Propane Dehydrogenation Using Transition Metal Cluster Catalysts

Our research seeks to determine the propane dehydrogenation (PDH) reaction pathways using various transition-metal cluster catalysts. We are studying the first step of the reaction, in which a C-H bond is broken. This has been previously shown to be the rate-limiting step of the PDH reaction. We are calculating the PDH activation energy (Ea) using the Vienna Ab-Initio Simulation Package (VASP) in conjunction with the nudged elastic band algorithm. Thus far, we have studied Pt, Ta, and Ni clusters ranging in size from 2-10 atoms. Our goal is to better understand the dependence of Ea on metal type and cluster size

A Sustainable Prototype for Renewable Energy: Optimized Prime-power Generator Solar Array Replacement

Remote locations such as disaster relief camps, isolated arctic communities, and military forward operating bases are disconnected from traditional power grids forcing them to rely on diesel generators with a total installed capacity of 10,000 MW worldwide. The generators require a constant resupply of fuel, resulting in increased operating costs, negative environmental impacts, and challenging fuel logistics. To enhance remote site sustainability, planners can develop stand-alone photovoltaic-battery systems to replace existing prime power generators. This paper presents the development of a novel cost-performance model capable of optimizing solar array and Li-ion battery storage size by generating tradeoffs between minimizing initial system cost and maximizing power reliability. A case study for the replacement of an 800 kW generator, the US Air Force’s standard for prime power at deployed locations, was analyzed to demonstrate the model and its capabilities. A MATLAB model, simulating one year of solar data, was used to generate an optimized solution to minimize initial cost while providing over 99% reliability. Replacing a single diesel generator would result in a savings of 1.9 million liters of fuel, eliminating 100 fuel tanker truck deliveries annually. The distinctive capabilities of this model enable designers to enhance environmental, economic, and operational sustainability of remote locations by creating energy self-sufficient sites, which can operate indefinitely without the need for resupply

Propane Dehydrogenation Using Transition Metal Cluster Catalysts

Our research seeks to determine the propane dehydrogenation (PDH) reaction pathways using various transition-metal cluster catalysts. We are studying the first step of the reaction, in which a C-H bond is broken. This has been previously shown to be the rate-limiting step of the PDH reaction. We are calculating the PDH activation energy (Ea) using the Vienna Ab-Initio Simulation Package (VASP) in conjunction with the nudged elastic band algorithm. Thus far, we have studied Pt, Ta, and Ni clusters ranging in size from 2-10 atoms. Our goal is to better understand the dependence of Ea on metal type and cluster size

Optimizing the Environmental and Economic Sustainability of Remote Community Infrastructure

Remote communities such as rural villages, post-disaster housing camps, and military forward operating bases are often located in remote and hostile areas with limited or no access to established infrastructure grids. Operating these communities with conventional assets requires constant resupply, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. For example, a 2000-member isolated village in northern Canada relying on diesel generators required 8.6 million USD of fuel per year and emitted 8500 tons of carbon dioxide. Remote community planners can mitigate these negative impacts by selecting sustainable technologies that minimize resource consumption and emissions. However, the alternatives often come at a higher procurement cost and mobilization requirement. To assist planners with this challenging task, this paper presents the development of a novel infrastructure sustainability assessment model capable of generating optimal tradeoffs between minimizing environmental impacts and minimizing life-cycle costs over the community’s anticipated lifespan. Model performance was evaluated using a case study of a hypothetical 500-person remote military base with 864 feasible infrastructure portfolios and 48 procedural portfolios. The case study results demonstrated the model’s novel capability to assist planners in identifying optimal combinations of infrastructure alternatives that minimize negative sustainability impacts, leading to remote communities that are more self-sufficient with reduced emissions and costs

A Multi-Criteria Logistics Analysis of Photovoltaic Modules for Remote Applications

Reliable electrical power grids are frequently unavailable or inaccessible in remote locations, including developing nation communities, humanitarian relief camps, isolated construction sites, and military contingency bases. This often requires sites to rely on costly generators and continuous fuel supply. Renewable energy systems (RES) in the form of photovoltaic (PV) arrays and energy storage present a rapidly improving alternative to power these remote locations. Previous RES literature and PV optimization models focused on economics, reliability, and environmental concerns, neglecting the importance of logistics factors in remote installations. This paper proposes additional optimization variables applicable to remote PV systems and compares PV module technologies. Logistics requirements such as system weight and volume are vital for shipment to remote applications. Furthermore, PV module efficiency and area power density are important factors because available land area can be limited in constrained sites. These factors should be considered, in addition to conventional economic and performance variables, to optimize an RES for remote locations. The present study evaluates 29 PV modules utilizing manufacturer datasheets and supplier pricing. For each module, cost, efficiency, panel weight, and volume were collected to calculate the proposed logistics variables: area power density, weight power density, and volume power density. These variables were compared against module costs per watt, demonstrating cost-performance tradeoffs and enabling planners to select the best PV module for their application. Monocrystalline modules appear to provide the best balance of these factors, but developing technologies may challenge crystalline cells as they continue to mature. The best monocrystalline panels had efficiencies of approximately 20%, costs of $0.60/W, and power densities of 17 W/kg, 200 W/m2, and 5,500 W/m 3 . By comparing the logistics variables of PV modules as presented here, RES planners can develop more efficient designs better suited to the logistics of installing and operating at remote sites

Quantifying Variation across 16S rRNA Gene Sequencing Runs in Human Microbiome Studies

Recent microbiome research has incorporated a higher number of samples through more participants in a study, longitudinal studies, and metanalysis between studies. Physical limitations in a sequencing machine can result in samples spread across sequencing runs. Here we present the results of sequencing nearly 1000 16S rRNA gene sequences in fecal (stabilized and swab) and oral (swab) samples from multiple human microbiome studies and positive controls that were conducted with identical standard operating procedures. Sequencing was performed in the same center across 18 different runs. The simplified mock community showed limitations in accuracy, while precision (e.g., technical variation) was robust for the mock community and actual human positive control samples. Technical variation was the lowest for stabilized fecal samples, followed by fecal swab samples, and then oral swab samples. The order of technical variation stability was inverse of DNA concentrations (e.g., highest in stabilized fecal samples), highlighting the importance of DNA concentration in reproducibility and urging caution when analyzing low biomass samples. Coefficients of variation at the genus level also followed the same trend for lower variation with higher DNA concentrations. Technical variation across both sample types and the two human sampling locations was significantly less than the observed biological variation. Overall, this research providing comparisons between technical and biological variation, highlights the importance of using positive controls, and provides semi-quantified data to better understand variation introduced by sequencing runs

A Framework for Estimating the United States Depression Burden Attributable to Indoor Fine Particulate Matter Exposure

Recently published exploratory studies based on exposure to outdoor fine particulates, defined as particles with a nominal mean diameter less than or equal to 2.5 μm (PM2.5) indicate that the pollutant may play a role in mental health conditions, such as major depressive disorder. This paper details a model that can estimate the United States (US) major depressive disorder burden attributable to indoor PM2.5 exposure, locally modifiable through input parameter calibrations. By utilizing concentration values in an exposure-response function, along with relative risk values derived from epidemiological studies, the model estimated the prevalence of expected cases of major depressive disorder in multiple scenarios. Model results show that exposure to indoor PM2.5 might contribute to 476,000 cases of major depressive disorder in the US (95% confidence interval 11,000–1,100,000), approximately 2.7% of the total number of cases reported annually. Increasing heating, ventilation, and air conditioning (HVAC) filter efficiency in a residential dwelling results in minor reductions in depressive disorders in rural or urban locations in the US. Nevertheless, a minimum efficiency reporting value (MERV) 13 filter does have a benefit/cost ratio at or near one when smoking occurs indoors; during wildfires; or in locations with elevated outdoor PM2.5 concentrations. The approach undertaken herein could provide a transparent strategy for investment into the built environment to improve the mental health of the occupants

Ten Questions Concerning the Built Environment and Mental Health

Most people spend the majority of their lives indoors. Research over the last thirty years has focused on investigating the mechanisms through which specific elements of the built environment, such as indoor air quality, influence the physical health of occupants. However, similar effort has not been expended in regard to mental health, a significant public health concern. One in five Americans has been diagnosed with a mental health disorder in the past year, and, in the United States, the number of suicide deaths are similar to the number of deaths due to breast cancer. Increases in mental health disorders in Western societies may be due, in part, to increased systemic inflammation, secondary to decreased exposures to a diverse microbial environment (i.e., the hygiene hypothesis, “Old Friends” hypothesis, “missing microbes” hypothesis, or biodiversity hypothesis), as well as increased environmental exposures that lead to chronic low-grade inflammation. In this review, we provide an assessment that integrates historical research across disciplines. We offer ten questions that highlight the importance of current lessons learned regarding the built environment and mental health, including a potential role for the microbiome of the built environment to influence mental health. Suggested areas for future investigation are also highlighted

Study Protocol: Identifying Transcriptional Regulatory Alterations of Chronic Effects of Blast and Disturbed Sleep in United States Veterans

Injury related to blast exposure dramatically rose during post-911 era military conflicts in Iraq and Afghanistan. Mild traumatic brain injury (mTBI) is among the most common injuries following blast, an exposure that may not result in a definitive physiologic marker (e.g., loss of consciousness). Recent research suggests that exposure to low level blasts and, more specifically repetitive blast exposure (RBE), which may be subconcussive in nature, may also impact long term physiologic and psychological outcomes, though findings have been mixed. For military personnel, blast-related injuries often occur in chaotic settings (e.g., combat), which create challenges in the immediate assessment of related-injuries, as well as acute and post-acute sequelae. As such, alternate means of identifying blast-related injuries are needed. Results from previous work suggest that epigenetic markers, such as DNA methylation, may provide a potential stable biomarker of cumulative blast exposure that can persist over time. However, more research regarding blast exposure and associations with short- and long-term sequelae is needed. Here we present the protocol for an observational study that will be completed in two phases: Phase 1 will address blast exposure among Active Duty Personnel and Phase 2 will focus on long term sequelae and biological signatures among Veterans who served in the recent conflicts and were exposed to repeated blast events as part of their military occupation. Phase 2 will be the focus of this paper. We hypothesize that Veterans will exhibit similar differentially methylated regions (DMRs) associated with changes in sleep and other psychological and physical metrics, as observed with Active Duty Personnel. Additional analyses will be conducted to compare DMRs between Phase 1 and 2 cohorts, as well as self-reported psychological and physical symptoms. This comparison between Service Members and Veterans will allow for exploration regarding the natural history of blast exposure in a quasi-longitudinal manner. Findings from this study are expected to provide additional evidence for repetitive blast-related physiologic changes associated with long-term neurobehavioral symptoms. It is expected that findings will provide foundational data for the development of effective interventions following RBE that could lead to improved long-term physical and psychological health