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DOE OSTI2025

OC7 phase I: Toward practical sea-state-dependent modeling of hydrodynamic viscous drag and damping

Here, this article presents a collaborative research campaign under the OC7 project on refining the engineering modeling approach for hydrodynamic viscous drag and damping on floating wind platforms, focusing on the adjustment of hydrodynamic drag and damping coefficients for different sea states. The participant simulation results show significant improvements over the previous OC6 project in predicting the low-frequency resonance motion under nonoperational conditions. The improvements are mainly due to enhanced modeling, including the adoption of wave stretching, and directly tuning the coefficients to measured platform motion in waves instead of free decay. For accurate predictions of mean- and slow-drift motion, the better performing models use a decreasing column splash zone drag coefficient and increasing surge damping/drag with increasing wave height. The model tuning for heave and pitch resonance shows less consistency. Generally, both quadratic drag and additional heave or pitch damping are needed for accurate predictions. Alternatively, a quadratic drag formulation with velocity filtering for the rectangular pontoons leads to improved predictions without additional damping. This model is also potentially more predictive, requiring minimal adjustment to its parameters for different conditions.

17 WIND ENERGY↗

DOE OSTI2020

Topological carbon materials: A new perspective

Carbon has numerous one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) allotropic structures. The study of carbon materials has long been a major focus of material sciences and condensed matter physics. Recently, a number of topological semi-metallic carbon allotropes with vastly different topological phases have been predicted from first-principles, showing exceptionally clean and robust topological properties near the Fermi surfaces. Here, we provide a brief summary of the development of carbon allotropes from 1D to 3D. Then, we discuss topological properties of pure carbon materials, despite their exceedingly small spin–orbit coupling. Instead, the nearest-neighbor p z orbit interaction has been identified as the origin for the non-trivial topological properties, which vary due to different p z connectivity: zigzag, armchair, or pentagonal, as well as the interactions among further neighboring atoms. Next, we consider possible expansions of the topological properties of carbon materials to other light-element materials such as boron. Finally, we discuss future prospects in light of experimental feasibility and the recent surge in pursue of exotic carbon physics due to twisted graphene, which may also be viewed as a subset of the topological physics involving sp 2 /p z carbon.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Language models for materials discovery and sustainability: Progress, challenges, and opportunities

Significant advancements have been made in one of the most critical branches of artificial intelligence: natural language processing (NLP). These advancements are exemplified by the remarkable success of OpenAI’s GPT-3.5/4 and the recent release of GPT-4.5, which have sparked a global surge of interest akin to an NLP gold rush. Here, in this article, we offer our perspective on the development and application of NLP and large language models (LLMs) in materials science. We begin by presenting an overview of recent advancements in NLP within the broader scientific landscape, with a particular focus on their relevance to materials science. Next, we examine how NLP can facilitate the understanding and design of novel materials and its potential integration with other methodologies. To highlight key challenges and opportunities, we delve into three specific topics: (i) the limitations of LLMs and their implications for materials science applications, (ii) the creation of a fully automated materials discovery pipeline, and (iii) the potential of GPT-like tools to synthesize existing knowledge and aid in the design of sustainable materials.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Investigating the impact of a multi-module operation environment on the task performance time of human operators – An explanatory study

The worldwide demand for Small Modular Reactors (SMRs) has surged in recent years due to their enhanced safety and versatility in supporting diverse industrial sectors. A unique feature of SMR operation is that a single human operator is responsible for managing multiple modules. Therefore, securing a sufficient amount of human performance data pertaining to this new environment is essential for the safe operation of SMRs. In this explanatory study, a series of experiments were conducted using the NuScale simulator, a representative SMR design, with student operators. A total of 12 student operators were assigned two types of off-normal events and asked to cope with them using paper-based procedures. Subsequently, their task performance times were compared with those of student operators responsible for a single unit based on the Task Complexity (TACOM) measure. Results indicate that the performance of student operators under the experimental conditions of this study degraded by a factor of 2 to 3, depending on the characteristics of the off-normal events.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Beneath the surface: Unsolved questions in soil virus ecology

Soil virus ecology is an exciting but still nascent field of research in soil microbiology. While there has been a recent surge in soil virus research studies, many fundamental questions remain unanswered, and a range of technical and bioinformatic challenges need to be overcome. In this perspective article, we present a series of key questions that highlight fruitful research areas for ongoing and future efforts. These include describing the challenges involved in understanding soil viral abundance and activity, spatiotemporal dynamics, life strategy prevalence, virus-mediated biogeochemical impacts, viral protein function, host prediction, and soil RNA virus discovery. In the near term, combining approaches (e.g., cultivation-based, meta-omics, biogeochemical, experimental, and bioinformatic) will be key to assessing the ecological and biogeochemical impacts of soil viruses from the microscopic to the field and global scales. Still, we stress that results must be tempered by current methodological limitations and highlight knowledge gaps that are most pressing to fill via new methods or measurements, such as the prevalence of different viral replication strategies across soils, the fate of microbial necromass carbon after viral lysis, the frequency of virus-host encounters that do not lead to successful infections yet could be bioinformatically mistaken as infections, and the diversity and ecological impacts of RNA viruses in soil.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Phosphate amendment drives bloom of RNA viruses after soil wet-up

Soil rewetting after a dry period results in a surge of activity and succession in both microbial and DNA virus communities. Less is known about the response of RNA viruses to soil rewetting—while they are highly diverse and widely distributed in soil, they remain understudied. We hypothesized that RNA viruses would show temporal succession following rewetting and that phosphate amendment would influence their trajectory, as viral proliferation may cause phosphorus limitation. Using 39 time-resolved metatranscriptomes and amplicon data, 2190 RNA viral populations were identified across five phyla, with 26 % of these predicted to infect bacteria, and 11 % fungi. Only 1.2 % of viral populations had annotated capsid genes, suggesting most persist via intracellular replication without a free virion phase. Phosphate amendment altered RNA viral community composition within the first week and amended vs. unamended communities remained distinguishable for up to three weeks. While the overall host community remained stable, certain bacterial populations showed reduced abundance in phosphate-amended soils, likely due to increased viral lysis, as RNA bacteriophages proliferated significantly. Notably, 60 % of the viruses with increased abundance under phosphate amendment belonged to basal Lenarviricota clades rather than well-known groups like Leviviricetes. We estimate RNA bacteriophage infections may affect 10 7 –10 9 bacteria per gram of soil, aligning with the total bacterial population (10 7 –10 10 g -1 soil), suggesting that RNA phages significantly influence bacterial communities post-wet-up, with phosphorus availability modulating this effect.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Next-Generation Reverse Logistics Networks of Photovoltaic Recycling: Perspectives and Challenges

With the growing adoption of solar energy as a key component of the global energy transition and its new industrial policy (the Inflation Reduction Act and others), the United States is witnessing a significant increase in solar investments. This surge in installations and domestic and reshored manufacturing of solar photovoltaic (PV) panels brings with it a pressing issue: the proper management of end-of-life (EoL) PV panels. As these panels are decommissioned, either due to reaching the end of their lifespans or due to breakage across the various stages of the forward supply chain, it becomes crucial to establish efficient reverse supply chain logistics systems to address the challenges associated with their disposal, while also unlocking the value of the inherent materials that are of critical value for other forward supply chains. This perspective article examines the challenges regarding EoL PV panels and relevant reverse supply chain and logistics networks, and proposes future research directions based on the gaps observed among academic research, industry, and policy-making challenges. We identify the main bottlenecks and hurdles including, among others, the lack of supportive regulations and absence of structured, optimized recycling infrastructure. To this end, it is proposed that the key to achieving a sustainable reverse supply chain network for solar PV panels lies in relentless end-to-end supply chain cost optimization efforts supported by enabling policies. Moreover, it is proposed that designing systematic decision-making modeling frameworks is vital for examining different possibilities and scenarios for state, region or nation-wide optimization of solar PV reverse supply chain networks. Indicative to this effect, we discuss the development of a Resource-Task-Network (RTN)-based model and demonstrate its application and benefits through a case study. We wrap up with conclusions and future research directions.

circular economy↗

DOE OSTI2025

Accelerating room air conditioner efficiency in India: Grid, economic, and policy implications through 2035

India is poised for a rapid surge in space cooling demand, driven by rising incomes, urbanization, and intensifying heat. Between 2025 and 2035, the country is expected to add 130–150 million new room air conditioners (ACs). If Minimum Energy Performance Standards (MEPS) continue to improve at the historical rate of 2–3 % annually, room ACs alone could contribute over 180 GW to peak electricity demand by 2035-nearly 30 % of the projected national total. This study evaluates the impact of an accelerated MEPS trajectory, proposing to raise the 1-star threshold to ISEER 5.0 by 2027, ISEER 6.3 by 2030, and ISEER 7.4 by 2033. Drawing on engineering cost analysis, stock turnover modeling, and retail pricing data, we find that this pathway could reduce peak demand by over 60 GW, save 118 TWh of electricity annually, avoid 49 MtCO₂ of electricity-related emissions per year, avert ₹7.5 trillion (∼US$85 billion) in power system investments, and yield ₹0.7–2.3 trillion (∼US$8–26 billion) in net consumer savings by 2035. Contrary to affordability concerns, empirical trends show that higher efficiency does not increase AC prices. These results highlight the value of ambitious MEPS as a cost-effective strategy for improving grid reliability, reducing emissions, and advancing consumer welfare in emerging economies.

Abhyankar, Nikit↗

DOE OSTI2025

E-scooter safety: How attitudinal factors influence risky behavior among shared e-scooter riders

In recent years, e-scooter usage for short-distance trips has grown rapidly. This surge in e-scooter use, combined with the high exposure of e-scooter riders to accident risk, has sparked concerns regarding e-scooter safety. Despite some studies focusing on e-scooter safety, little is known about how attitudinal factors lead e-scooter riders to engage in risky riding behaviors. In this paper, we developed a survey-based empirical model to identify the attitudinal factors influencing engagement in risky behaviors among e-scooter users. We used survey data collected from 420 shared e-scooter users in Chicago in 2022. The survey showed that 47.7% of respondents had experienced at least one collision or fall-off while riding e-scooters. We employed the Partial Least Squares Structural Equation Model (PLS-SEM) to examine the relationships between latent attitudinal factors and risky behavior engagement. Moreover, we conducted Permutation Multi-group Analysis (PMGA) to assess the moderating effect of socio-demographic factors within the estimated model. The findings suggest that riders’ unsafe riding attitude and riding confidence are the most influential factors shaping their risky behavior engagement. In addition, accident experience, infrastructure suitability, perceived enjoyment, traffic risk perception, and operational risk perception are among the other significant predictors. Among socio-demographic factors, gender, age, education, and car use frequency significantly influence riders’ engagement in risky behaviors. The results highlight the importance of infrastructure suitability and accident experience in analyzing e-scooter users’ riding behavior. The developed model advances our understanding of factors contributing to e-scooter riders’ risky behavior engagement. The findings offer valuable insights for policymakers and e-scooter vendors aiming to mitigate e-scooter users’ accident risk. Specifically, we recommend three safety countermeasures: (1) safety training programs to encourage a safer attitude, (2) practice-based initiatives to enhance riding confidence, and (3) infrastructure improvements, especially the expansion of bike lanes.

E-scooter↗

DOE OSTI2024

A modeling pipeline to relate municipal wastewater surveillance and regional public health data

As COVID-19 becomes endemic, public health departments require improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging. We have developed a generalized methodology to improve the predictive power of wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but all model estimates of prevalence significantly improved correlation compared to doing no correction. We also investigate the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We do this in a rolling manner for corrected wastewater data and for other prevalence indicators, and statistically compare the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We find that wastewater trends often lead other COVID-19 indicators in predicting new surges.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Catalysis for plastic deconstruction and upcycling

The surge in plastic waste has become one of the most pressing environmental challenges of our time. Traditional methods of plastic disposal, such as landfilling and incineration, pose significant environmental hazards, whereas mechanical recycling processes often yield downgraded materials with limited applications. Recent advancements in catalytic science have led to the development of innovative catalytic systems enabling the efficient deconstruction and upcycling of plastic polymers. In this Voices article, we ask a panel of experts worldwide: how can catalysis address this plastic crisis?

Ma, Ding↗

DOE OSTI2026

The Chemistry of CO 2 Conversion: A Review

For much of the past century, carbon dioxide (CO 2 ) has received little attention scientifically outside of its role as a byproduct in the industrialization of the global economy. This trend has recently been upended where, due to mounting environmental concerns, CO 2 has been brought squarely into the public consciousness. This surge in activity has contributed to a once unimaginable idea now pervading the scientific community: could CO 2 , a highly stable byproduct of hydrocarbon combustion, be recycled and converted back into useful chemicals and fuels? Owing to its ubiquitous nature and availability at truly massive quantities, it is thought that CO 2 -based products could offer a meaningful pathway toward lowering the environmental impact of many of the top industrial products while also enhancing supply chain diversification and resilience. In this manuscript we provide a holistic review of the pathways for CO 2 conversion, the underlying chemistry and challenges involved in the transformation to products, and considerations for commercialization.

09 BIOMASS FUELS↗

DOE OSTI2026

Seasonality and Declining Intensity of Methane Emissions from the Permian and Nearby US Oil and Gas Basins

We quantify weekly methane emissions and trends from oil and gas production in the US Permian Basin for 2019-2023, and in nearby basins for 2022-2023, by analytical inversion of Tropospheric Monitoring Instrument (TROPOMI) satellite observations with the Integrated Methane Inversion (IMI) at 25 km resolution. Permian oil and gas emissions averaged 4.0 ± 1.1 Tg a-1 over 2019-2023, with large seasonal variation but little interannual variability. Methane intensity fell from 5.2 to 3.2% as production surged. Intensity in the New Mexico Permian fell from 4.5 to 2.1%, approaching the state's 2026 target of <2%. Emissions were on average 50 ± 10% higher in winter than summer, which we corroborate with Permian Basin Tower Network measurements, Insight M aircraft data, and GHGSat satellite observations. This seasonality may be driven in part by higher winter emissions from liquid storage tanks due to decreased separator efficiency in cold conditions. Similar but weaker seasonality along with decreasing emissions and intensities is found in weekly inversions for the Anadarko, Barnett, Eagle Ford, and Haynesville basins in 2022-2023. Our work suggests that better weatherization of oil and gas facilities could significantly reduce methane emissions.

Permian↗

DOE OSTI2023

Reaction–Diffusion Coupling Facilitates the Sequential Precipitation of Metal Ions from Battery Feedstock Solutions

Here, the development of new technologies for chemical separations is urgently needed to meet the surging demand for critical materials that has strained resources and caused environmental challenges. Inspired by the classic Liesegang experiment, we demonstrated the separation of critical metal ions based on the coupling of ion diffusion and precipitation kinetics. For this purpose, a model feedstock solution simulating dissolved battery electrodes was placed on top of a hydrogel loaded with a precipitating agent, namely sodium hydroxide. As the lithium, manganese, cobalt, and nickel ions diffused into the gel, a gradient of precipitates formed along the length of the reactor. Elemental analysis of the spatially distributed precipitates showed the enrichment of nickel near the gel-solution interface, followed by the formation of an almost pure (>96%) manganese product further along the reactor. Optimization experiments revealed that a sodium hydroxide concentration of 10 mM and a gel/solution volume ratio of 2:1 favored efficient separations. The robustness of the method was demonstrated in four out of five feedstock compositions of typically used battery cathodes. Our proof-of-concept experiments present a paradigm for critical materials separations that does not require specialty chemicals, binding agents, membranes, or toxic solvents.

25 ENERGY STORAGE↗

DOE OSTI2022

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. In this work, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (~98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ~1.5 million atoms is discussed in the context of MuMMI.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Comparative Studies of the Structural and Transport Properties of Molten Salt FLiNaK Using the Machine-Learned Neural Network and Reparametrized Classical Forcefields

Despite surging interest in molten salt reactors and thermal storage systems, knowledge of the physicochemical properties of molten salts are still inadequate due to demanding experiments that require high temperature, impurity control, and corrosion mitigation. Therefore, the ability to predict these properties for molten salts from first-principles computations is urgently needed. Herein, we developed and compared a machine-learned neural network force field (NNFF) and a reparametrized rigid ion model (RIM) for a prototypical molten salt LiF–NaF–KF (FLiNaK). We found that NNFF was able to reproduce both the structural and transport properties of the molten salt with first-principles accuracy and classical-MD computational efficiency. Furthermore, the correlation between the local atomic structures and the dynamics was identified by comparing with RIMs, suggesting the significance of polarization of anions implicitly embedded in the NNFF. Furthermore, this work demonstrated a computational framework that can facilitate the screening of molten salts with different chemical compositions, impurities, and additives, and at different thermodynamic conditions suitable for the next-generation nuclear reactors and thermal energy storage facilities.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Structure of Molten Alkali Chlorides at Charged Interfaces and the Prediction and Interpretation of Their X-ray Reflectivity

The fundamental properties of molten salts have been the subject of research that spans a century. Yet, in the past few years, there has been an unprecedented surge in interest for these systems in the bulk and under confinement by walls and interfaces including under applied potentials. This is driven by the prospect of exciting and very practical energy technologies, including those in the solar and nuclear fields. This article sets to answer two simple but fundamental questions. How does the liquid structure of alkali chlorides change at a real interface when it is charged? Also, how would such changes on the liquid side of the interface be detected in X-ray reflectivity experiments? We use an interface mimicking conductive diamond, which because of its lattice spacing, is an excellent choice for reflectivity experiments. The reason for our interest in X-ray reflectivity is that, as opposed to electrochemical measurements alone, this is likely the only technique in which atomic level information at the liquid side of the interface can be gained under the extreme temperature environments of molten salts. As it will become apparent, the interpretation of reflectivity results in terms of atomic positions is complex when multiple species with different X-ray contrasts on the liquid side are considered. A theoretical scheme termed “the peaks and antipeaks analysis of reflectivity” originally introduced in our prior work (J. Phys. Chem. C2019, 123 (8), 4914–4925) is expanded to interpret the structural changes at the interface as a function of applied electrical bias.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Calibration-Free Quantitative Analysis of Lithium-Ion Battery (LiB) Electrode Materials Using Laser-Induced Breakdown Spectroscopy (LIBS)

Recent years have seen a surge in the demand for high-performance battery electrode materials for automotive and various electronic device applications. Improving battery performance requires precise knowledge of the structure–composition properties of active electrode materials. To this effect, quantitative and precise estimation of the composition of advanced electrode materials, containing trace amounts of dopants, provide immense value toward developing next-generation high-capacity battery materials. Herein, we demonstrate the application of calibration-free laser-induced breakdown spectroscopy (LIBS) as a powerful analytical tool for rapid and reliable quantitative spectrochemical characterizations of layered Li metal oxide cathodes containing Mo and Cr dopants (<5 atom %). Specifically, we employ the LIBS using an internal calibration methodology to establish the quantitative elemental ratios of major (Ni, Mn, Co) and trace dopant (Cr, Mo) transition metals to bulk Li contents in diverse cathode material samples such as LiNi 0.5 Mn 0.5 O 2 (NM-50/50), LiNi 0.33 Mn 0.33 Co 0.33 O 2 (NMC), LiNi 0.317 Mn 0.317 Co 0.317 Cr 0.05 O 2 (Cr-doped NMC), and LiNi 0.5– x /2 Mn 0.5– x /2 Mo x O 2 ( x = 0.03, 0.04, 0.05) (Mo-doped NM-50/50) that were synthesized via sol–gel routes. The LIBS-estimated elemental compositions are in good agreement with the nominal stoichiometric values. Furthermore, ex situ LIBS characterizations presented here pave the path for future high-efficacy application of calibration-free quantitative LIBS for rapid in situ analyses of elemental composition changes in battery electrode materials under operation that need not resort to off-site analytical techniques requiring cumbersome sample preparations and/or external standards.

25 ENERGY STORAGE↗