism or euthyroidism, was achieved in 72% of all patients at 3 months and in 90% at 1 year. A therapeutic success rate of at least 95% was found in the group of patients receiving doses of 200 Gy (p = 0.0483) and 330 Gy (p = 0.0131) when m{sub th} was measured by either USG or SCTG and A{sup ~} was determined by the integration of measured {sup 131}I activity in the thyroid gland and based on T{sub eff}, respectively. No statistically significant relationship was found between therapeutic response and patients’ age, administered {sup 131}I activity (MBq), 24-h thyroid {sup 131}I uptake (%) or T{sub eff} (p ≥ 0.064); nonetheless, a good relationship was found between the therapeutic response and m{sub th} (p ≤ 0.035). Conclusions: According to the results of this study, the most effective thyroid absorbed dose to be targeted in GD therapy should not be based on a fixed dose but rather should be individualized based on the patient'sm{sub th} and A{sup ~}. To achieve a therapeutic success (i.e., durable euthyroidism or hypothyroidism) rate of at least 95%, a thyroid absorbed dose of 200 or 330 Gy is required depending on the methodology used for estimating m{sub th} and A{sup ~}.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

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↗
DOE OSTI2018

Colloidal Cobalt Phosphide Nanocrystals as Trifunctional Electrocatalysts for Overall Water Splitting Powered by a Zinc–Air Battery

Highly efficient and stable electrocatalysts, particularly those that are capable of multifunctionality in the same electrolyte, are in high demand for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR). In this paper, highly monodisperse CoP and Co 2 P nanocrystals (NCs) are synthesized using a robust solution-phase method. The highly exposed (211) crystal plane and abundant surface phosphide atoms make the CoP NCs efficient catalysts toward ORR and HER, while metal-rich Co 2 P NCs show higher OER performance owing to easier formation of plentiful Co 2 P@COOH heterojunctions. Density functional theory calculation results indicate that the desorption of OH* from cobalt sites is the rate-limiting step for both CoP and Co 2 P in ORR and that the high content of phosphide can lower the reaction barrier. A water electrolyzer constructed with a CoP NC cathode and a Co 2 P NC anode can achieve a current density of 10 mA cm -2 at 1.56 V, comparable even to the noble metal-based Pt/C and RuO 2 /C pair. Finally and furthermore, the CoP NCs are employed as an air cathode in a primary zinc–air battery, exhibiting a high power density of 62 mW cm -2 and good stability.

36 MATERIALS SCIENCE↗