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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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DOE OSTI2022

International assessment of priority environmental issues for land-based and offshore wind energy development

Non-technical summary A substantial increase in wind energy deployment worldwide is required to help achieve international targets for decreasing global carbon emissions and limiting the impacts of climate change. In response to global concerns regarding the environmental effects of wind energy, the International Energy Agency Wind Technical Collaborative Program initiated Task 34 – Working Together to Resolve Environmental Effects of Wind Energy or WREN. As part of WREN, this study performed an international assessment with the global wind energy and environmental community to determine priority environmental issues over the next 5‒10 years and help support collaborative interactions among researchers, developers, regulators, and stakeholders. Technical summary A systematic assessment was performed using feedback from the international community to identify priority environmental issues for land-based and offshore wind energy development. Given the global nature of wind energy development, feedback was of interest from all countries where such development is underway or planned to help meet United Nations Intergovernmental Panel on Climate Change targets. The assessment prioritized environmental issues over the next 5–10 years associated with wind energy development and received a total of 294 responses from 28 countries. For land-based wind, the highest-ranked issues included turbine collision risk for volant species (birds and bats), cumulative effects on species and ecosystems, and indirect effects such as avoidance and displacement. For offshore wind, the highest-ranked issues included cumulative effects, turbine collision risk, underwater noise (e.g. marine mammals and fish), and displacement. Emerging considerations for these priorities include potential application to future technologies (e.g. larger turbines and floating turbines), new stressors and species in frontier regions, and cumulative effects for multiple projects at a regional scale. For both land-based and offshore wind, effectiveness of minimization measures (e.g. detection and deterrence technologies) and costs for monitoring, minimization, and mitigation were identified as overarching challenges. Social media summary Turbine collisions and cumulative effects among the international environmental priorities for wind energy development.

17 WIND ENERGY↗
DOE OSTI2022

The 5 Cs of Agrivoltaic Success Factors in the United States: Lessons from the InSPIRE Research Study

The concept of agrivoltaics (combining agriculture and solar photovoltaics technologies on the same land in novel configurations) has emerged as an approach to mitigate conflicts between solar and agricultural activities by providing mutual benefits and added values to each sector. The U.S. Department of Energy has supported agrivoltaics research since 2015 through its Innovative Solar Practices Integrated with Rural Economies and Ecosystems (InSPIRE) research project (National Renewable Energy Laboratory 2022). The InSPIRE project is the most comprehensive coordinated research effort on agrivoltaics in the United States and has examined opportunities and tradeoffs at over 25 sites across the country that span crop production, pollinator habitat, ecosystem services, animal husbandry, and d. Integrating research sites with active commercial agricultural operations can introduce unique challenges for conducting research. This synthesis aims to highlight the technical and non-technical insights from InSPIRE agrivoltaic field research sites from 2015-2021 to support i) appropriate deployment of agrivoltaic projects; ii) more successful research on agrivoltaics; and iii) more effective partnerships on agrivoltaic projects. The synthesized lessons discussed here are focused less on specific case study outcomes (i.e., the percent change in crop yield in an agrivoltaics configuration), and instead more on the elements that enable and facilitate agrivoltaics projects to be installed and operated along with research to be conducted at those sites. We find that there are some insights that are applicable across all types of agrivoltaic projects, while ecosystem service projects and crop production agrivoltaic projects can often have other unique considerations.

14 SOLAR ENERGY↗
DOE OSTI2022

Bioreactors: Design, Background, and Applications

A bioreactor is any reaction vessel designed to contain a biological reaction with the aims of either growing an organism, producing a biological product, bioremediating waste, or investigating the workings of a biological process within a controlled environment. Bioreactors compose a broad category of useful and ubiquitous technologies present throughout the bioeconomy, which are inherently “dualuse” technologies: holding both immense potential for both benefit and harm to society. Due to this duality, national security professionals must bridge technological understanding and policy acumen to understand how bioreactors are integrated into both, many peaceful applications throughout the global bioeconomy such as in developing or manufacturing life-saving pharmaceuticals, as well as potentially harmful and/or illicit uses, such as producing biowarfare agent. The following document gives a surface-level introduction into this broad category of technology. The first two sections of this document describe the historical origins, then typical contemporary uses of bioreactors as a critical component of the modern bioeconomy providing examples for each use case. The following two sections begin to give a technical description of bioreactor technology introducing the features commonly found in simple bioreactors and providing a non-exhaustive framework for categorizing bioreactors commonly found in wet-microbiology applications. The last two sections present two short discussions on the challenges facing modern bioreactor technology, firstly from a technical standpoint, and then from the non-technical standpoint with special attention to the dual-use dilemma and issue of responsible technological development inherent in most biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A case study in contrastive learning information combination: Application to technical forensics of additive manufacturing filament source identification

Combination of information from disparate data sources into a single decision is a core challenge in many fields, including the field of technical forensics. Technical forensics (TF) utilizes technical characterization of questioned samples to determine properties of that sample; these properties are then used to infer information of forensic interest, such as provenance, age, or attribution. TF is utilized in traditional forensic applications, such as the attribution of material fragments from an explosive, and in nuclear forensic applications, such as the attribution of actinides which have been interdicted out of regulatory control. The challenge of combining information from disparate sources, described alternately by many terms including “Data Fusion” and “Data Integration”, is exacerbated in the technical forensics domain due to at least two factors: the challenge of interpreting each information source singularly, and the relatively small data set sizes available. Extensive literature exists attempting to combine technical forensics information sources, both in manual and automated processes. These attempts are often bespoke to the specific information sources (such as the bi-, tri-, or quad-isotope chart (Moody, Grant, and Hutcheon 2005)), with some emerging examples of simple early- and late- fusion (, respectively). Simultaneous to the information combination efforts described in the previous paragraph, the field of natural language processing attempted (and largely succeeded) in combining information from multiple non-technical information sources. The ecosystem of “multi-modal” language models, which can take text and images as input, and generate text and images as output, became large and diverse by 2025 (Khan et al. 2025). In a generalized sense, many of these methods are trained by learning neural networks which can convert raw text or images into a vector of numbers describing the text or image, hereafter called “embeddings” and the neural networks performing the conversion are called “embedders”. By using a separate embedder for text and images, finding coincident text and images (such as images with their captions), and optimizing the parameters of the embedders such that the embeddings for the text and the image are similar, the field has found a bridge between text and images (Girdhar et al. 2023). It is the contention of the authors of this report that this insight is not limited to text and images but instead can be extended to any modality which can be found coincidently. The subject of the rest of this report is the application of this method to example multi-modal technical forensic data. Some details about the data used in this report are not appropriate for this report, and are included in a companion report (PNNL-38669).

36 MATERIALS SCIENCE↗