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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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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Mathematical Model of ATM Activation and Chromatin Relaxation by Ionizing Radiation

We propose a comprehensive mathematical model to study the dynamics of ionizing radiation induced Ataxia-telangiectasia mutated (ATM) activation that consists of ATM activation through dual mechanisms: the initiative activation pathway triggered by the DNA damage-induced local chromatin relaxation and the primary activation pathway consisting of a self-activation loop by interplay with chromatin relaxation. The model is expressed as a series of biochemical reactions, governed by a system of differential equations and analyzed by dynamical systems techniques. Radiation induced double strand breaks (DSBs) cause rapid local chromatin relaxation, which is independent of ATM but initiates ATM activation at damage sites. Key to the model description is how chromatin relaxation follows when active ATM phosphorylates KAP-1, which subsequently spreads throughout the chromatin and induces global chromatin relaxation. Additionally, the model describes how oxidative stress activation of ATM triggers a self-activation loop in which PP2A and ATF2 are released so that ATM can undergo autophosphorylation and acetylation for full activation in relaxed chromatin. In contrast, oxidative stress alone can partially activate ATM because phosphorylated ATM remains as a dimer. The model leads to predictions on ATM mediated responses to DSBs, oxidative stress, or both that can be tested by experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Bi-fated tendon-to-bone attachment cells are regulated by shared enhancers and KLF transcription factors

The mechanical challenge of attaching elastic tendons to stiff bones is solved by the formation of a unique transitional tissue. Here, we show that murine tendon-to-bone attachment cells are bi-fated, activating a mixture of chondrocyte and tenocyte transcriptomes, under regulation of shared regulatory elements and Krüppel-like factors (KLFs) transcription factors. High-throughput bulk and single-cell RNA sequencing of humeral attachment cells revealed expression of hundreds of chondrogenic and tenogenic genes, which was validated by in situ hybridization and single-molecule ISH. ATAC sequencing showed that attachment cells share accessible intergenic chromatin areas with either tenocytes or chondrocytes. Epigenomic analysis revealed enhancer signatures for most of these regions. Transgenic mouse enhancer reporter assays verified the shared activity of some of these enhancers. Finally, integrative chromatin and motif analyses and transcriptomic data implicated KLFs as regulators of attachment cells. Indeed, blocking expression of both Klf2 and Klf4 in developing limb mesenchyme impaired their differentiation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Chickpea NCR13 disulfide cross-linking variants exhibit profound differences in antifungal activity and modes of action

Small cysteine-rich antifungal peptides with multi-site modes of action (MoA) have potential for development as biofungicides. In particular, legumes of the inverted repeat-lacking clade express a large family of nodule-specific cysteine-rich (NCR) peptides that orchestrate differentiation of nitrogen-fixing bacteria into bacteroids. These NCRs can form two or three intramolecular disulfide bonds and a subset of these peptides with high cationicity exhibits antifungal activity. However, the importance of intramolecular disulfide pairing and MoA against fungal pathogens for most of these plant peptides remains to be elucidated. Our study focused on a highly cationic chickpea NCR13, which has a net charge of +8 and contains six cysteines capable of forming three disulfide bonds. NCR13 expression in Pichia pastoris resulted in formation of two peptide folding variants, NCR13_PFV1 and NCR13_PFV2, that differed in the pairing of two out of three disulfide bonds despite having an identical amino acid sequence. The NMR structure of each PFV revealed a unique three-dimensional fold with the PFV1 structure being more compact but less dynamic. Surprisingly, PFV1 and PFV2 differed profoundly in the potency of antifungal activity against several fungal plant pathogens and their multi-faceted MoA. PFV1 showed significantly faster fungal cell-permeabilizing and cell entry capabilities as well as greater stability once inside the fungal cells. Additionally, PFV1 was more effective in binding fungal ribosomal RNA and inhibiting protein translation in vitro. Furthermore, when sprayed on pepper and tomato plants, PFV1 was more effective in reducing disease symptoms caused by Botrytis cinerea, causal agent of gray mold disease in fruits, vegetables, and flowers. In conclusion, our work highlights the significant impact of disulfide pairing on the antifungal activity and MoA of NCR13 and provides a structural framework for design of novel, potent antifungal peptides for agricultural use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The mitochondrial Cu + transporter PiC2 (SLC25A3) is a target of MTF1 and contributes to the development of skeletal muscle in vitro

The loading of copper (Cu) into cytochrome c oxidase (COX) in mitochondria is essential for energy production in cells. Extensive studies have been performed to characterize mitochondrial cuproenzymes that contribute to the metallation of COX, such as Sco1, Sco2, and Cox17. However, limited information is available on the upstream mechanism of Cu transport and delivery to mitochondria, especially through Cu-impermeable membranes, in mammalian cells. The mitochondrial phosphate transporter SLC25A3, also known as PiC2, binds Cu + and transports the ion through these membranes in eukaryotic cells, ultimately aiding in the metallation of COX. We used the well-established differentiation model of primary myoblasts derived from mouse satellite cells, wherein Cu availability is necessary for growth and maturation, and showed that PiC2 is a target of MTF1, and its expression is both induced during myogenesis and favored by Cu supplementation. PiC2 deletion using CRISPR/Cas9 showed that the transporter is required for proliferation and differentiation of primary myoblasts, as both processes are delayed upon PiC2 knock-out. The effects of PiC2 deletion were rescued by the addition of Cu to the growth medium, implying the deleterious effects of PiC2 knockout in myoblasts may be in part due to a failure to deliver sufficient Cu to the mitochondria, which can be compensated by other mitochondrial cuproproteins. Co-localization and co-immunoprecipitation of PiC2 and COX also suggest that PiC2 may participate upstream in the copper delivery chain into COX, as verified by in vitro Cu + -transfer experiments. These data indicate an important role for PiC2 in both the delivery of Cu to the mitochondria and COX, favoring the differentiation of primary myoblasts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Effects of varying gelatin coating concentrations on RANKL induced osteoclastogenesis

Here, we assessed the effects of varying concentrations of gelatin coating on Receptor Activator of Nuclear Factor κ-B Ligand (RANKL)-induced RAW264.7 murine macrophage differentiation into osteoclast (OC) via osteoclastogenesis. The microstructures of coating surfaces with different concentrations of gelatin were examined by scanning electron microscopy and atomic force microscopy. Increased gelatin coating concentrations led to decreased gel rigidity but increased surface adhesion force attenuated OC differentiation and the decreased actin ring formation in RANKL-induced osteoclastogenesis. The decreased actin ring formation is associated with decreased lysosomal-associated membrane protein 1 (LAMP1) activity and bone resorption in the differentiated OCs with different gelatin coating concentrations as compared to the cells differentiated without gelatin coatings. In addition, increasing concentrations of gelatin coating attenuated the medium TGF-β1 protein levels and the expression levels of TGF-β and type-I (R1) and type-II (R2) TGF-β receptors in OCs, suggesting the gelatin-induced suppression of TGF-β signaling for the regulation of RNAKL-induced OC differentiation. Taken together, these findings showed that changes in gelatin coating concentrations, which were associated with altered gel thickness and substrate rigidity, might attenuate TGF-β signaling events to modulate OC differentiation and concomitant actin ring formation and bone matrix resorption in RANKL-induced osteoclastogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Machine learning of consistent thermodynamic models using automatic differentiation

In this study, we propose a data-driven method to describe consistent equations of state (EOS) for arbitrary systems. Complex EOS are traditionally obtained by fitting suitable analytical expressions to thermophysical data. A key aspect of EOS is that the relationships between state variables are given by derivatives of the system free energy. In this work, we model the free energy with an artificial neural network and utilize automatic differentiation to directly learn the derivatives of the free energy. We demonstrate this approach on two different systems, the analytic van der Waals EOS and published data for the Lennard-Jones fluid, and we show that it is advantageous over direct learning of thermodynamic properties (i.e., not as derivatives of the free energy but as independent properties), in terms of both accuracy and the exact preservation of the Maxwell relations. Furthermore, the method implicitly provides the free energy of a system without explicit integration.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Extensive cellular multi-tasking within Bacillus subtilis biofilms

Bacillus subtilis is a soil-dwelling bacterium that can form biofilms, or communities of cells surrounded by a self-produced extracellular matrix. In biofilms, genetically identical cells often exhibit heterogeneous transcriptional phenotypes, so that subpopulations of cells carry out essential yet costly cellular processes that allow the entire population to thrive. Surprisingly, the extent of phenotypic heterogeneity and the relationships between subpopulations of cells within biofilms of even in well-studied bacterial systems like B. subtilis remains largely unknown. To determine relationships between these subpopulations of cells, we created 182 strains containing pairwise combinations of fluorescent transcriptional reporters for the expression state of 14 different genes associated with potential cellular subpopulations. We determined the spatial organization of the expression of these genes within biofilms using confocal microscopy, which revealed that many reporters localized to distinct areas of the biofilm, some of which were co-localized. We used flow cytometry to quantify reporter co-expression, which revealed that many cells “multi-task,” simultaneously expressing two reporters. These data indicate that prior models describing B. subtilis cells as differentiating into specific cell types, each with a specific task or function, were oversimplified. Only a few subpopulations of cells, including surfactin and plipastatin producers, as well as sporulating and competent cells, appear to have distinct roles based on the set of genes examined here. These data will provide us with a framework with which to further study and make predictions about the roles of diverse cellular phenotypes in B. subtilis biofilms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

An expanded transcriptome atlas for Bacteroides thetaiotaomicron reveals a small RNA that modulates tetracycline sensitivity

Plasticity in gene expression allows bacteria to adapt to diverse environments. This is particularly relevant in the dynamic niche of the human intestinal tract; however, transcriptional networks remain largely unknown for gut-resident bacteria. Here we apply differential RNA sequencing (RNA-seq) and conventional RNA-seq to the model gut bacterium Bacteroides thetaiotaomicron to map transcriptional units and profile their expression levels across 15 in vivo-relevant growth conditions. We infer stress- and carbon source-specific transcriptional regulons and expand the annotation of small RNAs (sRNAs). Integrating this expression atlas with published transposon mutant fitness data, we predict conditionally important sRNAs. These include MasB, which downregulates tetracycline tolerance. Using MS2 affinity purification and RNA-seq, we identify a putative MasB target and assess its role in the context of the MasB-associated phenotype. These data—publicly available through the Theta-Base web browser (http://micromix.helmholtz-hiri.de/bacteroides/)—constitute a valuable resource for the microbiome community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Variable Inhibition of Nitrous Oxide Reduction in Denitrifying Bacteria by Different Forms of Methanobactin

Aerobic methanotrophic activity is highly dependent on copper availability, and methanotrophs have developed multiple strategies to collect copper. Specifically, when copper is limiting (ambient concentrations < 1 μM), some methanotrophs produce and secret a small modified peptide (< 1,300 Da) termed methanobactin (MB) that binds copper with high affinity. As MB is secreted into the environment, other microbes that require copper for their metabolism may be inhibited as MB may make copper unavailable; e.g., inhibition of denitrifiers as complete conversion nitrate to dinitrogen involves multiple enzymes, some of which are copper-dependent. Of key concern is inhibition of the copper-dependent nitrous oxide reductase (NosZ), the only known enzyme capable of converting nitrous oxide (N 2 O) to dinitrogen. Herein, we show that different forms of MB differentially affect copper uptake and N2O reduction by Pseudomonas stutzeri strain DCP-Ps1 (that expresses clade I NosZ) and Dechloromonas aromatica strain RCB (that expresses clade II NosZ). Specifically, in the presence of MB from Methylocystis sp. strain SB2 (SB2-MB), copper uptake and nosZ expression were more significantly reduced than in the presence of MB from Methylosinus trichosporium OB3b (OB3b-MB). Further, N2O accumulation increased more significantly for both P. stutzeri strain DCP-Ps1 and D. aromatica strain RCB in the presence of SB2-MB versus OB3b-MB. These data illustrate that copper competition between methanotrophs and denitrifying bacteria can be significant and that the extent of such competition is dependent on the form of MB that methanotrophs produce.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST)

This technical report summarized the contribution of the DADAIST project funded by the Data Model Convergence Initiative via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL). Specifically, we report the development of the NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations), a new open-source Scientific Machine Learning library for formulating and solving parametric constrained optimization problems, physics-informed system identification, and parametric optimal control problems. NeuroMANCER is using differentiable programming to combine modern data-driven models and optimization modeling language into a coherent algorithmic and software framework. NeuroMANCER is a Pytorch-based framework and adopts much of its philosophy focused on research and development, rapid prototyping, and streamlined deployment. Strong emphasis is given to extensibility, interoperability with the PyTorch ecosystem, and quick adaptability to custom domain problems. Neuromancer repository contains a comprehensive library of differentiable modules, including custom activation functions, matrix factorizations, deep learning architectures, neural differential equations, differential equation solvers, implicit layers such as iterative solvers, high-level API for symbolic expressions, API for modeling and control of dynamical systems, and extensive set of tutorial code examples in the form of python scripts and jupyter notebooks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Towards a Verifiable Domain-Specific Language for Hardware-Accelerated Stencils

Defining a domain-specific language (DSL) that supports vector-calculus abstractions eases the porting of partial differential equation (PDE) solvers to specialized architectures. Sufficiently high-level abstractions empower users to express universal laws with sufficient generality that the laws must always hold true within their domain of validity. A broad class of PDE solvers employs stencil-based algorithms, the target domain of Berkeley Lab's stencil accelerator chip co-design project. First released as open-source in January 2026, the Formal software framework lays a foundation for defining an embedded DSL based on composable operators that implement mimetic numerical methods -- stencil algorithms that guarantee satisfaction of discrete versions of important vector calculus theorems. The Formal DSL will be the frontend to a new class of stencil-PDE accelerators developed jointly by LBNL, UHCL, and UC Berkeley through the DOE Competitive Portfolios for Computer Science Project. This offers the potential of an order of magnitude acceleration for this important category of computational methods to serve the DOE mission. Future work on the Formal DSL will facilitate software verification via type-safe templates that enable problem-specific correctness proofs relying upon generic function theory and carefully crafted unit tests.

Rouson, Damian↗
DOE OSTI2023

Multidimensional quantitative phenotypic and molecular analysis reveals neomorphic behaviors of p53 missense mutants

Abstract Mutations in the TP53 tumor suppressor gene occur in >80% of the triple-negative or basal-like breast cancer. To test whether neomorphic functions of specific TP53 missense mutations contribute to phenotypic heterogeneity, we characterized phenotypes of non-transformed MCF10A-derived cell lines expressing the ten most common missense mutant p53 proteins and observed a wide spectrum of phenotypic changes in cell survival, resistance to apoptosis and anoikis, cell migration, invasion and 3D mammosphere architecture. The p53 mutants R248W, R273C, R248Q, and Y220C are the most aggressive while G245S and Y234C are the least, which correlates with survival rates of basal-like breast cancer patients. Interestingly, a crucial amino acid difference at one position—R273C vs. R273H—has drastic changes on cellular phenotype. RNA-Seq and ChIP-Seq analyses show distinct DNA binding properties of different p53 mutants, yielding heterogeneous transcriptomics profiles, and MD simulation provided structural basis of differential DNA binding of different p53 mutants. Integrative statistical and machine-learning-based pathway analysis on gene expression profiles with phenotype vectors across the mutant cell lines identifies quantitative association of multiple pathways including the Hippo/YAP/TAZ pathway with phenotypic aggressiveness. Further, comparative analyses of large transcriptomics datasets on breast cancer cell lines and tumors suggest that dysregulation of the Hippo/YAP/TAZ pathway plays a key role in driving the cellular phenotypes towards basal-like in the presence of more aggressive p53 mutants. Overall, our study describes distinct gain-of-function impacts on protein functions, transcriptional profiles, and cellular behaviors of different p53 missense mutants, which contribute to clinical phenotypic heterogeneity of triple-negative breast tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Selenomethionine protects oxidative-stress-damaged bone-marrow-derived mesenchymal stem cells via an antioxidant effect and the PTEN/PI3K/AKT pathway

Highlights: • Oxidative stress stimuli by H{sub 2}O{sub 2} suppressed osteogenic differentiation of BMSCs. • Selenomethionine could alleviate osteogenic dysfunction of oxidative-stress-damaged BMSCs. • Selenomethionine rescued osteogenic ability partly through the modulation of the PTEN/PI3K/AKT pathway. Dental implant surgery is currently a routine therapy for the repair of missing dentition or dentition defects. Both clinical and basic research have elucidated that oxidative stress caused by the accumulation of reactive oxygen species (ROS) for various reasons impairs the process of osteointegration after dental implantation. Therefore, the osteogenic micro-environment must be ameliorated to decrease the damage caused by oxidative stress. Selenomethionine (SEMET) has been reported to play an important role in alleviating oxidative stress and accelerating cell viability and growth. However, it remains unclear whether it exerts protective effects on bone-marrow-derived mesenchymal stem cells (BMSCs) under oxidative stress. In this study, we explored the influence of selenomethionine on the viability and osteogenic differentiation of BMSCs under oxidative stress and the underlying mechanisms. Results showed that 1 μM selenomethionine was the optimum concentration for BMSCs under H{sub 2}O{sub 2} stimulation. H{sub 2}O{sub 2}-induced oxidative stress suppressed the viability and osteogenic differentiation of BMSCs, manifested by the increases in ROS production and cell apoptosis rates, and by the decrease of osteogenic differentiation-related markers. Notably, the aforementioned oxidative damage and osteogenic dysfunction induced by H{sub 2}O{sub 2} were rescued by selenomethionine. Furthermore, we found that the PTEN expression level was suppressed and its downstream PI3K/AKT pathway was activated by selenomethionine. However, when PTEN was stimulated, the PI3K/AKT pathway was down-regulated, and the protective effects of selenomethionine on BMSC osteogenic differentiation diminished, while the inhibition of PTEN up-regulated the protective effects of selenomethionine. Together, these results revealed that selenomethionine could attenuate H{sub 2}O{sub 2}-induced BMSC dysfunction through an antioxidant effect, modulated via the PTEN/PI3K/AKT pathway, suggesting that selenomethionine is a promising antioxidant candidate for reducing oxidative stress during the process of dental implant osteointegration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Identification of functional non-coding variants associated with orofacial cleft

Oral facial cleft (OFC) comprises cleft lip with or without cleft palate (CL/P) or cleft palate only. Genome wide association studies (GWAS) of isolated OFC have identified common single nucleotide polymorphisms (SNPs) in many genomic loci where the presumed effector gene (for example, IRF6 in the 1q32 locus) is expressed in embryonic oral epithelium. To identify candidates for functional SNPs at eight such loci we conduct a massively parallel reporter assay in a fetal oral epithelial cell line, revealing SNPs with allele-specific effects on enhancer activity. We filter these SNPs against chromatin-mark evidence of enhancers and test a subset in traditional reporter assays, which support the candidacy of SNPs at loci containing FOXE1, IRF6, MAFB, TFAP2A, and TP63. For two SNPs near IRF6 and one near FOXE1, we engineer the genome of induced pluripotent stem cells, differentiate the cells into embryonic oral epithelium, and discover allele-specific effects on the levels of effector gene expression, and, in two cases, the binding affinity of transcription factors FOXE1 or ETS2. Conditional analyses of GWAS data suggest the two functional SNPs near IRF6 account for the majority of risk for CL/P at this locus. This study connects genetic variation associated with OFC to mechanisms of pathogenesis.

Kumari, Priyanka↗
DOE OSTI2020

Cytochrome P450s CYP380C6 and CYP380C9 in green peach aphid facilitate its adaptation to indole glucosinolate‐mediated plant defense

Abstract BACKGROUND Overexpressing CIRCADIAN CLOCK ASSOCIATED1 in Arabidopsis thaliana ( CCA1‐ox ) increases indole glucosinolate production and resistance to green peach aphid ( Myzus persicae ). Little is known of how aphids respond to this group of plant defense compounds or of the underlying molecular mechanism. Results Aphids reared on CCA1‐ox for over 40 generations (namely the CCA population) became less susceptible to CCA1‐ox than aphids maintained on the wild‐type Col‐0 (namely the COL population). This elevated tolerance was transgenerational as it remained for at least eight generations after the CCA population was transferred to Col‐0. Intriguingly, transcriptome analysis indicated that all differential cytochrome P450 monooxygenase genes ( MpCYPs ), primarily MpCYP4 s, MpCYP380s and MpCYP6 s, were more highly expressed in the CCA population. Application of a P450 inhibitor to the CCA population resulted in decreased aphid reproduction on CCA1‐ox , which was not observed if aphids were reared on Col‐0. When indole glucosinolate biosynthesis in CCA1‐ox was blocked using virus‐induced gene silencing, the effect of the P450 inhibitor on the CCA population was attenuated, affirming the essential role played by MpCYPs in counteracting the defense mechanism in CCA1‐ox that is low or absent in Col‐0. Furthermore, we used host‐induced gene silencing to identify MpCYP380C6 and MpCYP380C9 that specifically facilitated the CCA population to cope with CCA1 ‐mediated plant defense. Expression profiles revealed their possible contribution to the transgenerational tolerance observed in aphids. Conclusion MpCYP380C6 and MpCYP380C9 in aphids play a crucial role in mitigating indole glucosinolate‐mediated plant defense, and this effect is transgenerational.

Ji, Rui↗
DOE OSTI2020

Network Modeling of Complex Data Sets

We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.

Jones, Piet C.↗
DOE OSTI2023

Phase-field modeling of solid-state metathesis reactions with the charge neutrality constraint

In this work, we present a phase-field model that captures the evolution of ionic concentrations and phase fractions during solid-state metathesis (SSM) reactions where diffusion limits the rate of transformation. The evolution of the mole fraction of each ion is obtained via governing equations that describe the reduction of a free energy, which includes an energy landscape with local minima located at compositions corresponding to stable products. We utilized two Lagrange multipliers to impose constraints of electroneutrality as well as on the sum of mole fractions, which were then eliminated to derive set of two partial differential equations that describe the dynamics of the mole fraction evolution. From these governing equations, the expressions for effective mobilities for the cations and the anions were obtained. We first study the effect of mobilities of ions on the reaction kinetics, using a simple model considering the ions with an identical absolute value of charge numbers. The simulation results show that the overall characteristic mobility, defined as the sum of the two effective ionic mobilities, provides an excellent measure of the rate at which reaction progresses and that the ratio of the effective mobilities of the anions and the cations signifies the manner by which the reaction progresses. We then generalize the model to consider ions with different charge numbers and tuned the mobility of ions based on their diffusion coefficients reported in the literature and experimental data from a thin-film experiment for the synthesis of FeS 2 to demonstrate the capability of the model to predict the phase evolution during SSM reactions. In particular, the simulation predicts nonplanar phase evolution, which is recently observed in thin-film reactions for the synthesis of FeS 2 via transmission electron microscopy. The approach can serve as a basis for models for phase transformations in other multiphase ionic mixtures, such as in all-solid-state batteries and in ionic liquids.

36 MATERIALS SCIENCE↗