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All sources NASA NTRS DOE OSTI

DOE OSTI2018

Exploring parasite heterogeneity using single-cell RNA-seq reveals a gene signature among sexual stage Plasmodium falciparum parasites

The malaria parasite has a complex lifecycle, including several events of differentiation and stage progression, while actively evading immunity in both its mosquito and human hosts. Important parasite gene expression and regulation during these events remain hidden in rare populations of cells. Here, we combine a capillary-based platform for cell isolation with single-cell RNA-sequencing to transcriptionally profile 165 single infected red blood cells (iRBCs) during the intra-erythrocytic developmental cycle (IDC). Unbiased analyses of single-cell data grouped the cells into eight transcriptional states during IDC. Interestingly, we uncovered a gene signature from the single iRBC analyses that can successfully discriminate between developing asexual and sexual stage parasites at cellular resolution, and we verify five, previously undefined, gametocyte stage specific genes. Moreover, we show the capacity of detecting expressed genes from the variable gene families in single parasites, despite the sparse nature of data. In total, the single parasite transcriptomics holds promise for molecular dissection of rare parasite phenotypes throughout the malaria lifecycle.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Data Integration Aggregated Model and Ontology for Nuclear Deployment (DIAMOND): Preliminary Model and Ontology

The data of a nuclear utility or plant are stored in various isolated forms in different systems. These data have different structures and tools and are, therefore, used independently. The integration of data sources is often performed manually and on an as-needed basis to achieve a targeted cost saving. A holistic approach to the integration of the data would enable creating a data warehouse for a single nuclear power plant (NPP) or multiple plants. A data warehouse results in direct and indirect cost saving because it automates the manual search for data, enables sharing and comparison of data from various tools of a single or multiple plants, enable digital transformation of data, increase frequencies of needed data that are sparse in nature (such as failures signatures), reduces the need for training on various tools for plant staff, enables a holistic staff perception of plant activities, reveals cost-saving opportunities, and improves the visual perception of all parts a plant necessary for operations and maintenance. One of the main challenges to deploy a data warehouse is associated with the integration of data sources into a consistent data model using a standard ontology that is developed by and for the nuclear power industry. This model is non-existent and is a necessity to enable the nuclear data warehouse role. A data model contains objects that make up a given domain along with their attributes and relationships. It provides domain standardization and maintainability, setting forth the objects, attributes, and relationships within that domain and acting as the guide by which the various applications within that domain will structure their data and interchange. An ontology is defined as “a set of concepts and categories in a subject area or domain that shows their properties and the relations between them”. Ontologies consist of a taxonomy or directed rooted tree. A survey of the models and ontologies available to support a data integration aggregated model and ontology for nuclear deployment (DIAMOND) was performed and different standards that address specific aspects of the needed model and ontology were leveraged and integrated in this effort. In specific, the Basic Formal Ontology (BFO) and Lifecycle Modeling Language (LML) were leveraged for the base ontology. The ISO 4224 and 15926 were leveraged in the development of the overlapping nuclear and fossil data aspects. The electric power research institute (EPRI) power distribution common information model (CIM) was also reviewed and the experience gained was leveraged in the power generation scope of DIAMOND. The initial aim targeted by this effort was to develop the ontology definitions and model structure. This report documents the resulting model and ontology to develop DIAMOND. The report describes the various elements of the model development based on a growing tree development approach, i.e. DIAMOND’s tree structure is established with the core data aspects and is populated with a preliminary level of details. Fifteen sources of data in a nuclear power plant were considered and incorporated into DIAMOND at this stage of development, representing the majority of operations data sources in nuclear power plants. Additional data sources, scope, and a higher level of fidelity will be incorporated into DIAMOND next in collaboration with the nuclear industry and relevant stakeholders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Development of a Framework for Data Integration, Assimilation, and Learning for Geological Carbon Sequestration (DIAL-GCS) (Final Report)

This project aimed to develop and demonstrate a Data Integration, Assimilation, and Learning framework for geologic carbon sequestration projects (DIAL-GCS). DIAL-GCS is an intelligence monitoring system (IMS) for automating GCS closed-loop management by leveraging recent developments in machine learning technologies, complex event processing (CEP), and reduced-order modeling. The safe and efficient operation of GCS repositories requires integrated monitoring to track the injected CO¬2 as it moves within a storage reservoir. GCS projects are data intensive, as a result of proliferation of digital instrumentation and smart-sensing technologies. GCS projects are also resource intensive, often requiring multidisciplinary teams performing different monitoring, verification, accounting (MVA) tasks throughout the lifecycle of a project to ensure secure containment of injected CO2. The success of GCS thus depends in a large part on our ability to access, assimilate, and analyze heterogeneous data and information sources in a timely manner. This project included a number of meaningful and necessary tasks to transform the human domain knowledge into machine-interpretable rules for automating knowledge extraction and discovery in GCS. The specific technical objectives of the proposed DIAL-GCS project were to develop an ontology-driven GCS data management module for storing, querying, and exchanging GCS data (both historic and live sensor data) from multiple sources and in heterogeneous formats. Incorporate a CEP engine for detecting abnormal situations by seamlessly combining expert knowledge, rule-based reasoning, and machine learning. Enable uncertainty quantification and predictive analytics using a combination of coupled-process modeling, AI/ML methods, and reduced-order modeling, and integrate and demonstrate the system’s capabilities with both real and simulated data. As far as we know, this is one of the first projects aimed to develop intelligent monitoring systems (IMS) targeting the GCS. Under this project, the team had developed a large number of web applications and scientific algorithms that contribute the main theme of intelligent monitoring. The team has published more than a dozen peer reviewed papers and disseminated the research results at multiple technical meetings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

TASTI-GRID: An Overview of West Virginia’s Grid Resilience Challenges

West Virginia’s legacy as a top power producer with a legacy of mining and natural gas is well-known across the country. A state with historic underinvestment, vulnerability to natural hazards, and a large rural population – much of West Virginia’s critical energy infrastructure is beyond its recommended lifecycle at 75+ years old. West Virginian’s are feeling the effects of neighboring Virginia’s data center growth, as its grid interconnectedness with neighboring states have impacted energy affordability as power supply has not kept up with demand. The state aims to attract data centers to improve economic development, however, the age of infrastructure, utility reliability, weather-induced major outages, and cost of proposed improvements compound West Virginia’s resilience challenges.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Botnet behaviour analysis: How would a data analytics-based system with minimum a priori information perform?

Botnets, as one of the most aggressive threats, has used different techniques, topologies, and communication protocols in different stages of their lifecycle since 2003. Hence, identifying botnets has become very challenging specifically given that they can upgrade their methodology at any time. Various detection approaches have been proposed by the cyber-security researchers, focusing on different aspects of these threats. In this work, 5 different botnet detection approaches are investigated. These systems are selected based on the technique used and type of data used where 2 are public rule–based systems (BotHunter and Snort) and the other 3 use machine learning algorithm with different feature extraction methods (packet payload based and traffic flow based). On the other hand, 4 of these systems are based on a priori knowledge while one is using minimum a priori information. The objective here is to evaluate the effectiveness of these approaches under different scenarios (eg, multi-botnet and single-botnet classifications) as well as exploring how a system with minimum a priori information would perform. The goal is to investigate if a system with minimum a priori information could result in a competitive performance compared to systems using a priori knowledge. The evaluation is shown on 24 publicly available botnet data sets. Results indicate that a machine learning–based system with minimum a priori information not only achieves a very high performance but also generalizes much better than the other systems evaluated on a wide range of botnet structures (from centralized to decentralized botnets).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Data Architecture and Analytics Requirements for Artificial Intelligence and Machine Learning Applications to Achieve Condition-Based Maintenance

This report identified some of the important requirements that needs to be taken into consideration as part of the data evolution for the CBM application of a CWS in a NPP. In the data evolution process, the information is converted into insight leading into actions using advancements in AI/ML technologies. A notion of RESET AI: design, development, deployment, and operation principals are introduced to lifecycle of AI technologies. Towards the end of the report, we discussed how this CBM can be realized in a SDE. As path forward, this report lays the foundation for developing a more detailed industry guidance supporting data evolution for other plant applications like operations and plant support. These would be developed as part of ongoing research in the fiscal year 2023.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Representations of Precipitation Diurnal Cycle in the Amazon as Simulated by Observationally Constrained Cloud‐System Resolving and Global Climate Models

Abstract The ability of an observationally‐constrained cloud‐system resolving model (Weather Research and Forecasting; WRF, 4‐km grid spacing) and a global climate model (Energy Exascale Earth System Model; E3SM, 1‐degree grid spacing) to represent the precipitation diurnal cycle over the Amazon basin during the 2014 wet season is assessed. The WRF model coupled with a 3‐D variational data assimilation scheme reproduces the spatial variability of the precipitation diurnal cycle over the basin and the lifecycle of westward propagating MCSs initiated by the coastal sea‐breeze front. In contrast, a single morning peak in rainfall is produced by E3SM for simulations despite the nudging of large‐scale winds toward global reanalysis, indicating precipitation in E3SM is largely controlled by local convection associated with diurnal heating. The role of propagating MCS on the environment are discussed by using a multivariate perturbation analysis. We also find that the advection of moisture perturbations from ocean to inland regions have a higher correlation with the occurrence of MCSs in the Amazon than the intensity of colder air intrusion associated with sea breezes along the coast. Moreover, the presence of large cold pools over the central Amazon basin are responsible for the maintenance of propagating deep convection.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Battery Lifecycle Framework

The Battery Lifecycle (BLC) Framework is an open-source platform that provides tools to visualize and share battery data from material characterization, cell testing, manufacturing, and field testing through the technology development cycle. BLC has three components: data importers, a front-end for querying the data and creating visualizations, and an application programming interface to provide access to the data from Python. BLC has been deployed for tracking the development of a battery from the lab to a manufacturing line and systems installed in the field and for comparing studies of multiple cells of the same battery chemistry and configuration. The code was developed around Redash, a robust open-source extract-transform-load engine. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-4546 O

De Angelis, Valerio↗

DOE OSTI2015

NIF Experiment Editor Redesign. Setting up for the future

The Campaign Management project at the National Ignition Facility is embarking on a major redesign of the software used by experimentalists to setup experiments on the NIF. The current editor was architected over a decade ago and optimized for a different purpose than supporting production science campaigns. It has a steep learning curve and lacks many desirable features. Furthermore, its underlying technology stack and architecture do not support the kind of routine capability evolution that represents current and future growth needs of the Program. The principal stakeholders are the physicists who are users of the editing application, but the entire integrated suite of Campaign Management software has a broad customer base spanning experimentalists, NIF sub-system subject matter experts, planners, and shot operations staff. A recent DOE-mandated review of NIF activity, conducted with the goal of improving the shot rate at NIF, identified the experiment editor as a clear barrier to a more productive and efficient experiment lifecycle, exposing unnecessary levels of detail, lacking adequate automation, and lacking adequate integration with related activities and data sources in what should be a shared data environment.

42 ENGINEERING↗

DOE OSTI2020

Usable Data Abstractions for Next-Generation Scientific Workflows

Data- and computationally-intensive scientific research, such as numerical simulations and inversions or the training of large neural networks in machine learning applications, that are well suited for HPC environments also often require expert insight and evaluation throughout the computation which can be greatly facilitated with the use of interactive computing tools, such as those in the Jupyter ecosystem. HPC workflows and interactive workflows are typically treated as orthogonal, however, the next generation of research will require both. The first challenge we face in this project is thus designing the right level of abstractions to allow interactive capabilities in the JupyterLab environment to allow the working scientist to flexibly explore and query their data at multiple levels, with a minimal amount of customization required of the underlying optimized codes. In addition to these questions regarding the high-level representation of data for interactive use in HPC, we tackled two additional issues that are part of the entire lifecycle of research and that become particularly acute in HPC contexts: how to improve the experience of interfacing with the HPC system's scheduling environment for a scientist focused on exploratory questions, and how can that scientist then best share the results of their work with others in a self-contained, reproducible manner.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Circular Economy for Photovoltaics in Service of Energy Transition

The challenge of energy transition is immediate and immense; current projections target 75 TW of photovoltaics (PV) capacity by 2050. While any transition to renewable energy technology is preferable to the current fossil-based system, it is ideal to improve the sustainability of PV to minimize negative environmental and social impacts. Circular economy (CE) has been proposed as a method to improve the sustainability of PV, especially for emerging materials like perovskites. CE is a set of actions, principles, and systems which aim to design out waste and keep products and materials in use, to reduce environmental impacts and enable sustainable development. At the most basic level, CE is "reduce, reuse, recycle", the R-actions, in ranked order. CE of a PV technology can be metricized in a variety of ways, such as the Material Circularity Indicator (Smith and Jones, Ellen MacArthur Foundation, 2019) or recycling rates. Unfortunately, standard CE metrics have several shortcomings for measuring renewable energy technologies in the context of deployment for energy transition (Figge 2018, Saidani 2019): 1) Only measure mass flows; 2) De-prioritization of the use phase in favor of mass circularity when scoring; and 3) Tight focus on a single product scale The use phase and energy flows of PV are key to energy transition, and therefore need to be quantified. Additionally, correlating product-scale to system-scale is necessary for quantifying the environmental impacts of energy transition. Life Cycle Assessment (LCA) can address some of these concerns, but also focuses on a single product scale and has trouble capturing the dynamics of system-scale energy transition, such as the interaction of module lifetime with manufacturing demands for energy transition deployment schedules. Therefore, we developed an open-source Python-based system dynamics model to quantify the mass, energy and carbon impacts of CE R-actions for PV technologies in the energy transition; PV in the CE (PV ICE) (Ovaitt & Mirletz 2021). The tool captures supply chains from material extraction through end of life, incorporating 5 circular end of life pathways. PV ICE takes in any evolving bill of materials, module properties and deployment schedule to support researchers and decision makers with data-backed insights. In this work, we quantify and compare proposed CE sustainable PV module designs and lifecycle management strategies, spanning currently commercialized technologies, government and industry technology targets, and several low Technology Readiness Level (TRL) emerging PV technologies, including perovskites. Our analyses capture the projected evolutions of lifetime, efficiency and material circularity of these PV technologies, as well as their material supply chains. Our analyses emphasize the importance of examining a suite of metrics to identify priorities and tradeoffs, and inform design or lifecycle management decisions holistically. Previous analyses have demonstrated the central importance of PV module lifetime to support energy transition while minimizing impacts. High levels of material circularity (>90%) enable minimizing lifecycle wastes, can reduce virgin material demands if paired with improving efficiency, but demonstrate tradeoffs in energy return on investment. In the fervor of new material and technology development, it is important to remember that CE is not the end goal; decarbonization and energy transition are the end goal. CE should be used in service to improve the sustainability of PV, and R-actions evaluated for their usefulness and efficacy to this end.

carbon↗

DOE OSTI2025

A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.

Weaver, Gabriel [Idaho National Laboratory (INL), ↗

DOE OSTI2021

Uncertainties in Atmospheric River Lifecycles by Detection Algorithms: Climatology and Variability

Atmospheric rivers (ARs) are long and narrow filaments of vapor transport that are responsible for most poleward moisture transport outside of the tropics. Many AR detection algorithms have been developed to automatically identify ARs in climate data. The diversity of these algorithms has introduced appreciable uncertainties in quantitative measures of AR properties and thereby impedes the construction of a unified and internally consistent climatology of ARs. This paper compares nine global AR detection algorithms from the perspective of AR lifecycles following the propagation of ARs from origin to termination in the MERRA2 reanalysis over the period 1980–2017. Uncertainties in AR lifecycle characteristics, including event number, lifetime, intensity, and frequency distribution are discussed. Notably, the number of AR events per year in the Northern Hemisphere can vary by a factor of 5 with different algorithms. Although all algorithms show that the maximum AR origin (termination) frequency is located over the western (eastern) portion of ocean basins, significant disagreements appear in regional distribution. Spreads are large in AR lifetime and intensity. Here, the number of landfalling AR events produced by the algorithms can vary from 16 to 80 events per year, although the agreement improves for stronger ARs. By examining the ARs' connections with the Madden-Julian Oscillation and El Niño Southern Oscillation, we find that the overall responses of ARs (such as changes in AR frequency, origin, and landfall activity) to climate variability are consistent among algorithms.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

F*** workflows: when parts of FAIR are missing

The FAIR principles for scientific data (Findable, Accessible, Interoperable, Reusable) are also relevant to other digital objects such as research software and scientific workflows that operate on scientific data. The FAIR principles can be applied to the data being handled by a scientific workflow as well as the processes, software, and other infrastructure which are necessary to specify and execute a workflow. The FAIR principles were designed as guidelines, rather than rules, that would allow for differences in standards for different communities and for different degrees of compliance. There are many practical considerations which impact the level of FAIR-ness that can actually be achieved, including policies, traditions, and technologies. Because of these considerations, obstacles are often encountered during the workflow lifecycle that trace directly to shortcomings in the implementation of the FAIR principles. Here, we detail some cases, without naming names, in which data and workflows were Findable but otherwise lacking in areas commonly needed and expected by modern FAIR methods, tools, and users. We describe how some of these problems, all of which were overcome successfully, have motivated us to push on systems and approaches for fully FAIR workflows.

Wilkinson, Sean↗

DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2017

Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) Field Campaign Report

Cumulus convection is an important component in the atmospheric radiation budget and hydrologic cycle over the southern Great Plains and over many regions of the world, particularly during the summertime growing season when intense turbulence induced by surface radiation couples the land surface to clouds. Current convective cloud parameterizations contain uncertainties resulting in part from insufficient coincident data that couples cloud macrophysical and microphysical properties to inhomogeneities in land surface, boundary layer, and aerosol properties. The Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) campaign was designed to provide a detailed set of measurements that are needed to obtain a more complete understanding of the lifecycle of shallow clouds by coupling cloud macrophysical and microphysical properties to land surface properties, ecosystems, and aerosols. Some of the land-atmosphere-cloud interactions that can be studied using HI-SCALE data are shown in Figure 1. HI-SCALE consisted of two 4-week intensive operation periods (IOPs), one in the spring (April 24-May 21) and the other in the late summer (August 28-September 24) of 2016, to take advantage of different stages of the plant lifecycle, the distribution of “greenness” for various types of vegetation in the vicinity of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility Southern Great Plains (SGP) site, and aerosol properties that vary during the growing season. As expected, satellite measurements indicated that the Normalized Difference Vegetation Index (NDVI) was much “greener” in the vicinity of the SGP site during the spring IOP than the late summer IOP as a result of winter wheat maturing in the spring and being harvested in the early summer. As shown in Figure 2, temperatures were cooler than average and soil moisture was high during the spring IOP, while temperatures were warmer than average and soil moisture was low during the late summer IOP. These factors likely influence the occurrence and lifecycle of shallow clouds. Most of the instrumentation was deployed on the ARM Aerial Facility (AAF) Gulfstream 1 (G-1) aircraft, including those that measure atmospheric turbulence, cloud water content and drop size distributions, aerosol precursor gases, aerosol chemical composition and size distributions, and cloud condensation nuclei (CCN) concentrations. The specific instrumentation is listed in Table 1. The team of scientists participating in the G-1 flights were from Pacific Northwest National Laboratory (PNNL), Brookhaven National Laboratory (BNL), and the University of Washington. Routine ARM aerosol measurements made at the surface were supplemented with aerosol microphysical properties measurements, with support from the DOE Environmental Molecular Sciences Laboratory (EMSL) User Facility and the Atmospheric System Radiation (ASR) program. This included deploying a scanning mobility particle sizer (SMPS) to measure aerosol size distribution, a proton transfer reaction-mass spectrometer (PTR-MS) to measure volatile organic compounds, an aerosol mass spectrometer (AMS) to measure bulk aerosol composition, and the single-particle laser ablation time-of-flight mass spectrometer (SPLAT II) to measure single-particle aerosol composition at the SGP site Guest Instrumentation Facility. In this way, characterization of aerosol properties at the surface and on the G-1 were consistent. In addition, the HI-SCALE: Nanoparticle Composition and Precursors add-on campaign was conducted during the second IOP in which several state-of-the-science chemical ionization mass spectrometers were deployed to measure nanoparticle composition and precursors. Scientists participating in the surface measurements were from PNNL, BNL, University California–Irvine, Augsberg College, Colorado University, Aerodyne Inc., and Aerosol Dynamics Inc.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Interactions Between Aerosols, Meteorology, and Early Convective Cloud Lifecycle as Measured During CACTI (Final Technical Report)

The research supported by this award sought to improve the understanding and forecasting of thunderstorms. We did so by using data from the RELAMPAGO-CACTI project, which deployed a suite of instruments around a mountain range in Argentina that sees thunderstorms erupt over the same general area almost daily. The main portion of our research examined the relationship between the concentration of atmospheric particulates (dust, smoke, etc) on the intensity of thunderstorms. Despite prior studies finding that increased particulate concentration corresponded to more intense storms, we found no effect, or if anything a slightly opposite effect.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Morphologically-Directed Raman Spectroscopy as an Analytical Method for Subvisible Particle Characterization in Therapeutic Protein Product Quality

Abstract Subvisible particles (SVPs) are a critical quality attribute of injectable therapeutic proteins (TPs) that needs to be controlled due to potential risks associated with drug product quality. The current compendial methods routinely used to analyze SVPs for lot release provide information on particle size and count. However, chemical identification of individual particles is also important to address root-cause analysis. Herein, we introduce Morphologically-Directed Raman Spectroscopy (MDRS) for SVP characterization of TPs. The following particles were used for method development: (1) polystyrene microspheres, a traditional standard used in industry; (2) photolithographic (SU-8); and (3) ethylene tetrafluoroethylene (ETFE) particles, candidate reference materials developed by NIST. In our study, MDRS rendered high-resolution images for the ETFE particles (> 90%) ranging from 19 to 100 μm in size, covering most of SVP range, and generated comparable morphology data to flow imaging microscopy. Our method was applied to characterize particles formed in stressed TPs and was able to chemically identify individual particles using Raman spectroscopy. MDRS was able to compare morphology and transparency properties of proteinaceous particles with reference materials. The data suggests MDRS may complement the current TPs SVP analysis system and product quality characterization workflow throughout development and commercial lifecycle.

Science & Technology - Other Topics↗