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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Volcanic electrification: recent advances and future perspectives

Abstract The electrification of volcanic plumes has been described intermittently since at least the time of Pliny the Younger and the 79 AD eruption of Vesuvius. Although sometimes disregarded in the past as secondary effects, recent work suggests that the electrical properties of volcanic plumes reveal intrinsic and otherwise inaccessible parameters of explosive eruptions. An increasing number of volcanic lightning studies across the last decade have shown that electrification is ubiquitous in volcanic plumes. Technological advances in engineering and numerical modelling, paired with close observation of recent eruptions and dedicated laboratory studies (shock-tube and current impulse experiments), show that charge generation and electrical activity are related to the physical, chemical, and dynamic processes underpinning the eruption itself. Refining our understanding of volcanic plume electrification will continue advancing the fundamental understanding of eruptive processes to improve volcano monitoring. Realizing this goal, however, requires an interdisciplinary approach at the intersection of volcanology, atmospheric science, atmospheric electricity, and engineering. Our paper summarizes the rapid and steady progress achieved in recent volcanic lightning research and provides a vision for future developments in this growing field.

58 GEOSCIENCES↗

DOE OSTI2021

Contribution of advanced fluorescence nano microscopy towards revealing mitotic chromosome structure

The organization of chromatin into higherorder structures and its condensation process represent one of the key challenges in structural biology. This is important for elucidating several disease states. To address this long-standing problem, development of advanced imaging methods has played an essential role in providing understanding into mitotic chromosome structure and compaction. Amongst these are two fast evolving fluorescence imaging technologies, specifically fluorescence lifetime imaging (FLIM) and superresolution microscopy (SRM). FLIM in particular has been lacking in the application of chromosome research while SRM has been successfully applied although not widely. Furthermore, both these techniques are capable of providing fluorescence imaging with nanometer information. SRM or "nanoscopy" is capable of generating images of DNA with less than 50 nm resolution while FLIM when coupled with energy transfer may provide less than 20 nm information. Here, we discuss the advantages and limitations of both methods followed by their contribution to mitotic chromosome studies. Furthermore, we highlight the future prospects of how advancements in new technologies can contribute in the field of chromosome science.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Rapidly Changing Range Limits in a Warming World: Critical Data Limitations and Knowledge Gaps for Advancing Understanding of Mangrove Range Dynamics in the Southeastern USA

Climate change is altering species’ range limits and transforming ecosystems. For example, warming temperatures are leading to the range expansion of tropical, cold-sensitive species at the expense of their cold-tolerant counterparts. In some temperate and subtropical coastal wetlands, warming winters are enabling mangrove forest encroachment into salt marsh, which is a major regime shift that has significant ecological and societal ramifications. Here, we synthesized existing data and expert knowledge to assess the distribution of mangroves near rapidly changing range limits in the southeastern USA. We used expert elicitation to identify data limitations and highlight knowledge gaps for advancing understanding of past, current, and future range dynamics. Mangroves near poleward range limits are often shorter, wider, and more shrublike compared to their tropical counterparts that grow as tall forests in freeze-free, resource-rich environments. The northern range limits of mangroves in the southeastern USA are particularly dynamic and climate sensitive due to abundance of suitable coastal wetland habitat and the exposure of mangroves to winter temperature extremes that are much colder than comparable range limits on other continents. Thus, there is need for methodological refinements and improved spatiotemporal data regarding changes in mangrove structure and abundance near northern range limits in the southeastern USA. Advancing understanding of rapidly changing range limits is critical for foundation plant species such as mangroves, as it provides a basis for anticipating and preparing for the cascading effects of climate-induced species redistribution on ecosystems and the human communities that depend on their ecosystem services.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Toward ML-Assisted Advanced Manufacturing

Abstract The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging advanced manufacturing process, shear-assisted processing and extrusion (ShAPE). We generate realistic images conditioned on temper and either experimental parameters or material properties. In doing so, we are able to integrate machine learning into the development cycle, by allowing a user to immediately visualize the microstructure that would arise from particular process parameters or properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes in the absence of first-principle models. By characterizing microstructure from a topological perspective, we are able to evaluate our models’ ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples. Our method is successful in capturing the visual and general microstructural features arising from the considered process, with analysis highlighting directions to further improve the topological realism of our synthetic imagery.

36 MATERIALS SCIENCE↗

DOE OSTI2025

From PINNs to PIKANs: recent advances in physics-informed machine learning

Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.

Kolmogorov-Arnold networks↗

DOE OSTI2022

Chapter 10: Advances in Protein Engineering and its Application in Synthetic Biology

Protein engineering has been used successfully in fields ranging from medicine to food science to biofuels. Applications of protein engineering include developing antiviral peptides or other protein therapeutics, antibody engineering, designing protein-based logic circuits, engineering enzymes to be more specific or to function under industrially relevant conditions such as at higher temperatures or high/low pH, modifying cell signaling or regulatory functions, and so on. Advances in recombinant DNA, "omics," and CRISPR-Cas (clustered regularly interspaced short palindromic repeats and its associated proteins) technologies, combined with high-throughput screening facilities, will lead to improved methods for protein engineering, enabling easy modification of more proteins/enzymes for new specific applications. New methods for rational design, directed evolution, and computer-aided protein design will further accelerate the speed of protein evolution and expand the scope for protein engineering. In this chapter we discuss general protein engineering strategies and advances in engineering proteins with desired functions, focusing on the "design" and "build" part of the design-build-test-learn cycle.

BIOMASS FUELS↗

DOE OSTI2023

Advances in arene alkylation and alkenylation catalyzed by transition metal complexes based on ruthenium, nickel, palladium, platinum, rhodium and iridium

Over the past several years, significant advancements have been made for transition metal catalyzed arene alkylation and alkenylation that operate by metal mediated arene C–H activation. These catalytic processes provide a route for arene alkylation and alkenylation that is complementary to traditional acid catalyzed reactions (e.g., Friedel-Crafts reactions and zeolite-based arene alkylations). Catalysts from Groups 8, 9 and 10 have dominated new developments. As a result, this monograph will overview advancements for catalytic arene alkylation and alkenylation for hydrocarbon substrates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Recent advancements in the genetic engineering of microalgae

The development of more sustainable food, feed, and bio-products is critical to mitigating the environmental stresses facing our world today. Algae, which includes seaweeds, eukaryotic microalgae, and cyanobacteria, are a promising platform to achieving this, as they have low energy and space requirements, are safe for human and animal consumption, and can be manipulated to produce a diversity of valuable bioproducts. This review focuses on microalgae, both eukaryotic and cyanobacteria. In the past, addressing the major challenges of bringing microalgal production systems to an economically viable scale only had a relatively small genetic toolset to work with, in comparison to other microbial systems such as bacteria and yeast. Expanding the molecular tools available for genetic engineering of microalgae will lead to higher product yields, and accelerate the development of new microalgal bioproducts for commercial applications, thereby supporting the shift towards more environmentally friendly products. In this review, we highlight significant advances from recent years on the design of microalgal expression vectors, discovery of genetic regulatory elements (promoters and transcription factors), optimization of transformation methods, and development of new strain improvement techniques, all aimed at advancing microalgae to become a more efficient biomanufacturing platform. We then discuss how these tools have been applied to improving recombinant protein production, and to enhance metabolic pathway engineering.

09 BIOMASS FUELS↗

DOE OSTI2024

Griffin: A MOOSE-based reactor physics application for multiphysics simulation of advanced nuclear reactors

Griffin is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor physics application for multiphysics simulations of advanced reactor designs jointly developed by Idaho National Laboratory and Argonne National Laboratory. This paper summarizes the motivation, significance, architecture, design, and features of Griffin. Griffin offers flexible and extensible features to address the challenges associated with advanced reactor designs. These features range from fundamental particle transport to specific reactor physics tasks. The features cover a wide range including on-the-fly and traditional two-step cross-section generation methods, steady-state and transient transport solvers suitable for both heterogeneous and homogeneous models, high-fidelity depletion where thousands of isotopes can be tracked and low-fidelity depletion characterized by burnup, etc. The most fundamental aspect that sets Griffin apart from other reactor analysis codes is that it is developed based on the MOOSE framework. A modular development approach is strongly enforced, with multiphysics being an essential element considered since the beginning of Griffin’s development. Griffin links various MOOSE physics modules and couples to other MOOSE-based applications and non-MOOSE-based applications for multiphyiscs simulations. Griffin includes three modules: ISOXML for preparing and managing multigroup cross sections, radiation transport for solving the neutron transport equation, and reactor analysis for user-oriented reactor physics analysis functionalities. Griffin uses various finite element methods for spatial discretization, multigroup approximation for energy discretization and discrete ordinates method, spherical harmonics expansion method, and diffusion approximation for streaming direction discretization to solve the neutron transport equation. Griffin’s flexibility is evidenced through Griffin’s various applications to fast reactor, high-temperature reactor, pebble bed reactor, molten salt reactor, and microreactor designs. Griffin development follows the software quality assurance procedure for MOOSE-based applications and with software requirements consistent with the ASME NQA-1 standard. Griffin has been adopted into the reactor analysis system for the U.S. NRC and is in use at U.S. companies, universities and national laboratories.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Experimental investigation of advanced turbine control strategies and load-mitigation measures with a model-scale floating offshore wind turbine system

To advance the control co-design of offshore wind energy systems, the authors perform basin-scale experiments with a fully instrumented and actuated floating offshore wind turbine model. The model consists of a 1:70 scale performance-matched model of the International Energy Agency Wind Technology Collaboration Programme 15-MW reference turbine atop the VolturnUS-S semisubmersible platform. The Reference OpenSource Controller provides real-time blade pitch and generator torque control. Here we aim to develop an open data set for the validation of numerical models in predicting the influence of turbine control and load-mitigation measures. For this purpose, we measure the effects of advanced turbine control features, including peak shaving and floating feedback as well as hull-based control using tuned mass dampers on the system. Overall, the results demonstrate measurable and consistent influences from the control and load-mitigation measures, thus confirming the usefulness as a validation data set. Peak shaving attenuates the response to wind turbulence at near-rated wind speed. Floating feedback reduces the load and platform pitch motion associated with the negative damping induced by blade pitch control. The tuned mass dampers also attenuate the system response near the targeted frequencies under suitable conditions. We also identify detrimental side effects of each load-mitigation measure.

17 WIND ENERGY↗

DOE OSTI2024

Technical and economic evaluation of heat transfer fluids for a TES system integrated to an advanced nuclear reactor

The utilization of thermal energy storage technology has emerged as a potential enhancement for nuclear power plants. Although nuclear energy systems possess the ability to adjust their energy output, prolonged variations in production can impact the plant's overall efficiency and economic viability. However, the nuclear industry can capitalize on thermal energy storage to bolster the power plant's economics by leveraging ancillary services and market hedging strategies. Here this research aims to advance the development of thermal energy storage for advanced nuclear reactors by assessing the thermophysical properties of various heat transfer fluids, including FLiNaK, KCl-MgCl 2 , LiCl-KCl, (LiNaK) 2 CO 3 , NaF-ZrF 4 , and KF-ZrF 4 , at temperatures ranging from 500 to 900oC. The assessment is conducted based on thermophysical correlations established in previous studies and employs figures of merit as a metric to evaluate the heat transfer performance of different salt candidates. Additionally, a cost analysis is conducted to provide an idea of the potential cost associated with each salt candidate.

25 ENERGY STORAGE↗

DOE OSTI2023

Growth of carbon nanotube forests on flexible metal substrates: Advances, challenges, and applications

Owing to their remarkable properties and ordered architecture, vertically aligned CNT forests (VACNT) hold promises for countless applications ranging from energy storage to multifunctional fiber production. Over the last three decades, studies of carbon nanotube (CNT) growth have provided invaluable insight into the CNT nucleation and growth mechanism and enabled critical advancements in nanotechnology. Nevertheless, the ability to fully harness the exceptional VACNT properties in commercial devices is bottlenecked by compatibility of current CNT synthesis processes with large-scale manufacturing. Metal foil substrates present an economic alternative to traditional insulating substrates, like silicon or quartz, and are compatible with both roll-to-roll and automated, large-scale, batch processes. Compared to CNT growth on Si substrates, metal foils present additional challenges, such as increased roughness and reactivity at high temperatures. Nonetheless, many successful VACNT growths on conducting metal foils have been reported, which demonstrate the feasibility of metal substrate use for large-volume production of high-quality CNTs. Here, in this review, we discuss advancements in the field of VACNT growth on metal foil and specifically examine different choices for metal substrates, barrier and catalyst layers, deposition and growth methods, resulting CNT characteristics, and highlight a number of applications that benefit from CNT growth on metal substrates and transition to large-scale manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Improving anaerobic digestion of sewage sludge to renewable natural gas by the Advanced Pretreatment & Anaerobic Digestion technology (APAD): Pilot testing

Conventional anaerobic digestion (AD) of sewage sludge in wastewater treatment facilities suffers from low carbon conversion efficiency (CCE = 40%) and requires costly CO2 removal for injection of the produced CH4 into the natural gas grid. To address these limitations, we developed the Advanced Pretreatment and Anaerobic Digestion (APAD) process. This integrates Advanced Wet Oxidation & Steam Explosion (AWOEx) pretreatment of residual sludge after conventional AD, followed by biogas upgradation using a novel methanogenic strain, Methanothermobacter wolfeii BSEL, converting CO2 with H2 into CH4 or RNG (renewable natural gas). Pilot-scale results demonstrated that AWOEx pretreatment achieved a CCE of 62% for the residual sludge, 68% higher than the conventional AD process. The CH4 production was further increased by 79%. Subsequent biogas upgrading in a trickling bed reactor with H2 further enhanced total methane output by 100% and resulted in a final CO2 concentration of =3%. The integrated APAD process achieved a remarkable overall CCE of 83%, resulting in a 200% increase in RNG output when compared to conventional AD. Techno-economic analysis revealed that AWOEx pretreatment alone reduced sludge treatment costs from $494 to $253 per ton of dry solids. The complete APAD process incurred a higher cost of treatment of $530 per ton, driven by prices for bottled H2. The process did, however, show gains in energy recovery and decarbonization. Renewable H2, which may reduce in price in the near future, can positively improve the economics of biogas upgrading for the APAD process.

Life Cycle Assessment (LCA)↗

DOE OSTI2026

Advancing specialized biofoundries via automated adaptive laboratory evolution

Adaptive laboratory evolution (ALE) is a powerful strategy for improving microbial phenotypes by harnessing natural selection under defined environmental conditions. Through applying selection regimes, beneficial mutations accumulate, enabling the generation of strains with enhanced properties. However, conventional ALE is labor-intensive and difficult to scale, limiting reproducibility and broader discovery of evolutionary principles. Recent advances in robotics, automation, and computational infrastructure are transforming ALE into a scalable, data-rich experimental paradigm. Automated platforms enable standardized and complex protocols, real-time monitoring, and highly parallel evolution campaigns, improving consistency while generating longitudinal datasets that reveal convergent adaptive mechanisms. Here, we discuss the role of specialized biofoundries in advancing automated ALE and enabling large-scale evolutionary engineering. We review major automated ALE formats and outline key design principles for effective ALE biofoundries, highlighting how automated ALE can support autonomous experimentation and AI-guided strain engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Advances in building data management for building performance standards using the SEED platform

Reducing energy consumption and greenhouse gas emissions in the built environment is a critical step in achieving emission goals to mitigate climate change impacts. Local, federal, and international jurisdictions are deploying several methods to reduce energy and emissions such as voluntary and mandatory benchmarking and building performance standards, requiring building owners to reach energy and emission targets. Jurisdictions leveraging benchmarking and building performance standards require knowledge of the buildings covered; which is a large task due to staffing constraints, limited information on building characteristics and tax parcel data, and the need for advanced data management techniques to align datasets. This paper describes an open-source platform's recent advances to create consistent taxonomies, identify erroneous data, enable auditability, and track building performance. The paper concludes with two use cases on how the platform has been used by jurisdictions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Multi-stage heat release of multi-component fuels: Insights and implications for advanced engine operation

Multi-stage heat release (MSHR) is a unique phenomenon typically seen in lean/diluted and low- to intermediate-temperature combustion. Despite its relevance to advanced engine operation, the MSHR of multi-component fuels has barely been quantified. This study aims to characterize the MSHR of multi-component fuels in a rapid compression machine (RCM) at conditions representative of advanced combustion engines. New experimental data are first reported in the RCM at an equivalence ratio of 0.4, pressure of 60 bar and temperatures from 702 to 795 K for a research grade, multi-component gasoline surrogate, termed PACE-20. Here, experiments confirm the existence of MSHR for PACE-20 at all temperatures, and reveal the strong inhibiting effect of temperature on MSHR, where increasing temperature inhibits MSHR by suppressing first stage heat release and promoting second and third stage heat release. A detailed chemical kinetic model is also adopted to model the experiments, with good agreement observed. The response of MSHR characteristics to changes in different engine operating parameters (i.e., temperature, pressure, equivalence ratio and CO 2 dilution level) and fuel compositions (i.e., mole fraction of n-pentane, n-heptane, isooctane, cyclopentane, 1-hexene, ethanol and aromatics in PACE-20) is further evaluated via statistical analysis coupling quasi-random sampling, extensive computer experiments and high-dimensional model representation. The change in MSHR is characterized through 8 quantities of interest (QOIs), i.e., duration and extent of each heat release stage, and their standard deviations. The analysis highlights the dependence of the QOIs on the individual parameters as well as their interplays, with temperature exhibiting the strongest impact among all parameters. Furthermore, it is demonstrated that if MSHR is to be facilitated and combustion phasing is to be extended to enable engine operation at higher compression ratios, it is recommended to operate the engine at low intake temperatures, boosted intake pressures and high CO 2 dilutions (i.e., high EGR levels) with gasoline fuels containing more n-pentane and less aromatics.

42 ENGINEERING↗

DOE OSTI2026

Real-time plasma monitoring framework for advanced plasma control and ML-research in DIII-D

Real-time and adaptive plasma control is crucial for robust tokamak operation, requiring sensitivity and tolerance measurements of the plasma state. This paper presents the implementation of an integrated real-time plasma monitoring framework on the DIII-D tokamak to support advanced control approaches, including machine-learning (ML) methods. The system is built on the SHIELD framework, a high-performance modular architecture that provides a unified pipeline for integrating diverse diagnostics. The framework leverages high-bandwidth digitizers, fast numerical processing, and deterministic, low-latency interconnects to stream high-fidelity data from diagnostics such as electron cyclotron emission (ECE), beam emission spectroscopy (BES), CO interferometers, and a visible tangential divertor camera (TangTV). The system’s validity is demonstrated through direct comparisons of real-time and offline data. Furthermore, we present two key applications of the developed plasma monitoring system with ML-based plasma control strategies, including real-time divertor detachment and active Alfvén Eigenmode control. As a result, this work presents a robust and scalable approach for integrating high-frequency, multidimensional diagnostics into advanced control algorithms for future fusion devices.

AI/ML↗

DOE OSTI2025

DECOVALEX-2023: An international collaboration for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems

The DECOVALEX initiative is an international research collaboration (www.decovalex.org), initiated in 1992, for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems. DECOVALEX stands for “DEvelopment of COupled Models and VALidation against EXperiments”. The creation of this international initiative was motivated by the recognition that prediction of these coupled effects is an essential part of the performance and safety assessment of geologic disposal systems for radioactive waste and spent nuclear fuel. DECOVALEX emphasizes joint analysis and comparative modeling of the complex perturbations and coupled processes in geologic repositories and how these impact long-term performance predictions. The most recent phase of the DECOVALEX Project, here referred to as DECOVALEX-2023, started in early 2020 and ended in late 2023. More than fifty research teams associated with 17 international DECOVALEX partner organizations participated in the comparative evaluation of eight modeling tasks covering a wide range of spatial and temporal scales, geological formations, and coupled processes. This Virtual Special Issue on DECOVALEX-2023 provides an in-depth overview of these collaborative research efforts and how these have advanced the state-of-the-art of understanding and modeling coupled THMC processes. While primarily focused on radioactive waste, much of the work included here has wider application to many geoengineering topics.

Coupled processes↗