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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Energy savings and life cycle cost analysis of advanced double skin facade system applied to old apartments in South Korea

South Korea is one of the most densely populated countries in the world, and the population density in urban areas is much higher among others. Apartments are the most common form of residential buildings due to their high population density. More than 60% of domestic residential buildings are apartments, with more than 10 million households. The high population density is a problem that has long plagued South Korea because more than 45% of apartments are old apartments that are more than 20 years old. Old apartment buildings have low thermal performance that results in lots of energy consumption. Balconies, which worked as a thermal buffer, are also being removed by residents to expand indoor space. According to the green remodeling project supported by the Korean government, the typical retrofitting method is replacing external windows with the high-efficiency window in old apartments in South Korea. Here, this paper proposes a retrofitting method using an extended double-skin façade (DSF) system that replaces exterior windows and acts as a thermal buffer. The simulation model is developed with the EnergyPlus simulation program to conduct energy performance analyses. The simulation model has been calibrated using actual experimental data collected between October 1, 2019, and January 15, 2020. Results show that the cooling, heating, and lighting energy can be reduced up to 44.1% by fully utilizing electric energy generated by solar PV panels integrated with the DSF system. The payback period is about 15 years based on the energy price provided by the utility company. Although the payback period is long, it has great potential of energy savings and CO 2 emission reductions. The DSF system should be considered as a way of renovation, considering other factors such as increased real estate values and energy cost increases in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Description of the multinucleon transfer mechanism for Ca 48 + Pu 244 and Kr 86 + Pt 198 reactions in a quantal transport approach

Multinucleon transfer (MNT) reactions involving heavy projectile and target combinations stand as a promising method for synthesizing new neutron-rich exotic nuclei, which may not be possible using hot or cold fusion reactions or fragmentation. Exploring the mechanisms behind MNT reactions is essential and it requires a comprehensive theoretical framework that can explain the physical observables in these reactions. This work aims to show that the quantal diffusion approach based on the stochastic mean-field (SMF) theory is capable of explaining the reaction dynamics observed in MNT reactions. Primary product mass distributions in 48 Ca + 244 Pu reaction at E c.m. = 203.2 MeV and 86 Kr + 198 Pt reaction at E c.m. = 324.2 MeV are calculated and compared with the available experimental data. In this work, we utilize the time-dependent Hartree-Fock (TDHF) calculations to analyze the mean-field reaction dynamics computationally in the reactions 48 Ca + 244 Pu and 86 Kr + 198 Pt for a broad range of initial angular momenta. Quantal transport description based on the SMF approach is used to calculate quantal diffusion coefficients and mass variances in 48 Ca + 244 Pu and 86 Kr + 198 Pt systems. The primary products arising from quasifission reactions are described by joint probability distribution in the SMF approach and those arising from fusion-fission are estimated by using the statistical deexcitation code gemini + +. Mean values of charge and mass numbers, scattering angles of the primary reaction products, and the total kinetic energies after the collision are calculated within the TDHF framework for a broad range of initial angular momenta. Throughout all the collisions, drift toward the mass symmetry and large mass dispersion associated with this drift are observed. Here, the calculated primary fragment and mass distributions using the SMF approach successfully explain experimental observations for the 48 Ca + 244 Pu and 86 Kr + 198 Pt systems. The primary mass distributions, mean values of binary products, and mass dispersions are determined and results are compared with the available experimental data. The observed agreement between the experimental data and SMF results highlights the effectiveness of the quantal diffusion mechanism based on the SMF approach, which does not include any adjustable parameters other than standard parameters of Skyrme energy density functional.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Data for Reshaping the 2-Pyrone Synthase Active Site for Chemoselective Biosynthesis of Polyketides

Engineering enzymes with novel reactivity and applying them in metabolic pathways to produce valuable products are quite challenging due to the intrinsic complexity of metabolic networks and the need for high in vivo catalytic efficiency. Triacetic acid lactone (TAL), naturally generated by 2-pyrone synthase (2PS), is a platform molecule that can be produced via microbial fermentation and further converted into value-added products. However, these conversions require extra synthetic steps under harsh conditions. We herein report a biocatalytic system for direct generation of TAL derivatives under mild conditions with controlled chemoselectivity by rationally engineering the 2PS active site and then rewiring the biocatalytic pathway in the metabolic network of E. coli to produce high-value products, such as kavalactone precursors, with yields up to 17 mg/L culture. Computer modeling indicates sterics and hydrogen-bond interactions play key roles in tuning the selectivity, efficiency, and yield.

Conversion↗
DOE OSTI2022

Application of the Redlich-Kister expansion for estimating the density of molten fluoride psuedo-ternary salt systems of nuclear industry interest

The development of next-generation molten salt reactors relies on accurate knowledge of the thermophysical properties of the candidate coolant and fueled molten salts. These thermophysical properties include density, viscosity, thermal conductivity, and heat capacity. Because of difficulties in measuring thermophysical properties of molten salts, there are many gaps in the current state of thermophysical property knowledge of these salts, particularly those that contain actinides or beryllium. Therefore, leveraging modeling techniques to estimate unknown molten salt thermophysical properties and guide future experimental measurements has high value for the nuclear industry. Here, the densities of molten fluoride pseudo-ternary salt systems, which are of interest to the nuclear industry, were estimated using Redlich-Kister expansion and Muggianu interpolation techniques. The pseudo-ternary systems considered for estimation in this study were NaF-LiF-ZrF 4 , LiF-BeF 2 -ZrF 4 , LiF-BeF 2 -ThF 4 , NaF-LiF-BeF 2 , NaF-KF-BeF 2 , NaF-ZrF 4 -UF 4 , and NaF-BeF 2 -UF 4 . This Redlich-Kister estimation approach accounts for nonideal mixing behavior based on pseudo-binary subsystem interaction parameters determined from experimentally measured pseudo-binary system density data sets. The Redlich-Kister estimation was compared with the method of additive molar volumes, which assumes ideal mixing. Additionally, the Redlich-Kister approach was used to determine previously unknown binary and ternary interaction parameters based on experimentally measured density data sets for select pseudo-ternary salt systems. The results of this study show improvement in density estimation using the Redlich-Kister approach for all systems considered compared with estimation by additive molar volumes. Furthermore, this analysis allowed for the estimation of nonideal density behavior in experimentally unstudied ZrF 4 -UF 4 and BeF 2 -UF 4 , as well as the quantification of ternary interaction in NaF-LiF-ZrF 4 , NaF-BeF 2 -UF 4 , and NaF-ZrF 4 -UF 4 .

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

Extended families of critical and stationary droplets for nonequilibrium phase transitions in spatially discrete bistable systems

Bistable nonequilibrium systems are realized in catalytic reaction-diffusion processes, biological transport and regulation, spatial epidemics, etc. Behavior in spatially continuous formulations, described at the mean-field level by reaction-diffusion type equations (RDEs), often mimics that of classic equilibrium van der Waals type systems. When accounting for noise, similarities include a discontinuous phase transition at some value, p eq , of a control parameter, p , with metastability and hysteresis around p eq . For each p , there is a unique critical droplet of the more stable phase embedded in the less stable or metastable phase which is stationary (neither shrinking nor growing), and with size diverging as p → p eq . Spatially discrete analogs of these mean-field formulations, described by lattice differential equations (LDEs), are more appropriate for some applications, but have received less attention. It is recognized that LDEs can exhibit richer behavior than RDEs, specifically propagation failure for planar interphases separating distinct phases. Herein, we show that this feature, together with an orientation dependence of planar interface propagation also deriving from spatial discreteness, results in the occurrence of entire families of stationary droplets. The extent of these families increases approaching the transition and can be infinite if propagation failure is realized. In addition, there can exist a regime of generic two-phase coexistence where arbitrarily large droplets of either phase always shrink. Such rich behavior is qualitatively distinct from that for classic nucleation in equilibrium and spatially continuous nonequilibrium systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Verifying status of a termination end cap of a microinverter chain

A photovoltaic (PV) system includes a system control module that determines the presence of a microinverter chain termination end cap. The end cap includes an embedded circuit. The embedded circuit includes components having resistive, reactive, or impedance values. A signal source provides a signal through the microinverter chain. A parameter for a sensed signal detected by the system control module is used to determine the presence of the end cap using a change between the parameter for the sense signal and a reference parameter.

Chapman, Patrick L.↗
DOE OSTI2022

Oak Ridge National Laboratory Pilot Demonstration of an Attestation and Anomaly Detection Framework using Distributed Ledger Technology for Power Grid Infrastructure

This report summarizes the design and pilot demonstration of a framework called Grid Guard that was created to provide increased data and device trustworthiness to electric grid devices by leveraging distributed ledger technology (DLT), specifically blockchain. Grid Guard contains a combination of core cryptographic methods such as the secure hash algorithm (SHA), and asymmetric cryptography, private permissioned blockchain, baselining configuration data, consensus algorithm (Raft) and the Hyperledger Fabric (HLF) framework. The system implements a low energy, fast, and robust enhancement to system trustworthiness within and across electric grid systems such as substations, control centers and metering infrastructures. Blockchain is a distributed database structured that provides a practically unalterable (immutable) timeline of stored transactions. By relying on hashing and the Raft consensus algorithm, if an entity tries to illegitimately alter a record at one instance of the database the other ledger nodes are not altered. They work to cross-reference each other and easily locate any incorrectly added data and remove it. The bulk raw data is stored in an off-chain storage (outside of the blockchain ledger) and a hash of this baseline data is stored in the Blockchain ledger via hashing windows of time-series and configuration data, after aggregation and filtering. The bulk off-chain data repository is then considered to be trust-anchored using the hashes stored in the blockchain. To secure the electric grid testbed devices and data, device configuration baselines were compared to those baselines that had been previously stored in the ledger. Statistical baselines for device configurations, network communication patterns, and high-speed sensor data are calculated and then stored off-chain and hashes stored in the ledger. Measurements such as three-phase voltage and current, frequency, breaker status, protection scheme settings, network configuration settings (and other device configuration artifacts) and network traffic features (packet interarrival times) are compared every minute or other selected time windows. During phase 1 of the Grid Guard DLT project different DLT technologies were studies, and an assessment was performed on DLT technology vulnerabilities, uses, and key characteristics. DLT consensus protocols were studies (e.g., RAFT, named after Reliable, Replicated, Redundant, And Fault-Tolerant). Also, cryptography, public, private and permissioned or permissionless systems were assessed. Grid Guard implements a permissioned private DLT. Consensus algorithm selection and choice of DLT implementation depended heavily on the use-case. For this use-case, parameters were selected to measure performance and existing tools for assessment. Benchmarking was performed theoretically and practically. During phase 2 hashed transactions/blocks were inserted into the ledger every second. During phase 2 of the Grid Guard DLT project, a prototype framework was developed and demonstrated for attestation of critical substation devices and data using precision timing systems that use PTP and IRIG-B protocols) on a testbed of operational devices that emulated a distribution substation, control center, and power metering infrastructure using real Operational Technology (OT). The testbed includes OT devices such as protective relays, human machine interfaces (HMI), and power meters. To determine when to collect and compare system and network baselines, an initial examination of an anomaly detection capability to identify malicious manipulation of data streams was conducted. The resulting anomaly detection was demonstrated in a set of experiments and leveraged to trigger device artifact attestation checks. Attestation checks occur against device configuration baselines when compared with the immutable blockchain-stored baselines, which provided a cryptographically supported means by which to store baselines. The electrical substation-grid testbed was created to test the Grid Guard framework. The testbed emulates the operations of a portion of a power grid and SCADA systems as closely as possible. The testbed integrates real protocols, mainly IEC 61850 standard protocols, such as the Sampled Value (SV) and the GOOSE protocols. The testbed also supports DNP3 and other layer 2 and layer 3 protocols such as Telnet, SSH, SFTP/FTP and other proprietary protocols needed to connect to industrial control system equipment. The testbed emulates real power conditions using the OpalRT hardware-in-the-loop (HIL) device which can create fault situations that cannot be easily tested on real systems. The electrical substation-grid testbed was created using real measurement, communication, and protection devices that electrical utilities commonly use.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Synchrophasor Data Compression Under Disturbance Conditions via Cross-Entropy-Based Singular Value Decomposition

The increasing deployment of phasor measurement units and the advances of their reporting rates are challenging the present data centers in terms of storing and analyzing large-volume data. Under power system disturbance conditions, it is difficult to retain critical information while compressing the synchrophasor data effectively. This article combines the cross entropy and the singular value decomposition, proposing a novel model to compress the synchrophasor data to an extremely small size yet keep superior accuracy. The proposed model is extensively tested and compared with the state-of-the-art algorithms using the simulated and the FNET/GridEye field-collected data. The result indicates that the proposed algorithm has superior performance in compressing the data while retaining critical information under disturbance conditions.

42 ENGINEERING↗
DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Fast neutron leakage spectra of the EUCLID experiment

Special nuclear material in sub-critical and critical configurations measured in integral experiments are important for validation and adjustment of nuclear data. Many different evaluations of nuclear data exist, and these different evaluations can provide different values for individual cross sections that vary due to the uncertainties in differential experiments or lack of such data. For integral experiments, differences in these individual cross sections can have compensating errors, which lead to the same answer. One example of this is the Jezebel critical assembly, where k eff of the system is correctly computed by both ENDF/B-VIII.0 and JEFF-3.3, despite having substantially different underlying evaluated values for specific reactions (such as elastic and inelastic cross sections). To reduce compensating errors in nuclear data, the Experiments Underpinned by Computational Learning for Improvements in Nuclear Data (EUCLID) project has utilized machine learning to design a set of sub-critical and critical experiments. These experiments include slab- and cube-like configurations of 239 Pu in the form of the ZPPR plates. Six different responses were measured on a total of thirteen different configurations. One of these responses, the neutron leakage spectrum, was measured using an EJ301D detector. Finally, the results of the neutron leakage spectra show good agreement (within 1–2 σ ) with the expected spectrum from simulations and will be used in the subsequent nuclear data adjustment done by the EUCLID team.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Colorado State University (CSU) X-Band Precipitation Radar Plan Position Indicator Data Processed with Corrected Moments in Antenna Coordinates (CMAC) Value-Added Product Report

In 2010 the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility procured 3- and 5-cm wavelength radars for documenting the macrophysical, microphysical, and dynamical structure of precipitating systems. To maximize the scientific impact, ARM supported the development of an application chain to correct for various phenomena in order to retrieve the “point” values of moments of the radar spectrum and polarimetric measurements. We have now used the lessons learned from the processing of the 3- and 5-cm wavelength radars obtained by ARM to help process X-band radar data from the Surface Atmospheric Integrated Field Laboratory (SAIL) field campaign. This report details the motivation, science, and progress to date as well as charting a path forward.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Fast growing high-yield wheat and canola for efficient nutrient recycling systems

Wheat and canola are two major rotation crops in the U.S. each offering advantageous traits for the agricultural value chain. Canola, a dicot rotation crop, generally offers high seed oil content. Wheat, a monocot rotation crop, offers more cellulose and other nutritious carbohydrates. As rotation crops, traits such as rapid growth rates, increased harvest yields, and high quality (digestibility, deconstructability) are desirable for food, biorefinery, and also nutrient recycling for crop rotation. Improvement of such traits has been the goal of numerous biotechnology efforts. However, alternative biotechnology techniques are typically constitutive and when applied to commercial crops have resulted in net negative consequences. For example, efforts to improve quality by lowering lignin biosynthesis have resulted in plants with poor structural integrity and diminished mass at maturity. With supports by SBIR programs from DOE and USDA, JBEI/LBNL and AFINGEN previously demonstrated engineered healthy switchgrass and alfalfa which grew with 20-30% more fermentable sugar release, 25% less lignin, faster growth, and more biomass yields than control plants. In this CRADA project supported by NASA STTR program, AFINGEN again collaborated with JBEI/LBNL and transferred the simple tissue-targeting technology from the demonstrated switchgrass and alfalfa to the two major rotation crops wheat and canola for efficient nutrient recycling systems.

09 BIOMASS FUELS↗
DOE OSTI2020

Accurate and complete genomes from metagenomes

Genomes are an integral component of the biological information about an organism; thus, the more complete the genome, the more informative it is. Historically, bacterial and archaeal genomes were reconstructed from pure (monoclonal) cultures, and the first reported sequences were manually curated to completion. However, the bottleneck imposed by the requirement for isolates precluded genomic insights for the vast majority of microbial life. Shotgun sequencing of microbial communities, referred to initially as community genomics and subsequently as genome-resolved metagenomics, can circumvent this limitation by obtaining metagenome-assembled genomes (MAGs); but gaps, local assembly errors, chimeras, and contamination by fragments from other genomes limit the value of these genomes. Here, we discuss genome curation to improve and, in some cases, achieve complete (circularized, no gaps) MAGs (CMAGs). To date, few CMAGs have been generated, although notably some are from very complex systems such as soil and sediment. Through analysis of about 7000 published complete bacterial isolate genomes, we verify the value of cumulative GC skew in combination with other metrics to establish bacterial genome sequence accuracy. The analysis of cumulative GC skew identified potential misassemblies in some reference genomes of isolated bacteria and the repeat sequences that likely gave rise to them. We discuss methods that could be implemented in bioinformatic approaches for curation to ensure that metabolic and evolutionary analyses can be based on very high-quality genomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Effectively Considering the Distribution System in Integrated Resource Plans

While electricity planning practices vary by state and utility based on utility type and market structure, integrated resource planning (IRP) remains a prominent vehicle — even in states with centrally-organized wholesale electricity markets. IRP focuses on meeting forecasted long-term electricity needs. Typically, utilities have not considered impacts of design and operation of the low-voltage distribution network in IRP. With advanced capabilities of grid-edge technologies to generate and store electricity and provide load flexibility, and large utility investments in distribution systems, it's increasingly important to consider at least some distribution planning elements in IRP. This report considers the value proposition for doing so, such as reducing utility costs through resource co-optimization and strategic siting of grid-edge resources, and idenfities the most important touchpoints between planning for bulk power and distribution systems and provide a range of tactics for integrating these two processes.

Relf, Grace [Lawrence Berkeley National Laboratory↗
DOE OSTI2024

Weighted Composition Operators for Learning Nonlinear Dynamics

Operator theoretic methods in dynamical system have been dominated by the use of Koopman operators and their continuous time counterparts, such as Koopman Generators and Liouville Operators. The advantage gained from their use primarily stems from the ability to extract subspaces and eigenfunctions within a space of observables that are invariant with respect to the Koopman operator over that space. When this occurs, a dynamic mode decomposition of the systems state provides a linear model for the dynamical system. Not all Koopman operators have eigenfunctions that may be exploited in this manner. However, the framework can still be leveraged for approximations using other operators. In this setting, we present a different operator for the study of dynamical systems, the weighted composition operator. These operators are compact for a wide range of dynamics and spaces, and through their interactions with occupation kernels and vector valued kernels, they admit an estimation of the underlying dynamics. Here, this manuscript presents a new algorithm for the data driven study of dynamical systems from data, and also provides two numerical experiments where convergence is achieved as a proof of concept.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Managing Increased Electric Vehicle Shares on Decarbonized Bulk Power Systems

Transportation electrification and power sector decarbonization - the convergence of these two trends present a complex planning problem requiring realistic, region-specific modeling and analysis to ensure that both can happen rapidly and cost effectively. This project, funded through the U.S. Department of Energy's Vehicle Technologies Office, models the evolution of the U.S. bulk power system (through 2050) in response to large-scale EV charging across all on-road vehicle segments. We will assess the opportunity and value of demand-side flexibility (i.e., "smart" charging) for reducing energy costs, increasing renewable generation shares, and managing future EV loads on the bulk power system. Overall, this study aims to provide an improved understanding of least-cost solutions for managing EV load growth on the grid and will make high-resolution EV load data sets publicly available for further analysis.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2025

Assessing the Performance of Exchange‐Correlation Density Functionals in Describing the Iron‐Catalyzed Ammonia Synthesis System

Density functional theory (DFT) has been widely employed for elucidating mechanistic aspects of heterogeneous catalysis. However, the accuracy of DFT calculations relies heavily on selecting an exchange-correlation (XC) functional that correctly describes the electronic structure of materials involved in the reactions. This study assesses the accuracy of several XC density functionals for modeling the iron-catalyzed ammonia synthesis system. In the assessment of functional accuracy, experimental references are compared to DFT-calculated values for the formation energy of gas-phase ammonia and nitrogen, bulk Fe/Fe-nitride (γ′-Fe 4 N) lattice constants and cohesive/formation energies, and nitrogen and ammonia binding energies on Fe(100), Fe(111), Fe(110), and γ′-Fe 4 N(111). It is observed that the experimental value for each of these descriptors is accurately modeled by at least one functional. RPBE alone provides reliable estimates for both the lattice constant and cohesive energy of Fe and γ′-Fe 4 N. Temperature-programmed desorption experiments led to estimates for N and NH 3 adsorption across several Fe-based facets that are best captured by RPBE. These results highlight the importance of choosing an appropriate XC functional that accurately describes experimental systems and offer insights into effectively modeling the interactions between nitrogen and ammonia on Fe-based surfaces.

adsorption↗