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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Algal Biomass Production via Open Pond Algae Farm Cultivation: 2023 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office (BETO). It allows for the impact of research progress (both directly achieved in-house at the National Renewable Energy Laboratory [NREL] and furnished by partner organizations) to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked toward out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae SOT benchmarking efforts historically focused both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under the Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) consortium efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted primarily under NREL research and development projects. This report focuses on front-end algal biomass production, documenting the pertinent algal biomass cultivation parameters that were input to the NREL open pond algae farm model. Through partnerships under DISCOVR, collaborators at ASU furnished details on cultivation performance metrics including biomass productivity and harvest densities for recent growth trials done at the AzCATI site. The resulting biomass productivity was calculated at 16.7 g/m 2 /day (ash-free dry weight [AFDW], annual average) for seasonal cultivation of Picochlorum celeri TG2 and Monoraphidium minutum 26B-AM biomass strains at the ASU site. Picochlorum celeri achieved the best productivity from April to September, with Monoraphidium minutum 26B-AM being used between October and March. Tetraselmis striata LANL1001, usually part of the strain rotation in previous cultivation SOTs, was supplanted by Monoraphidium minutum 26B-AM in this year's outdoor cultivation trials. Finally, building from an industry case study presented in the 2022 SOT report, in the Appendix of this report we provide an update on further improved data furnished by an industry collaborator and resultant impacts on economics reflecting several seasonal scenarios. This case study provides a supplementary datapoint on work being performed elsewhere with a more dedicated focus on improved compositional quality, producing biomass enriched in lipids as may be more optimal for conversion upgrading to fuels and products.

09 BIOMASS FUELS↗
DOE OSTI2024

Atom Probe Tomography Investigation of Clustering in Model P 2 O 5 -Doped Borosilicate Glasses for Nuclear Waste Vitrification

Atom probe tomography (APT) has been utilized to investigate the microstructure of two model borosilicate glasses designed to understand the solubility limits of phosphorous pentoxide (P 2 O 5 ). This component is found in certain high-level radioactive defence wastes destined for vitrification, where phase separation can potentially lead to a number of issues relating to the processing of the glass and its long-term chemical and structural stability. The development of suitable focused ion beam (FIB)-preparation routes and APT analysis conditions were initially determined for the model glasses, before examining their detailed microstructures. In a 3.0 mol% P 2 O 5 -doped glass, both visual inspection and sensitive statistical analysis of the APT data show homogeneous microstructures, while raising the content to 4.0 mol% initiates the formation of phosphorus-enriched nanoscale precipitates. This study confirms the expected inhomogeneities and phase separation of these glasses and offers routes to characterizing these at near-atomic scale resolution using APT.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Algal Biomass Conversion to Fuels via Combined Algae Processing (CAP): 2020 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office. It allows for the impact of research progress to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked towards out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae state of technology benchmarking efforts focus both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under the Development of Integrated Screening, Cultivar Optimization, and Verification Research consortium (DISCOVR) efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted under NREL research and development projects. This report focuses on back-end conversion of algal biomass through the CAP pathway, highlighting the 2020 updates to minimum fuel selling price (MFSP). Notably, this analysis marks the first time that polyurethane (PU) is included as a value-added coproduct in the SOT case. Relative to prior 2019 benchmarks, the 2020 CAP SOT achieved further MFSP reductions of over $4/gallon gasoline equivalent, reflective of (a) the inclusion of PU co-production, (b) reduced SOT biomass production costs, and (c) higher pretreatment sugar yield data.

09 BIOMASS FUELS↗
DOE OSTI2021

Algal Biomass Production via Open Pond Algae Farm Cultivation: 2020 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office. It allows for the impact of research progress to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked towards out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae state of technology benchmarking efforts focus both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) consortium efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted under NREL research and development projects. This report focuses on front-end algal biomass production, documenting the pertinent algal biomass cultivation parameters that were input to the NREL open pond algae farm model based on the latest DISCOVR cultivation performance data. Relative to prior 2019 benchmarks, the 2020 algae farm SOT achieved further reductions in modeled minimum biomass selling price of approximately $70/ton (roughly 10%), primarily attributable to a 16% demonstrated improvement in annual average cultivation productivity rates.

09 BIOMASS FUELS↗
DOE OSTI2022

Algal Biomass Production via Open Pond Algae Farm Cultivation: 2021 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office. It allows for the impact of research progress to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked towards out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae state of technology benchmarking efforts focus both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) consortium efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted under NREL research and development projects. This report focuses on front-end algal biomass production, documenting the pertinent algal biomass cultivation parameters that were input to the NREL open pond algae farm model based on the latest DISCOVR cultivation performance data. Relative to the fiscal year (FY) 2020 SOT at $683/ton or $603/ton for ASU and FA evaporation scenarios, respectively (unlined pond basis), the FY 2021 SOT represents a slight increase in MBSP of 1%-2%. This is primarily attributed to a slight 4% reduction in annual cultivation productivity achieved at the AzCATI site (supported by the efforts under the DISCOVR consortium noted above) observed during FY 2021 cultivation campaigns.

09 BIOMASS FUELS↗
DOE OSTI2022

Algal Biomass Conversion to Fuels via Combined Algae Processing (CAP) (2021 State of Technology and Future Research)

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office. It allows for the impact of research progress to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked towards out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae state of technology benchmarking efforts focus both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under the Development of Integrated Screening, Cultivar Optimization, and Verification Research consortium (DISCOVR) efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted under NREL research and development projects. This report focuses on back-end conversion of algal biomass through the CAP pathway, highlighting the 2021 updates to minimum fuel selling price (MFSP). This update maintains an important recent inclusion of polyurethane (PU) previously incorporated in the 2020 SOT as a value-added coproduct. Relative to the 2020 SOT case, this indicates a minimal increase of $0.10-$0.14/GGE (roughly 2%) for both the acids and BDO pathways, attributed to minimal increases in upstream algal biomass costs from slightly lower demonstrated cultivation productivities in the 2021 SOT.

09 BIOMASS FUELS↗
DOE OSTI2022

In Situ Growth of Crystalline and Polymer-Incorporated Amorphous ZIFs in Polybenzimidazole Achieving Hierarchical Nanostructures for Carbon Capture

We report mixed matrix materials (MMMs) hold great potential for membrane gas separations by merging nanofillers with unique nanostructures and polymers with excellent processability. In situ growth of the nanofillers is adapted to mitigate interfacial incompatibility to avoid the selectivity loss. Surprisingly, functional polymers have not been exploited to co-grow the nanofillers for membrane applications. Herein, in situ synergistic growth of crystalline zeolite imidazole framework-8 (ZIF-8) in polybenzimidazole (PBI), creating highly porous structures with high gas permeability, is demonstrated. More importantly, PBI contains benzimidazole groups (similar to the precursor for ZIF-8, i.e., 2-methylimidazole) and induces the formation of amorphous ZIFs, enhancing interfacial compatibility and creating highly size-discriminating bottlenecks. For instance, the formation of 15 mass% ZIF-8 in PBI improves H- 2 permeability and H- 2 /CO 2 selectivity by approximate to 100% at 35 degrees C, breaking the permeability/selectivity tradeoff. This work unveils a new platform of MMMs comprising functional polymer-incorporated amorphous ZIFs with hierarchical nanostructures for various applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Uncertainty quantification of mass models using ensemble Bayesian model averaging

Developments in the description of the masses of atomic nuclei have led to various nuclear mass models that provide predictions for masses across the whole chart of nuclides. These mass models play an important role in understanding the synthesis of heavy elements in the rapid neutron capture ( r ) process. However, it is still a challenging task to estimate the size of uncertainty associated with the predictions of each mass model. In this work, a method called ensemble Bayesian model averaging (EBMA) is introduced to quantify the uncertainty of one-neutron separation energies (S 1 n ) which are directly relevant in the calculations of r -process observables. Here, this Bayesian method provides a natural way to perform model averaging, selection, and uncertainty quantification, by combining the mass models as a mixture of normal distributions whose parameters are optimized against the experimental data, employing the Markov chain Monte Carlo method using the no-u-turn sampler. The EBMA model optimized with all the experimental S 1 n from the AME2003 nuclides are shown to provide reliable uncertainty estimates when tested with the new data in the AME2020.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

A versatile and low-cost chip-to-world interface: enabling ICP-MS characterization of isotachophoretically separated lanthanides on a microfluidic device

Microfluidics offer novel and state-of-the-art pathways to process materials. Microfluidic systems drastically reduce timeframes and costs associated with traditional lab-scale efforts in the area of analytical sample preparations. The challenge arises in effectively connecting microfluidics to off-chip analysis tools to accurately characterize samples after treatment on-chip. Here, a fused silica microfluidic device (MFD) is connected to a fused-silica capillary in order to create a world-to-chip connection between the MFD and an inductively coupled plasma mass spectrometer (ICP-MS). Specifically, this is demonstrated by utilizing isotachophoresis (ITP) on a microfluidic chip to separate samples containing up to 14 lanthanides into elementally pure bands. The separated sample is successfully transferred across a 7 nL dead space at the microchip-capillary junction, such that separation resolution is maintained and even increased through the interface and into the ICP-MS, where the elemental composition of the separated sample is analysed. Lanthanide samples of varying composition are detected using ICP-MS, demonstrating this versatile and cost-effective approach that does not degrade the separation quality achieved on the MFD. This simple and inexpensive connection enables fast, low-cost sample preparation immediately prior to injection into an ICP-MS or other analytical instruments.

Lackey, Hope E.↗
DOE OSTI2024

Sulfonated polybenzimidazole membrane with graphene oxide additive for 2,3-butanediol/water separation: A molecular simulation

Membrane separation for 2,3-butanediol (2,3-BDO) recovery from fermentation broth is highly valued for sustainable and renewable processes, but it requires efficient membrane materials. Here, this work evaluates the sulfonated polybenzimidazole (sPBI) and its graphene oxide (GO) doped composite membrane for separating 2,3-BDO and water via atomistic simulations. Density functional theory calculations are applied to identify various forms of sPBI structures and quantify their binding interactions with 2,3-BDO and water. Classical molecular dynamic simulations are used to evaluate the structural changes, diffusivity, and selectivity of 2,3-BDO and water in different sPBI models, GO surfaces, and GO-doped sPBI composite models. Our results suggest that sPBI slightly increases the crystallinity of the membrane structures, enhances the adsorption strength for both 2,3-BDO and water, and improves the water/2,3-BDO selectivity by 2–3 times. The GO surfaces display a maximum selectivity at a surface coverage of 0.1–0.15 for both hydroxyl and epoxy surface groups. The addition of GO flakes to sPBI creates new interaction sites for 2,3-BDO and water at the interface of sPBI and GO, and the water/2,3-BDO selectivity of GO-doped sPBI models is further increased up to 3 times. This work illustrates how the integrated addition of sPBI and GO flakes offers a promising approach to selective separation of 2,3-BDO and water, providing theoretical guidance for polybenzimidazole-based membranes in the potential application of 2,3-BDO recovery.

2,3-butanediol↗
DOE OSTI2021

Controlling the Morphology of Dynamic Thia-Michael Networks to Target Pressure-Sensitive and Hot Melt Adhesives

A series of multistage (pressure-sensitive/hot melt) adhesives utilizing dynamic thia-Michael bonding motifs are reported. The benzalcyanoacetate Michael acceptors used in this work undergo bond exchange under ambient conditions without external catalysis, facilitating pressure-sensitive adhesion. A key feature of this system is the dynamic reaction-induced phase separation that lends reinforcement to the otherwise weakly bonded materials, enabling weak, repeatable pressure-sensitive adhesion under ambient conditions and strong adhesion when processed as a hot melt adhesive. Furthermore, by using different pairs of benzalcyanoacetate cross-linking units, the phase separation characteristics of the adhesives can be directly manipulated, allowing for a tailored adhesive response.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Development of metallic nuclear material purification process via simultaneous chlorination and volatilization

Here, a purification process involving simultaneous chlorination and volatilization is reported using aluminum, iron, gallium, and uranium separation from cerium metal as the demonstration system. Anhydrous Cl 2 gas was reacted with the metal after it had been converted to small particles of hydride at temperatures ranging from 523 to 973 K. Each of the impurities should form chlorides with relatively high saturated vapor pressures. The objective of the process was to volatilize these chlorides after first achieving near-complete conversion of the metals to hydride and then chlorides. Complete conversion of hydrides to chlorides could not be achieved using relatively low-grade (99.5%) Cl 2 . High decontamination factors for all of the metal impurities required near complete conversion to chloride. It was concluded that oxygen contamination of the feed gas stream likely limits the conversion to chlorides by forming oxides instead, which then limits the volatilization of contaminants. Uranium was particularly challenging to remove in experiments suspected of having high oxygen contamination in the feed gas stream. After switching to ultra-high purity (99.999%) Cl 2 , complete conversion to chlorides within the measurement error was possible for the entire temperature range studied. Decontamination factors for aluminum, iron, gallium, and uranium were found to have average values of 1.9, 9.8, 14, and 5, respectively, at 973 K. While the process was most extensively studied using a once-through gas flow, recycling unreacted Cl 2 gas succeeded at minimizing waste while still maintaining complete conversion and similar decontamination factors.

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

Utah FORGE: InSAR Data Best Pairs

This submission provides Interferometric Synthetic Aperture Radar (InSAR) data covering the Utah FORGE site via the TerraSAR-X and TanDEM-X satellite missions operated by the German Space Agency (DLR). Data was collected between 2019/01/01 and 2023/06/30. Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software. The best 112 pairs were selected based on having short orbital separations (perpendicular baseline less than 5 meters in absolute value).

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Generation of Isocurvature from Curvature Inhomogeneities on Super-Horizon Scales

Here it is shown 1) how isocurvature inhomogeneities correlated on causally disconnected (super-horizon) scales are generated from curvature inhomogeneities which are known to be correlated on these scales 2) that super-horizon isocurvature generation is nearly inevitable for non-equilibrium chemical processes 3) that the amplitude of the compositional isocurvature correlations a) can be large for production of rare objects, b) falls off rapidly with separation c) falls off at scales below the horizon when these modes are generated. These two fall-offs results in an "isocurvature bump" in the power spectrum. Isocurvature generation is illustrated by the process of dark matter freeze-in, computed here with both separate universe modelling and linear perturbation theory. For freeze-in the most prominent isocurvature modes are inhomogeneities in the ratio of dark matter to standard model matter. Much smaller inhomogeneities in the ratio of baryons to standard model entropy are also produced. Previous constraints on freeze-in from Ly-$\alpha$ clouds limit the bump enhancement to $\lesssim10\%$ on comoving scales $\lesssim1\,$Mpc. Current observations are not sensitive to the isocurvature modes generated in viable freeze-in models. Results are obtained using a somewhat novel framework to describe cosmological inhomogeneities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
DOE OSTI2022

Comprehensive Study of the Potential of Extracting and Processing Critical Minerals from Coal-Based Resources - Phase I

The Phase I report prepared for the Department of Energy addresses U.S. Executive Order 13817 titled A Federal Strategy to Ensure Secure and Reliable Supplies of Critical Minerals, issued on December 20, 2017, that lists 35 critical minerals that are vulnerable to supply disruption. A comprehensive review of each of the minerals was conducted to determine the criticality based primarily on extractability from coal-based resources. Several other factors were also considered such as gaps in supply and demand, use in current technology, and the existence of viable substitutes. It was determined that the critical minerals that show highest potential for extraction from coal-based resources are lithium, rare earth elements (REEs), cobalt, and manganese. All four of the critical minerals listed serve an important role in the technology industry, have few substitutes, and have a heavy import reliance. Most notably are lithium, which is widely used in the electric vehicle industry, and the REEs which can be found in virtually all electronic devices. The research of critical mineral extraction from coal-based resources was completed using a combination of literature review from public sources as well as cooperation from coal mines and power plants across the United States. Samples collected from six different geographical locations across the U.S. were subjected to sample preparation (i.e., pH measurement, moisture content, particle size analysis) and characterization studies using Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS) and Scanning Electron Microscopy, Energy Dispersive X-Ray Spectroscopy (SEM-EDX) instruments. 27 samples of coal waste materials such as refuse, sludge, and fly ash were tested to characterize the rare earth element concentration by total rare earth elements (TREEs), heavy rare earth elements (HREEs), and light rare earth elements (LREEs). Of the 27 samples tested, 22 contained a TREE concentration higher than the threshold of 300 ppm, which is considered a viable feedstock material. 3 samples contained less than 300 ppm of TREEs; however, they were within 20 ppm of the threshold, and could potentially be considered viable sources in the future pending the advancement of more efficient extraction technologies. 2 of the 27 samples had significantly low TREE concentrations, which does not imply any potential for being a source for REEs. For the minerals identified as most critical in the literature review, a conceptual process flow diagram (PFD) was developed for their extraction from different coal-based feedstocks. The process targets selective recovery of one commodity (i.e., rare earths, lithium, cobalt, and manganese) via several hydrometallurgical separation methods. By identifying potentially extractable coal-based critical mineral resources, a study of the current and future market environments for each critical mineral, and a review of current processing methodologies for critical mineral extraction from coal-based resources, the foundation has been laid to further characterize and explore new resources and extraction techniques. As reliance on technologies in industries such as the production of electronic devices, batteries, and alloys containing critical minerals utilizing critical minerals continues to increase, a sound understanding of our nation’s dependence on and even the global criticality of certain critical minerals, will serve as a catalyst for innovation in virtually all fields of science.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Control of iodine and tellurium for use in domestic molybdenum-99 production efforts

Three initial proof-of-concept experiments were performed to test the removal of I and Te from solutions containing Mo for potential abatement during chemical processing to purify 99 Mo. All three proof-of-concept experiments produced results indicating that further optimization could provide methods for I and/or Te abatement from multiple matrices. Decamp and Happel (2013) describe the use of CL resin from Eichrom Technologies loaded with Ag + combined with Amberlite XAD-4 in the chromatographic separation of irradiated fission products in 1 M HNO 3 to separate iodine species where iodide and iodate react with Ag + and I2 adheres to the Amberlite resin. The method worked well under processing conditions in a medical isotope facility although many separation methods use higher concentrations of HNO 3 which could adversely impact the Ag + loading. The experiment performed here used a roughly 2 mL column of CL resin loaded with Ag + and Amberlite XAD-4 to separate µg quantities of I, Te, and Mo in 3 M HNO 3 .

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗