Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
Not Available
36 MATERIALS SCIENCE↗
SEARCH · Search NASA
Results for “Chemical properties”
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.
Physical and chemical properties of sea salt deliquescent brines as a function of temperature and relative humidity
Not Available
54 ENVIRONMENTAL SCIENCES↗
Recent advancements in chiral spintronics: from molecular-level insights to device applications. A prospect based on the interplay between physical and chemical properties of chiral systems
This review discusses recent advancements in chiral-based spin-electronic devices achieved using suitable combinations of organic, inorganic and hybrid materials. The focus is on how chirality can be effectively used to control spin in practical applications, particularly in spintronic sensors and consumer devices. Interestingly, the underlying mechanism for this control is the chiral-induced spin selectivity (CISS) effect, which links the structural chirality of a material to its spin-selective electronic transport properties. The CISS effect arises from the observation that charge transmission in chiral systems is spin selective. Experimental evidence suggests a relationship between electron spin and the structural handedness of the material. In conclusion, this unique structure-transport relationship can be exploited to design a wide range of spintronics devices, including memory, transistors, logic gates and molecular q-bits.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Impact of fire on chemical properties and respiration of boreal forest soils
We collected both organic-rich and sandy soil cores from 12 sites (site_location_and_texture.csv) within Wood Buffalo National Park, Alberta, Canada. Laboratory burns were conducted by exposing intact soil cores to 60 kW m^-2 in a mass loss calorimeter to simulate boreal forest crown fires in order to measure the effects of burning (temp_data.csv) on soil properties including pH, total C, and total nitrogen (N) (metadata.csv). We used 70-day soil incubations and two-pool exponential decay models to characterize the impacts of burning and burn-induced changes in soil properties on soil respiration (soil_moisture_at_end_of_incubation.csv; soil_mass_at_end_of_incubation.csv; respiration_data.csv). Laboratory burns successfully captured a range of soil temperatures that were realistic for natural wildfire events.
54 ENVIRONMENTAL SCIENCES↗
Studies of optical and chemical properties of aged and fresh biomass burning absorbing aerosols for climate models
Particulate emissions from burning fuels from sub-Saharan Africa is currently poorly characterized. With increasing population and industrialization, emissions are expected to grow. Optical methods for measuring light absorption in the ultraviolet (UV) and visible (vis) regions of the spectrum are crucial for understanding the optical properties of BB aerosols (Bond and Bergstrom, 2006; Lack and Cappa, 2010; Moschos et al., 2021). One such instrument is the dual-spot AE33 model aethalometer is a common filter-based instrument that can be used to measure aerosol absorption properties (Hansen et al., 1984). This instrument has many figures of merit; however, the method's limitations include potential biases from scattering within the filter material, multiple scattering effects of the particles, filter loading (i.e. particle shadowing), and the need for wavelength-specific correction factors (Cλ), which depend on accurate intercomparison with other in situ techniques (Bond et al., 1999; Collaud Coen et al., 2010). This work aims to: 1. Optically characterize BB aerosol generated from African fuel 2. Correct the aethalometer at multiple wavelengths 3. Parametrize these correction factors. 4. Combusting the same fuels with two difference tube furnace systems assess the general applicability of this flexible means of producing BB aerosol. This was accomplished by conducting laboratory studies at Los Alamos National Laboratory (LANL) and North Carolina A&T State University (NCA&T), where aethalometer measurements were compared to in situ optical measurements. Mass cross sections of BB aerosol from these under-studied fuels will be determined along with their wavelength dependence.
09 BIOMASS FUELS↗
Chemical properties and single-particle mixing state of soot aerosol in Houston during the TRACER campaign
Abstract. A high-resolution soot particle aerosol mass spectrometer (SP-AMS) was used to selectively measure refractory black carbon (rBC) and its associated coating material using both the ensemble size-resolved mass spectral mode and the event trigger single particle (ETSP) mode in Houston, Texas, in summer 2022. This study was conducted as part of the Department of Energy Atmospheric Radiation Measurement (ARM) program's TRacking Aerosol Convection interactions ExpeRiment (TRACER) field campaign. The study revealed an average (±1σ) rBC concentration of 103 ± 176 ng m−3. Additionally, the coatings on the BC particles were primarily composed of organics (59 %; 219 ± 260 ng m−3) and sulfate (26 %; 94 ± 55 ng m−3). Positive matrix factorization (PMF) analysis of the ensemble mass spectra of BC-containing particles resolved four distinct types of soot aerosol, including an oxidized organic aerosol (OOABC,PMF) factor associated with processed primary organic aerosol, an inorganic sulfate factor (SO4,BC,PMF), an oxidized rBC factor (O-BCPMF), and a mixed mineral dust–biomass burning aerosol factor with significant contribution from potassium (K-BBBC,PMF). Additionally, K-means clustering analysis of the single-particle mass spectra identified eight different clusters, including soot particles enriched in hydrocarbon-like organic aerosol (HOABC,ETSP), sulfate (SO4,BC,ETSP), two types of rBC, OOA (OOABC,ETSP), chloride (ClBC,ETSP), and nitrate (NO3,BC,ETSP). The single-particle measurements demonstrate substantial variation in BC coating thickness with coating-to-rBC mass ratios ranging from 0.1 to 100. The mixing state index (χ), which denotes the degree of homogeneity of the soot aerosol, varied from 4 % to 94 % with a median of 40 %, indicating that the aerosol population lies in between internal and external mixing but has large temporal and source type variability. In addition, a significant fraction of BC-containing particles, a majority enriched with oxidized organics and sulfate, exhibit sufficiently high κ values and diameters conducive to activation as cloud nuclei under atmospherically relevant supersaturation conditions. This finding bears significance in comprehending the aging processes of rBC-containing particles and their activation into cloud droplets. Our analysis highlights the complex nature of soot aerosol and underscores the need to comprehend its variability across different environments for accurate assessment of climate change.
54 ENVIRONMENTAL SCIENCES↗
Soil moisture, physical and chemical properties coincident with airborne SAR data collections for 2017 and 2019, Seward Peninsula, Alaska
Soil samples were collected coincident with in-situ soil moisture and thaw depth measurements at NGEE Arctic study sites on the Seward Peninsula, Alaska in August 2017 and 2019 Field measurements and flights were conducted during both summers as a collaboration between the NASA ABoVE Project's Airborne SAR Campaign and the NGEE Arctic Project. Airborne overflights of L-band SAR instruments occurred during the soil sampling periods. Laboratory measurements of soil properties include bulk density, volumetric and gravimetric water content, carbon and nitrogen content, and particle size of mineral components. Soil samples processed and reported in this dataset were collected coincident with in situ measurements of soil moisture and thaw depth and airborne P-band and L-band SAR measurements as a collaboration between NGEE Arctic and NASA ABoVE. To learn more about how ABoVE protocols were applied for sampling site selection and making in situ measurements, see the following datasets: (2017: https://doi.org/10.5440/1423892 and 2019: https://doi.org/10.5440/1856042). Contained in this dataset are four .csv data files (including data dictionaries) and one zipped folder of *.pdf files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort (with some overlap with Covid-19 pandemic) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
Insights into the Physical-Chemical Properties of a CO2-Responsive Fracturing Fluid
Here we determine the phase behavior of StimuFrac, a CO2 responsive fracturing fluid, under geothermal wellbore conditions. StimuFrac is an aqueous poly(allylamine) fluid that crosslinks in the presence of CO2. StimuFrac significantly reduces the net pressure required to induce fractures, relative to other fracture fluids, and has potential to reduce water use. However, the phase behavior and equations of state to describe StimuFrac’s phase behavior remain unavailable. Here we determine the density and molar volume of the fluid as a function of geothermal relevant temperatures, pressures, and weight fractions of StimuFrac added. In general, experiments find that StimuFrac’s density decreases as temperature and pressure increase. Using these results, equations of saturated state and phase diagrams for different polymer concentrations are reported. These results are critical inputs for planned numerical simulation efforts.
Pease, Leonard F.↗
Fundamental Linkage Between Structure, Electrochemical Properties, and Chemical Compositions of LiNi1-x-yMnxCoyO2 Cathode Materials
Explore the source record for details and available documents.
99 GENERAL AND MISCELLANEOUS↗
Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties
The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic–lipophilic difference equation used to predict microemulsions’ phase behavior expresses the oils’ physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. As a result, the best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R² score of 0.9
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Molecular Properties and Chemical Transformations Near Interfaces
The properties of bulk water and aqueous solutions are known to change in the vicinity of an interface and/or in a confined environment, including the thermodynamics of ion selectivity at interfaces, transition states and pathways of chemical reactions, and nucleation events and phase growth. Here we describe joint progress in identifying unifying concepts about how air, liquid, and solid interfaces can alter molecular properties and chemical reactivity compared to bulk water and multicomponent solutions. Furthermore, we also discuss progress made in interfacial chemistry through advancements in new theory, molecular simulation, and experiments.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Advanced biorefinery feedstock from non-recyclable municipal solid waste by mechanical preprocessing
Because of accelerated urbanization and the development of a global economy, a large quantity of municipal solid waste (MSW) has been collected and disposed of by the municipalities. Due to this drastic increase in the disposal of MSW, the need for its management is a must to preserve the environment. Currently, approximately 50% of the total MSW generated in the United States has been utilized through various recycling, combustion, and composting technologies, which means the remaining 50% is sent to landfill; this is often known as non-recyclable MSW (nMSW). As this nMSW is physically and chemically heterogenous and contains very high amounts of inorganic material, processing is required prior to using it as a biorefinery feedstock. Thus, this study focused on how mechanical preprocessing advanced the physical and chemical properties of nMSW. The physical and chemical properties were investigated in terms of particle size distribution, bulk density, ultimate and proximate analysis, and the higher heating value (HHV). The combustion properties were examined in terms of ignition temperature, peak heat release rate, and combustion efficiency. Results showed that the variability of physical and chemical properties of nMSW can be reduced by mechanical preprocessing. For example, the variability of the bulk density of the as-received nMSW was approximately 17.3% while it reduced to 5.8% when the sample size was reduced to 2 mm. Similarly, the variability of ash and HHV reduced from 49.2% to 11.0% and 13.4%–4.2%, respectively. Combustion thermograms showed that the size reduction positively improved the combustion properties. For example, 2 mm of spec sample started to ignite approximately 4 times earlier and took 6.5 folds less time to reach the peak heating rate compared to as-received nMSW. Overall, the mechanical preprocessing reduced the variability of physical and chemical properties in addition to the improvement of combustion behavior of the nMSW which is one step forward toward the biorefinery feedstock.
09 BIOMASS FUELS↗
Method for simultaneous characterization and expansion of reference libraries for small molecule identification
A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.
Colby, Sean M.↗
Correlating the role of nanofillers with active layer properties and performance of thin-film nanocomposite membranes
Thin-film nanocomposite (TFN) membranes are emerging water-purification membranes that could provide enhanced water permeance with similar solute removal over traditional thin-film composite (TFC) membranes. However, the effects of nanofiller incorporation on active layer physico-chemical properties have not been comprehensively studied. Accordingly, we aimed to understand the correlation between nanofillers, active layer physico-chemical properties, and membrane performance by investigating whether observed performance differences between TFN and control TFC membranes correlated with observed differences in physico-chemical properties. Throughout this work, the effects of nanofiller loading, surface area, and size on membrane performance, along with active layer physico-chemical properties, were characterized in TFN membranes incorporated with Linde Type A (LTA) zeolite and zeolitic imidazole framework-8 (ZIF-8). Results show that nanofiller incorporation up to ~0.15 wt% resulted in higher water permeance and unchanged salt rejection, above which salt rejection decreased 0.9–25.6 % and 26.1–48.3 % for LTA-TFN and ZIF-8-TFN membranes, respectively. Observed changes in active layer physico-chemical properties were generally unsubstantial and did not explain observed changes in TFN membrane performance. Therefore, increased water permeance in TFN membranes could be due to preferential water transport through porous structures of nanofillers or along polymer-nanofiller interfaces. These findings offer new insights into the development of high-performance TFN membranes for water/ion separations.
42 ENGINEERING↗