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DOE OSTI2020

Evaluation of an Additively Manufactured Respirator for Personnel Protection from Particulates

Additively manufactured respirators (AMRs) made using rigid plastic can be fitted with repurposed filter media to yield a wearable mask capable of passing a quantitative fit test with criteria set for commercial, N95 health care particulate respirators. This has been verified through an evaluation study assessing AMR masks fabricated using open-source design files and a commercial fused-filament fabrication machine, using N95 filter cloth and a P100 respirator cartridge. A few examples of filter media not designed for human respiratory protection were also evaluated in an AMR mask but did not demonstrate filtering sufficient to pass the N95 test criteria. All attributes of an AMR mask system must be considered during design and when testing performance, including the mask frame, gasket, and the cord used to secure the mask to the face. AMR masks are not the same as commercially available respiratory masks. If AMR masks are used as personal protective equipment (PPE) during a crisis when there is a shortage of regular PPE it is important that wearers are fully informed of the differences, tradeoffs, and risks associated with their use. Quantitative fit testing and proper training on how to don and doff the AMR mask are important to help achieve the best possible outcome when they are used.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Cultivating a culture of inclusivity in heliophysics

A large number of heliophysicists from across career levels, institution types, and job titles came together to support a poster at Heliophysics 2050 and the position papers for the 2024 Heliophysics decadal survey titled “Cultivating a Culture of Inclusivity in Heliophysics,” “The Importance of Policies: It’s not just a pipeline problem,” and “Mentorship within Heliophysics.” While writing these position papers, the number of people who privately shared disturbing stories and experiences of bullying and harassment was shocking. The number of people who privately expressed how burned out they were was staggering. The number of people who privately spoke about how they considered leaving the field for their and their family’s health was astounding. And for as much good there is in our community, it is still a toxic environment for many. If we fail to do something now, our field will continue to suffer. While acknowledging the ongoing growth that we as individuals must work toward, we call on our colleagues to join us in working on organizational, group, and personal levels toward a truly inclusive culture, for the wellbeing of our colleagues and the success of our field. This work includes policies, processes, and commitments to promote: accountability for bad actors; financial security through removing the constant anxiety about funding; prioritization of mental health and community through removing constant deadlines and constant last-minute requests; a collaborative culture rather than a hyper-competitive one; and a community where people can thrive as whole persons and do not have to give up a healthy or well-rounded life to succeed.

mental health↗

DOE OSTI2023

Phenopacket-tools: Building and validating GA4GH Phenopackets

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Coal-dependent Communities in Transition: Identifying Best Practices to Ensure Equitable Outcomes

The U.S. coal industry is experiencing a sharp increase in the numbers of retired and/or decommissioned coal-fired power plants across the U.S. In the years between 2010-2019, around 102 gigawatts (GW) of coal-fired generating capacity has been announced to be decommissioned, representing more than 546 coal-fired power plant units, and an additional 17 GW is planned to be decommissioned by 2025. This change in the energy production landscape presents an impact on the social, environmental, and economic prospects of coal-dependent communities. This report examined the role of communities in the coal power plant decommissioning process and provided community-identified best practices to ensure an equitable process. The experiences of four coal-dependent communities—Wise County, VA, Muskegon, MI, Anderson County, TN, and Becker, MN—are presented as case studies to understand the impacts of the decommissioning process, and associated best practices, from the communities’ perspective. The report results highlight the need to recognize that the decommissioning decision-making process must be community-based to be equitable. Each community’s input is key to the transition away from coal power because there is no one-size-fits-all development plan. In other words, each community’s trajectory through the decommissioning process—from the retirement decision-making stage to the final site redevelopment phase—is unique because each community has distinct needs and wants from the energy transition. What is best for one community may not be suited for another. Ultimately, the framework for site development and community revitalization post-decommissioning cannot be universal because each community’s profile—from a social, cultural, and economic perspective—is different. Community impacts of power plant decommissioning are not limited to job and revenue losses. Communities are likely to be impacted culturally, socially, environmentally, and have long-term health-based impacts that should be acknowledged and addressed in post-retirement plans. Commonly identified decommissioning best practices include: Early and continued engagement throughout, with a number of mediums for communication and feedback (e.g., in-person sessions, virtual meetings, written comment opportunities); Early planning of post-decommissioning projects to replace lost jobs, revenue, and economic activity; Recognition (and mitigation, if possible) of social impacts on the community due to plant closure; Transparency throughout the process, with trusted information being provided about the decommissioning process and timeline; potential impacts on the workforce, economy, and environment; and the feasibility of alternative site uses; Identification of funding sources, technical experts, and/or strategic partnerships to support decommissioning and the affected communities upfront; and, Acknowledgment of communities as stakeholders who have a role in the conversation and right to determine their futures. Three key areas for assisting coal-dependent communities affected by the energy transition: Technical assistance: assessment of site feasibility for alternative uses or to repower with new technologies; Cross-partnership engagement and collaboration: facilitate knowledge-sharing of “lessons learned” about the decommissioning process between communities and provide guidance for decision-making processes; and, Financial assistance: access to grant and/or loan programs to assist with redevelopment survey, bolster community economic security through job creation, and cover environmental clean-up costs. Technical assistance, cross-partnership engagement, and financial aid can be mobilized to help communities throughout various stages of the decommissioning process, including the retirement decision, the site reclamation phase, and eventual revitalization of the site and surrounding community.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Gut microbiota and its therapeutic implications in tumor microenvironment interactions

The development of cancer is not just the growth and proliferation of a single transformed cell, but its tumor microenvironment (TME) also coevolves with it, which is primarily involved in tumor initiation, development, metastasis, and therapeutic responses. Recent years, TME has been emerged as a potential target for cancer diagnosis and treatment. However, the clinical efficacy of treatments targeting the TME, especially its specific components, remains insufficient. In parallel, the gut microbiome is an essential TME component that is crucial in cancer immunotherapy. Thus, assessing and constructing frameworks between the gut microbiota and the TME can significantly enhance the exploration of effective treatment strategies for various tumors. In this review the role of the gut microbiota in human cancers, including its function and relationship with various tumors was summarized. In addition, the interaction between the gut microbiota and the TME as well as its potential applications in cancer therapeutics was described. Furthermore, it was summarized that fecal microbiota transplantation, dietary adjustments, and synthetic biology to introduce gut microbiota-based medical technologies for cancer treatment. This review provides a comprehensive summary for uncovering the mechanism underlying the effects of the gut microbiota on the TME and lays a foundation for the development of personalized medicine in further studies.

Feng, Pengya↗

DOE OSTI2021

A NIR fluorescent smart probe for imaging tumor hypoxia

Abstract Background Tumor hypoxia is a characteristic of paramount importance due to low oxygenation levels in tissue negatively correlating with resistance to traditional therapies. The ability to noninvasively identify such could provide for personalized treatment(s) and enhance survival rates. Accordingly, we recently developed an NIR fluorescent hypoxia‐sensitive smart probe ( NO 2 ‐Rosol ) for identifying hypoxia via selectively imaging nitroreductase (NTR) activity, which could correlate to oxygen deprivation levels in cells, thereby serving as a proxy. We demonstrated proof of concept by subjecting a glioblastoma (GBM) cell line to extreme stress by evaluating such under radiobiological hypoxic ( p O 2 ≤ ~0.5%) conditions, which is a far cry from representative levels for hypoxia for brain glioma ( p O 2 = ~1.7%) which fluctuate little from physiological hypoxic ( p O 2 = 1.0‐3.0%) conditions. Aim We aimed to evaluate the robustness, suitability, and feasibility of NO 2 ‐Rosol for imaging hypoxia in vitro and in vivo via assessing NTR activity in diverse GBM models under relevant oxygenation levels ( p O 2 = 2.0%) within physiological hypoxic conditions that mimic oxygenation levels in GBM tumor tissue in the brain. Methods We evaluated multiple GBM cell lines to determine their relative sensitivity to oxygenation levels via measuring carbonic anhydrase IX (CAIX) levels, which is a surrogate marker for indirectly identifying hypoxia by reporting on oxygen deprivation levels and upregulated NTR activity. We evaluated for hypoxia via measuring NTR activity when employing NO 2 ‐Rosol in in vitro and tumor hypoxia imaging studies in vivo. Results The GBM39 cell line demonstrated the highest CAIX expression under hypoxic conditions representing that of GBM in the brain. NO 2 ‐Rosol displayed an 8‐fold fluorescence enhancement when evaluated in GBM39 cells ( p O 2 = 2.0%), thereby establishing its robustness and suitability for imaging hypoxia under relevant physiological conditions. We demonstrated the feasibility of NO 2 ‐Rosol to afford tumor hypoxia imaging in vivo via it demonstrating a tumor‐to‐background of 5 upon (i) diffusion throughout, (ii) bioreductive activation by NTR activity in, and (iii) retention within, GBM39 tumor tissue. Conclusion We established the robustness, suitability, and feasibility of NO 2 ‐Rosol for imaging hypoxia under relevant oxygenation levels in vitro and in vivo via assessing NTR activity in GBM39 models.

Hettie, Kenneth S.↗

DOE OSTI2022

Extending the Brick schema to represent metadata of occupants

Here, energy-related behaviors of occupants constitute a key factor influencing building performance; accordingly, the measured occupant data can support the objective assessment of the indoor environment and energy performance of buildings, which can inform building design and operational decisions. Existing data schemas focus on metadata of sensors, meters, physical equipment, and IoT devices in buildings; however, they are limited in representing the metadata of occupant data, including occupants' presence in spaces, movement between spaces, interactions with building systems or IoT devices, and preference of indoor environmental needs. To address this gap, an extension to the widely adopted metadata schema, Brick, is proposed to represent the contextual, behavioral, and demographic information of occupants. The proposed extension includes four parts: (1) a new “Occupant” class to represent occupants' demography and energy related behavioral patterns, (2) new subclasses under the Equipment class to represent envelope system and personal thermal comfort devices, (3) new subclasses under the Point class to represent occupant sensing and status, and (4) new auxiliary properties for occupant interactable equipment to represent the level of controllability for each piece of equipment by occupants. The extension is implemented in the Brick schema and has been tested using multiple occupant datasets from the ASHRAE Global Occupant Database. The extension enables Brick schema to capture diverse types of occupant sensing data and their metadata for FAIR data research and applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Intubation Containment System for Improved Protection From Aerosolized Particles During Airway Management

Worldwide efforts to protect front line providers performing endotracheal intubation during the COVID-19 pandemic have led to innovative devices. Authors evaluated the aerosol containment effectiveness of a novel intubation aerosol containment system (IACS) compared with a recently promoted intubation box and no protective barrier. In a simulation center at the authors’ university, the IACS was compared to no protective barrier and an intubation box. Aerosolization was simulated using a commercial fog machine and leakage of aerosolize mist was visually assessed. The IACS appeared to contain the aerosolized mist, while the intubation box allowed for mist to contact the laryngoscopist and contaminate the clinical space through arm port holes and the open caudal end. Both devices protected the laryngoscopist better than no protective barrier. The IACS with integrated sleeves and plastic drape appears to offer superior protection for the laryngoscopist and assistant providers from aerosolized particles.

aerosols↗

DOE OSTI2020

An Assessment of the Usability of Machine Learning Based Tools for the Security Operations Center

Gartner, a large research and advisory company, anticipates that by 2024 80% of security operation centers (SOCs) will use machine learning (ML) based solutions to enhance their operations. 1 1 https://www.ciodive.com/news/how-data-science-tools-can-lighten-the-load-for-cybersecurity-teams/572209/ In light of such widespread adoption, it is vital for the research community to identify and address usability concerns. This work presents the results of the first in situ usability assessment of ML-based tools. With the support of the US Navy, we leveraged the national cyber range-a large, air-gapped cyber testbed equipped with state-of-the-art network and user emulation capabilities-to study six US Naval SOC analysts' usage of two tools. Our analysis identified several serious usability issues, including multiple violations of established usability heuristics for user interface design. We also discovered that analysts lacked a clear mental model of how these tools generate scores, resulting in mistrust a and/or misuse of the tools themselves. Surprisingly, we found no correlation between analysts' level of education or years of experience and their performance with either tool, suggesting that other factors such as prior background knowledge or personality play a significant role in ML-based tool usage. Our findings demonstrate that ML-based security tool vendors must put a renewed focus on working with analysts, both experienced and inexperienced, to ensure that their systems are usable and useful in real-world security operations settings.

Oesch, T↗

DOE OSTI2020

Household Materials Selection for Homemade Cloth Face Coverings and Their Filtration Efficiency Enhancement with Triboelectric Charging

The COVID-19 pandemic is currently causing a severe disruption and shortage in the global supply chain of necessary personal protective equipment (e.g., N95 respirators). The U.S. CDC has recommended use of household cloth by the general public to make cloth face coverings as a method of source control. We evaluated the filtration properties of natural and synthetic materials using a modified procedure for N95 respirator approval. Common fabrics of cotton, polyester, nylon, and silk had filtration efficiency of 5–25%, polypropylene spunbond had filtration efficiency 6–10%, and paper-based products had filtration efficiency of 10–20%. An advantage of polypropylene spunbond is that it can be simply triboelectrically charged to enhance the filtration efficiency (from 6 to >10%) without any increase in pressure (stable overnight and in humid environments). Using the filtration quality factor, fabric microstructure, and charging ability, we are able to provide an assessment of suggested fabric materials for homemade facial coverings.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Data-driven modeling of dynamic occupant thermostat override behavior for demand response applications

Buildings consume nearly 40% of global energy and produce similar emissions. Whiletechnological advances address efficiency, occupant behavior causes energy use variations up to 300% between identical buildings. This gap between predicted and actual building performance impacts building design, operations, and grid demand management programs. Through analyses of smart thermostat data from 1,400 single-occupant homes, the researchdemonstrates that occupants respond to 8°F thermostat setpoint changes within a median of 15 minutes, while 2°F changes trigger responses within a median of 30 minutes. This highlights an understudied temporal relationship between thermostat setbacks and response time of occupant behaviors. Models of such behavior dynamics are required to incorporate occupant impacts into building performance simulation. A key contribution of this dissertation is the Thermal Frustration Theory (TFT), which positsthat thermal discomfort driven behaviors are caused by the time-accumulation of discomfort, not simply a temperature deviation threshold or a delay from an initiating event. Using a dataset of 634 thermostats, each with 25+ manual setpoint changes, a comparative analysis of TFT and comfort zone and a delayed response theories demonstrated that personalized TFT models better predict when manual setpoint change occur. This was measured by the area under the curve statistical measure (AUC); all three models perform similarly by a Matthews Correlation Coefficient measure. Higher AUC performance is especially important for modeling occupant behavior in demand response programs where false negatives of rare occupant interactions could adversely affect grid stability. EnergyPlus based simulations were conducted with TFT-derived occupant models, demonstrating the ability to identify parameters of known TFT models from only data observable with smart thermostats, even under the presence of noise from routine overrides. Overall, the dissertation highlights that thermostat interactions are neither static,instantaneous, nor driven solely by the environment. Instead, temporal accumulation of discomfort and routine-based behavior play important roles. The methodology and results offer a pathway towards more accurate modeling of human-building interactions for policy assessment, building design, and demand response programs.

Sharma, Kunind [Northeastern University] (ORCID:00↗

DOE OSTI2020

Protein Molecular Dynamics Simulations with Approximate QM: What Can We Learn?

Classical force fields are essential for computer simulations of proteins and are typically parameterized to reproduce secondary and tertiary structure of isolated proteins. However, while protein-protein interactions are ubiquitous in nature, they are not considered in parameterization efforts and are far less understood than isolated proteins. A better characterization of intermolecular interactions is widely recognized as a key to revolutionizing drug and therapeutic developments with high-throughput computational screening. Urgently needed is a critical assessment of the performance of modern protein force fields against first-principles electronic structure methods and experiments. In a daring step toward this goal, we here describe a comparison of peptide folding dynamics as predicted by a molecular mechanics force field on the one hand and by an approximate electronic structure quantum mechanical (QM) method based on density-functional tight-binding (DFTB) on the other. We further compare the dynamics from straightforward DFTB simulations with a near-linear scaling version of DFTB for massively parallel computation based on the fragment molecular orbital (FMO-DFTB) method. We illustrate differences between the phenomenology of the folding dynamics from these three methods for a small model peptide, as well as charge polarization and dynamic fluctuations, point out possible correlations and implications for force field developers, and discuss the lessons learned that might become applicable to future predictive high-throughput computer screening for personalized neoantigen cancer therapy.

Irle, Stephan↗

DOE OSTI2021

Connectivity‐based parcellation of normal and anatomically distorted human cerebral cortex

Abstract For over a century, neuroscientists have been working toward parcellating the human cortex into distinct neurobiological regions. Modern technologies offer many parcellation methods for healthy cortices acquired through magnetic resonance imaging. However, these methods are suboptimal for personalized neurosurgical application given that pathology and resection distort the cerebrum. We sought to overcome this problem by developing a novel connectivity‐based parcellation approach that can be applied at the single‐subject level. Utilizing normative diffusion data, we first developed a machine‐learning (ML) classifier to learn the typical structural connectivity patterns of healthy subjects. Specifically, the Glasser HCP atlas was utilized as a prior to calculate the streamline connectivity between each voxel and each parcel of the atlas. Using the resultant feature vector, we determined the parcel identity of each voxel in neurosurgical patients ( n = 40) and thereby iteratively adjusted the prior. This approach enabled us to create patient‐specific maps independent of brain shape and pathological distortion. The supervised ML classifier re‐parcellated an average of 2.65% of cortical voxels across a healthy dataset ( n = 178) and an average of 5.5% in neurosurgical patients. Our patient dataset consisted of subjects with supratentorial infiltrating gliomas operated on by the senior author who then assessed the validity and practical utility of the re‐parcellated diffusion data. We demonstrate a rapid and effective ML parcellation approach to parcellation of the human cortex during anatomical distortion. Our approach overcomes limitations of indiscriminately applying atlas‐based registration from healthy subjects by employing a voxel‐wise connectivity approach based on individual data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Modeling charging infrastructure impact on the electric vehicle market in China

The plug-in electric vehicle (PEV) is deemed as a critical technological revolution, and the governments are imposing various vehicle policies to promote its development. Meanwhile, the market success of PEVs depends on many aspects. The study reported herein integrates one’s use of charging infrastructure at home, public place and workplace into the market dynamics analysis tool, New Energy and Oil Consumption Credits (NEOCC) model, to systematically assess the charging infrastructure (home parking ratio, public charging opportunity, and charging costs) impact on PEV ownership costs and analyze how the PEV market shares may be affected by the attributes of the charging infrastructure. Compared to the charging infrastructure, the impact of battery costs is incontrovertibly decisive on PEV market shares, the charging infrastructure is still non-negligible in the PEV market dynamics. The simulation results find that the public charging infrastructure has more effectiveness on promoting the PEV sales in the PEV emerging market than it does in the PEV mature market. However, the improvement of charging infrastructure does not necessarily lead to a larger PEV market if the charging infrastructure incentives do not coordinate well with other PEV policies. Besides, the increase of public charging opportunities has limited motivations on the growth of public PEV fleets, which are highly correlated to the number of public fast charging stations or outlets. It also finds that more home parking spaces can stimulate more sales of personal plug-in hybrid electric vehicles instead of personal battery electric vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Insulation Activation and Contamination in Low-Power Experiments

Among the diverse advanced nuclear reactor concepts being developed are designs that expect to operate at much higher temperatures than conventional light-water reactors, and therefore require alternative materials for components capable of withstanding these extreme temperatures. Insulation is one such component where alternative high-temperature industrial composites are being considered. Low-power experiments can provide an opportunity for advancing our understanding of material behavior under irradiation; however, these experiments are not without occupational hazards. The focus of this paper is the potential for activation of insulating composites when exposed to neutron fluence during reactor experiments, and to discuss the subsequent contamination of the testing area. This paper will also discuss practical measures for preventing the creation and inhalation of activated particulate matter (i.e., dust), such as engineering and administrative controls and personal protective equipment. This paper evaluates the suitability of several insulation materials for use in reactor experiments based on their activation from neutron irradiation. The materials considered are Pyrogel XTE by Aspen Aerogel, Cerablanket and Kaowool by Morgan Advanced Materials, Thermo-12 Gold by Johns Manville, and Maxsil CF6-2000 by McAllister Mills. Although these materials are not yet widely used in the nuclear industry, they are common in other industries due to their thermal insulating properties. These materials produce dust while handling that can pose health and safety hazards, which could be further exacerbated by activation. The analysis presented in this paper utilizes a deterministic method developed to predict the activation source term of the provided insulation materials. The method calculates the atomic density of each element within the composites and simulates their exposure to a specified neutron flux density representative of low power experimental conditions. During the irradiation phase and throughout the extensive cooling period, neutron absorptions and nuclear decays are tracked for relevant isotopes. The residual activity within each composite is then assessed to identify the material with the lowest potential radiological hazard to workers. Dust will be created during installation and removal of insulating materials and may also be generated during the experiment. Contamination of surrounding surfaces due to dispersion is highly likely unless preventative measures are taken. The results of this analysis are used to estimate an internal dose from inhalation of activated dust, which is significantly lower than the allowable whole-body dose. However, the risk of internal exposure may increase if the dust becomes a transport vector for nuclear material. The goals of this paper are to enhance the understanding of the potential activation and contamination hazards from the use of these insulating materials in low-power nuclear experiments and to foster safer work environments through informed decision-making and strategic planning. This work ultimately contributes to a broader understanding of how new non-nuclear materials for advanced reactors impact the radiological safety of co-located workers, helping to ensure the responsible management of radioactive materials during installation and decommissioning activities.

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗

DOE OSTI2024

ToF-SIMS evaluation of PEG-related mass peaks and applications in PEG detection in cosmetic products

Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Equitable Employment Access Assessed Through the Mobility Energy Productivity (MEP) Metric

This paper examines commuting options for an underserved neighborhood in Columbus, Ohio to a major employment center. The analysis is based on an emerging metric called the Mobility Energy Productivity (MEP) metric developed by the National Renewable Energy Laboratory (NREL) on behalf the Department of Energy (DOE). The purpose of the analysis is twofold. The first is to quantify relative attractiveness of commute modes between the two locations, using a perspective that includes travel time, energy and cost, while providing an equity lens to compare commute options between privately owned vehicles and pooled transportation options. The second objective is to apply MEP in a specific origin-destination (O-D) scenario, whereas previously it has been used primarily as an aggregate metropolitan-wide statistical measure. In so doing, parameters in MEP are further customized and the methodology is refined to account for unique aspects of this case study. Four commute options between the neighborhood and the industry employment based are analyzed: drive alone option, public transit express bus (historical), public transit normal route (current), and a proposed shuttle specific to the O-D pair. This analysis identified issues applying MEP that required further customization: (1) deprecation functions customized to modes other than driving, (2) accounting for first-mile last-mile travel times with transit, (3) accounting for transit frequency without resorting to full simulation. The results provide quantitative insights on the employment accessibility between these two locations, both across modes, and as equity of job accessibility for those who can and cannot operate a personal vehicle.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Pre-Symptomatic COVID Screening

Temperature checks for fever are extensively used for preliminary COVID screenings but are ineffective during the incubation stage of infection when a person is asymptotic. Researchers at the European Centre for Disease Prevention and Control concluded that approximately 75% of passengers infected with COVID-19 and traveling from affected Chinese cities would not be detected by early screening. Core body temperature is normally kept within a narrow range and has the smallest relative standard deviation of all vital signs. Heat in the body is prioritized around internal organs at the expense of the periphery by controlling blood flow. In fact, blood flow to the skin may vary by a factor of 100 depending on thermal conditions. This adaptation causes rapid temperature fluctuations in different skin regions from changes in cardiac output, metabolism, and likely cytokine diffusion during inflammation that would not be seen in average core body temperature. Current IR and thermal scanners used for temperature checks are not necessarily reflective of core body temperatures and require cautious interpretation as they frequently result in false positive and false negative diagnosis. Hand held thermometers measure average skin temperatures and can get readings that differ from core body temperature by as much as 7°. Rather than focusing on a core body temperature threshold assessment we believe that variability of temperature patterns using a novel wearable transdermal microneedle sensor will be more sensitive to infections in the incubation stage and propose to develop a wearable transdermal temperature sensor using established Sandia microneedle technology for pre-symptomatic COVID screening that can additionally be used to monitor disease progression at later stages.

47 OTHER INSTRUMENTATION↗