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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An argument for using anaerobes as microbial cell factories to advance synthetic biology and biomanufacturing

Anaerobes thrive in the absence of oxygen and are an untapped reservoir of biotechnological potential. Therefore, bioprospecting efforts focused on anaerobic microbial diversity could rapidly uncover new enzymes, pathways, and chassis organisms to drive biotechnology innovation. Despite their potential utility, anaerobic fermenters are viewed as inefficient from a biochemical perspective because their metabolisms produce fewer ATP (~2) per molecule of glucose processed than heterotrophic respirers (~32–38 ATP). While aerobes excel at ATP generation, they are often less efficient than anaerobes at processes that compete with ATP generation for cellular resources. This perspective highlights how anaerobic adaptations are advantageous for synthetic biology and biomanufacturing applications through the engineering of microbial cell factories. We further highlight emerging applications of anaerobic bioprocessing, including the use of anaerobic metabolisms for lignocellulosic bioprocessing, human and environmental health, and value-added bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Exploring inflammation‐related protein expression and its relationship with TSPO PET in Alzheimer's disease

Abstract INTRODUCTION To understand the role of neuroinflammation in Alzheimer's disease (AD), we characterized immune‐related proteins in central and peripheral biofluids. METHODS Selection of participants from the Translational Biomarker of Aging and Dementia (TRIAD) cohort with available translocator protein (TSPO) positron emission tomography (PET), cerebrospinal fluid (CSF) (n = 97), and plasma (n = 165). Biofluid samples analyzed with Olink technology (368 inflammation proteins). RESULTS Elevated proteins levels in CSF of TSPO‐positive individuals were identified. Functional enrichment analysis of CSF proteins revealed processes implicated in AD (MAPK, ERK cascades, cytokine, and leukocyte signaling). Selected candidates (CXCL1 and TNFRSF11B) showed high correlation with each other in CSF and with TSPO PET signal, but weaker associations with amyloid and tau PET. No significantly changed proteins in plasma between TSPO groups were found. DISCUSSION This explorative study identified two potential targets in CSF showing correlations with TSPO, amyloid and tau PET, suggesting a direct link between neuroinflammation, expression of these proteins and their potential implication in AD. Highlights Several proteins are elevated in CSF of TSPO PET‐positive individuals, linking them to neuroinflammation. Elevated CSF proteins were enriched in pathways such as MAPK, ERK, and cytokine signaling, linking them to the AD pathophysiology. Candidate proteins (CXCL1 and TNFRSF11B) correlated strongly with TSPO PET, particularly in brain regions known to be affected in AD. Although none of the plasma proteins remained significant after multiple comparisons correction when comparing their expression between TSPO groups, as done for CSF, candidate CSF proteins were found to correlate with plasmatic proteins, highlighting the complexity of the immune system.

Neurosciences & Neurology↗
DOE OSTI2024

Electrochemical Imaging of Precisely‐Defined Redox and Reactive Interfaces

Abstract Understanding the diverse electrochemical reactions occurring at electrode‐electrolyte interfaces (EEIs) is a critical challenge to developing more efficient energy conversion and storage technologies. Establishing a predictive molecular‐level understanding of solid electrolyte interphases (SEIs) is challenging due to the presence of multiple intertwined chemical and electrochemical processes occurring at battery electrodes. Similarly, chemical conversions in reactive electrochemical systems are often influenced by the heterogeneous distribution of active sites, surface defects, and catalyst particle sizes. In this mini review, we highlight an emerging field of interfacial science that isolates the impact of specific chemical species by preparing precisely‐defined EEIs and visualizing the reactivity of their individual components using single‐entity characterization techniques. We highlight the broad applicability and versatility of these methods, along with current state‐of‐the‐art instrumentation and future opportunities for these approaches to address key scientific challenges related to batteries, chemical separations, and fuel cells. We establish that controlled preparation of well‐defined electrodes combined with single entity characterization will be crucial to filling key knowledge gaps and advancing the theories used to describe and predict chemical and physical processes occurring at EEIs and accelerating new materials discovery for energy applications.

Edgecomb, Joseph↗
DOE OSTI2024

Electrochemical Imaging of Precisely‐Defined Redox and Reactive Interfaces

Abstract Understanding the diverse electrochemical reactions occurring at electrode‐electrolyte interfaces (EEIs) is a critical challenge to developing more efficient energy conversion and storage technologies. Establishing a predictive molecular‐level understanding of solid electrolyte interphases (SEIs) is challenging due to the presence of multiple intertwined chemical and electrochemical processes occurring at battery electrodes. Similarly, chemical conversions in reactive electrochemical systems are often influenced by the heterogeneous distribution of active sites, surface defects, and catalyst particle sizes. In this mini review, we highlight an emerging field of interfacial science that isolates the impact of specific chemical species by preparing precisely‐defined EEIs and visualizing the reactivity of their individual components using single‐entity characterization techniques. We highlight the broad applicability and versatility of these methods, along with current state‐of‐the‐art instrumentation and future opportunities for these approaches to address key scientific challenges related to batteries, chemical separations, and fuel cells. We establish that controlled preparation of well‐defined electrodes combined with single entity characterization will be crucial to filling key knowledge gaps and advancing the theories used to describe and predict chemical and physical processes occurring at EEIs and accelerating new materials discovery for energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

The impact of plant‐derived fire management prescriptions on fire‐responsive bird species

Abstract In fire‐prone regions, the occurrence of some faunal species is contingent on the presence of resources that arise through post‐fire plant succession. Through planned burning, managers can alter resource availability and aim to provide the conditions required to promote biodiversity. Understanding how species occurrence changes at different spatial and temporal scales after fire is essential to achieve this goal. However, many fire prescriptions are guided primarily by the responses of fire‐sensitive plants when setting tolerable fire intervals. This approach assumes that maintaining floristic diversity will satisfy the requirements of fauna. We surveyed bird species in two semi‐arid vegetation types across an environmental gradient in south‐eastern Australia. We conducted four surveys at each of 253 sites across a 75‐year chronosequence of time since fire and used generalized additive mixed models to examine changes in the occurrence of birds in response to time since fire. Model predictions were compared to plant‐derived fire prescriptions currently guiding fire management in the region. Time since fire was a significant predictor for 18 of 28 species modeled, in at least one vegetation type, over a gradient of 1.3° of latitude. We detected considerable variation in the responses of some species, both between vegetation types and geographically within a vegetation type. Our evaluation of plant‐derived fire prescriptions suggests that the intervals considered acceptable for maintaining floristic diversity may not be sustainable for populations of birds requiring longer unburnt vegetation, with 6 of the 12 species assessed attaining a mean occurrence probability of 20.3% by the minimum tolerable fire interval, and 57.3% by the maximum tolerable fire interval, in their respective vegetation types. Our findings highlight the potential vulnerability of fire‐responsive bird species if fire prescriptions are applied in a manner that fails to account for the slow development of habitat resources needed by some species, and the variation detected within the responses of species. This highlights the need for species‐specific data collected at an appropriate spatial scale to inform management plans.

Makdissi, Rhys↗
DOE OSTI2024

Utilizing digitized occurrence records of Midwestern feral Cannabis sativa to develop ecological niche models

Hemp (Cannabis sativa L.) has historically played a vital role in agriculture across the globe. Feral and wild populations have served as genetic resources for breeding, conservation, and adaptation to changing environmental conditions. However, feral populations of Cannabis, specifically in the Midwestern United States, remain poorly understood. This study aims to characterize the abiotic tolerances of these populations, estimate suitable areas, identify regions at risk of abiotic suitability change, and highlight the utility of ecological niche models (ENMs) in germplasm conservation. The Maxent algorithm was used to construct a series of ENMs. Validation metrics and MOP (Mobility-oriented Parity) analysis were used to assess extrapolation risk and model performance. We also projected the final projected under current and future climate scenarios (2021–2040 and 2061–2080) to assess how abiotic suitability changes with time. Climate change scenarios indicated an expansion of suitable habitat, with priority areas for germplasm collection in Indiana, Illinois, Kansas, Missouri, and Nebraska. This study demonstrates the application of ENMs for characterizing feral Cannabis populations and highlights their value in germplasm conservation and breeding efforts. Populations of feral C. sativa in the Midwest are of high interest, and future research should focus on utilizing tools to aid the collection of materials for the characterization of genetic diversity and adaptation to a changing climate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Implications of Changes in Water Stress and Precipitation Extremes for Cocoa Production in Côte D'ivoire and Ghana

ABSTRACT Climate change induces high variability in drought patterns and extreme precipitation indices in rainfed cocoa farming, impacting cocoa production. This study evaluated water stress, meteorological and agricultural drought conditions, and critical extreme precipitation indices in the world's two largest cocoa‐producing nations from 1981 to 2022. The results revealed a significant reduction in total annual precipitation (PRCPTOT), in the last three decades, with the greatest decline in the 1991–2000 and 2011–2022 periods. Ghana experienced the most significant reduction up to 15% (200 mm/year) in the last decade, attributed to a substantial decrease in wet days number (RR1) up to 25 days per year, a reduction in maximum consecutive wet days (CWD) up to 6 days per year, and an increase in maximum consecutive dry days (CDD) up to 15 days per year. Moreover, there was a notable decline in the Simple Daily Intensity Index (SDII), with reductions of up to 4 mm/day in certain areas, contributing to increased drought frequency, severity, and duration. In the most recent decade (2011–2022), particularly during the extremely dry years of 2013 and 2015, cocoa‐growing regions in Ghana (GHA) and eastern Côte d'Ivoire (CIV) experienced prolonged agricultural drought expressed by soil moisture deficit, typically extending from May to September. Additionally, large portions of central and eastern Ghana, as well as northeastern Côte d'Ivoire experienced sustained water stress, with over three consecutive months of total monthly precipitation falling below 100 mm, negatively impacting cocoa productivity. The decrease in the yield in the range of 2.5% to 37% was noted in the dry years and the following years, varying according to the country depending on the severity of the drought. Sensitivity analysis highlights cocoa yield's responsiveness to drought and water stress, particularly in specific years when water stress occurred, such as 1984,1985, 1989, 1995, 1999, 2000, and 2008. Considering the observed trends in precipitation patterns and their impact on cocoa production, it is crucial to acknowledge the inherent uncertainty of future precipitation patterns due to climate change. To address this challenge effectively, our study underscores the importance of identifying and closely monitoring regions currently facing water stress, as determined by precipitation and drought indicators. Over the analysed period (1981–2022), we have noted shifts in the distribution of water‐stressed areas, highlighting the dynamic nature of this issue. Consequently, we advocate for a targeted approach to implement cocoa supplementary irrigation in consistently water‐stressed regions.

Obahoundje, Salomon [International Water Managemen↗
DOE OSTI2025

A comprehensive review on valorization of chestnut processing wastes into bio‐based composites and bioplastics

Abstract This review examines the characterization and utilization of chestnut processing wastes (35%) in the production of bioplastics and biocomposites. In this review, a Web of Science search without any publishing year restriction on the biochemical compositions of all the components of Castanea sativa . The obtaining of bioplastics and biocomposites based on C. sativa was reviewed. First, it highlights the biochemical composition and antioxidant properties of chestnut fruit, shell, burrs, leaves, flowers, and wood focusing on the most important compounds, such as phenolic acids, flavonoids, carbohydrates, Klason lignin, cellulose, and glucan, which can enhance the properties of these materials. Then the review covers using several chestnut extracts and fillers in bioplastics production through solvent casting technique. The mechanical, structural, bioactive properties, and moisture content were optimized through the composition and production. The color, UV absorption, antioxidant, and antimicrobial activity were also discussed. Biocomposites reinforced with chestnut burs, shells, or wood flour increased the intended properties. The enhancements in tensile strength, elastic modulus, and the effects of a pre‐treatment were evaluated. Additionally, it discusses material recovery, recycling, and reuse, particularly how it affects the biodegradability of composites incorporating chestnut waste residues. Highlights Chestnut fruit, shells, and burrs are rich in starch, lignin, and cellulose. The waste of chestnut processing can be used in bioplastics and biocomposites. Chestnut‐based films and biocomposites exhibit promising mechanical properties. The antimicrobial activity, making films, and composites proper for food packaging. New techniques boost performance, offering alternatives to conventional plastics.

Silva, Simão B. [REQUIMTE/LAQV, ISEP, Polytechnic ↗
DOE OSTI2024

A novel digital lifecycle for Material‐Process‐Microstructure‐Performance relationships of thermoplastic olefins foams manufactured via supercritical fluid assisted foam injection molding

Abstract This research significantly enhances the applicability of thermoplastic olefins (TPOs) in the automotive industry using supercritical N 2 as a physical foaming agent, effectively addressing the limitations of traditional chemical agents. It merges experimental results with simulations to establish detailed material‐process‐microstructure‐performance (MP2) relationships, targeting 5–20% weight reductions. This innovative approach labeled digital lifecycle (DLC) helps accurately predict tensile, flexural, and impact properties based on the foam microstructure, along with experimentally demonstrating improved paintability. The study combines process simulations with finite element models to develop a comprehensive digital model for accurately predicting mechanical properties. Our findings demonstrate a strong correlation between simulated and experimental data, with about a 5% error across various weight reduction targets, marking significant improvements over existing analytical models. This research highlights the efficacy of physical foaming agents in TPO enhancement and emphasizes the importance of integrating experimental and simulation methods to capture the underlying foaming mechanism to establish material‐process‐microstructure‐performance (MP2) relationships. Highlights Establishes a material‐process‐microstructure‐performance (MP2) for TPO foams Sustainably produces TPO foams using supercritical (ScF) N 2 with 20% lightweighting Shows enhanced paintability for TPO foam improved surface aesthetics Digital lifecycle (DLC) that predicts both foam microstructure and properties DLC maps process effects & microstructure onto FEA mesh for precise prediction

Engineering↗
DOE OSTI2025

Acetylcholinesterase: Structure, dynamics, and interactions with organophosphorus compounds

Acetylcholinesterase (AChE) is an enzyme that hydrolyzes the neurotransmitter acetylcholine (ACh), removing it from the synaptic cleft after the transmission of an electrical signal, making it an essential component of chemical neurotransmission. AChE is a serine hydrolase, containing a catalytic triad of Ser/His/Glu. AChE is a prime target for pharmaceuticals treating a variety of neurological disorders. It is also the target of synthetic organophosphorus (OP) compounds that have been used as pesticides and chemical warfare agents. OP compounds contain a potent leaving group, such as fluorine, and act by forming a covalent adduct with the catalytic serine of the AChE active site. A wealth of structural information is available for AChE, including over 300 structures, including a subset of structures in complex with drugs as well as OP compounds. This review will highlight the interactions between OP compounds and AChE from a structural and computational perspective, with a discussion of access to the active site, as well as side reactions that lead to dealkylation of the OP-catalytic serine adduct, a process known as aging. We conclude that while the majority of the conformational changes needed to accommodate the OP compounds are localized to the acyl loop in the crystal structures, molecular dynamics simulations highlight the potential for a far more dynamic enzyme.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Root and Leaf Traits of Alfalfa Exhibit Distinct Responses to Soil Microbial Communities and Environmental Stresses

Ongoing climate change is negatively impacting crop productivity globally. Past research has highlighted that a diverse soil microbial community and variation in plant traits for resource acquisition can mitigate the negative impacts of climate change factors on crop productivity. This study investigates the effects of two major environmental stressors—drought and salinity stress, on plant productivity, biomass allocation, and root and leaf trait responses under distinct soil microbial diversities. Our results showed that salinity stress had stronger negative impacts on plant productivity than drought stress. Shoot biomass decreased by 30% and 32.5% under drought and salinity stress, respectively, whereas the root biomass decreased by 32% only under salinity stress. Soil microbial diversity did not affect plant productivity. Next, root traits were mainly impacted by drought and salinity stress, whereas leaf traits were impacted by both environmental stresses and soil microbial diversity. Specific root length and specific root area decreased under drought, and root tissue density was minimal under salinity stress. Root traits were not affected by soil microbial communities. In contrast, the leaf nitrogen content increased, whereas pheophytin content (a breakdown product of chlorophyll) decreased when plants were grown in diverse microbial communities under environmental stresses, especially drought. These results highlight the importance of soil microbial diversity in impacting plant traits in response to environmental stresses. We showed that the soil microbial diversity influences both aboveground and belowground plant traits, indicating the need for better management practices to conserve and promote soil microbial diversity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Accelerating Discovery of Solid‐State Thin‐Film Metal Dealloying for 3D Nanoarchitecture Materials Design through Laser Thermal Gradient Treatment

Thin‐film solid‐state metal dealloying (thin‐film SSMD) is a promising method for fabricating nanostructures with controlled morphology and efficiency, offering advantages over conventional bulk materials processing methods for integration into practical applications. Although machine learning (ML) has facilitated the design of dealloying systems, the selection of key thermal treatment parameters for nanostructure formation remains largely unknown and dependent on experimental trial and error. To overcome this challenge, a workflow enabling high‐throughput characterization of thermal treatment parameters is demonstrated using a laser‐based thermal treatment to create temperature gradients on single thin‐film samples of Nb‐Al/Sc and Nb‐Al/Cu. This continuous thermal space enables observation of dealloying transitions and the resulting nanostructures of interest. Through synchrotron X‐ray multimodal and high‐throughput characterization, critical transitions and nanostructures can be rapidly captured and subsequently verified using electron microscopy. The key temperatures driving chemical reactions and morphological evolutions are clearly identified. While the oxidation may influence nanostructure formation during thin‐film treatment, the dealloying process at the dealloying front involves interactions solely between the dealloying elements, highlighting the availability and viability of the selected systems. Further, this approach enables efficient exploration of the dealloying process and validation of ML predictions, thereby accelerating the discovery of thin‐film SSMD systems with targeted nanostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Identifying preferential flow from soil moisture time series: Review of methodologies

Abstract Identifying and quantifying preferential flow (PF) through soil—the rapid movement of water through spatially distinct pathways in the subsurface—is vital to understanding how the hydrologic cycle responds to climate, land cover, and anthropogenic changes. In recent decades, methods have been developed that use measured soil moisture time series to identify PF. Because they allow for continuous monitoring and are relatively easy to implement, these methods have become an important tool for recognizing when, where, and under what conditions PF occurs. The methods seek to identify a pattern or quantification that indicates the occurrence of PF. Most commonly, the chosen signature is either (1) a nonsequential response to infiltrated water, in which soil moisture responses do not occur in order of shallowest to deepest, or (2) a velocity criterion, in which newly infiltrated water is detected at depth earlier than is possible by nonpreferential flow processes. Alternative signatures have also been developed that have certain advantages but are less commonly utilized. Choosing among these possible signatures requires attention to their pertinent characteristics, including susceptibility to errors, possible bias toward false negatives or false positives, reliance on subjective judgments, and possible requirements for additional types of data. We review 77 studies that have applied such methods to highlight important information for readers who want to identify PF from soil moisture data and to inform those who aim to develop new methods or improve existing ones. Core Ideas Soil moisture data can be used to identify the occurrence of preferential flow (PF) and its initiating conditions. Various data‐analysis methods to identify PF differ in susceptibility to error, bias, and subjectivity. These methods can utilize vast amounts of data from soil moisture monitoring networks to develop understanding of when, where, and under what conditions PF occurs. Newly developed methods may lead to better accuracy and reliability, and reduce the need for subjective judgments. Plain Language Summary Preferential flow through soil occurs when a large amount of water is suddenly available, as during an intense storm. This type of flow moves rapidly through the soil in distinct narrow pathways rather than moving evenly throughout the body of soil, with major consequences for groundwater resources, ecosystems, spreading of contaminants, and other vital concerns. Methods of detecting preferential flow have been developed that utilize measurements of soil water content made by sensors installed at various depths. This measurement technology has been widely implemented, many locations now having datasets years in length, and various methods have been developed for using these to identify preferential flow. The various methods are based on different features in the soil moisture records and vary in their advantages and shortcomings. In this review, we explain and evaluate these methods, highlighting important information for their implementation to identify preferential flow from soil moisture data and for efforts to develop new methods or improve existing ones.

Nimmo, John R↗
DOE OSTI2026

Ecological connectivity and habitat loss shape patterns of genetic diversity in a threatened salamander

Context The maintenance of genetic diversity is essential for preserving adaptive potential in populations, yet it is increasingly threatened by landscape alteration. The field of landscape genetics offers a framework for assessing how patch-level landscape conditions, modeled at multiple scales, influence genetic diversity. Objectives We sought to assess how local environmental features and connectivity influence genetic diversity across 74 four-toed salamander (Hemidactylium scutatum) breeding wetlands in the southeastern United States. Methods Using next-generation sequencing data and hierarchical Bayesian models, we examined genome-wide heterozygosity in relation to local landscape features and ecological connectivity. We also assessed the scale of effect of landscape features and tested for temporal lag effects. Results Genetic diversity was lower in wetlands with higher levels of historic deforestation and lower connectivity. An interaction between deforestation and connectivity indicated that deforestation had stronger negative effects in isolated wetlands but weaker effects in well-connected wetlands. Accounting for scale of effect and temporal lags was critical for detecting these relationships. Conclusions Our analyses highlight the importance of assessing the spatial scale (scale of effect) and temporal lag of landscape features to detect key drivers of genetic diversity. In line with population genetic theory, our results indicate that the genetic consequences of habitat loss do not affect populations uniformly and are most severe in isolated populations where gene flow cannot buffer against loss of diversity. Altogether, we highlight the importance of considering the interaction of habitat loss and connectivity in conservation genetic management.

Hemidactylium scutatum↗
DOE OSTI2025

Harnessing Machine Learning to Predict MoS 2 Solid Lubricant Performance

Physical vapor deposited (PVD) molybdenum disulfide (MoS 2 ) solid lubricant coatings are an exemplar material system for machine learning methods due to small changes in process variables often causing large variations in microstructure and mechanical/tribological properties. Here, in this work, a gradient boosted regression tree machine learning method is applied to an existing experimental data set containing process, microstructure, and property information to create deeper insights into the process-structure–property relationships for molybdenum disulfide (MoS 2 ) solid lubricant coatings. The optimized and cross-validated models show good predictive capabilities for density, reduced modulus, hardness, wear rate, and initial coefficients of friction. The contribution of individual deposition variables (i.e., argon pressure, deposition power, target conditioning) on coating properties is highlighted through feature importance. The process-property relationships established herein show linear and non-linear relationships and highlight the influence of uncontrolled deposition variables (i.e., target conditioning) on the tribological performance.

MoS2↗
DOE OSTI2026

Enhancing Building Resilience: Maintaining Energy Efficiency and Thermal Comfort During Power Outages in Cold Climates

The increasing frequency and intensity of extreme weather events, such as heatwaves and cold snaps, present significant challenges to building energy performance and occupant comfort. Highly correlated with climate events are widespread long duration power interruptions that may affect thousands of buildings and millions of customers. This study evaluates the impact of building energy performance and occupant thermal comfort in medium-sized office buildings in a cold climate region. Using energy models representing pre-1980 and 2019 vintages, simulations were conducted to assess energy performance under typical weather conditions and occupant thermal comfort during power interrupted extreme cold snap and heatwave climate events under both current 2020s and future 2050s weather conditions. The results show a projected 33% increase in cooling energy demand and a 19% reduction in heating energy by 2050. Findings reveal that older buildings are more susceptible to cold discomfort during cold snaps, while modern airtight buildings are more vulnerable to overheating during heatwaves. Various passive energy efficiency measures, such as improved infiltration control, thermal windows, solar-controlled windows, and cool envelopes, were evaluated for their ability to mitigate thermal discomforts. Solar controlled windows and weatherstripping contribute to reducing cold thermal discomfort by 21% during a power-interrupted cold snap. Solar-controlled windows were found to reduce hot thermal discomfort by 34% during a future power-interrupted heatwave. The study highlights the importance of targeted retrofitting strategies to enhance thermal resilience, especially during power outages, to ensure occupant safety and comfort during extreme climate events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Chemical trends favoring interstitial cluster formation in bcc high-entropy alloys from first-principles calculations

Achieving high strength and ductility is a common goal in structural alloy design. Body-centered cubic high-entropy alloys (HEAs) commonly highlight the conflict between these properties, with stronger alloys being brittle and vice versa. Recent reports suggest interstitial solutes can be used to overcome this trade-off, in some cases providing both strength and ductility enhancements. This effect has been correlated with interstitial cluster formation, although the conditions favoring their formation remain incompletely understood. Using first-principles calculations of solution energies and diffusivities, we provide insights into thermodynamic and kinetic factors favoring interstitial solute clusters. Among C, N and O solutes, O interstitials display most desirable diffusion kinetics. Further, the results highlight the importance of local composition fluctuations in the HEAs to enable the formation of clusters of appreciable size. The results are explained in terms of bonding and distortion trends across solutes and HEA compositions to provide guidelines for alloy design.

Borges, Pedro P P O↗
DOE OSTI2026

Phase diagram of magnetic shape memory alloy Ni 50 Mn $50–x$ In $x$ , 0 < $x$ , 25 from first principles, via spin cluster expansion and phonon vibrational entropies

The metamagnetic shape memory Heusler alloy Ni 50 Mn $50–x$ In $x$ exhibits a rich phase diagram featuring competing magnetic states, coupled magnetic–structural phase transitions, and strong compositional sensitivity. Existing first-principles approaches struggletocapturetheintertwinedchemical, magnetic, andvibrationaleffectsinthesealloys, necessitating a more integrated modeling framework. We develop a spin cluster expansion (spin-CE) framework augmented by a quasi-harmonic phonon model to capture both configurational (chemical and magnetic) and vibrational contributions to the free energy of Ni 50 Mn $50–x$ In $x$ over the full compositional range 0 ≤ x ≤25. The spin-CE includes both chemical clusters and composition-dependent Ising spin interactions, with parameters fit to a first-principles density functional theory (DFT) dataset. Using this approach, we predict the complete magnetostructural phase diagram and transformation temperatures of Ni 50 Mn $50–x$ In $x$ across the composition space. We find that vibrational entropy alone is insufficient to reproduce the martensitic transformation in the magnetic shape memory alloy regime, highlighting the essential role of magnetism. Incorporating both magnetic and vibrational contributions allows us to reproduce all experimentally known phases, including the disappearance of the stable martensite phase at a critical In concentration and the Curie temperature of the austenite phase. The method also captures the transition with increasing In in martensite from antiferromagnetic to ferromagnetic order and predicts re-entrant ferromagnetism, though the latter occurs at higher In content than reported experimentally. We discuss possible sources of this discrepancy and highlight the broader applicability of the method to other magnetostructurally complex materials, where it may offer mechanistic insight and predictive design capabilities.

Cluster expansion↗