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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Spectra-to-exposure conversion using polynomial response models for gamma-ray field characterization

Accurate measurement of exposure rate from gamma-ray spectral data remains a critical challenge during radiological emergency response operations. Conventional methods rely on pre-defined static conversion factors derived from fixed geometries and isotopic compositions, which often fail to capture real-world environmental variability. This study presents a generalized approach as a "next-step" for converting gamma-ray spectral data into exposure rate using polynomial response models. The method introduces a flexible weighting scheme based on the in-situ detector response to distributed sources, enabling a pathway towards improved correspondence between measured spectra and "ground-truth" exposure rates. Experimental data from sodium iodide NaI(Tl) detectors were used to validate the approach as, at least equivalent to the current count-to-exposure method employed in emergency response CONOPS. Results show that the polynomial weighting model is sufficiently equal to the count-to-exposure method and may help improve accuracy given its adaptability to real-world conditions.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Addressing Inherent Challenges to Chemical Relithiation of Cycled End‐of‐Life Cathode Materials

Recycling end‐of‐life (EOL) lithium‐ion batteries (LIBs) is important to retain valuable resources from critical materials present in EOL battery waste. Direct recycling methods offer an opportunity to recover intact valuable cathode materials with minimal re‐processing. An important step of the direct recycling process is relithiation which is used to restore lithium content to EOL cathode materials. However, little has been done to study how preprocessing steps such as washing or binder removal may affect relithiation methods in the direct recycling process. Here, the evolution of fluorine byproducts left over from preprocessing steps during a low‐temperature chemical redox mediator relithiation process is tracked. A facile washing step is presented as a solution for mediating adverse effects of surface contamination on the chemical relithiation performance. The structure, lithium content, and electrochemical performance of relithiated EOL NMC 622 material that underwent a pre‐relithiation washing step to remove fluorine byproducts is shown to match that of pristine NMC 622. In this work, it is showed that redox mediator relithiation as a part of a direct recycling process is a promising low energy method that can be applied to EOL material with inherent surface impurities if the proper pre‐relithiation processing steps are implemented.

25 ENERGY STORAGE↗
DOE OSTI2024

Semi-automatic image annotation using 3D LiDAR projections and depth camera data

Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗