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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Scientific and stakeholder evidence-based assessment: Ecosystem response to floating solar photovoltaics and implications for sustainability

Floating solar photovoltaic (FPV) installations are increasing globally. However, their interaction with the hosting water body and implications for ecosystem function is poorly understood. Understanding potential impacts is critical as water bodies provide many ecosystem services on which humans rely and are integral for delivering the United Nations Sustainable Development Goals (SDGs). Here, we used scientific evidence from a systematic review and stakeholder expertise, captured through an international survey and a workshop, alongside existing understanding of the role of water bodies in delivering ecosystem services and the SDGs. We found 22 evidence outcomes that indicated potential physical, chemical and biological impacts of FPV on water bodies. Assessment by stakeholders from across sectors indicated that reduced water evaporation is the greatest opportunity, whilst changes to water chemistry, including nitrification and deoxygenation, are the greatest threat. Despite these findings, FPV operators reported no observed water quality or ecosystem impacts. However, only 15% of respondents had performed water quality analysis; visual inspection alone cannot ascertain all water quality impacts. Based on the integration of these findings, we determined that FPV could impact nine ecosystem services. Furthermore, established linkages between ecosystem services and SDGs indicate the potential for impacts on eight SDGs, although whether the impact is positive or negative is likely to depend on FPV design and water body type. Our results further the understanding of the effects of FPVs on host water bodies and may help to ensure the anticipated growth in FPVs minimises threats and maximises opportunities, safeguarding overall sustainability.

14 SOLAR ENERGY↗
DOE OSTI2025

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI

We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Utility Regulation Through Legislation: A Cautionary Tale for Legislators, Regulators, Stakeholders, and Utilities

State legislatures in the U.S. are becoming increasingly involved in utility regulation. While state utility legislation can expand regulator authority, for example in the case of renewable energy or energy efficiency standard administration, it can also reduce it. Given the challenging nature of for-profit monopoly regulation, state utility legislation often reduces regulator authority unintentionally. This editorial begins with an examination of the outcomes of state legislation related to baseload generation in the U.S. and the resulting impacts to customers, shareholders, and state economies. The record indicates that utility regulation through state legislation has caused significant economic harm, generally by encouraging for-profit utilities to make riskier investments. A description of more recent state legislation involving distribution grids is presented, along with evidence that increases in grid investment encouraged by state legislation has not improved the reliability of U.S. for-profit utilities. The authors claim that features common to state utility legislation, particularly the requirement that utilities provide investment plans to regulators in advance, practically eliminates cost disallowance risk. This, in turn, reduces regulator authority despite legislative provisions intended to protect consumers. The authors hypothesize that the practical elimination of cost disallowance risk, combined with information and expertise asymmetry among regulators and stakeholders, encourages for-profit monopoly utilities to make grid investments that are not cost effective. The editorial concludes that legislators should avoid utility regulation through legislation, thereby preserving regulator authority, whenever possible. The authors also present their fifth annual Customer Value Ranking of U.S. investor-owned electric utilities. The ranking represents a comparison of the benefits utilities deliver to customers (measured by reliability performance and customer satisfaction) to the costs customers pay (measured by O&M and capital spending per customer).

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗
DOE OSTI2021

The bioprocess TEA calculator: An online technoeconomic analysis tool to evaluate the commercial competitiveness of potential bioprocesses

Techno-economic analysis connects R&D, engineering, and business. By linking process parameters to financial metrics, it allows researchers to understand the factors controlling the potential success of their technologies. In particular, metabolic and bioprocess engineering, as disciplines, are aimed at engineering cells to synthesize products with an ultimate goal of commercial deployment. As a result it is critical to be able to understand the potential impact of strain engineering strategies and lab scale results on commercial potential. To date, while numerous techno-economic models have been developed for a wide variety of bioprocesses, they have either required process engineering expertise to adapt and/or use or do not directly connect financial outcomes to potential strain engineering results. Despite the clear value of techno-economic analysis, these challenges have made it inaccessible to many researchers. Furthermore, the tool, currently focused on aerobic fermentation processes, can be used to understand the impact of fermentation level metrics on the commercial potential of a bioprocess for the production of a wide variety of organic molecules. Using the calculator, I review the commercially relevant targets for an aerobic bioprocess for the production of diethyl malonate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Defending Weapons Inspections from the Effects of Disinformation

The intentional spread of disinformation is not a new challenge for the scientific world. We have seen it perpetuate the idea of a flat earth, convince communities that vaccines are more dangerous than helpful, and even suggest a connection between the “5G” communication infrastructure and COVID-19. Nor is disinformation a new phenomenon in the weapons inspection arena. Weapons inspectors themselves are often forced to sift through alternative narratives of events and inconsistent reporting, and they regularly see their credibility and conclusions questioned in the face of government politics or public biases. But certain recent disinformation campaigns have become so overwhelmingly comprehensive and effective that they constitute a new kind of threat. By preventing accountability for clear violations of international law, these campaigns have created a challenge to the survival of arms control treaties themselves. If weapons inspectors cannot regain the trust of the international community in the face of this challenge, it will be increasingly difficult to ensure compliance with arms control and disarmament treaties going forward. In this essay, I will briefly discuss one of the most comprehensive disinformation efforts of the past decade: the disinformation campaign used to prevent accountability for Syria's repeated use of chemical weapons. After this discussion, I will propose one possible approach to help protect the credibility of disarmament experts and weapons inspectors in the face of pervasive disinformation. This approach will require a concerted effort to connect and support compliance experts and to understand and explain their expertise across cultural, political, national, economic, and religious divides.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Multidisciplinary concurrent optimization framework for multi-phase building design process

Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. Here this paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. Here, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s weakest bond are used in simple physical models of site-selectivity and reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Bioinspired Supercharging of Photoredox Catalysis for Applications in Energy and Chemical Manufacturing

For more than a decade, photoredox catalysis has been demonstrating that when photoactive catalysts are irradiated with visible light, reactions occur under milder, cheaper, and environmentally friendlier conditions. Furthermore, this methodology allows for the activation of abundant chemicals into valuable products through novel mechanisms that are otherwise inaccessible. The photoredox approach, however, has been primarily used for pharmaceutical applications, where its implementation has been highly effective, but typically with a more rudimentary understanding of the mechanisms involved in these transformations. From a global perspective, the manufacture of everyday chemicals by the chemical industry as a whole currently accounts for 10% of total global energy consumption, and generates 7% of the world's greenhouse gases annually. In this context, the Bio-Inspired Light-Escalated Chemistry (BioLEC) Energy Frontier Research Center (EFRC) was founded to supercharge the photoredox approach for applications in chemical manufacturing aimed at reducing its energy consumption and emissions burden, by using bio-inspired schemes to harvest multiple electrons to drive endothermically uphill chemical reactions. The Center comprises a diverse group of researchers with expertise that includes synthetic chemistry, biophysics, physical chemistry, and engineering. Here the team works together to gain a deeper understanding of the mechanistic details of photoredox reactions while amplifying the applications of these light-driven methodologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Electrical Shock During Phone Assisted Troubleshooting of Laboratory Equipment

We report researchers have the potential to be exposed to a wide variety of hazards inherent to the equipment they use and maintain. When equipment does not function as expected, researchers sometimes reach out to their vendors for assistance. Early diagnostic or troubleshooting interactions between researcher and vendor are often conducted over the telephone and can lead to researchers performing work outside of their area of expertise and exposure to unknown hazards. This type of interaction significantly contributed to an incident where during diagnostic activities a researcher accidentally contacted, and discharged, a capacitor in an X-ray diffraction instrument. While this incident did not produce a serious injury, if the capacitor discharge path had occurred hand-to-hand across the heart, a serious injury may have been possible.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. Herein, we cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗
DOE OSTI2025

Postfire Biogeochemical Processes: Implications to Source Water Quality in Fire-Influenced Watersheds

Forested watersheds are instrumental in providing purified and reliable water to millions of people worldwide. The changing climate has increased the frequency and severity of global fire events. Forested watersheds and their ecosystem functions are greatly disrupted during fire activity. Postfire concerns in forested watersheds include unpredictable and potentially simultaneous alterations in source water quality and hydro-biogeochemical processes. Here, the degree of fire severity can complexly modify water quality through the production of fire-transformed constituents on the burned forest floor (i.e., nutrients, metal(loid)s, dissolved organic matter, and the formation of disinfection byproducts). Correspondingly, fire severity and postfire rainfall patterns can refine hydro-biogeochemical processes that influence the transport of the fire-transformed constituents (i.e., vegetation function, soil structure, hydrological pathways, and microbial communities). Postfire alterations to water quality and hydro-biogeochemical processes introduce further complexity with varying temporal influence, which ranges from months to decades. As postfire water quality and watershed response research progresses, it is essential to homogenize interdisciplinary expertise to bridge knowledge gaps between fields ranging from forest ecology, hydrology, microbiology, and geochemistry. A multidisciplinary approach in wildfire research will facilitate a comprehensive perception of the diverse water quality risks associated with fire activity and mitigate fire concerns on a global level.

Disinfection Byproducts↗
DOE OSTI2026

A Proxy Method to Bridge LCA Data Gaps Using Automated Material Classification and Probabilistic Under-Specification

Life cycle assessments (LCAs) are essential for understanding the environmental impacts of material production. However, gaps in life cycle inventory (LCI) data for material and chemical inputs present a key challenge for LCA practitioners, especially in the early design stages. Strategies for filling in these gaps require additional time and expertise, which can hinder the LCA’s completion. This study combined automatic material classification and probabilistic under-specification to create a time-efficient method to fill material LCI data gaps. To illustrate the proposed method, proxy environmental impact distributions were generated using publicly available material LCI data classified into the ChemOnt chemical taxonomy using the open-source chemical classification software ClassyFire. Input materials with data gaps were then classified into the same taxonomy, where proxy environmental impact values could be selected from the available distributions to quickly fill in any data gaps. Although these methods were applied to classify material production processes available in the Federal LCA Commons and Ecoinvent databases, they can be applied to any LCA database. This study shows that classifying materials by their chemical structure produces taxonomies with increased granularity relative to industrial classification, improving the ability of under-specified proxy data to be used for differentiating the environmental impacts of competing designs.

biological databases↗
DOE OSTI2025

A Comprehensive Machine Learning Model for Metal–Ligand Binding Prediction: Applications in Chemistry and Biology

A machine-learning (ML) model that predicts metal–ligand binding constants was developed using the open-source Chemprop software. The model was trained on over 30,000 experimental log K 1 values, which include both protonation and metal–ligand stability constants, comprising over 3500 ligands and 10 2 metal ions from 73 total elements, thus generalizing beyond existing limited approaches, which focus only on specific metals or ligand families. The best-performing model included a combination of SMILES-based molecular representations along with descriptors for the metal ion and experimental conditions. It had an external test R 2 value of 0.942, and MAE value of 0.834. A “SMILES-only” simpler version also produced accurate predictions and preserved the binding trends, serving as a quick and easily accessible alternative for users without computational expertise. The SMILES-only model performed comparably to density functional theory (DFT) calculations but utilized a fraction of the computational resources. The model was successfully applied across diverse domains, including bioinorganic chemistry, heavy metal remediation, and sensor development and demonstrated its effectiveness as a rapid and reliable screening tool for both academic and industrial uses.

Ligands↗
DOE OSTI2025

MODE: A Web Application for Interactive Visualization and Exploration of Omics Data

Studies generating transcriptomics, proteomics, lipidomics, and metabolomics (colloquially referred to as “omics”) data allow researchers to find biomarkers or molecular targets, or understand complex biological structures and functions by identifying changes in biomolecule abundance and expression between experimental conditions. Omics data is multi-dimensional and oftentimes summarization techniques such as principal component analysis (PCA) are used to identify high-level patterns in data. Though useful, these summaries don’t allow exploration of detailed patterns in omics data that may have biological relevance. The use of interactive HTML displays with plots allows researchers to interact with omics data at a detailed level, but building these displays requires significant coding expertise. To overcome this barrier, the software MODE was built to empower users to build their own interactive HTML displays to support scientific discovery. These displays are easily shareable, do not depend on a specific operating system, and allow users to effortlessly sort and filter plots by categorical or numerical variables. MODE allows users to build and share these displays with several options for plot design and meta selection. In conclusion, the MODE web application and its capabilities are presented and then demonstrated on lipidomics data from a leaf wounding study.

lipidomics↗
DOE OSTI2022

How to Get the Best Gas Separation Membranes from State-of-the-Art Glassy Polymers

Herein we focus on fundamental polymer science factors in membrane-based gas separation to open another chapter on this important topic. Realistically thinking about nano and angstrom scale matrix attributes that are translatable into higher performance thin glassy polymer asymmetric membrane layers in practical large-scale devices is crucial. Such thinking should be guided by expertise in both polymer and membrane communities to advance the state of the art. Polymer structures, membrane morphologies and effects of operating conditions are discussed here, using specific examples to illustrate key issues. Rubbery polymers are not the focus of this discussion, since they lack diffusive discrimination for size-similar penetrants, which glassy polymers provide and make them the focus here. Four scalable subtopics guide necessary thinking: (i) plasticization; (ii) anti-plasticization†2; (iii) dual-mode transport involving saturation of unrelaxed free volume, and (iv) non-uniform free volume and stress profiles in thin skin glassy polymer asymmetric or composite membranes. Furthermore, using these subtopics in the context of macromolecular science we discuss issues needed to expand the state-of-the-art in gas separation membranes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The Magnetic Genome of Two-Dimensional van der Waals Materials

Magnetism in two-dimensional (2D) van der Waals (vdW) materials has recently emerged as one of the most promising areas in condensed matter research, with many exciting emerging properties and significant potential for applications ranging from topological magnonics to low-power spintronics, quantum computing, and optical communications. In the brief time after their discovery, 2D magnets have blossomed into a rich area for investigation, where fundamental concepts in magnetism are challenged by the behavior of spins that can develop at the single layer limit. However, much effort is still needed in multiple fronts before 2D magnets can be routinely used for practical implementations. In this comprehensive review, prominent authors with expertise in complementary fields of 2D magnetism (i.e., synthesis, device engineering, magneto-optics, imaging, transport, mechanics, spin excitations, and theory and simulations) have joined together to provide a genome of current knowledge and a guideline for future developments in 2D magnetic materials research.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Robust Atomic-Resolution Imaging of Lithium in Battery Materials by Center-of-Mass Scanning Transmission Electron Microscopy

The performance of energy storage materials is often governed by their structure at the atomic scale. Conventional electron microscopy can provide detailed information about materials at these length scales, but direct imaging of light elements such as lithium presents a challenge. While several recent techniques allow lithium columns to be distinguished, these typically either involve complex contrast mechanisms that make image interpretation difficult or require significant expertise to perform. In this work, we demonstrate how center-of-mass scanning transmission electron microscopy (CoM-STEM) provides an enhanced ability for simultaneous imaging of lithium and heavier element columns in lithium ion conductors. Through a combination of experiments and multislice electron scattering calculations, we show that CoM-STEM is straightforward to perform and produces directly interpretable contrast for thin samples, while being more robust to variations in experimental parameters than previously demonstrated techniques. As a result, CoM-STEM is positioned to become a reliable and facile method for directly probing all elements within energy storage materials at the atomic scale.

25 ENERGY STORAGE↗