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LiYSi is half-Heusler structured and crystallizes in the cubic F-43m space group. The structure is three-dimensional and consists of four lithium molecules and one YSi framework. In the YSi framework, Y3+ is bonded to four equivalent Si4- atoms to form corner-sharing YSi4 tetrahedra. All Y–Si bond lengths are 2.88 Å. Si4- is bonded to four equivalent Y3+ atoms to form corner-sharing SiY4 tetrahedra.
In 2018 13.7 EJ of fuel were consumed by the global commercial aviation industry. Worldwide, demand will increase into the foreseeable future. Developing Sustainable Aviation Fuels (SAFs), with decreased CO 2 and soot emissions, will be pivotal to the on-going mitigation efforts against global warming. Minimizing aromatics in aviation fuel is desirable because of the high propensity of aromatics to produce soot during combustion. Because aromatics cause o-rings to swell, they are important for maintaining engine seals, and must be present in at least 8 vol% under ASTM-D7566. Recently, cycloalkanes have been shown to exhibit some o-ring swelling behavior, possibly making them an attractive substitute to decrease the aromatic content of aviation fuel. Cycloalkanes must meet specifications for a number of other physical properties to be compatible with jet fuel, and these properties can vary greatly with the cycloalkane chemical structure, making their selection difficult. Building a database of structure-property relationships (SPR) for cycloalkanes greatly facilitates their furthered inclusion into aviation fuels. The work presented in this paper develops SPRs by building a data set that includes physical properties important to the aviation industry. The physical properties considered are energy density, specific energy, melting point, density, flashpoint, the Hansen solubility parameter, and the yield sooting index (YSI). Further, our data set includes cycloalkanes drawn from the following structural groups: fused cycloalkanes, n-alkylcycloalkanes, branched cycloalkanes, multiple substituted cycloalkanes, and cycloalkanes with different ring sizes. In addition, a select number of cycloalkanes are blended into Jet-A fuel (POSF-10325) at 10 and 30 wt%. Comparison of neat and blended physical properties are presented. One major finding is that ring expanded systems, those with more than six carbons, have excellent potential for inclusion in SAFs. Our data also indicate that polysubstituted cycloalkanes have higher YSI values.
This dataset contains processed output from AquaBOT, which combines 30-second interval measurements of GPS location and YSI water quality parameters (temperature (degrees Celsius), dissolved oxygen (mg/l), specific conductance(microSiemens/cm at 25 degrees Celsius), and turbidity (NTU)), for 3 sections of the Muskegon River in Michigan, USA. The file aquabot2024_MuskegonRiver.csv has information on locations, dates and times, and observations. The data were processed by removing observations recorded before and after AquaBOT was in the water and observations where no data values were recorded. No other QA/QC was done. This research was performed as part of the DOE Research Development and Partnership Pilot award “Expanding Collaborative Capacity to Address Climate Resiliency in the Great Lakes Region”, which aims to expand collaborations between researchers at Central Michigan University and U.S. Department of Energy labs and projects focused on enhancing climate resilience in Great Lakes communities and ecosystems.
A new open-source code for the simulation of the diffusion flamelet equations is proposed. Emphasis is placed on using an approximate Jacobian to reduce the computational cost of the matrix operations. Performance of the proposed solvers is tested by performing flamelet calculations with kinetic mechanisms of varying sizes. For the unity Lewis number equations, the present iterative Newton solver using an approximate Jacobian greatly outperforms direct Newton solvers using exact Jacobians. The computation cost scales linearly with the number of species, leading to a reduction in solution times by two orders of magnitude for mechanisms containing thousands of species. The applicability of the Jacobian approximations to the solution of the non-unity Lewis number flamelet equations is assessed. The approximations are generally inadequate to solve the full non-unity Lewis number equations but can be used in some applications depending on the balance of terms in the flamelet equations. As an example, the flamelet solver is applied to the study of sooting tendencies in laminar co-flow diffusion flames where modified non-unity Lewis number flamelet equations, previously shown to accurately reproduce experimentally-measured Yield Sooting Indices (YSI), are solved. Here, the accelerated flamelet solver is well suited for sensitivity analysis and uncertainty quantification with large detailed kinetic mechanisms, tasks for which the computational cost was previously prohibitive.
While the sooting tendencies of regular hydrocarbons, oxygenates, and complex fuel mixtures have been well-studied, far less research has been devoted to analyzing the influence of fuel-nitrogen on soot formation. The effect of nitrogen on soot formation becomes relevant for diesel fuels with nitrogen-containing additives, as well as biomass or biomass-derived fuels, which can contain up to 30% nitrogen-containing compounds by dry weight. To begin closing these gaps in the literature, the sooting tendencies of 14 C4 and C6 amines were measured. Sooting tendencies were quantified by re-scaling relative soot concentrations measured in fuel-doped methane flames into Yield Sooting Indices (YSI). The relative soot concentrations were measured with line-of-sight spectral radiance, and validation experiments confirmed that the presence of nitrogen in the test compounds did not interfere with this diagnostic. All of these amines had lower sooting tendencies than the structurally analogous hydrocarbons and oxygenates. The sooting tendencies of amine isomers with the same chemical formula varied significantly. Secondary amines with linear substituents were found to offer the lowest sooting propensity, while primary amines with branched substituents were observed to yield the largest sooting tendencies. In conclusion, the relationship between sooting propensity and chemical structure of the amines hints at the complex nature of soot formation, and highlights an interesting and unexplored area of combustion chemistry for further studies.
Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.
Abstract The conversion of biomass using fast pyrolysis has the potential to be significantly less expensive at scale compared to alternative methods such as fermentation and gasification. Selective upgrading of the products of fast pyrolysis through chemical catalysis produces compounds with lower oxygen content and lower acidity; however, identifying the specific catalytic pathways for producing viable fuels and fuel additives often requires a trial-and-error approach. Specifically, key properties of the compounds must be experimentally tested to evaluate the viability of the resultant compounds. The present work proposes predictive models constructed with artificial neural networks (ANNs) for cetane number (CN), yield sooting index (YSI), kinematic viscosity (KV), and cloud point (CP), with blind test set median absolute errors of 5.14 cetane units, 3.36 yield sooting index units, 0.07 millimeters squared per second, and 4.89 degrees Celsius, respectively. Furthermore, the cetane number, yield sooting index, kinematic viscosity, and cloud point were predicted for over three hundred expected products from the catalytic upgrading of pyrolysis oil. It was discovered that 130 of these compounds have predicted cetane numbers greater than 40, with four of these compounds possessing predicted yield sooting index values significantly less than that of diesel fuel and predicted viscosities and cloud points comparable to that of diesel fuel.
This resource includes turbidity and suspended sediment data collected at two sampling stations located on Gwynns Falls in Baltimore County, MD, USA. In addition, two forested reference sites, Baisman Run and Pond Branch at Oregon Ridge, and two urban sites, Dead Run and Maiden's Choice Run (tributaries to Gwynns Falls), were sampled in Baltimore County and Baltimore City, MD, USA. Turbidity sensor data were collected at a 5-minute frequency using YSI EXO2 sondes. Suspended sediment was collected using ISCO samplers for the purpose of establishing correlations between turbidity and suspended sediment concentration. The six sites are co-located with USGS stream gages. This resource is part of the Baltimore Social-Environmental Collaborative Urban Integrated Field Laboratory supported by Department of Energy as well as the Critical Zone Collaborative Network supported by National Science Foundation. This resource includes a technical report summarizing the findings.
This study introduces a Drought Adaptation Index (DAI), derived from Best Linear Unbiased Prediction (BLUP), as a method to assess drought resilience in switchgrass (Panicum virgatum L.). A panel of 404 genotypes was evaluated under drought-stressed (CV) and well-watered (UC) conditions over four consecutive years (2019–2022). BLUP-estimated biomass yields were used to calculate the DAI, which enabled classification of genotypes into four adaptation groups: very well-adapted, well-adapted, adapted, and unadapted. The DAI was compared with conventional drought tolerance indices, including the Stress Susceptibility Index (SSI), Stress Tolerance Index (STI), Geometric Mean Productivity (GMP), and Yield Stability Index (YSI). Correlation analyses demonstrated strong agreement between DAI and these indices, supporting its validity and consistency. Biplot analyses using the Genotype plus Genotype-by-Environment Interaction (GGE) and Additive Main Effects and Multiplicative Interaction (AMMI) models revealed significant genotype-by-environment interactions (GEI) and identified J222.A, J463.A, and J295.A. A as high-performing genotypes, with J222.A exhibiting greater yield stability across treatments and years. Additionally, DAI isoline curves provided a graphical representation of differential genotype performance under drought and control conditions. These visualizations aided in distinguishing genotypes with stable and superior biomass yield across contrasting environments. Overall, the BLUP-based DAI is a robust and practical selection tool that improves the accuracy of identifying drought-resilient, high-yielding switchgrass genotypes. Its integration into breeding programs offers a comprehensive framework for improving biomass productivity and stress adaptation under variable climatic conditions. The application of DAI supports the development of climate-resilient cultivars and contributes to sustainable bioenergy and forage production systems.
A unique exercise facility has been developed and used to perform tipper body ergometry tests for space applications. Originally designed to simulate the muscular, cardiovascular and thermoregulatory responses to working in zero gravity, this facility may be used to conduct basic thermoregulatory investigations applicable to multiple sclerosis patients. An environmental chamber houses the tipper body ergometer and permits control of temperature, air now and humidify. The chamber is a closed system and recirculate-s air after conditioning if. A Cybex Lipper body ergometer has been mounted horizontally on the wall of the environmental chamber. In this configuration, the subject lies underneath the arm crank on a supine seat in order to turn the crank. The supine seat can be removed in order to introduce other equipment into the chamber such as a stool to allow upright arm cranking, or a treadmill to allow walk-run experiments. Physiological and environmental signals are fed into a Strawberry Tree data acquisition system while being monitored and logged using the Workbench software program. Physiological monitoring capabilities include 3-lead EKG using an H-P patient monitor, 5 site skin temperature and core temperature using YSI thermistors, and O2 consumption and CO2 production using AMFTFK Applied Electrochemistry analyzers and sensors. This comprehensive data acquisition set tip allows for calculation of various thermoregulatory indices including heat storage, evaporative heat loss, latent heat loss, and metabolic rate. The current system is capable of adding more data acquisition channels if needed. Some potential studies that could be carried out using the facility include: 1) An investigation into the efficiency of cooling various segments of the body to lower Tc 1-2 F. 2) A series of heat and mass balance studies comparing various LCG configurations.
Sustainably-produced biofuels, including furans derived from lignocellulosic biomass, are being studied as potential next-generation fuels. Biofuels have unique structures that are absent in conventional fuels and they potentially emit fewer particulates than conventional fuels because of the presence of oxygen atoms. In this work, we focus on quantification and measurement of sooting tendency. A set of furans was selected and their sooting tendencies were characterized by Yield Sooting Index (YSI), which was obtained by measuring the line-of-sight spectral radiance (LSSR) of a coflow laminar diffusion flame doped with 1000 ppm of each test fuel. The measured YSIs are lower than traditional gasoline fuels and benzenoid aromatics (e.g., YSIbenzene = 100). We also performed numerical simulations with a detailed chemical kinetic mechanism to analyze the flame chemistry of 2-methylfuran (2MF). 4→5 H-atom migration in the furan ring causes 31.0% of the total decomposition and is the dominant fuel consumption pathway leading to soot. DFT calculations via the G4 composite method were carried out to obtain activation energies (Ea) and bond dissociation energies (BDE). The results showed that the 4→5 H-atom migration in the furan ring has the second lowest Ea and subsequently forms a 1,2-diene-substituted ketone derivative as a stable species. The ring-opening products of the 4→5 H-atom migration are propyne + a linear ketene for 2-n-alkylfurans, and 1-butyne + a methyl-substituted ketene for 2-t-butylfuran. These results can explain the trends of the measured YSIs of 2-alkylfurans.
The files included in this data package provide site wide water level data, geochemical data, and borehole information associated with the Rifle site in Colorado during the Lawrence Berkeley National Lab (LBNL) led Integrated Field Research Challenge (IFRC) and Scientific Focus Area (SFA) research projects. The data informs hydrogeochemical interactions occurring in the watershed. The readme file contains detailed metadata for the files, including naming conventions, column headers, and data validation.The borehole information is provided in the file called rifle_boreholes.csv. It provides names and location for boreholes/wells at the Rifle. The waterlevel data is provided in the folder rifle_waterlevels. The geochemical data is provided in two folders: the rifle_floodplain_geochemistry folder has data which was collected from wells across the floodplain and a variety of offsite locations that contribute solutes to the floodplain given their upgradient locations. The rifle_plotC_geochemistry folder has data which was collected primarily in plot C, which was a focused study area in which a large number of groundwater manipulation / biostimulation experiments were conducted. While not all geochemical constituents are included for each well location and sampling date, they include anions, cations, trace metals, dissolved organic and inorganic carbon, stable isotopes of water and sulfur (as sulfate), and water quality parameters including pH, groundwater electrical conductivity (EC) and dissolved oxygen (DO). All files are in csv or txt format.