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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Physical Origins of the Transient Absorption Spectra and Dynamics in Thin-Film Semiconductors: The Case of BiVO 4

Transient absorption (TA) spectroscopy is uniquely suited for understanding kinetic processes initiated by light over vast ranges of time. In combination with white light probes, the recorded differential absorption spectra can contain spectroscopic signatures characteristic of specific charge carrier population densities. However, disentangling the often-complex and convoluted spectra is made challenging without robust analysis methods relating the underlying physical mechanisms to the spectral components. In this work, we address the origin of the transient spectra of a model system of emerging solar energy harvesting materials using a monoclinic BiVO 4 thin film. Using ground-state optical properties of the semiconductor, we find the main derivative-like spectral response to be related to shifting and broadening of oscillators, rather than specific carrier-related transitions. However, by using the Drude optical model of free carriers, we also identify the transient response related to free-hole density. Importantly, sample heating from the optical pumping, which begins at ~10 ps and plateaus by ~200 ps, dominates the overall spectral response at longer times. On the basis of a physical model of the spectral response, a kinetic model is developed that describes the pump power dependence of the free-hole density, as well as the temporal evolution of the spectral changes associated with shifting and broadening of oscillators. First-principles density functional theory calculations are used to rationalize experimental measurements. Here, this comprehensive approach to analyzing and modeling the TA spectra offers a generalizable basis for understanding the complex pump-probe data, reveals thermal heating artifacts that are frequently erroneously assigned to long-lived photocarriers, and offers a path to eliminating ambiguity in analysis of photocarrier dynamics in solid-state systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Solving the sample size problem for resource selection functions

Abstract Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS‐based animal telemetry. Cited thresholds for sufficiency include a number of captured animals and as many relocations per animal N as possible. These thresholds render many RSF‐based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed‐form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies.

Street, Garrett M.↗
DOE OSTI2022

Phenomenological Model of Nonlinear Dynamics and Deterministic Chaotic Gas Migration in Bentonite: Experimental Evidence and Diagnostic Parameters

Understanding gas migration in compacted clay materials, e.g., bentonite and claystone, is important for the design and performance assessment of an engineered barrier system of a radioactive waste repository system, as well as many practical applications. Existing field and laboratory data on gas migration processes in low-permeability clay materials demonstrate the complexity of flow and transport processes, including various types of instabilities, caused by nonlinear dynamics of coupled processes of liquid–gas exchange, dilation, fracturing, fracture healing, etc., which cannot be described by classical models of fluid dynamics in porous media. We here show that the complexity of gas migration processes can be explained using a phenomenological concept of nonlinear dynamics and deterministic chaos theory. To do so, we analyzed gas pressure and gas influx (i.e., input) and outflux (i.e., output), recorded during the gas injection experiment in the compact Mx80-D bentonite sample, and calculated a set of the diagnostic parameters of nonlinear dynamics and chaos, such a global embedding dimension, a correlation dimension, an information dimension, and a spectrum of Lyapunov exponents, as well as plotted 2D and 3D pseudo-phase-space strange attractors, based on the univariate influx and outflux time series data. These results indicate the presence of phenomena of low-dimensional deterministic chaotic behavior of gas migration in bentonite. In particular, during the onset of gas influx in the bentonite core, before the breakthrough, the development of gas flow pathways is characterized by the process of chaotic gas diffusion. After the breakthrough, with inlet-to-outlet movement of gas, the prevailing process is chaotic advection. During the final phase of the experiment, with no influx to the sample, the relaxation pattern of gas outflux is resumed back to a process of chaotic diffusion. The types of data analysis and a proposed phenomenological model can be used to establish the basic principles of experimental data-gathering, modeling predictions, and a research design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning for domain transfer between simulated and experimental 2D X-ray diffraction patterns using generative adversarial networks

X-ray diffraction (XRD) is a well-established technique for analyzing materials at an atomic level. Dynamic compression experiments (DCE), in which materials are subject to extreme pressures, can provide fundamental understanding to pressure-induced phase transitions and compression of the crystal lattice. The analysis of XRD patterns from highly compressed samples is non-trivial given the sparsity of data, high experimental costs, and the fact that the data is often marred with X-ray background and other artifacts. While accurate computational frameworks exist, they solve the forward problem—from structures and orientations to XRD patterns. Solving the inverse problem for 2D experimental diffraction patterns is currently a complex manual process of matching and comparing experimentally observed patterns to computationally generated ones. Machine learning is a promising tool for automating the matching process but often requires data-intensive architectures. Here, in this study, we use a CycleGAN to translate the domain of limited experimental data to a domain in which there is readily available simulated data. This domain shift allows data-intensive machine learning models that have only been trained on simulated XRD patterns to be used in the analysis of experiments.

Brozak, Samantha Jean [Sandia National Laboratorie↗
DOE OSTI2024

Thermal Activation of Zirconium(IV) Acetylacetonate Catalysts to Enhance Polyurethane Synthesis and Reprocessing

Carbamate formation and exchange catalysts enable efficient polyurethane (PU) manufacturing, as well as emerging recycling and reprocessing methods for PU thermosets. Zirconium β-diketonate complexes, such as Zr acetylacetonate [Zr(acac) 4 ], are effective alternatives to toxic organotin catalysts that have been used for PU reprocessing. Here, we report that Zr(acac) 4 undergoes a thermally activated process in the PU network during reprocessing that transforms it into a more active carbamate exchange catalyst. This process is associated with the irreversible loss of acetylacetonate ligands and is not observed for the more sterically hindered Zr 2,2,6,6-tetramethyl-3,5-heptanedione [Zr(tmhd) 4 ] complex. Crossover experiments between PU thermoplastics indicated enhanced carbamate exchange after the thermal activation of Zr(acac) 4 in the presence of one of the PUs, whereas a sample of Zr(acac) 4 activated in the absence of the PU had no catalytic activity. Thermal gravimetric analysis suggested that this process is associated with the loss of one protonated acac ligand. Stress relaxation analysis of PU thermosets indicated a distinct change in the characteristic relaxation time associated with the thermal activation of Zr(acac) 4 at temperatures above 140 °C; no such change was observed for samples reprocessed using Zr(tmhd) 4 . Density functional theory and molecular experiments suggest that irreversible ligand exchange of acac with alkoxide or carbamate reduces the activation energy for urethane formation and reversion. Furthermore, the Zr(acac) 4 catalyst activated in the presence of a PU’s polyol precursor provided more porous and less dense PU foams compared to those made using the unactivated Zr(acac) 4 catalyst. Furthermore, these findings are important for developing improved PU synthesis and recycling processes. Thermally activating a catalyst during reprocessing may provide more nuanced control of the in-use and reprocessing characteristics of PU thermosets.

Alcohols↗
DOE OSTI2019

Online supercritical fluid extraction mass spectrometry (SFE-LC-FTMS) for sensitive characterization of soil organic matter

We report a novel technical approach for subcritical fluid extraction (SFE) for organic matter characterization in complex matrices such as soil. The custom platform combines on-line SFE with micro-solid phase extraction, nano liquid chromatography (LC), electrospray ionization and Fourier transform mass spectrometry (SFE-LC-FTMS). We demonstrated the utility of SFE-LC-FTMS, including results from both Orbitrap and FTICR MS, for analysis of complex mixtures of organic compounds in a solid matrix by characterizing soil organic matter in peat, a high-carbon soil. For example, in a single experiment, >6000 molecular formulas can be assigned based upon FTICR MS data from 1–50 μL of soil samples (roughly 1–50 mg of soil, dependent on soil density), nearly twice that typically obtained from direct infusion liquid solvent extraction (LSE) from an order of magnitude larger volume of the same soil. The detected species consisted predominately of lipid-like, lignin-like and protein-like compounds, based on their O/C and H/C ratios, with predominantly CHO and CHONP molecular compositions. These results clearly demonstrate that SFE has the potential to effectively extract a variety of molecular species and could become an important member of a suite of extraction methods for studying SOM and other natural organic matter. This is especially true when comprehensive coverage, minimal sample volumes, and high sensitivity are required, or when the presence of organic solvent residue in residual soil is problematic. Lastly, the SFE based extraction protocol could potentially enable spatially resolved characterization of organic matter in soil with a resolution of ~1 mm3 to facilitate studies probing the spatial heterogeneity of soil.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effect of Micro-Structural Dispersity of SiMo Ductile Iron on High Temperature Performance during Static Oxidation

High silicon and molybdenum (SiMo) ductile iron is commonly used for car exhaust systems, and its micro-structural dispersity depends on intrinsic parameters, which include alloy composition and inoculation efficiency, as well as extrinsic factors, such as casting wall thickness and molding material, which define cooling rate during solidification. Micro-structural dispersity is referred to as the degree of heterogeneity of sizes of structural constituencies within the microstructure. A variation in the micro-structural dispersity could impact the high temperature performance of SiMo ductile iron during static oxidation and transient thermo-mechanical loading conditions. In this study, static high temperature tests were performed on SiMo ductile iron solidified in a casting with varying wall thicknesses from 5 mm to 100 mm. The faster solidified specimens (taken from near chilled casting surfaces) had extremely high micro-structural dispersity as compared to the thicker section samples. After thermal exposure, each of the samples were characterized using 2D sections and 3D µCT images, and the results indicated an order of magnitude difference in graphite phase dispersity. The surface degradation was quantified after static oxidation experiments were implemented at temperature intervals between 650 °C and 800 °C. Non-destructive µCT 3D analysis and SEM/EDS were performed on cross sections and used to quantify the scale topology and structure. Carbon analysis was used to decouple the scale formation and decarburization phenomena that occurred within the samples. These methods enabled the quantification of the oxidation of the SiMo cast iron with different micro-structural dispersity levels after being exposed to high temperature static oxidation. Additionally, the complex material behavior during oxidation-assisted transient thermo-mechanical loading will be presented in a separate article.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Complementary Metagenomic Approaches Improve Reconstruction of Microbial Diversity in a Forest Soil

ABSTRACT Soil ecosystems harbor diverse microorganisms and yet remain only partially characterized as neither single-cell sequencing nor whole-community sequencing offers a complete picture of these complex communities. Thus, the genetic and metabolic potential of this “uncultivated majority” remains underexplored. To address these challenges, we applied a pooled-cell-sorting-based mini-metagenomics approach and compared the results to bulk metagenomics. Informatic binning of these data produced 200 mini-metagenome assembled genomes (sorted-MAGs) and 29 bulk metagenome assembled genomes (MAGs). The sorted and bulk MAGs increased the known phylogenetic diversity of soil taxa by 7.2% with respect to the Joint Genome Institute IMG/M database and showed clade-specific sequence recruitment patterns across diverse terrestrial soil metagenomes. Additionally, sorted-MAGs expanded the rare biosphere not captured through MAGs from bulk sequences, exemplified through phylogenetic and functional analyses of members of the phylum Bacteroidetes . Analysis of 67 Bacteroidetes sorted-MAGs showed conserved patterns of carbon metabolism across four clades. These results indicate that mini-metagenomics enables genome-resolved investigation of predicted metabolism and demonstrates the utility of combining metagenomics methods to tap into the diversity of heterogeneous microbial assemblages. IMPORTANCE Microbial ecologists have historically used cultivation-based approaches as well as amplicon sequencing and shotgun metagenomics to characterize microbial diversity in soil. However, challenges persist in the study of microbial diversity, including the recalcitrance of the majority of microorganisms to laboratory cultivation and limited sequence assembly from highly complex samples. The uncultivated majority thus remains a reservoir of untapped genetic diversity. To address some of the challenges associated with bulk metagenomics as well as low throughput of single-cell genomics, we applied flow cytometry-enabled mini-metagenomics to capture expanded microbial diversity from forest soil and compare it to soil bulk metagenomics. Our resulting data from this pooled-cell sorting approach combined with bulk metagenomics revealed increased phylogenetic diversity through novel soil taxa and rare biosphere members. In-depth analysis of genomes within the highly represented Bacteroidetes phylum provided insights into conserved and clade-specific patterns of carbon metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Dynamics and lipid membrane coupling of the RAS-RAF complex revealed via multiscale simulations

To gain molecular and mechanistic insights into initiation of the RAS-RAF signaling cascade, we developed and used a combination of multiscale simulation and experimental approaches. The influence and impact of the membrane on RAS and RAF proteins is a factor we are just beginning to understand and appreciate in more detail. Molecular simulation is an ideal methodology to further study this complicated relationship between the membrane and associated proteins. Our previous work using Multiscale Machine-learned Modeling Infrastructure investigated different lipid compositions solely around the KRAS4b protein and the interplay between protein behavior and these membrane environments. Multiscale Machine-learned Modeling Infrastructure uses machine learning to couple adjacent simulation scales and has been efficiently scaled across some of the world’s largest high-performance computers. Recently, we have expanded this multiresolution framework to include the all-atom simulation scale and to incorporate the RAF RBDCRD domains. Here, we present the overall analysis results from this new simulation campaign comprising a mixture of RAS and RAF RBDCRD proteins. Approximately 35,000 coarse-grained and 10,000 all-atom molecular dynamics simulations were completed, sampled from a variety of protein/lipid composition configurations that were generated from a micron-scale continuum simulation containing hundreds of copies of the proteins. Our studies suggest that orientations of the RAS-RBDCRD complex on the membrane occupy distinct configurational states, and the spatial patterns of lipid arrangements around these different protein states are unique to each state. The extent and size of lipid “fingerprints” imposed on the membrane by the RAS-RBDCRD protein complex are significantly larger than observed for just the RAS protein on its own. These protein complexes strongly associate, but we do not observe statistically significant preferred protein-protein orientations. These observations indicate that spatial colocalization of RAS-RBDCRD proteins in the same vicinity may be assisted by specific membrane environments, acting to increase the probability of signaling complex formation.

Carpenter, Timothy S. [Lawrence Livermore National↗
DOE OSTI2021

Characterization of shale using Helium and Argon at high pressures

In order to estimate the shale gas in place and the eventual recovery during shale gas operations, one of the key requirements is to accurately characterize the shale’s petrophysical and transport properties such as porosity, permeability, diffusivity, and storage capacity. Despite the many efforts reported in the technical literature aiming to provide an improved understanding of the complex pore structures and the associated fluid flow in gas shales, complete characterization of organic-rich shale samples still poses a big challenge. Here, we have characterized mass transfer and sorption in shale at different length scales using Helium (He) and Argon (Ar) as probe gases. Thermogravimetric analysis (TGA) with a shale cube of ~1 cm 3 in size and gas expansion experiments with a full-diameter core (3.5” in diameter) were used to measure sorption kinetics/isotherms and mass transfer, respectively. Both samples are from the same depth/location in the Marcellus shale formation. The TGA steady-state technique was initially used to generate excess sorption isotherms for Ar, while dynamic TGA experiments were used to study its sorption kinetics. The TGA experiments demonstrate that Ar, which has a similar sorption potential as Methane, but is generally assumed to be inert, adsorbs onto the surfaces of the mesoporous and microporous regions of the shale samples according to a Langmuir-type behavior. Helium expansion experiments, on the full-diameter core, were used to measure the overall porosity, on the basis that He is a non-sorbing and inert gas as compared to Ar. The He expansion experiments, furthermore, allow us to delineate the mass transfer of gas across the inherent hierarchy of pore sizes, including macropores (macro- and microcracks), mesopores and micropores. Similar expansion experiments were also performed with Ar to study the combined impact of mass transfer and sorption. A triple-porosity model (TPM) was utilized to interpret the He expansion experiments with the shale core and to extract (estimate) relevant transport parameters. We report and compare here the diffusivities and permeabilities of the whole core for both He and Ar, as calculated from the modeling and fitting of the experimental data. On the premise that the shale cube is representative of the matrix region of the core, the Ar sorption kinetics from the cube experiments were subsequently combined with the transport parameters extracted from the He experiments to predict the behavior of the Ar expansion test with the full-diameter core. An excellent agreement is observed between the model predictions and the experimental data. The experimental observations and their interpretation indicate that one must be cautious when using Ar to estimate the true porosity and permeability of shales. In addition, we demonstrate that He and Ar probe gases, when used in tandem, can be employed effectively as a tool to characterize shales in terms of mass transfer and sorption dynamics across scales.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2024

Phase retrieval for refraction-enhanced x-ray radiography using a deep neural network

X-ray refraction-enhanced radiography (RER) or phase contrast imaging is widely used to study internal discontinuities within materials. The resulting radiograph captures both the decrease in intensity caused by material absorption along the x-ray path, as well as the phase shift, which is highly sensitive to gradients in density. A significant challenge lies in effectively analyzing the radiographs to decouple the intensity and phase information and accurately ascertain the density profile. Conventional algorithms often yield ambiguous and unrealistic results due to difficulties in including physical constraints and other relevant information. We have developed an algorithm that uses a deep neural network to address these issues and applied it to extract the detailed density profile from an experimental RER. To generalize the applicability of our algorithm, we have developed a technique that quantitatively evaluates the complexity of the phase retrieval process based on the characteristics of the sample and the configuration of the experiment. Accordingly, this evaluation aids in the selection of the neural network architecture for each specific case. Beyond RER, the model has potential applications for other diagnostics where phase retrieval analysis is required.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Sparse optimization of two-dimensional terahertz spectroscopy

Two-dimensional terahertz spectroscopy (2DTS) is a low-frequency analog of two-dimensional optical spectroscopy that is rapidly maturing as a probe of a wide variety of condensed matter systems. However, a persistent problem with 2DTS is the long experimental acquisition times, which prevent its broader adoption. A potential solution, requiring no increase in experimental complexity, is signal reconstruction via compressive sensing. In this work, we apply the sparse exponential mode analysis (SEMA) technique to 2DTS of a cuprate superconductor. We benchmark the performance of the algorithm in reconstructing terahertz nonlinearities and find that SEMA reproduces the asymmetric photon echo line shapes at sampling rates as low as 10%, reaching the reconstruction noise floor at sampling rates beyond 20%–30%. The success of SEMA in reproducing such subtle, asymmetric line shapes confirms compressive sensing as a general method to accelerate 2DTS and multidimensional spectroscopies more broadly.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

High‐speed 4‐dimensional scanning transmission electron microscopy using compressive sensing techniques

Abstract Here we show that compressive sensing allows 4‐dimensional (4‐D) STEM data to be obtained and accurately reconstructed with both high‐speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4‐D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal‐to‐noise ratio in the recovered phase using 0.3% of the total data. Lay abstract : Four‐dimensional scanning transmission electron microscopy (4‐D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2‐dimensional signal is acquired at each 2‐D probe location, equating to a 4‐D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4‐D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100‐1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4‐D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10‐100 times. The acquired data is then processed to fill‐in or inpaint the missing data, taking advantage of the inherently low‐complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4‐D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4‐D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100‐300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera.

Robinson, Alex W.↗
DOE OSTI2019

Kinetics of the Carbothermic Reduction of Manganese Oxide from Slag

Experiments were performed using a range of test conditions to elucidate the rate controlling step during the reaction of liquid iron-carbon droplets and slags containing manganese oxide. Four conditions were tested in the system: initial MnO content in the slag (5, 10, and 15 wt pct), initial carbon content of the metal (1, 2.5, 4.3 wt pct), initial droplet mass (0.5, 1.0, and 1.5 g), and reaction temperature (1823 K [1550 °C], 1873 K [1600 °C], and 1923 K [1650 °C]). Data were collected using the Constant Volume Pressure Increase (CVPI) technique which tracked the continuous pressure increase in the sealed furnace over time. Samples were quenched at the end of each experiment and chemistry was checked using LECO Carbon Analysis and ICP (Inductively Coupled Plasma) for manganese. The rate of reaction can be broken into a faster initial period related to internal CO formation, and a slower second reaction controlled by a complex mechanism involving transport of oxygen from slag to metal via CO{sub 2} and decomposition of the CO{sub 2} at the gas–metal interface.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Imaging and spatially resolved mass spectrometry applications in nephrology

The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly growing. These powerful methods, which enable label-free and multiplexed detection of many molecular classes across omics domains (including metabolites, drugs, proteins and protein post-translational modifications), are beginning to reveal new molecular insights related to kidney health and disease. Further, the complexity of the kidney often necessitates multiple scales of analysis for interrogating biofluids, whole organs, functional tissue units, single cells and subcellular compartments. Various MS methods can generate omics data across these spatial domains and facilitate both basic science and pathological assessment of the kidney. Optimal processes related to sample preparation and handling for different MS applications are rapidly evolving. Emerging technology and methods, improvement of spatial resolution, broader molecular characterization, multimodal and multiomics approaches and the use of machine learning and artificial intelligence approaches promise to make these applications even more valuable in the field of nephology. Overall, spatially resolved MS and MS imaging methods have the potential to fill much of the omics gap in systems biology analysis of the kidney and provide functional outputs that cannot be obtained using genomics and transcriptomic methods.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Temperature distribution in a laser-heated diamond anvil cell as described by finite element analysis

Finite element analysis (FEA) is a powerful tool for numerically solving partial differential equations over complex geometries and is thus useful for analyzing heat transport in laser-heated diamond anvil cell (LHDAC) experiments. Our models expand on previously published simulations by calculating the volume-averaged temperatures of both the sample and insulation/pressure media under steady-state heating to determine the thermal pressure of the hot sample. Our goal is to produce an accurate relationship between the measured surface temperature of the absorbing sample and the temperature of the transparent insulating media, which is used to determine thermal pressure but susceptible to steep temperature gradients. We find that in doing so, our FEA models of temperature within the pressure/insulation media can differ from simplified estimates of temperature gradients by more than a factor of 2. We also explore temperature-dependent and temperature-independent thermal conductivity models and find that the volume-averaged temperatures differ by up to a factor of 1.3, forcing the predicted thermal pressures determined to also differ by up to a factor of 1.5 at a temperature of 2000 K at 50 GPa for neon. Higher temperatures exacerbate this difference. We also find that unintentional asymmetric sample insertion and sample heating, which are common in LHDAC experiments, do not have a first-order effect on volume-averaged temperatures. The FEA models, available in both Python and FlexPDE, are versatile across different sample geometries, materials, and heat source laser shapes.

Farah, Frederick↗
DOE OSTI2018

Increasing the Separation Capacity of Intact Histone Proteoforms Chromatography Coupling Online Weak Cation Exchange-HILIC to Reversed Phase LC UVPD-HRMS

Top-down proteomics is an emerging analytical strategy to characterize combinatorial protein post-translational modifications (PTMs). However, sample complexity and small mass differences between chemically closely related proteoforms often limit the resolution attainable by separations employing a single liquid chromatographic (LC) principle. In particular, for ultra-modified proteins like histones, extensive and time-consuming fractionation is needed to achieve deep proteoform coverage. Herein, we present the first online nano-flow comprehensive two-dimensional liquid chromatography (nLC×LC) platform top-down mass spectrometry analysis of histone proteoforms. The described two-dimensional LC system combines weak cation exchange chromatography under hydrophilic interaction LC conditions (i.e. charge- and hydrophilicity-based separation) with reversed phase liquid chromatography (i.e. hydrophobicity-based separation). The two independent chemical selectivities were run at nanoflows (300 nL/min) and coupled online with high-resolution mass spectrometry employing ultraviolet photodissociation (UVPD-HRMS). The nLC×LC workflow increased the number of intact protein masses observable relative to one-dimensional approaches and allowed characterization of hundreds of proteforms starting from limited sample quantities (~1.5 µg).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Colloidal State Machines as Smart Tracers for Chemical Reactor Analysis

A widely utilized tool in reactor analysis is passive tracers that report the residence time distribution, allowing estimation of the conversion and other properties of the system. Recently, advances in microrobotics have introduced powered and functional entities with sizes comparable to some traditional tracers. This has motivated the concept of Smart Tracers that could record the local chemical concentrations, temperature, or other conditions as they progress through reactors. Herein, the design constraints and advantages of Smart Tracers by simulating their operation in a laminar flow reactor model conducting chemical reactions of various orders are analyzed. It is noted that far fewer particles are necessary to completely map even the most complex concentration gradients compared with their conventional counterparts. Design criteria explored herein include sampling frequency, memory storage capacity, and ensemble number necessary to achieve the required accuracy to inform a reactor model. Cases of severe particle diffusion and sensor noise appear to bind the functional upper limit of such probes and require consideration for future design. The results of the study provide a starting framework for applying the new technology of microrobotics to the broad and impactful set of problems classified as chemical reactor analysis.

97 MATHEMATICS AND COMPUTING↗