Comparative Investigation of Gasoline-like Surrogate Fuels using 3D Computed Tomography.
Abstract not provided.
Karathanassis, Ioannis↗
SEARCH · Engineering Papers
Results for “Comparators”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Optimizing Material Selection and Operational Conditions for XHV Systems: Lessons from AISI 1020 and 316L Comparative Studies
In this study, AISI 1020 low-carbon steel is investigated as a cost-effective alternative to SS316 stainless steel for reaching extreme high vacuum (XHV) conditions. After being baked at 400°C, a vacuum chamber made of the low-carbon steel material exhibited an outgassing rate approximately 2000 times smaller than a similar chamber made of stainless steel. Its activation energy for hydrogen diffusion (27 kJ/mol) is less than half that of stainless steel (60.3 kJ/mol), indicating more efficient hydrogen removal during bakeout. MolFlow+ simulations supported the experimental data and demonstrated the importance of system geometry optimization and minimizing stainless steel content for achieving optimal vacuum performance. AISI 1020's magnetic properties, typically considered disadvantageous for accelerator applications, could benefit spin-polarized electron sources by shielding photocathodes from stray fields while simultaneously providing improved vacuum through reduced outgassing. To optimize AISI 1020's performance in XHV systems, practical considerations include pre-baking protocols and careful system design to minimize stainless steel components.
Al-Allaq, Aiman H.↗
KBase Narrative - Comparing Proteomes of Cupriavidus basilensis 4G11 and Cupriavidus necator H16
Species of Cupriavidus are among the best bioplastic producing microorganisms known, though intracellular poly-hydroxybutyrate (PHB) is made exclusively during unbalanced growth (e.g. nitrogen limitation), a state characterized by poor growth and cellular stress. Much work has been done studying bioplastic production in the model/type strain C. necator H16. We use an RB-TnSeq library of the closely related C. basilensis 4G11 to assess for genes which decouple PHB production from growth state. In this narrative we compute genome-wide protein homology between these two organisms, and generate a synteny plot across the genomes as well.
Sander, Kyle↗
Calculation of monomer yields, verification of PG concentration approximation, catalyst comparison for neat aromatic RCF, SEM micrographs for postreaction pulps, yields of aqueous-phase sugar derivatives after PG-H2O RCF, reactor pressure transients, GPC comparing pre- and postreaction organic solvent composition, PG-H2O miscibility testing, GPC probing dimer/oligomer fractionation in PG-H2O mixtures for
Reductive catalytic fractionation (RCF) is an effective lignin-first biorefining method to extract lignin as a stabilized oil from lignocellulosic biomass. To realize RCF at scale, process modeling has shown that minimizing the use of exogenous organic solvents is critical. To this end, here we investigate the ability of lignin-derived monomers to act as either solvents or cosolvents for RCF. We begin by examining the influence of lignin-derived aromatic compounds (4-propylguaiacol, 4-propylphenol, and propylbenzene) on RCF monomer yields and subsequently extend our analysis to mixtures of 4-propylguaiacol and either methanol or water. We demonstrate that 4-propylguaiacol is an effective solvent for lignin extraction and depolymerization during RCF, especially when used in combination with water as a cosolvent. Cosolvent mixtures of 4-propylguaiacol and water enable up to 81% lignin extraction, monomer yields up to 25 wt %, and postreaction phase separation. However, unlike methanol, water as a cosolvent fails to inhibit aromatic ring hydrogenation when conducted over Ru/C as a catalyst, potentially leading to excess hydrogen consumption in a process utilizing this approach. Nonetheless, these results suggest a promising strategy for eliminating external organic solvents from RCF by utilizing mixtures of lignin-derived compounds and water as alternative extraction solvents.
biorefining↗
ITeM: Independent temporal motifs to summarize and compare temporal networks
We report networks are a fundamental and flexible way of representing various complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Temporal networks are a specialization of general networks where every relationship occurs at a discrete time. The temporal evolution of such networks is as important to understand as the structure of the entities and relationships. We present the Independent Temporal Motif (ITeM) to characterize temporal graphs from different domains. ITeMs can be used to model the structure and the evolution of the graph. In contrast to existing work, ITeMs are edge-disjoint directed motifs that measure the temporal evolution of ordered edges within the motif. For a given temporal graph, we produce a feature vector of ITeM frequencies and the time it takes to form the ITeM instances. We apply this distribution to measure the similarity of temporal graphs. We show that ITeM has higher accuracy than other motif frequency-based approaches. We define various ITeM-based metrics that reveal salient properties of a temporal network. We also present importance sampling as a method to efficiently estimate the ITeM counts. We present a distributed implementation of the ITeM discovery algorithm using Apache Spark and GraphFrame. We evaluate our approach on both synthetic and real temporal networks.
97 MATHEMATICS AND COMPUTING↗
Comparative Study of Hydrology and Icemelt in Three Nepal River Basins Using the Glacio-Hydrological Degree-Day Model (GDM) and Observations From the Advanced Scatterometer (ASCAT)
An assessment of the water supply and its seasonal and annual changes over the century in the High Mountain Asia (HMA) region is of increasing interest due to its potential impact on one-sixth of the global population. In order to understand the changing hydrology and snow and ice melt, we used remotely sensed Advanced Scatterometer (ASCAT) observations of glacier melt (GM) and a distributed and gridded Glacio-hydrological Degree-day Model (GDM) in three river basins: Tamor, Trishuli and Marsyangdi. The GDM-estimated contribution of snowmelt, icemelt, rainfall and baseflow in river flows is found to be most accurate in the Trishuli River basin, with Nash-Sutcliffe efficiency (NSE) between the estimated and observed discharges of 0.81 and volume differences of -0.5%, and reasonably accurate in the Tamor River basin, with NSE of 0.69 and volume difference of -7.51%. Similarly, NSE of 0.81 and volume difference of 4.64% in Marsyangdi River basin. We find strong similarities in the timing of glacier melting using the GDM and from observations from the ASCAT GM, determining the seasonal start of glacier melting to within 6 days on average. In all basins ASCAT GM observes melting at higher elevations relative to GDM, average of 5,328 m a.s.l. Systematic differences in glacier melting area determined by modeling and satellite observations indicate ASCAT may have suboptimal resolution, view geometry and/or polarimetry for delineating glacier melting at the process-scale in complex topography, especially in the ablation zone. This is the first step in examining the remote sensing products that could potentially be incorporated into hydrologic models to increase the accuracy of the hydrologic flow as well as the ability to estimate river discharge in other basins with limited data.
54 ENVIRONMENTAL SCIENCES↗
Comparative Analysis of Microbial Diversity Across Temperature Gradients in Hot Springs From Yellowstone and Iceland
Geothermal hot springs are a natural setting to study microbial adaptation to a wide range of temperatures reaching up to boiling. Temperature gradients lead to distinct microbial communities that inhabit their optimum niches. We sampled three alkaline, high temperature (80–100°C) hot springs in Yellowstone and Iceland that had cooling outflows and whose microbial communities had not been studied previously. The microbial composition in sediments and mats was determined by DNA sequencing of rRNA gene amplicons. Over three dozen phyla of Archaea and Bacteria were identified, representing over 1700 distinct organisms. We observed a significant non-linear reduction in the number of microbial taxa as the temperature increased from warm (38°C) to boiling. At high taxonomic levels, the community structure was similar between the Yellowstone and Iceland hot springs. We identified potential endemism at the genus level, especially in thermophilic phototrophs, which may have been potentially driven by distinct environmental conditions and dispersal limitations.
59 BASIC BIOLOGICAL SCIENCES↗
Comparative study of the effects of prenatal sevoflurane exposure at different cortical stages on forebrain development and maturation in offspring
Introduction Brain development involves several critical stages, such as proliferation, neuronal migration, axonal pathfinding, and connection formation. Sevoflurane, a γ -aminobutyric acid (GABA) receptor agonist, is widely used as an inhaled general anesthetic. However, its impact on brain development has raised increasing concerns, particularly regarding prenatal exposure. This study aims to investigate the effects of prenatal sevoflurane exposure (PSE) at different cortical stages, focusing on its impact on the migration of glutamatergic and GABAergic neurons and neuronal behavior in offspring. Methods PSE was administered at two critical prenatal stages: embryonic day (E) 12.5 and E18.5. Double in situ hybridization was used to identify the coexpression of GABA receptors in Pax6- and Mash1-positive cells in the forebrain. The radial migration of glutamatergic neurons and the tangential migration of GABAergic neurons were analyzed. Behavioral tests, including the open-field test, elevated plus-maze test, forced swim test, tail suspension test, sucrose preference test, and Morris water maze, were performed on offspring to assess anxiety-like behaviors, depression, and learning and memory impairments. Results PSE inhibits the radial migration of glutamatergic neurons and promotes the tangential migration of GABAergic neurons. Specifically, early exposure (E12.5) inhibited the expression of the Pax6–Tbr2–Tbr1 cascade and the radial migration of Tbr1 in the ventral prefrontal cortex (PFC), whereas late exposure (E18.5) inhibited this process on the dorsal side. In addition, offspring mice with PSE exhibited increased anxiety-like behaviors, rather than depression, as demonstrated by reduced time spent in the center of the open-field test and in the open arms of the elevated plus-maze test. No significant differences were observed in the forced swim test, tail suspension test, or sucrose preference test. Furthermore, learning and memory impairments were observed in the Morris water maze. Conclusion Our results indicate that PSE at E12.5 and E18.5 leads to abnormalities in the migration of glutamatergic and GABAergic neurons, affecting long-term anxiety-like behaviors and causing learning and memory impairments in offspring mice.
Wang, Tianyuan↗
Comparing Deep Learning Approaches for Understanding Genotype × Phenotype Interactions in Biomass Sorghum
We explore the use of deep convolutional neural networks (CNNs) trained on overhead imagery of biomass sorghum to ascertain the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. We consider both CNNs trained explicitly on the classification task of predicting whether an image shows a plant with a reference or alternate version of various SNPs as well as CNNs trained to create data-driven features based on learning features so that images from the same plot are more similar than images from different plots, and then using the features this network learns for genetic marker classification. We characterize how efficient both approaches are at predicting the presence or absence of a genetic markers, and visualize what parts of the images are most important for those predictions. We find that the data-driven approaches give somewhat higher prediction performance, but have visualizations that are harder to interpret; and we give suggestions of potential future machine learning research and discuss the possibilities of using this approach to uncover unknown genotype × phenotype relationships.
Zhang, Zeyu↗
Comparative study of machine learning techniques for post-combustion carbon capture systems
Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO 2 -capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO 2 -capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO 2 -capture property prediction and design optimization.
97 MATHEMATICS AND COMPUTING↗
Comparing the response of the indigenous microbial community to crude oil amendment in oxic versus hypoxic conditions
The Caspian Sea is the world’s largest landlocked saline lake which lies between Europe and Asia. This region is particularly known for its large-scale oil reserves, pipelines, and drilling activities, which have contributed to the environmental decline of this lake. In addition to pollution from the petroleum industry, drainage from various river basins brings an influx of residential, industrial, and agricultural effluents that induce eutrophication and hypoxic conditions in deeper, colder waters, creating an oxygen gradient. The temperature and oxygen stratification in this environment has presented a unique opportunity to investigate the potential of the biodegradative processes carried out by the indigenous microbial community. We believe these indigenous microbes possess different metabolic capabilities to degrade oil as they adapted to declining oxygen concentrations and temperatures with increasing depths over a prolonged period. Hence, community structure and composition will vary with depth. Microcosms were set up to observe the indigenous microbial reaction after a 60 ppm native crude oil amendment over 115 days. Surface water microcosms were incubated at 28ºC and aerated while deep water microcosms were incubated at 8ºC under anaerobic conditions. These two environmental conditions represent the temperature and oxygen extremes along the gradient and were selected as we try to simulate the indigenous community’s response to this oil contamination. DNA was extracted and amplified from these microcosms and sequenced. Bioinformatic analysis was performed to track changes in the abundance of taxa present and biodiversity over different time points to show the progression of community structure. All microcosms showed the presence of hydrocarbon-degrading phyla, whose presence is consistent with other reports from oil-enriched environments. However, distinct communities were observed in oxic versus hypoxic microcosms. Orders of Bacteria related to sulfate and nitrogen cycling were found in hypoxic microcosms, indicating a possible mechanism for the anaerobic biodegradation of crude oil. GC-MS analysis of initial and final microcosms also provided evidence of degradation of hydrocarbon fractions in both warm, oxic and cold, hypoxic conditions.
59 BASIC BIOLOGICAL SCIENCES↗
Comparative Analysis via CFD Simulation on the Impact of Graphite Anode Morphologies on the Discharge of a Lithium-Ion Battery
The morphology of electrode materials plays a crucial role in determining the performance of lithium-ion batteries. Traditional computational models often simplify graphite flakes as uniformly sized spheres, which limits their predictive accuracy. In this study, we present a computational workflow that overcomes these limitations by incorporating a more realistic representation of graphite morphologies. This workflow is designed to be flexible and reproducible, enabling efficient evaluation of electrochemical performance across diverse material structures. By exploring different graphite morphologies, our approach accelerates the optimization of material preparation techniques and processing conditions. Our findings reveal that incorporating greater morphological complexity leads to significant deviations from classical model predictions. Instead, our refined model offers a more accurate representation of battery discharge behavior, closely aligning with experimental data. This improvement underscores the importance of detailed morphological descriptions in advancing battery design and performance assessments. To promote accessibility and reproducibility, we provide the developed code for seamless integration with the COMSOL API, allowing researchers to implement and adapt it easily. This computational framework serves as a valuable tool for investigating the impact of graphite morphology on battery performance, bridging the gap between theoretical modeling and experimental validation to enhance lithium-ion battery technology.
25 ENERGY STORAGE↗
Comparative Molecular Analysis of Cancer Behavior Cultured In Vitro, In Vivo, and Ex Vivo
Current pre-clinical models of cancer fail to recapitulate the cancer cell behavior in primary tumors primarily because of the lack of a deeper understanding of the effects that the microenvironment has on cancer cell phenotype. Transcriptomic profiling of 4T1 murine mammary carcinoma cells from 2D and 3D cultures, subcutaneous or orthotopic allografts (from immunocompetent or immunodeficient mice), as well as ex vivo tumoroids, revealed differences in molecular signatures including altered expression of genes involved in cell cycle progression, cell signaling and extracellular matrix remodeling. The 3D culture platforms had more in vivo-like transcriptional profiles than 2D cultures. In vivo tumors had more cells undergoing epithelial-to-mesenchymal transition (EMT) while in vitro cultures had cells residing primarily in an epithelial or mesenchymal state. Ex vivo tumoroids incorporated aspects of in vivo and in vitro culturing, retaining higher abundance of cells undergoing EMT while shifting cancer cell fate towards a more mesenchymal state. Cellular heterogeneity surveyed by scRNA-seq revealed that ex vivo tumoroids, while rapidly expanding cancer and fibroblast populations, lose a significant proportion of immune components. This study emphasizes the need to improve in vitro culture systems and preserve syngeneic-like tumor composition by maintaining similar EMT heterogeneity as well as inclusion of stromal subpopulations.
59 BASIC BIOLOGICAL SCIENCES↗
Comparative Study of Variations in Quantum Approximate Optimization Algorithms for the Traveling Salesman Problem
The traveling salesman problem (TSP) is one of the most often-used NP-hard problems in computer science to study the effectiveness of computing models and hardware platforms. In this regard, it is also heavily used as a vehicle to study the feasibility of the quantum computing paradigm for this class of problems. In this paper, we tackle the TSP using the quantum approximate optimization algorithm (QAOA) approach by formulating it as an optimization problem. By adopting an improved qubit encoding strategy and a layer-wise learning optimization protocol, we present numerical results obtained from the gate-based digital quantum simulator, specifically targeting TSP instances with 3, 4, and 5 cities. We focus on the evaluations of three distinctive QAOA mixer designs, considering their performances in terms of numerical accuracy and optimization cost. Notably, we find that a well-balanced QAOA mixer design exhibits more promising potential for gate-based simulators and realistic quantum devices in the long run, an observation further supported by our noise model simulations. Furthermore, we investigate the sensitivity of the simulations to the TSP graph. Overall, our simulation results show that the digital quantum simulation of problem-inspired ansatz is a successful candidate for finding optimal TSP solutions.
97 MATHEMATICS AND COMPUTING↗
Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting
A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%.
14 SOLAR ENERGY↗
Piezoelectric Energy Harvesting Gyroscopes: Comparative Modeling and Effectiveness
Given its versatility in drawing power from many sources in the natural world, piezoelectric energy harvesting (PEH) has become increasingly popular. However, its energy harvesting capacities could be enhanced further. Here, a mathematical model that accurately simulates the dynamic behavior and energy harvested can facilitate further improvements in the performance of piezoelectric devices. One of the goals of this study is to create a dependable reduced-order model of a multi-purpose gyroscope. This model will make it possible to compute the harvested voltage and electrical power in a semi-analytical manner. The harvested voltage is often modeled as an average value across the whole electrode surface in piezoelectric devices. We propose a model which provides practical insights toward optimizing the performance of the system by considering a spatially varying electric field across the electrode surface length. Our framework allows investigation of the limits of applicability of the modeling assumptions across a range of load resistances. The differential quadrature method (DQM) provides the basis for the suggested numerical solution. The model is also employed to examine energy harvesting under various resistance loads. The newly developed spatially varying model is evaluated for open- and closed-circuit conditions and is proved to be accurate for various values of load resistance that have not previously been considered. The results show that using a spatially varying model is more versatile when modeling the performance of the piezoelectric multifunctional energy harvester. The performance may be accurately captured by the model for load resistances ranging between 103 Ω and 108 Ω. At optimum load resistance and near 65 KHz, the maximum power output predicted by the spatially varying (SV) model is 1.3 mV, 1.5 mV for the open-circuit (OC) model, and 2.1 mV for the closed circuit (CE) model. At a high-load resistance, the SV and OC models all predict the maximum power output to be 1.9 mV while the CE model predicted the maximum voltage to be 3 mV.
42 ENGINEERING↗
A Comparative Analysis of Waste-as-a-Feedstock Accounting Methods in Life Cycle Assessments
Global waste generation is a ubiquitous challenge, driving a paradigm shift towards viewing waste as a valuable resource for a circular economy across diverse sectors. While innovative waste-to-resource pathways are crucial, rigorous Life Cycle Assessment (LCA) is essential to ensure the pathways are an important part of current practices. However, LCA application to waste valorization varies, leading to incomparable results due to differing methodological choices. This paper examines three key nuances in waste-as-resource LCAs: the zero-burden assumption, the biogenic carbon neutrality assumption, and the benchmark assumption for emissions avoidance. Using a waste gasification to hydrogen case study, we demonstrate how these methodological decisions impact LCA outcomes. Our findings reveal that waste composition significantly influences the results and highlight challenges associated with biogenic carbon accounting under various system boundary assumptions. Emissions avoidance accounting requires multi-functional unit perspectives and robust benchmark selection. This paper clarifies these accounting approaches, empirically illustrates their influence, and discusses broad implications for accurate sustainability assessment, emphasizing the critical role of transparent LCA choices for effective policy and investment in circular economy solutions.
LCA method↗
A Comparative Multi-System Approach to Characterizing Bioactivity of Commonly Occurring Chemicals
A 2019 retrospective study analyzed wristband personal samplers from fourteen different communities across three different continents for over 1530 organic chemicals. Investigators identified fourteen chemicals (G14) detected in over 50% of personal samplers. The G14 represent a group of chemicals that individuals are commonly exposed to, and are mainly associated with consumer products including plasticizers, fragrances, flame retardants, and pesticides. The high frequency of exposure to these chemicals raises questions of their potential adverse human health effects. Additionally, the possibility of exposure to mixtures of these chemicals is likely due to their co-occurrence; thus, the potential for mixtures to induce differential bioactivity warrants further investigation. This study describes a novel approach to broadly evaluate the hazards of personal chemical exposures by coupling data from personal sampling devices with high-throughput bioactivity screenings using in vitro and non-mammalian in vivo models. To account for species and sensitivity differences, screening was conducted using primary normal human bronchial epithelial (NHBE) cells and early life-stage zebrafish. Mixtures of the G14 and most potent G14 chemicals were created to assess potential mixture effects. Chemical bioactivity was dependent on the model system, with five and eleven chemicals deemed bioactive in NHBE and zebrafish, respectively, supporting the use of a multi-system approach for bioactivity testing and highlighting sensitivity differences between the models. In both NHBE and zebrafish, mixture effects were observed when screening mixtures of the most potent chemicals. Observations of BMC-based mixtures in NHBE (NHBE BMC Mix) and zebrafish (ZF BMC Mix) suggested antagonistic effects. In this study, consumer product-related chemicals were prioritized for bioactivity screening using personal exposure data. High-throughput high-content screening was utilized to assess the chemical bioactivity and mixture effects of the most potent chemicals.
60 APPLIED LIFE SCIENCES↗