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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Real time propagation of the exact two component time-dependent density functional theory

We report the development of a real time propagation method for solving the time-dependent relativistic exact two-component density functional theory equations (RT-X2C-TDDFT). The method is fundamentally non-perturbative and may be employed to study nonlinear responses for heavy elements which require a relativistic Hamiltonian. Here, we apply the method to several group 12 atoms as well as heavy-element hydrides, comparing with the extensive theoretical and experimental studies on this system, which demonstrates the correctness of our approach. Because the exact two-component Hamiltonian contains spin-orbit operators, the method is able to describe the non-zero transition moment of otherwise spin-forbidden processes in non-relativistic theory. Furthermore, the two-component approach is more cost effective than the full four-component approach, with similar accuracy. The RT-X2C-TDDFT will be useful in future studies of systems containing heavy elements interacting with strong external fields.

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

Selective acetate recognition and sensing using SWCNTs functionalized with croconamides

Croconamides are a new class of dual-hydrogen bond donors that have attracted interest in anion recognition and sensing applications due to their strong H-bond donation capability. In this work, we report chemiresistive sensors for the detection of anions based on poly(4-vinylpyridine) (P4VP)-wrapped single-walled carbon nanotubes (SWCNTs) functionalized with croconamide-based selectors. Model structures of asymmetric N,N-substituted croconamide selectors with N,N-substitutional groups of alkyl cationic pyridinium and electron-withdrawing moieties such as aniline (6) and 3,5-bis(trifluoromethyl)phenyl (7) groups were synthesized and their binding affinities for various anions were characterized using UV–vis and 1 H NMR titrations. Switchable binding of oxoanions was observed with selector 7, wherein HSO 4 – formed hydrogen bonding interactions and acetate (AcO – ) induced deprotonation of the NH- protons of the croconamide. Large sensitivity transitions were obtained toward AcO – for both P4VP-6-SWCNT (S = 95.68 ± 2.11 %) and P4VP-7-SWCNT (S = 140.91 ± 3.68 %) at 83.33 mM, which was attributed to deprotonation of the croconamide selectors upon addition of AcO – . In conclusion, three serial sensors were assembled in a sensor array that rapid and selective screening of AcO – was demonstrated against other oxoanions such as HSO 4 – and H 2 PO 4 – in real-time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Modeling the interface of platinum and α-quartz(001): Implications for sintering

We present a first-principles study which aims to understand the metal–support interaction of platinum nanoparticles on α-quartz(001) and, more generally, silica. The thermodynamic stability of the α-quartz(001) surface and its interface with Pt(111) are investigated as a function of temperature and partial pressure of H 2 O and O 2 . Potential defects in the α-quartz(001) surface as well as the adsorption energies of the Pt atom are also studied. This allows us to draw conclusions concerning nanoparticle shape and the resistance toward particle migration based on the interface free energies. We find that, as for the clean α-quartz(001) surface, a dry, reconstructed interface is expected at temperatures that are high but within experimentally relevant ranges. On an ideal, dry, reconstructed surface, particle migration is predicted to be a fast sintering mechanism. On real surfaces, defects may locally prevent reconstruction and act as anchoring points. Finally, the energetics of the adsorption of platinum atoms on α-quartz(001) do not support surface-mediated single-atom migration as a viable path for sintering on the investigated surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Real-space pseudopotential study of vibrational properties and Raman spectra in Si–Ge core-shell nanocrystals

We examine the vibrational properties and Raman spectra of Si–Ge core-shell nanostructures using real-space pseudopotentials constructed within density functional theory. Our method uses no empirical parameters, unlike many popular methods for predicting Raman spectra for nanocrystals. Here, we find the dominant features of the Raman spectrum for the Si–Ge core-shell structure to be a superposition of the Raman spectra of the Ge and Si nanocrystals with optical peaks around 300 and 500 cm -1 , respectively. We also find a Si–Ge “interface” peak at 400 cm -1 . The Ge shell causes the Si core to expand from the equilibrium structure. This strain induces significant redshift in the Si contribution to the vibrational and Raman spectra, while the Ge shell is largely unstrained and does not exhibit this shift. We find that the ratio of peak heights is strongly related to the relative size of the core and shell regions. This finding suggests that Raman spectroscopy may be used to characterize the size of the core and shell in these structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Digital Twin Framework Utilizing Machine Learning for Robust Predictive Maintenance: Enhancing Tire Health Monitoring

We introduce a novel digital twin (DT) framework for the predictive maintenance of long-term physical systems. Using monitoring tire health as an application, we show how the DT framework can be used to enhance automotive safety and efficiency, and how the technical challenges can be overcome using a three-step approach. First, to manage the data complexity over a long operation span, we employ data reduction techniques to concisely represent physical tires using historical performance and usage data. Relying on these data, for fast real-time prediction, we train a transformer-based model offline on our concise dataset to predict future tire health over time, represented as remaining casing potential (RCP). Based on our architecture, our model quantifies both epistemic and aleatoric uncertainties, providing reliable confidence intervals around predicted RCP. Second, to incorporate real-time data, we update the predictive model in the DT framework, ensuring its accuracy throughout its lifespan with the aid of hybrid modeling and the use of the discrepancy function. Third, to assist decision-making in predictive maintenance, we implement a tire state decision algorithm, which strategically determines the optimal timing for tire replacement based on RCP forecasted by our transformer model. This approach ensures that our DT accurately predicts system health, continually refines its digital representation, and supports predictive maintenance decisions. Furthermore, our framework effectively embodies a physical system, leveraging big data and machine learning (ML) for predictive maintenance, model updates, and decision-making.

advanced computing infrastructure↗
DOE OSTI2015

MO-FG-303-02: BEST IN PHYSICS (THERAPY): Cherenkov Emission Dosimetry: Feasibility for Electron Radiotherapy

Purpose: To investigate from first principles, corroborated by Monte Carlo simulations and experimental measurements, the feasibility of developing a relative Cherenkov emission (CE) dosimetry protocol for electron beam radiotherapy. Methods: Monte Carlo (MC) simulations of mono-energetic electrons incident on water were carried out in Geant4. Percent depth Cherenkov emission (PDCE) and dose (PDD) distributions were scored for incidence energies of 4, 6, 9, 12, 15, and 18 MeV. PDCE-to-PDD analytical conversion models were developed from least-squares data fits generated for PDD as a function of PDCE at the same depth and at different depths. Experimental techniques for validation of these models are examined. Results: Same-depth PDD versus PDCE data fits indicate that although the relationship is linear to first order (correlation r > 0.9 for all energies), it is much more accurately approximated by separate linear and quadratic models for the build-up and drop-off regions, respectively (r > 0.999), which is theoretically underpinned. To understand the source of this relationship and its basis for developing robust conversion models, an approximate quadratic first-principles model was derived and found in agreement with MC/measured data (20% deviation at worst). Conversely, data fits of PDD versus different-depth PDCE unveiled a depth-invariant effective point of measurement of 1.5–2.1 mm downstream with 4–18 MeV incidence, respectively (r > 0.999 in the drop-off region). We present an analytical first-principles justification for this shift. This method led to errors of <1% in drop-off region PDD (<2% for PDD<20% with 4 MeV incidence) and <0.2 mm in practical range prediction. Conclusion: We present robust quantitative prediction models, derived from first-principles and supported by simulation and measurement, for relative dose from Cherenkov emission by high-energy electrons. This constitutes a major step towards development of protocols for routine clinical quality assurance as well as real-time in vivo Cherenkov dosimetry in radiotherapy. The authors acknowledge partial support by Fonds de recherche du Quebec - Nature et technologies (FRQNT), CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council of Canada (NSERC), CREATE Integrated Sensor Systems grant of NSERC, the Canadian Institutes of Health Research (CIHR), and NSERC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Site-Selective Real-Time Observation of Bimolecular Electron Transfer in a Photocatalytic System Using L-Edge X-Ray Absorption Spectroscopy

Here, time-resolved X-ray absorption spectroscopy has been utilized to monitor the bimolecular electron transfer in a photocatalytic water splitting system. This has been possible by uniting the local probe and element specific character of X-ray transitions with insights from high-level ab initio calculations. The specific target has been a heteroleptic [Ir III (ppy) 2 (bpy)] + photosensitizer, in combination with triethylamine as a sacrificial reductant and Fe 3 (CO) 12 as a water reduction catalyst. The relevant molecular transitions have been characterized via high-resolution Ir L-edge X-ray absorption spectroscopy on the picosecond time scale and restricted active space self-consistent field calculations. The presented methods and results will enhance our understanding of functionally relevant bimolecular electron transfer reactions and thus will pave the road to rational optimization of photocatalytic performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Unified architecture for data-driven metadata tagging of building automation systems

This article presents a Unified Architecture (UA) for automated point tagging of Building Automation System (BAS) data, based on a combination of data-driven approaches. Advanced energy analytics applications—including fault detection and diagnostics and supervisory control—have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical BAS implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time-series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: (i) commercial retail buildings and (ii) office buildings from the National Renewable Energy Laboratory (NREL) campus. We report the proposed methodology correctly applied 85–90% and 70–75% of the tags in each of these test scenarios, respectively for two significantly different building types used for testing UA's fully-functional prototype. The proposed UA, therefore, offers promising approach for automatically tagging BAS data as it reaches close to 90% accuracy. Further building upon this framework to algorithmically identify the equipment type and their relationships is an apt future research direction to pursue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Integrated Process Optimization for Biochemical Conversion

This research is motivated by the challenges faced during biomass processing in bioenergy plants. It has been observed that variations in biomass characteristics, such as moisture, ash, and carbohydrate contents cause variations in feeding of the system which led to underutilization of equipment and the reactor. The objective of this research is to ensure a continuous flow of biomass to the reactor in plants that use the biochemical conversion process to generate liquid fuels. The overall goal is to lower the cost of producing biofuels, which could lead to improving US’s energy independency and growing US’s rural economy. The research team developed analytical models, such as discrete element method (DEM) models and mathematical models. The DEM models are unit-level models that explicitly capture biomass characteristics and quantify the impacts of biomass characteristics on bulk material properties and the performance of specific equipment. The mathematical models are system-level models that capture the impacts of system infeed rate, equipment processing rate, storage location and capacity, and biomass characteristics on system throughput. The functional relations predicting the bulk material properties from DEM models are incorporated to the mathematical models. The models developed were validated and evaluated using data collected at Idaho National Laboratory’s biomass processing facility. Via these models, we identified process control strategies that ensure a continuous flow of biomass to the reactor, while meeting the requirements of biochemical conversion process. Our analysis indicates that sequencing of biomass bales based on moisture level, and carbohydrate contents could have a positive impact on reducing processing time and inventory level and increasing throughput rate. Short bale sequences that repeat frequently, seem to have the greatest impact on improving system’s performance. Based on our experiments, the total annual system operating costs reduced by 20-30%, and the maximum inventory level reduced by 3 to 4 times. The operating costs include the annual equipment amortization cost and processing cost. The implementation of the models developed requires the use of standardized bale format, Radio Frequency Identification technology, sensing and real time monitoring of material attributes, automated material handling equipment, and automated process control. The scope of the model proposed can be extended to include the whole supply chain. The supply chain models help identify how many bales of different biomass feedstock to purchase given biomass availability in the region, biomass price and quality, and the biomass processing capabilities of the biorefinery. Thus, the outcomes of supply chain models can be used to inform the design of long-term contracts among farmers and the biorefinery.

09 BIOMASS FUELS↗
NASA NTRS1999

A Second-Generation Volatile Organic Analyzer for the International Space Station

Early in the development of the Crew Health Care System (CHECS) for the International Space Station (ISS), it was recognized that detection of target volatile organic compounds would be a key component of the air monitoring strategy. Experiences during the NASA/Mir program supported the decision to include a real-time volatile organic analyzer (VOA) aboard ISS to help assess the impact of air quality events on crew health and determine the effectiveness of decontamination efforts. Toward this end, a joint development by the Toxicology Laboratory at Johnson Space Center and Graseby Dynamics produced a VOA that has been delivered and is ready for the first 5 years of ISS operation. The first-generation VOA selection criteria included minimizing size, weight, and power consumption while maintaining analytical performance. Measuring available technologies against these criteria, a VOA system based upon gas chromatography/ion mobility spectrometry (GC/IMS) was selected in the mid-90's. However, as NASA looks forward to later-stage ISS operations and to new frontiers such as human exploration of Mars, the ISS VOA (weighing 43 kg and consuming 160 watts) must be replaced by a smaller, less resource-intensive device. This paper will present a possible second-gene ration VOA based upon the same technology as the first-generation unit. Utilizing GC/IMS technology again will permit the instrumental data and experience gained during the initial phase of ISS to be applied to later ISS phases and advanced spacecraft missions. During the past 3 years, efforts to reduce the size of ion mobility spectrometers have been pursued by Graseby Dynamics, the manufacturer of the first-generation VOA. The concept of operation, expected analytical performance, and estimated size of a fully functional second-generation VOA based upon GC/mini-IMS technology will be presented. Furthermore, results of initial laboratory evaluations will be shown.

Limero, Thomas↗
DOE OSTI2018

Experimental and Theoretical Insights into the Structure of Tellurium Chloride Glasses

Here, the structure of the binary chalcohalide glasses Te 1-x CL x (0.35 <= x <= 0.65) is considered by combining experimental and theoretical results. The structural network properties are influenced by a competition between ionic and covalent bonding in such glasses. At first, a focus is placed on the detailed information available by using the complementary high-energy X-ray and the neutron diffractions in both the reciprocal and real spaces. The main characteristic suggested by the structure factors S(Q) concerns the presence of three length scales in the intermediate range order. The total correlation function T(r) lets us also suppose that the structure of these glasses is more complicated than Te-chain fragments with terminal Cl as demonstrated in crystalline Te 3 Cl 2 . Molecular dynamics simulations were subsequently performed on Te 3 Cl 2 and Te 2 Cl 3 , and coupled with the experimental data, a highly reticulated network of chalcogen atoms, with a fair amount of chlorine atoms bonded in a bridging mode, is proposed. The simulations clearly lead to a glass description that differs markedly from the simple structural model based on only Te atom chains and terminal Cl atoms. Solidstate NMR experiments and NMR parameters calculations allowed validation of the presence of Te highly coordinated with chlorine in these glasses.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Reduced scaling Hilbert space variational Monte Carlo

We show that for both single-Slater-Jastrow and Jastrow geminal power wave functions the formal cost scaling of Hilbert space variational Monte Carlo can be reduced from fifth to fourth order in the system size, thus bringing it in line with the long-standing scaling of its real space counterpart. While traditional quantum chemistry methods can reduce costs related to the two-electron integral tensor through various tensor decomposition methods, we show that such approaches are ineffective in the presence of Hilbert space Jastrow factors. Instead, we develop a simple semi-stochastic approach that can take similar advantage of the near-sparsity of this four-index tensor. Through demonstrations on alkanes of increasing length, we show that accuracy and overall statistical uncertainty are not meaningfully affected and that a total cost crossover is reached as early as 50 electrons when using a minimal basis. Further study will be needed to assess where the crossover occurs in more compact molecular geometries and larger basis sets and to explore how in that context the crossover can be accelerated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Celestial Mapping System and Digital Lunar Library Initiative

We are preparing to create an interactive, global 3D lunar environment with integrated dataset and AI/ML tools to provide unique value to mission planners, scientists and the entire lunar community. This lunar environment will be based on NASA Ames Celestial Mapping System (CMS) [1] and Digital Lunar Library (DLL) Initiative. CMS provides a 3D virtual Lunar Globe with extensive user friendly tool sets, that include high resolution terrain visualization, elevation profiles, measurement kits, slope analysis, path optimization, line of sight analysis, equipment planning and placement tools and many other functionalities [1]. It has a thick client with less overhead to access hardware resources. This allows features such as terrain profiling and distance calculations to be performed on the client and on the fly. The application is developed to provide situational and domain awareness on the Lunar surface, planning capabilities for equipment placements and traverse path optimization. As data becomes available, CMS has the capabilities to integrate data sets that change dynamically in real-time, which will be useful for monitoring satellites and remotely-sensed data on the Lunar surface. CMS supports importing synthetic features in a variety of 3D, 2D, vector and raster formats. In the future, these capabilities will be enhanced by incorporating AI/ML tools and a plug-in architecture to enable customization by the user groups. With the help of DLL we will be able to : 1) Amplify the value of lunar information with AI-powered data enhancements 2) Acquire and integrate lunar data with AI-assisted georectification and homogenization 3) Analyze lunar data with advanced 3D visualization, intelligent search-by-example 4) Apply lunar data insights to specific use cases with an open plug-in architecture. The CMS-DLL initiative will have several potential use cases for NASA and the lunar community in general, including subsurface lava tube visualization and analysis, soil analysis, in-situ lunar resource visualization and representation on 3D globe, and data analytics for utilization. REFERENCES: [1] https://celestial.arc.nasa.gov/

3D Globe↗
DOE OSTI2022

Real-time equation-of-motion CC cumulant and CC Green’s function simulations of photoemission spectra of water and water dimer

Newly developed coupled-cluster (CC) methods enable simulations of ionization potentials and spectral functions of molecular systems in a wide range of energy scales ranging from core-binding to valence. This paper discusses the results obtained with the real-time equation-of-motion CC cumulant (RT-EOM-CC) approach and CC Green’s function (CCGF) approaches in applications to the water and water dimer molecules. Here, we compare the ionization potentials obtained with these methods for the valence region with the results obtained with the coupled-cluster with singles, doubles, and perturbative triples formulation as a difference of energies for N and N – 1 electron systems. All methods show good agreement with each other. They also agree well with the experiment with errors usually below 0.1 eV for the ionization potentials. We also analyze unique features of the spectral functions, associated with the position of satellite peaks, obtained with the RT-EOM-CC and CCGF methods employing single and double excitations, as a function of the monomer OH bond length and the proton transfer coordinate in the dimer. Finally, we analyze the impact of the basis set effects on the quality of calculated ionization potentials and find that the basis set effects are less pronounced for the augmented-type sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Harnessing Heterologous Bacterial Two-Component Systems as Biosensors to Address Challenges in Fermentation Scale-Up

Scaling up bacterial fermentation from bench to industrial scale often results in unpredictable performance losses, possibly in part due to changes in microenvironmental conditions such as pH. To investigate this, we developed a suite of pH-sensitive biosensors from bacterial two-component systems (TCSs) that provide a dynamic, fluorescent readout in response to extracellular pH changes. TCSs consist of a periplasmic sensor histidine kinase (HK) that, in response to an extracellular stimulus, autophosphorylates intracellularly and subsequently transfers the phosphate to a cognate response regulator (RR) that modulates transcription of target genes. We utilized three pH-responsive TCSs (referred to here as CVJ1, CVJ30, and CVJ79) and linked their output to GFP. This was achieved by placing the RR promoter upstream of GFP or by constructing a chimeric RR composed of the native receiver domain and the DNA-binding domain of another well-characterized RR with a defined promoter. All components - HK, RR (native or chimeric), and GFP under its corresponding promoter - were cloned into a broad-host-range plasmid. Sensors were validated in Escherichia coli and Pseudomonas putida, including the muconic acid-producing strain P. putida TL207. All three biosensors successfully reported pH, with fluorescence (normalized to optical density) correlating strongly with media pH. Among the native sensors, CVJ79 showed the most robust performance while CVJ1 also performed best in its native form; CVJ30 exhibited improved functionality as a chimera, suggesting that modular RR design can enhance compatibility in some heterologous hosts. Further, CVJ79 was activated by alkaline conditions, while CVJ30 responded to acidic environments. Notably, CVJ1 was induced by high pH in wild-type E. coli and P. putida, but low pH in TL207. The observed differences in sensor activation between strains - particularly the divergent response of CVJ1 - suggest that host-specific regulatory pathways may influence how cells perceive and adapt to pH stress. Moving forward, these biosensors can be used to guide the rational design of more robust strains, optimize process conditions in real time, and inform strategies to minimize physiological heterogeneity during scale-up. Integrating these tools into high-throughput screening and bioreactors will be a key step toward improving predictability and performance in industrial bioprocesses.

09 BIOMASS FUELS↗
NASA NTRS2016

Functional Fault Model Development Process to Support Design Analysis and Operational Assessment

A functional fault model (FFM) is an abstract representation of the failure space of a givensystem. As such, it simulates the propagation of failure effects along paths between the origin ofthe system failure modes and points within the system capable of observing the failure effects. Asa result, FFMs may be used to diagnose the presence of failures in the modeled system. FFMsnecessarily contain a significant amount of information about the design, operations, and failuremodes and effects. One of the important benefits of FFMs is that they may be qualitative, ratherthan quantitative and, as a result, may be implemented early in the design process when there ismore potential to positively impact the system design. FFMs may therefore be developed andmatured throughout the monitored system's design process and may subsequently be used toprovide real-time diagnostic assessments that support system operations. This paper provides anoverview of a generalized NASA process that is being used to develop and apply FFMs. FFMtechnology has been evolving for more than 25 years. The FFM development process presented inthis paper was refined during NASA's Ares I, Space Launch System, and Ground SystemsDevelopment and Operations programs (i.e., from about 2007 to the present). Process refinementtook place as new modeling, analysis, and verification tools were created to enhance FFMcapabilities. In this paper, standard elements of a model development process (i.e., knowledgeacquisition, conceptual design, implementation & verification, and application) are describedwithin the context of FFMs. Further, newer tools and analytical capabilities that may benefit thebroader systems engineering process are identified and briefly described. The discussion isintended as a high-level guide for future FFM modelers.

Melcher, Kevin J.↗
DOE OSTI2024

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Real-Time KMC Simulation of Vacancy-Mediated Intermixing in Au@Ag Octahedral Core–Cubic Shell Nanocrystals with Ab Initio-Guided Kinetics

Utilization of core–shell rather than monometallic nanocrystals (NCs) facilitates fine-tuning of NC properties for applications. However, compositional evolution via intermixing can degrade these properties prompting recent experimental studies. We develop an atomistic-level stochastic model for vacancy-mediated intermixing exploiting a formalism which allows incorporation at an ab initio density functional theory level of not just the thermodynamics of vacancy formation, but also relevant diffusion barriers for a vast number of possible local environments (in the core and in the shell, at the interface, and in the intermixed phase). This facilitates a predictive treatment and comprehensive understanding of intermixing on the relevant time scale (e.g., 10 1 –10 3 s). In contrast, previous modeling at the atomistic level utilized only unrealistic generic prescriptions of barriers or employed simplified continuum treatments. For Au@Ag octahedral core–cubic shell NCs, our modeling not only captures the experimentally observed rate or time scale for intermixing of ~100 s at 450 °C for 60 nm NCs, but also elucidates the underlying rate controlling processes and the effective intermixing barrier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗