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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cluster analysis of occupancy schedules in residential buildings in the United States

The energy performance of residential buildings significantly depends on the building occupants’ behavior, which can be highly variable. When the heating, ventilation and air conditioning (HVAC) system is controlled based on the presence or absence of occupants in a building, occupant behavior is of even further importance to its energy performance. In current practice, building energy simulation tools generally use a single occupancy profile to represent the building’s occupancy schedule, the schedule of which is considered to be the same, regardless of the type of household being modeled. Thus, there is significant potential for improvement to allow for more flexibility and accuracy in calculation of occupancy. The objective of this study is to assess the variations in the typical types of occupancy schedules followed by the U.S. population using cluster analysis. American Time Use Survey data, which statically represents the overall U.S. population’s activities, across 12 years (2006–2017), is used. The ATUS data is segregated into smaller groups based on age and weekday/weekend, then divided into activities that are considered “at home” and “away from home”, which are mapped to the presence or non-presence of occupants in the home. Cluster analysis is then used to identify common types of occupancy schedule patterns for each age group. Three main types of patterns are obtained from cluster analysis for each age group, which together represent approximately 88% of people in the United States. The output of the cluster analysis is further analyzed to evaluate the variation in characteristics, including the number of times leaving home, time of day when leaving the home, and the timespan of absence from the home. The results of this study provide detailed insights on how typical occupants in the United States spend their time in residential spaces which can be used to create occupancy profiles for residential buildings. Finally, these occupancy profiles could be utilized inform an assessment of the energy use impact of occupancy-based controls of energy consuming systems and technologies.

42 ENGINEERING↗
DOE OSTI2023

Assessing individual movement, habitat use, and behavior of non-breeding marine birds in relation to prey availability in the US Atlantic

Resource availability is a key factor driving marine bird movements and distributions, but direct information on prey availability is difficult to obtain at relevant scales. We present novel methods for describing multi-scale trophic associations, combining movement analyses of marine birds with estimates of forage fish surface aggregations from digital aerial survey data and species occupancy from bottom trawl survey data. We analyzed satellite telemetry data from northern gannets Morus bassanus , red-throated loons Gavia stellata , and long-tailed ducks Clangula hyemalis in the US Atlantic during the non-breeding period. Using discrete-time hidden Markov models to distinguish area-restricted (i.e. putative foraging) from transit movements, we examined how environmental factors influence movement, and how forage fish species distributions and surface aggregations influence habitat use by gannets and loons that have greater dietary reliance. Our results suggest that chlorophyll a concentration significantly affected movement behavior across species, highlighting the importance of higher-productivity areas around estuaries during colder months when regional productivity is low. Though variable across species and seasons, spatial cross-correlation analysis revealed that herring species (Family Clupeidae), including Atlantic menhaden Brevoortia tyrannus , may be important resources; it also showed positive spatial correlations with forage fish aggregations. This suggests that prey patch dynamics and factors driving aggregation formation may be as important as species composition. However, spatial patterns were generally low (<0.3), suggesting a mismatch in spatiotemporal resolution, exemplifying the challenges in quantifying trophic relationships in marine systems. Disentangling predator-prey relationships is critical to understanding the mechanisms driving marine bird behavior in rapidly changing marine systems.

Environmental Sciences & Ecology↗
DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A Study of Cost-Saving Potential of Load Flexibility Measures in Grid-Interactive Multifamily Buildings

With recent advances in smart technologies, more and more smart devices are penetrating the residential and commercial buildings market. The introduction of these smart devices is also helping IoT companies emerge with load aggregator roles in the sector. With more utility companies on the track of supporting OpenADR protocols, the aggregators could play a significant role in providing load flexibilities by automatically responding to demand response (DR) events and coordinating load flexibility measures between customers. This would benefit utility companies by reducing stress on the grid during critical peak demand hours as well as customers by allowing them to utilize utility rate structures advantageous to those able to reduce electric usage during high-demand hours. This study evaluates cost and energy savings from adopting multiple load flexibility measures in multifamily buildings. Combinations of different load flexibility measures, including space temperature floating, light dimming, automatic window shading, and water heater temperature floating, are considered. The simulations are performed using OpenStudio®, an open-source U.S. Department of Energy (DOE) simulation platform. For the case study, we used a midrise apartment building with weather conditions from Denver, Colorado. To compare climate zone differences, simulations were also performed for Los Angeles, California, and Chicago, Illinois. Initial results indicate that the application of automated load flexibility measures without careful consideration of dispatching strategies and DR program enrollments could significantly affect the savings. To get meaningful cost savings, aggregators need to encourage tenant awareness to curtail energy usage through occupant behavior in addition to dispatching automatic load flexibility measures. The outcomes from this study are believed to help load aggregators understand the risks and benefits of load flexibility opportunities.

building energy modeling↗
DOE OSTI2022

Abundance and Migration Success of Overshoot Steelhead in the Upper Columbia River

Abstract Summer steelhead Oncorhynchus mykiss may enter freshwater almost a year before spawning and potentially make long migrations (>1,000 km) to interior headwater habitats. However, in response to suboptimal freshwater habitat conditions (e.g., warmer water temperatures), adult summer steelhead may exhibit complex behaviors during upstream migration in the Columbia River basin. Steelhead may migrate upstream of their natal tributary (hereafter, referred to as “overshoot”) and spend days to several months before subsequently migrating downstream (hereafter, referred to as “fallback”) to their natal tributary to spawn. An expansion of an existing Bayesian patch occupancy model, derived from observations of adult steelhead that were PIT-tagged to estimate population-specific abundance upstream of the tagging location, incorporated downstream detection locations to estimate the abundance of overshoot fallbacks. Overshoot steelhead abundance at the tagging location was estimated based on the relationship between the number of known overshoot fallbacks (i.e., the number of steelhead that overshot and successfully migrated downstream to their natal tributary) and their model-estimated abundance. During the study period (2010–2017), the annual mean proportion of overshoot steelhead that successfully migrated downstream of the tagging location (Priest Rapids Dam) was 0.59 (SD = 0.14). The number of dams encountered by overshoot steelhead during their downstream migration was negatively correlated with their downstream migration success probability. Improved downstream passage survival for adult steelhead will increase the abundance of affected populations while reducing potential genetic introgression of upstream populations (i.e., strays). This is the first study to estimate the abundance of overshoot and fallback steelhead, providing the data necessary for scientists to estimate potential conservation benefits of improved downstream survival. For example, surface flow passage routes (e.g., sluiceways and temporary spillway weirs) are very effective in guiding and passing adult steelhead downstream of Columbia River hydroelectric projects and data from this assessment show that changes in dam operations throughout the downstream migration period may maximize conservation benefits.

Murdoch, Andrew R. (ORCID:0000000324827689)↗
DOE OSTI2025

Energy performance of an operational government building retrofitted with ceiling phase change material tiles in a mixed-humid climate

The aging U.S. building stock requires various retrofit measures to enhance their energy efficiency. Here, this study explores the integration of thermal energy storage and advanced building controls as viable retrofit solutions for load flexibility and peak demand response while maintaining the occupants' comfort. A detailed assessment is conducted on the energy use of an administrative building in Sumner County, Kansas, focusing on the implementation of phase change materials (PCMs) in the ceiling of occupied zones. First, a time-resolved, whole-building energy model is developed in EnergyPlus, incorporating complex thermal behaviors such as air exchange between the plenum space and occupied zones, envelope leakage, and operational schedules. The model is then validated using experimental field test data, and subsequently a parametric assessment of key PCM properties and application strategies is performed to evaluate cooling electricity demand benefits. The parametric study shows that the optimal retrofit strategy, comprising a PCM with 23°C peak melting temperature, 0.125 in. (3.17 mm) thickness, and 150 kJ/kg latent heat, combined with active controls that include 8 h of precooling, forced convection under the ceiling, and a 2°C thermostat setback during peak hours, can result in a maximum load shift during the peak period of 99.6 % and the total electricity savings during the peak period of 98.9 % for the optimum case and thus provide significant cost savings under time-of-use pricing scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Material efficiency and climate change mitigation of passenger vehicles

A transition to electric vehicles and renewable energy is currently underway but may not be rapid enough in order to reach ambitious climate change mitigation targets. Therefore, additional, preferably instantaneous, measures are needed for quick emissions reductions, which is where material efficiency (ME) could constitute a promising solution. ME strategies include but are not limited to vehicle lightweighting through material substitution, increased recycling of materials, reuse and remanufacturing of vehicle components, vehicle downsizing (switching to a smaller vehicle) and more intensive use by means of increased vehicle occupancy through sharing practices. While recent analyses have focused on a narrow subset of these, we find striking differences in the overall potential of different strategies to decrease vehicular carbon footprints. Downsizing and more intensive use offer the largest mitigation potential but strongly depend on consumer behavior and are highly sensitive to modeling assumptions. Combined, the analyzed strategies can achieve emission reductions of up to 57% over the life cycle of a single vehicle, which is comparable to up to 83% achieved through a shift to low-carbon energy supply. ME can cut carbon footprints of already efficient vehicles charging renewable electricity by half again. This makes ME both an excellent short-term solution for climate change mitigation targeting the light vehicle sector but also an important complementary strategy to the long-term transition towards electric vehicles and renewable energy supply.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Selectivity mechanisms of ion intercalation in Prussian blue analogs

Prussian blue analogs (PBAs) are a family of materials with facile, reversible, and selective ion transport capability for various ions via electrochemical intercalation, owing to their vacancy structure. The large tunable compositional space of PBAs allows for manipulation of intercalation behavior and selectivity by controlling structural vacancy level through choice of transition metal centers and modifications to the synthesis process. However, a lack of understanding of the mechanisms of ion selectivity hinders the material’s design process. Here, for this work, we investigated the origins of ion selectivity using a model PBA, copper hexacyanoferrate, and focused on eight technologically and biologically prominent ions, for which we determined a sequence of selectivity: Rb + > K + > Na + > Ba 2+ > Sr 2+ ≈ Ca 2+ > Mg 2+ > Li + . We provide electrochemical, structural, and redox evidence of strong correlation between the ion identity, the dominant charge-compensating redox, and preferred occupancy site. Specifically, using synchrotron anomalous X-ray diffraction (AXRD), we reveal that monovalent ions exhibit significant association with the corner sites of the unit cell and iron redox, whereas divalent ions display affinity toward the center site with higher ratios of copper redox. Informed by selectivity results, we applied CuHCFe to Li purification and achieved 99.9% purity. Our findings demonstrate an approach to elucidating ion intercalation behavior in order to distinguish and manipulate material properties to optimize separation performance.

Prussian blue analog↗
DOE OSTI2026

Correlating and Simulating Socio-Demographically Driven Residential End-Use Activity Schedules

Incorporating socio-demographic and behavioral considerations into decision-support tools is crucial for identifying gaps and addressing consumer needs to ensure reliable and affordable energy solutions. In energy simulation models, the correlation between socio-demographics and time-use behavior is not well-captured. Thus, we developed a large-scale simulation workflow to generate schedules for 10 residential activities across 24 population segments defined by age, income, and employment status. Using pre-pandemic 2015-2019 American Time Use Survey (ATUS) data, we used ANOVA to confirm the correlation between demographic factors and time use. We explored three k-modes clustering methods-backward, forward, and a new hybrid approach-to delineate the occupancy patterns based on demographics. Using the probability of cluster membership for each population segment and a time inhomogeneous Markov chain to generate activity transition probabilities for each cluster, we simulated 50,000 schedules per segment and validated them against the ATUS data. The hybrid method produced the most socio-demographically differentiated clusters while demonstrating comparable performance to other approaches, with an overall root mean square error of 0.12 for both weekday and weekend schedules. Thus, the hybrid method, where each cluster is dominated by certain demographic segments and occupancy patterns, offers more modeling versatility in terms of scenario analysis. The new workflow improves the socio demographic differentiation of energy consumption by considering differences in time use. This approach enables future research on demographically segmented time of use (TOU) energy consumption, including impacts of TOU utility bills and rate analysis, long-run marginal emissions, and energy retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Entanglement and seniority

In quantum mechanics entanglement is the most striking phenomenon which has no counterpart in classical systems. Although, different approaches have already been developed to study correlations in the case of indistinguishable particles, the exploration of the so-called mode entanglement is still in its initial stage in nuclear physics. Study of mode-entanglement in the seniority model, derivation of analytic formulas for the one-body reduced density matrix of states with seniority $ν$ = 0, 1, 2, and $ν$ = 3, and also determination of the particle number dependence of the one-body reduced density matrix for arbitrary seniority. In addition, comparison of the predictions of the seniority model with the results of the shell model to gain insight into the structure and correlations of the ground and lowest yrast states. In the seniority model the analytic results are calculated using the quasi-spin formalism. The numerical shell model calculations are carried out in the standard shell model framework using an inert core. The applied realistic effective interactions are derived earlier using the $G$-matrix formalism. The density matrix renormalization group method is also applied in order to directly calculate the mode entropies. In the seniority model simple analytical expressions are given for the mode entropies. The peculiar behavior of the half-filled shells and the seniority-zero states are revealed. Numerical results are presented for the lightest stable calcium isotopes and for 94 Ru nucleus. For 94 Ru, the seniority model accounts for the 0$g$ 9/2 mode entropies, but seniority mixing is important for certain yrast states. Furthermore, interaction induced quantum fluctuations decrease the occupation of the 0$f$ 5/2 , 1$p$ 3/2 and 1$p$ 1/2 shells, and amount in non-negligible mode entropies on these shells, too, clearly outside the scope of the simple (0$g$ 9/2 ) 4 seniority model. The 0$f$ 7/2 shell-based seniority model is more accurate for the Ca isotopes, but seniority mixing is substantial for some 44 Ca yrast states, too. Mode and one-body entanglement entropies are useful tools to investigate the structure of quantum correlations in nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Visualization of the HIV-1 Env glycan shield across scales

The dense array of N-linked glycans on the HIV-1 envelope glycoprotein (Env), known as the “glycan shield,” is a key determinant of immunogenicity, yet intrinsic heterogeneity confounds typical structure–function analysis. Here, we present an integrated approach of single-particle electron cryomicroscopy (cryo-EM), computational modeling, and site-specific mass spectrometry (MS) to probe glycan shield structure and behavior at multiple levels. We found that dynamics lead to an extensive network of interglycan interactions that drive the formation of higher-order structure within the glycan shield. This structure defines diffuse boundaries between buried and exposed protein surface and creates a mapping of potentially immunogenic sites on Env. Analysis of Env expressed in different cell lines revealed how cryo-EM can detect subtle changes in glycan occupancy, composition, and dynamics that impact glycan shield structure and epitope accessibility. Importantly, this identified unforeseen changes in the glycan shield of Env obtained from expression in the same cell line used for vaccine production. Finally, by capturing the enzymatic deglycosylation of Env in a time-resolved manner, we found that highly connected glycan clusters are resistant to digestion and help stabilize the prefusion trimer, suggesting the glycan shield may function beyond immune evasion.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A synthetic building operation dataset

Abstract This paper presents a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years’ historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants’ diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Ride-hailing and taxi versus walking: Long term forecasts and implications from large-scale behavioral data

Introduction: Although ride-hailing and taxi trips can potentially reduce single-occupant vehicle trips and auto ownership, they can also replace pedestrian trips. Because physical activity is associated with improved health outcomes, the extent to which ride-hailing and taxi travel captures walking's mode share is of interest to policymakers. Methods: Based on large-scale behavioral data from the 2017 U.S. National Household Travel Survey, this paper reports on the development of a full Bayesian logistic regression model for determining the mode split between (1) ride-hailing and taxi and (2) walk while accounting for unobserved heterogeneity. The results from the stand-alone model inform two longer-term travel forecasting scenarios: a) higher risk of walk trips converting to ride-hailing and taxi, specifically in the future with high prevalence of automated vehicles, b) higher probability of such trips remaining as walking. Results: The results revealed that some of the important characteristics that increase the likelihood of a traveler using the ride-hailing and taxi mode versus walking include having a longer trip, using a smartphone to access the internet, having an interest in technologies, having a medical condition, and living in a metropolitan area with rail access. Further, the results from the first scenario suggest that an overall increase of up to 2.9% in the ride-hailing and taxi mode share may be expected. The second scenario shows that between 68% and 76% of ride-hailing and taxi trips could be diverted to walking if supportive pedestrian infrastructure were provided in the case study locations. The planning process can be adapted to consider not only congestion, crash, and emissions impacts of such shifts but also the effects of a loss of physical activity. Conclusions: The study findings show how the ride-hailing and taxi mode competes with walking. Further, the findings enable planners to update their regional travel forecasting models; policy makers can thus encourage active travel by prioritizing pedestrian infrastructure investments that may divert ride-hailing and taxi trips to walking. However, equity should be a key consideration to ensure that addressing the competition between these two modal choices does not hinder the provision of pedestrian facilities in communities that depend on walking.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

HIVE™ [SWR-19-36]

The HIVE™ platform is a mobility services simulation platform developed to provide insight on the energy, infrastructure, service, and economic outcomes of various mobility as a service (MaaS) options. The HIVE platform takes a set of spatiotemporal travel origin-destination pairs and simulates the operation of a predefined mobility service fleet, incorporating request pooling, and various operational and charging behaviors. Hive specializes at modeling fleets of automated electric vehicles (AEVs) and can be used to site and size direct current fast charge (DCFC) stations and measure grid impacts of large-scale AEV fleets serving real-world MaaS trip demand (similar to taxis, Uber, Lyft, etc.). Potential outcomes from a Hive simulation include level of service, total vehicle miles traveled (VMT), deadheading (zero passenger) miles, simultaneous and total energy loads, average occupancy, and more. Hive is developed to generalize to new regions and can be customized to handle many scenarios and operating conditions.

Rames, Clement↗
DOE OSTI2022

Hierarchical Model-Free Transactive Control of Building Loads to Support Grid Services

Residential buildings consume 4.4 quads of electricity annually, approximately 37% of the total electricity consumption in the United States. This represents a vast resource that can be used for demand management and other ancillary services. This project aims to develop a robust, scalable hierarchical transactional control mechanism incorporating elements of model-free control (MFC) and game theory to harness buildings to provide ancillary services to the grid. This approach is being taken to address the challenges of incorporating traditional transactional control schemes into existing buildings. The challenges include small individual building sizes requiring aggregation of many buildings, unpredictable energy usage that makes model identification difficult, and satisfying the sensitive occupant comfort constraints. In the proposed approach, by separating the control mechanism into two layers above and below the load aggregator, MFC can be used below the aggregator to modulate flexible building loads in response to pricing signals with guaranteed performance. This allows the burden of identifying an accurate model of the system to be shifted to the above-aggregator layer, where fluctuations in individual building usage have less impact on predicted building system behavior. Game theory concepts can then be used to determine pricing curves and control signals among regional aggregators. Managing this control in a game-theoretic approach will allow us to build in financial incentives that increase customer engagement. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable on small, embedded devices. In a broader sense, developing a strategy capable of effectively incorporating residential and small commercial buildings will allow greater throughput of existing and emerging grid services in addition to future transactive energy grid management methods. Using MFC within a hierarchical control architecture will allow the shifting of existing forecasting challenges to an aggregate level, where dynamics are slower and more predictable. This will enable a smooth interface between the grid services requests of utilities and the reliable control required by participating buildings. MFC, which supports distributed control architecture, permits a scalable solution that can be deployed to neighborhood-size systems as well as individual buildings. This project focuses on three objectives: (1) developing the mathematical framework, algorithm toolkit, and software toolset of the two-layer transactive control testbed; (2) developing a scalable solution for application over many residential and small-size commercial buildings with sparse distributed communication; and (3) field testing and implementation on hardware of the control strategies developed in the previous two objectives. The research and development activities are focused and designed to be impactful within the relevant 2025 targets timeframe. An open-source control framework for exploiting variability and dispatchability of building loads will be delivered as the outcome of the project. This capability enables greater participation of loads in electricity markets and ancillary services that are both useful for the utility and financially beneficial for building owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Additively manufactured high-entropy alloy mimics the pressure-induced structural transition of iron

We report a pressure-induced structural phase transformation in an additively manufactured (AM) AlCrFe 2 Ni 2 high-entropy alloy. High laser scan-speed processing during AM has been shown to strongly suppress FCC formation, yielding BCC/B2-dominant microstructures. Synchrotron x-ray diffraction reveals a reversible BCC/B2 → HCP transition near ∼13 GPa that mirrors the α-Fe → ɛ-Fe transformation in elemental iron. Notably, this iron-like phase change occurs despite substantial chemical disorder and multi-element site occupancy, demonstrating that the BCC lattice instability leading to close-packed polymorphs can persist in a highly disordered matrix. At ambient pressure, AlCrFe 2 Ni 2 exhibits robust ferromagnetic behavior associated with the BCC/B2 phase. The observation of an α-Fe–like polymorphic pathway in a chemically complex alloy shows that classic cubic-to-close-packed transformation physics is not extinguished by compositional complexity. As a result, AlCrFe 2 Ni 2 emerges as a model system for exploring pressure-driven polymorphism and potential magneto-structural coupling in high-entropy alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Individual low-carbon behaviors and influencing factors: Insights from a behavior survey study in China

Climate change is a major global concern. Greenhouse gas emissions that cause global climate change are directly or indirectly affected by human activities. Individual low-carbon behaviors are crucial in reducing CO 2 emissions and improving environmental and ecological health. To effectively promote individual low-carbon behavior, this study designed a questionnaire on the factors influencing individual low-carbon intentions and behavior based on theoretical models of environmental behavior. A total of 2430 valid questionnaires were collected in China. This study focuses on analyzing the impact of demographic characteristics, internal and external factors on individual low-carbon behaviors and their interrelationships. The research shows correlations between internal and external factors in determining low-carbon intention or behaviors. Internal factors-related low-carbon behavior is not closely linked with demographic variables, whereas the external factors-related low-carbon behavior vary significantly by age, residence, education, marital status, occupation, and income. The findings have important implications for designing effective policies to promote low-carbon behaviors, such as creating a more favorable external environment and increasing the use of policy tools for reducing CO 2 emission.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Simple self-consistent method for excited states in density functional theory to characterize defect-derived behavior in wide-band-gap-based microelectronic materials

This final report summarizes the results of the Laboratory Direct Research and Development (LDRD) Project Number 229740. Wide band gap semiconductors such as gallium nitride (GaN) have features highly desirable for multiple mission electronic applications. Realization of their potential requires atomic-scale understanding of electronic behavior. The principal experimental tools for electronically probing defects in GaN are chemically undifferentiating and lack a practical theoretical counterpart needed to identify and characterize specific defects. This project investigated whether a simple idea for modeling defect excited states and their associated photoluminescence (PL) energies is viable, as a path to accelerate the understanding of defect behavior and gain valuable insights into engineering new electronic materials and devices. The research implemented a non-self-consistent total-energy evaluation of a Koopmans-type estimation of an excited electronic state energy in density functional theory (DFT) calculations, and proceeded to design, implement, and assess a self-consistent method for computing excited states based upon an OCcupation-Constrained-DFT (occ-DFT). The occ-DFT was verified in test calculations of defect excited states and validated against well-characterized PL data for 3d transition metal defects in GaN. The method proved stable and robust in computing excited states and gave accurate predictions compared to experimental PL data. The combined ground state/excited-state capability proved capable of chemically differentiating defect species in GaN. In application to 3d dopants in GaN, we reinterpreted extensive experimental literature, proposed new defects as prospective candidates for use in quantum information applications, and outlined design strategies to create and exploit these potentially useful functional defects in GaN.

36 MATERIALS SCIENCE↗