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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Nonlinear Impact-Echo Test for Quantitative Evaluation of ASR Damage in Concrete

Nonlinear acoustic methods demonstrate high sensitivity for concrete damage evaluation. Among these methods, nonlinear resonance acoustic methods have been widely used in laboratory tests on small scale test samples. In this study, a nonlinear impact-echo (IE) method for concrete damage evaluation was introduced, in which the IE frequency shift was measured at different impact force levels. The nonlinear IE test and the nonlinear impact resonance acoustic spectroscopy (NIRAS) test share similarities in the experimental setup and analysis method. However, the nonlinear IE test excites a local thickness resonance mode of a member instead of the global resonance vibration. Therefore, the analyzed mode is unaffected by member boundary conditions and is applicable to large concrete structural members. To identify the fundamental IE frequencies, multiple impacts were applied along the members. Once the fundamental IE frequencies were determined, the effectiveness of the nonlinear IE method was evaluated by testing seven concrete beam specimens with varying levels of alkali-silica reaction (ASR) damage. In conclusion, the nonlinear IE test demonstrates high sensitivity to concrete damage and allows for quantitative assessment of the damage state of concrete without a baseline measurement.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Evaluation and calibration of MERRA-2 and CAMS reanalysis for PM 2.5 in a semi-urbanized area in the south of the Amazon

Air pollution has significant implications for the climate and poses irreversible risks to human health. The Amazon region of Brazil is severely affected by biomass burning (BB) emissions, yet air quality monitoring remains highly inadequate. Given the scarcity of surface-based observations, reanalysis models have become essential tools for assessing air pollution. Although MERRA-2 and CAMS PM 2.5 products are widely utilized, their validation and comprehensive evaluation for the Amazon Basin remain limited. Here, this study assesses the performance of these products in a semi-urbanized region in the southern Amazon. The calibrated time series was employed to analyze PM 2.5 concentrations from 2003 to 2023. Our results showed satisfactory performance of both products for the 24-h averages of PM 2.5 , with linear correlations above 0.76. However, it was found that both products overestimate surface concentrations. MERRA-2 performed better, with approximately 30% lower bias than CAMS. Time series analysis showed that the study area is strongly impacted by emissions BB in the dry period, mainly in August and September. Furthermore, our findings indicate a positive trend in increasing PM 2.5 concentrations, with a notable rise observed since 2014. The average PM 2.5 levels frequently exceed the daily air quality guidelines established by the WHO in 2021. It has been estimated that the population of this region is exposed to concentrations above 15 μg.m -3 , on average, more than 30 days per year. Our results contribute to the evaluation of MERRA-2 and CAMS products for Amazon and provide a corrected estimate for surface PM 2.5 . Recent concerns about air quality and the implementation of new surface monitoring networks may improve the evaluation of reanalysis products. In the short term, the need for this information makes our assessments indispensable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Non-contact electrochemical evaluation of biofilms

Abstract Here, we demonstrate a non-contact technique for electrochemical evaluation of biofilms on surfaces in relation to corrosion. Electrochemical impedance spectrometry was employed, incorporating flat patterned electrodes positioned over the surfaces of aluminum and glass with and without biofilms. Signal communication from the working electrode to the counter electrode followed electric field lines passing tangentially through the biofilms. Electrochemical impedance parameters that were evaluated included complex impedance, phase angle, imaginary (out of phase) conductivity and Cole–Cole plots with a corresponding equivalent circuit. Changes in the impedance properties due to the presence of biofilms were monitored and correlated through microbiological, chemical and electrochemical assays. Impedance parameters associated with microbial activity correlated with biofilms on aluminum and glass surfaces. This technical approach provides impedance information about the biofilm without the signal traveling through the underlying conductive media or disrupting the biofilm. In this way, biological contributions to surface fouling can be evaluated with minimal contribution from the inorganic surface under the biofilm. In addition, this technique can be used to monitor biofilms on electrochemically inert surfaces as well as electrically conductive surfaces. Graphic abstract

Turick, Charles E. (ORCID:0000000307815846)↗
DOE OSTI2021

Quantitative resilience evaluation on recovery from emergency situations in nuclear power plants

Here, this paper mainly introduces how to develop a resilience evaluation model by quantifying the relationship between resilience and resilience components in regard to recovering from emergency situations in nuclear power plants (NPPs). It is an extension of the author’s previous researches. To develop the model, the first step was to analyze event reports published in Republic of Korea in terms of resilience factors from different safety perspectives. Second, statistical methods such as factor analysis, principal component analysis, and multivariate regression analysis were applied to the dataset to identify relations among resilience factors and develop a quantitative resilience evaluation model for recovery from emergency situations in NPPs. Third, the model was validated using the recent event report data released in Korea. Lastly, we present a discussion on how to apply the quantitative resilience evaluation model to results from stress tests performed in Korea.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Laboratory testing methods to evaluate the reliability of occupancy sensors for commercial building applications

The energy performance of commercial buildings is greatly influenced by occupants which are highly variable and among the most unpredictable components of a building's operation. While most building control systems use fixed, predetermined occupancy schedules, these fixed occupancy levels can be quite different from actual occupancy. This can cause unnecessary energy consumption, particularly from heating, ventilation, and air conditioning (HVAC) and lighting systems which are responsible for approximately 60% of commercial buildings' energy use. The use of occupancy counting sensor systems integrated with building management system controls is one method that can be used to improve the energy-consuming performance of buildings. However, there is no standardized universal methodology and metrics to evaluate their reliability. The aim of this research is to develop a uniform evaluation methodology to assess the reliability of occupancy counting sensor systems in a controlled laboratory environment. The developed testing methodology includes both “typical” scenarios representing the occupancy scenarios of a typical commercial building, and “failure” testing scenarios which represent a range of potential scenarios that may impact a sensor system's reliability. These methods were then implemented in a case study to evaluate the performance of two novel occupancy counting sensor systems (i.e., door-centric, and camera-based). Results suggest that typical testing results can be used to compare the overall performance of the occupancy counting sensor systems; however, failure testing is also important to understand the weaknesses of the sensor system in order to select the suitable one for the intended use of the commercial building. In addition, the proposed methodology includes a modified confusion matrix which enables the ability to identify if failures are caused by over or under counting occupants and to what extent this occurs over the testing period.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Evaluation of supply air temperature control performance with different control strategies at air handling units

Due to its nonlinear feature, supply air temperature control systems at air handling units using single-loop fixed gain control frequently suffer unstable response under dynamic conditions. This paper is aimed at identifying a more effective control strategy to stabilize the valve control and achieve desirable supply air temperature response under various operation conditions by evaluating three control strategies, cascade, gain scheduling and cascade plus gain scheduling controls. Firstly, a control-oriented model is developed and calibrated through open-loop tests. Then, the control issues with single-loop fixed gain control are evaluated through simulations and field tests, which indicates that the system has more aggressive response or becomes unstable under lower load conditions when the chilled-water flow rate is less than 0.1 L/s. Finally, three control strategies are proposed and evaluated. Further, the results reveal that all three proposed controls can achieve better robustness with the integral square error of supply air temperature decreased to 16%, 47% and 5%, respectively, under lower load condition. However, since high-frequency oscillation of the control valve occurs with cascade and cascade plus gain scheduling control, the gain scheduling control is recommended considering a trade-off among the supply air temperature response, valve lifetime and implementation complexity.

42 ENGINEERING↗
DOE OSTI2025

General field evaluation in high-order meshes on GPUs

Robust and scalable function evaluation at any arbitrary point in the finite/spectral element mesh is required for querying the partial differential equation solution at points of interest, comparison of solution between different meshes, and Lagrangian particle tracking. This is a challenging problem, particularly for high-order unstructured meshes partitioned in parallel with MPI, as it requires identifying the element that overlaps a given point and computing the corresponding reference space coordinates. Here, we present a robust and efficient technique for general field evaluation in large-scale high-order meshes with quadrilaterals and hexahedra. In the proposed method, a combination of globally partitioned and processor-local maps are used to first determine a list of candidate MPI ranks, and then locally candidate elements that could contain a given point. Next, element-wise bounding boxes further reduce the list of candidate elements. Finally, Newton’s method with trust region is used to determine the overlapping element and corresponding reference space coordinates. Since GPU-based architectures have become popular for accelerating computational analyses using meshes with tensor-product elements, specialized kernels have been developed to utilize the proposed methodology on GPUs. The method is also extended to enable general field evaluation on surface meshes. The paper concludes by demonstrating the use of the proposed method in various applications ranging from mesh-to-mesh transfer during r-adaptivity to Lagrangian particle tracking.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evaluating dynamic thermal performance of building envelope components using small-scale calibrated hot box tests

The hot box test method has been applied to evaluate both the steady-state (U-value) and dynamic thermal properties of building envelopes. However, the high construction cost of full-scale hot box apparatus and the testing time required (usually several days) may prevent its wider adoption. To overcome the limitations of full-scale hot box tests, here we propose a novel method to evaluate the dynamic thermal performance of building envelope components using a small-scale calibrated hot box and scaled-down specimen. In this paper, the scaling relationships of thermal properties evaluated using a full-size specimen and a scaled-down specimen are established based on the Laplace transform of the heat transfer equations. In addition, dynamic thermal properties obtained from scaled-down experimental tests are compared to the values calculated by the EN ISO13768 (ISO) method. A small-scale hot box with a 355 mm × 355 mm metering area was constructed and calibrated to validate the correlations. Three scaled-down concrete sandwich wall panels were then tested and the scaling relationship was cross-validated using the experimental results, finite difference (FD) simulations, and the ISO method. The results indicate that the dynamic thermal properties obtained from a scaled-down hot box test can be correlated to its full-size counterpart when certain conditions are met. The scaled-down hot box test is demonstrated to be an effective yet economical alternative to a full-scale test with significantly reduced experimentation cost and turn-around time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Development and testing of a performance evaluation methodology to assess the reliability of occupancy sensor systems in residential buildings

With the emergence of advanced occupancy sensor technologies to better detect occupancy in buildings, a universal methodology and metrics are required to evaluate and report sensor systems’ reliability and compare the performance across multiple sensor systems. Herein this research presents a methodology to assess the reliability of occupancy sensor systems in residential buildings in a controlled laboratory environment, including both “typical” and “failure” testing scenarios. The developed methodology was then implemented to evaluate a novel occupancy detection sensor system’s reliability. “Typical” testing evaluates the overall accuracy of the sensor system, which suggest how reliable the occupancy sensor system is over time. Results show that on average, the precision and recall are 0.75 and 0.70, indicating similar numbers of false positives and false negatives across the dataset. The overall accuracy of the tested sensor system was 62.4% to 76.4%. Failure testing results indicate whether there are influential variables impacting the sensor performance. For the tested sensor system, the number of occupants, presence of large objects, presence of interior light sources, and number of doors are not influential, while lighting level, location of occupants, additional door in the entry/exit area, and having the TV on are variables determined to impact the sensor system performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Evaluating performance of different generative adversarial networks for large-scale building power demand prediction

We report as an unsupervised-learning data-driven model, Generative Adversarial Networks (GANs) have recently attracted a lot of attention for various applications. There is potential to apply GANs for large-scale building power demand prediction, which is needed for power grid operation. However, there are many GAN variations and it is unclear which GAN is suitable for this application. To answer this question, this paper identifies five promising GANs (Original GAN, cGAN, SGAN, InfoGAN, and ACGAN) and evaluates their performance for predicting building power demand at a large scale. Physics-based building energy models are developed to generate training and reference data. A new evaluation indicator that combines accuracy and reproducibility is proposed to evaluate the performance of different GANs in predicting building power demand. The results show that SGAN and InfoGAN are not suitable because they cannot control the number of generated building samples for different building types. The prediction performance among the Original GAN, cGAN, and ACGAN can vary depending on training sample sizes and number of building types. If the training sample size is sufficiently large, Original GAN and cGAN can predict building power demand more accurately than ACGAN with the same number of samples. If training samples are limited, Original GAN provides better accuracy than cGAN and ACGAN. When the number of building types increase, the prediction accuracy increases for cGAN, decreases for ACGAN, and remains the same for Original GAN. As a result, cGAN and Original GAN are recommended for large-scale building power demand prediction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Evaluating offshore legacy wells for geologic carbon storage: A case study from the Galveston and Brazos areas in the Gulf of Mexico

In this article, federal offshore waters in the Gulf of Mexico are of interest for large-scale geologic carbon storage (GCS). However, more than 80,000 offshore oil and gas wells exist in the region, which could impact the integrity of sealing intervals. In this study, we propose a screening methodology for ranking offshore legacy wells based on the challenge they may present to GCS. The methodology relies on the review of well regulatory records to 1) identify leakage pathways and assess the potential hazards that wells pose to planned GCS operations, 2) evaluate well features that impact the accessibility of wells to determine the feasibility of potential corrective actions, and 3) rank wells based on the overall challenge they may pose for GCS. We demonstrate our framework by evaluating the construction and abandonment of 156 wells across eight areas of interest (AOIs) in shallow federal waters along the Texas Gulf Coast. The majority (99.3 %) of wells considered were constructed and plugged in a manner that did not isolate prospective GCS targets in the Upper and Lower Miocene formations and may potentially require a challenging or uncertain corrective action prior to GCS. Dataset trends suggest that the observed well construction and plugging designs may be common in shallow offshore federal waters along the Texas Gulf Coast. Consequently, operators pursuing offshore GCS projects in the region may consider selecting areas that avoid challenging wells or performing robust evaluations of legacy well leakage risks to plan corrective action prior to CO 2 injection.

58 GEOSCIENCES↗
DOE OSTI2020

Voltammetry measurements in lithium chloride-lithium oxide (LiCl–Li 2 O) salt: An evaluation of working electrode materials

Instrumentation to provide process monitoring (PM) and safeguards for the oxide reduction (OR) step in the electrochemical processing of used oxide nuclear fuel is necessary to ensure equipment is operated as declared. Cyclic voltammetry (CV) has been proposed for real-time monitoring of the operation of an OR process for safeguards purposes. In this study, different materials including 316 stainless steel (SS), tantalum (Ta), molybdenum (Mo), tungsten (W), platinum (Pt), and iridium (Ir) were evaluated as potential working electrode (WE) materials based on their chemical inertness, corrosion resistance, and sensitivity in detecting Li 2 O and other chloride salts in the OR electrolyte. Of the electrodes evaluated, 316 SS, Ir, and Pt all performed reasonably well in the LiCl-Li 2 O electrolyte. Here, stainless steel was operated in the cathodic potential and had reasonable corrosion resistance and is relatively inexpensive. Iridium could be operated in both the cathodic and anodic potentials and was the most corrosion resistant of those evaluated. Platinum is limited to the anodic potential range, had reasonable corrosion resistance, and was the most sensitive to Li 2 O concentrations in the salt. In the development of a stand-alone safeguards instrumentation, our recommendation is a CV sensor fitted with both Ir and Pt WEs. The results in this study may also be helpful for anode material selection in other electrometallurgy industries, such as molten salt electrolysis.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Evaluation of neutron dosimetry capabilities with the MC-15 portable multiplicity counter

This work proposes a preliminary neutron dose rate estimation method for a neutron multiplicity detector through measurement- and simulation-based analyses. Uncharacterized neutron-emitting sources may be encountered in situations such as nuclear emergency response, safeguards, and treaty verification. These circumstances may present irradiation risk to personnel conducting field assay, search, and characterization measurements. It is therefore of interest to provide a field neutron dosimetry capability with the existing neutron multiplicity counting (NMC) capabilities. To date, no commercially-available neutron detection systems are capable of both accurate NMC and real-time neutron dosimetry. This work will focus on estimating dose rate using input from a single fielded NMC called the MC-15. The energy-dependent neutron detection efficiency response of the MC-15 was quantified in monoenergetic neutron simulations and evaluated in response to two neutron-emitting sources and to a polyethylene-moderated source. The results were compared to existing neutron dosimeters and established the proof of concept for further investigation of the MC-15 for dose estimation. Measurement results were also replicated in simulations; additional simulations were then conducted to expand upon the limited empirical data. The initial empirical results provided a conversion factor appropriate for use when measuring 252 Cf neutrons that is independent of polyethylene shielding presence and thickness. The simulated data sets were then used to evaluate a fit equation allowing estimation of the neutron dose rate for less restricted geometric configurations and dependent only on the distance between the source and the detector. Additionally, the energy dependence of the efficiency response indicates that further empirical evaluations could provide energy-dependent conversion factors for broader neutron dosimetry capabilities with a wider range of neutron-emitting sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Experimental demonstration and performance evaluation of a complex fenestration system for daylighting and thermal harvesting

Two significant design strategies for mitigating building energy consumption are daylight redirection and solar energy harvesting. Effective daylighting implementation enhances the amount of useful natural light within a space and offsets the need for electric lighting. Solar energy harvesting systems can mitigate energy costs from mechanical systems by managing incoming thermal loads or capturing solar energy that can be used to supplement thermal systems in the building. While there are many available façade-based technologies that can perform daylighting or solar thermal energy harvesting, there remains a limitation in available systems that can perform both of them simultaneously. This paper, describes the design and experimental demonstration of a Liquid Filled Prismatic Louver (LFPL) system, which combines both energy-saving strategies. The LFPL system was installed in a south-facing room in New York City and evaluated for indoor daylight penetration and potential for thermal energy harvesting. Daylight redirection is achieved through the prismatic louver geometry and proper orientation, whereas thermal energy harvesting is achieved through IR absorption from the liquid (e.g., water) within the prisms. Daylighting performance was evaluated by illuminance measurements at key locations within the space, whereas thermal harvesting performance was evaluated through temperature measurements and thermal imaging analysis. We show that the LFPL system, with all prismatic elements oriented at the same angle, achieve a 2-fold and 8-fold enhancement in daylight redirection to the ceiling, for prism orientations of 10° and 20°, respectively. We also demonstrate the system’s capability to adjust to specific lighting needs, within the space, through the dynamic individual orientation of prismatic elements; thus, achieving a concentrated ceiling illuminance enhancement of ~100 times and ~200 times at 2.5 and 4.3 m away from the window sill, providing workplane illuminance enhancement of 6 and 2 times more than in the case of the control room with regular windows. Furthermore, we illustrate a reduction of potential heating loads at locations close to the window from the combination of infrared absorption in the water volume and the redirection of the incoming solar radiation, leading to a reduction of the workplane temperature by an average of 7–10 °C.

14 SOLAR ENERGY↗
DOE OSTI2021

Evaluation of clear-sky and satellite-derived irradiance data for determining the degradation of photovoltaic system performance

Knowing the degradation in performance of a photovoltaic (PV) system over time is important for estimating the lifetime energy produced and the financial return. A key parameter for normalizing performance and determining degradation is the plane-of-array (POA) irradiance. Because accurate long-term POA measurements are not always readily available, three methods of providing irradiance data for determining the degradation rate of PV systems were evaluated—a method using irradiance data modeled with the Ineichen clear-sky model and monthly Linke turbidity coefficients, a method using the supplemental clear-sky irradiance data from the National Solar Radiation Data Base (NSRDB), and a method using the NSRDB solar irradiance data for both cloudy and clear-sky conditions (all-sky). The irradiance data from the three methods were evaluated using measured irradiance data from 1998 through 2018 for the seven-station SURFRAD network and for 3-, 5-, and 10-year periods that might be used for evaluating PV system performance. Only the two clear-sky methods for the 10-year periods had less uncertainty with respect to determining PV system degradation than the expected median degradation rate for PV systems of -0.5%/year to -0.6%/year. Shorter periods and the all-sky method had larger uncertainties, making their use questionable for determining the degradation rates of PV systems.

14 SOLAR ENERGY↗
DOE OSTI2022

Evaluating WRF-Solar EPS cloud mask forecast using the NSRDB

Improving the accuracy of day-ahead solar forecasts using numerical weather prediction models requires improving the forecasting of cloud occurrence and properties. Validating cloud forecasts is challenging because this requires evaluating different types of clouds over a wide variety of regions. This study analyzes the cloud occurrence (or cloud mask) over the contiguous United States (CONUS), predicted by the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS), to identify the strengths and limitations of the model in reproducing cloud fields. To enable the in-depth analysis of cloud mask forecasts covering CONUS, we use satellite observations from the National Solar Radiation Database (NSRDB). Two evaluation methods are implemented to consider all clouds and partially removed clouds from the 2-km NSRDB in evaluating the 9-km WRF-Solar EPS cloud mask. Cloud detection metrics as well as the frequency of cloud occurrence are used to quantify the monthly performance of WRF-Solar EPS. Mismatched cloud frequency (MCF) is used to assess the model's capability to predict different types of clouds, which are classified using three levels of cloud optical depth (COD) and cloud top height (CTH). The day-ahead forecasts covering the full year of 2018 demonstrate that WRF-Solar EPS produces MCFs ranging from 27%-46%, 13%-34%, and 8%-19% for thin, mid-thickness, and thick clouds, respectively. For three CTH levels, the model shows MCFs ranging from 19%-46%, 16%-33%, and 8%-27% for low-level, middle-level, and high-level clouds, respectively. This comprehensive characterization of model performance helps identify model weakness and will eventually lead to improvements in cloud and solar radiation forecasting.

14 SOLAR ENERGY↗
DOE OSTI2020

Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions

Simulated historical precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices. The model indices are evaluated against corresponding indices from the CPC unified gauge-based analyses of precipitation over seven geographical regions across the contiguous US (CONUS). The regions assessed match those in recent US National Climate Assessment Reports. To estimate observational uncertainty, precipitation indices for three other observational datasets (HadEx2, Livneh and PRISM) are evaluated against the CPC analyses. Both the moderate and extreme mean precipitation intensities are overestimated over the western CONUS and underestimated in the areas of the Central Great Plains (CGP) in most CMIP6 models tested. Most CMIP6 models overestimate the mean and variability of wet spell durations and underestimate the mean and variability of dry spell durations across the CONUS. Biases in interannual variability of most of the indices have similar patterns to those in corresponding mean biases. The median and interquartile model spreads in CMIP6 model biases are clearly smaller than those in CMIP5 model biases for wet spell durations. Multimodel medians of CMIP6 (CMIP6-MMM) and CMIP5 (CMIP5-MMM) have similar biases in climatology and variability but biases tend to be smaller in CMIP6-MMM. Depending on the index, extreme precipitation is slightly better in parts of the eastern half of the CONUS in CMIP6-MMM, otherwise, the biases in climatology and variability are similar to CMIP5-MMM. CMIP6-MMM performs better than individual models and even observational datasets in some cases. Differences between observational datasets for most indices are comparable to the CMIP6 interquartile model spread. The better-performing observational and model datasets are different in different parts of the CONUS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Evaluation of Force Fields for Molecular Dynamics Simulations of Platinum in Bulk and Nanoparticle Forms

Here, understanding the size- and shape-dependent properties of platinum nanoparticles is critical for enabling the design of nanoparticle-based applications with optimal and potentially tunable functionality. Toward this goal, we evaluated nine different empirical potentials with the purpose of accurately modeling faceted platinum nanoparticles using molecular dynamics simulation. First, the potentials were evaluated by computing bulk and surface properties - surface energy, lattice constant, stiffness constants, and the equation of state - and comparing these to prior experimental measurements and quantum mechanics calculations. Then, the potentials were assessed in terms of the stability of cubic and icosahedral nanoparticles with faces in the {100} and {111} planes, respectively. Although none of the force fields predicts all the evaluated properties with perfect accuracy, one potential - the embedded atom method formalism with a specific parameter set - was identified as best able to model platinum in both bulk and nanoparticle forms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗