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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 73 records · Page 4

Thermodynamics of MgO Atomic Layer Deposition Surface Reactions

The realities of atomic layer deposition (ALD) surface reactions often deviate from the simple ligand exchange frequently used to illustrate the technique. A detailed understanding of these reactions is necessary to develop greater surface synthetic control including chemical selectivity for patterning or to target defects. Here, the thermodynamics of surface reactions relevant to MgO ALD were investigated using pyroelectric calorimetry to measure the time-resolved heat generation. These reactions show exothermic heat generation of 0.12 mJ/cm 2 and 0.15 mJ/cm 2 for alternating Mg(CpEt) 2 and H 2 O reactions, respectively. The total reaction heat closely matches the standard reaction enthalpy for bulk MgO, supplemented with first-principles molecular calculations. First-principles models further reveal that while simple ligand exchange is favorable during surface reactions, the required increase in Mg-coordination number from two in the precursor to six in bulk MgO requires additional coverage-dependent surface reactions, which depend on the availability and stability of proximal surface hydroxyls.

36 MATERIALS SCIENCE↗

High-performance light-driven heterogeneous CO 2 catalysis with near-unity selectivity on metal phosphides

Akin to single-site homogeneous catalysis, a long sought-after goal is to achieve reaction site precision in heterogeneous catalysis for chemical control over patterns of activity, selectivity and stability. Herein, we report on metal phosphides as a class of material capable of realizing these attributes and unlock their potential in solar-driven CO 2 hydrogenation. Selected as an archetype, Ni 12 P 5 affords a structure based upon highly dispersed nickel nanoclusters integrated into a phosphorus lattice that harvest light intensely across the entire solar spectral range. Motivated by its panchromatic absorption and unique linearly bonded nickel-carbonyl-dominated reaction route, Ni 12 P 5 is found to be a photothermal catalyst for the reverse water gas shift reaction, offering a CO production rate of 960 ± 12 mmol g cat –1 h –1 , near 100% selectivity and long-term stability. Successful extension of this idea to Co2P analogs implies that metal phosphide materials are poised as a universal platform for high-rate and highly selective photothermal CO 2 catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An uncertainty-aware strategy for plasma mechanism reduction with directed weighted graphs

In this work, we present a framework for the analysis and reduction of plasma mechanisms by means of weighted directed graphs, in which reactions and species are both treated as nodes. The methodology consists of two distinct analyses. The first, which is qualitative, relies on graph spatializations via force-directed algorithms to discover the predominant global patterns in the chemical model. The second ranks the reactions based on their shortest paths' lengths from/to the species of interest and their relative contributions to the power balance. Further, this quantitative investigation enables a strategy for mechanism reduction that is fully automatized, as it does not require any expert knowledge, highly effective, as it generates reduced mechanisms that are highly accurate while relying on a small number of processes, and easily interpretable, as the algorithm justifies the importance of the retained reactions by outputting their related chemical pathways. Additionally, the work proposes a methodology extension that employs ensembles of graphs to improve the robustness of the reduced mechanism to reaction parameter uncertainties. The approach, here tested for steady-state predictions of a plasma system characterizing negative hydrogen ion sources, is general and can be used in a wide variety of applications outside the particular nuclear fusion context demonstrated in this work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Manipulation of Dirac Fermions in Nanochain-Structured Graphene

Graphene has afforded an ideal 2D platform for investigating a rich and fascinating behavior of Dirac fermions. Here, we develop a theoretical mechanism for manipulating the Dirac fermions in graphene, such as from type-I to type-II and type-III, by a top-down nanopatterning approach. We demonstrate that by selective chemical adsorption to pattern the 2D graphene into coupled 1D armchair chains (ACs), the intrinsic isotropic upright Dirac cone becomes anisotropic and strongly tilted. Based on model analyses and first-principles calculations, we show that both the shape and tilt of Dirac cone can be tuned by the species of chemisorption, e.g., halogen vs hydrogen, which modifies the strength of inter-AC coupling. Furthermore, the topological edge states and transport properties of the engineered Dirac fermions are investigated. Our work sheds lights on understanding the Dirac fermions in a nanopatterned graphene platform, and provides guidance for designing nanostructures with novel functionality.

Physics↗

Advanced Analysis of Plutonium: Pre- and Post-Detonation Scenarios

A Dissertation Presented for the Doctor of Philosophy Degree in Nuclear Engineering. Overarching research question: Do nuclear reactions in surface environment lead to distinctive chemical and isotopic signatures that are not present in nature? Testable hypothesis: Nuclear processes will lead to distinctive chemical and isotopic patterns of partitioning because high-temperature processes under high radiation rarely occur in natural surface environment.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Predicting Fracture Porosity Evolution in Sandstone

To better understand the porosity, strength, chemical reactivity, and patterns of fractures in the subsurface, we used evidence from mineral deposits in open fractures to unravel how fracture growth and diagenesis interact to create and destroy fracture porosity. Quartz cement textures and associated fluid inclusions and thermal histories provided data used to infer the duration and rates at which fractures open.

54 ENVIRONMENTAL SCIENCES↗

Predicting Fracture Porosity Evolution in Sandstone (Final Report)

To better understand porosity, strength, chemical reactivity, and patterns of fractures, we developed methods using mineral deposits to unravel how fracture growth and diagenesis interact to create and destroy fracture porosity. Quartz and dolomite cement textures and associated fluid inclusion assemblage sequences and thermal histories provided data to infer the duration and rates at which fractures open. We developed and tested a model that accounts for how fracture porosity and rock properties change as a function of thermal exposure and how fracture size and arrangement evolve. Effects of cement deposits on growth were investigated with new geomechanical models and new methods to quantify fracture spatial arrangement. We tested these concepts in a wide range of rock types using outcrop and core-based datasets. We documented opening histories of fractures and fracture arrays in contrasting tectonic and thermal regimes. Our methods set a new standard for fracture characterization and our diagenetic-geomechanical model accurately predicts fluid flow characteristics of fractured rocks based on coupled effects of diagenesis and deformation. Results represent a significant advance in fundamental understanding of fractures in the Earth and have application to improving subsurface engineering operations in a wide range of contexts including geothermal and sequestration applications. The breakthrough of successfully reconstructing the timing and opening rates of parts of single fractures and portions of fracture arrays to document the growth and linkage of fracture arrays was an accomplishment of this grant that is being followed up in our current BES grant DE-SC0022968, ‘Reconstructing and Predicting Fracture Pattern Evolution‘

58 GEOSCIENCES↗

Adverse Neurological Effects of Wildfire Smoke

The respiratory and cardiovascular effects of smoke exposure are well documented from cigarette smoking and particulate matter (PM) associated with air-pollution studies, but epidemiological data also suggests exposure to high levels of woodsmoke correlate with central-nervous-system (CNS) dysfunction, including elevated rates of Alzheimer’s disease (AD) and dementia. Neuro-inflammation is believed to initiate the majority of Alzheimer’s and other neurodegenerative diseases attributed to environmental factors. We investigated whether inhaled PM and specific classes of compounds in wildfire smoke condensate extract (WSE) reach the brain and cause neuro-inflammation that can lead to progressive neurological degeneration, using isotope tracing of labeled particulate matter and major compound classes, biomarkers of blood-brain barrier (BBB) integrity, and biomarkers of neuro-inflammation. This project showed that inhalation exposure to PM and major compound classes present in WSE reached the brain through absorption in the nasal cavity and systemic circulation across the BBB. Twenty-four exposures of WSE to brain endothelial cells that form the main physical barrier of the BBB produced dose-dependent increases in biomarkers of inflammation and decreases in tight junction markers between cells. Intranasal exposure to WSE caused inflammatory disfunction in lung and brain. Biomarkers of inflammation increased and an enzyme inhibiting inflammation decreased. Similar patterns of chemical-induced changes in the lung and brain suggest either: (1) chemicals may act through shared molecular pathways upon entering lung and brain, or (2) that mediators originating in the lung circulate systemically to the brain and drive neuroinflammation. These findings warrant further mechanistic studies to delineate the temporal sequence of events to establish the lung-brain axis.

54 ENVIRONMENTAL SCIENCES↗

Phenotypically anchored transcriptomics across diverse agrichemicals reveals conserved pathways and unique gene expression signatures in zebrafish

Agrichemicals such as herbicides, fungicides, insecticides, and biocides are widely used in agriculture, yet some are associated with adverse effects in humans and the environment. While many of these chemicals have been extensively studied in vitro and are included in the EPA’s ToxCast program, comprehensive in vivo comparisons using RNA sequencing across structurally diverse agrichemicals, in a single screening platform, are lacking. In this study, we examined structurally diverse agrichemicals found in the U.S. Environmental Protection Agency’s (EPA) Toxcast Phase I and II library by statically exposing early life stage zebrafish at 6 h post fertilization (hpf) until 120 hpf at concentrations ranging from 0.25 to 100 µM. Morphological outcomes were assessed at 120 hpf across 10 endpoints, including yolk sac edema, craniofacial malformations, and axis abnormalities. Chemicals that produced robust concentration-response relationships were selected for transcriptomic profiling. For transcriptomic analysis, zebrafish were statically exposed to each chemical and sampled at 48 hpf, prior to the onset of morphological effects observed at 120 hpf. Differential expression analysis identified between 0 and 4,538 differentially expressed genes (DEGs) per chemical, with no clear correlation to morphological severity. Both DEG and co-expression network analyses revealed chemical-specific expression patterns that converged on shared biological pathways, including neurodevelopment and cytoskeletal organization. Key regulatory genes such as mylpfa and krt4 were identified within co-expression modules, suggesting their potential role in conserved toxicity mechanisms. Semantic similarity analysis of enriched gene ontology (GO) terms, when compared to existing datasets, highlighted gaps in the annotation of neurodevelopmental processes, indicating that some in vivo effects may not be fully captured by current curated resources. The results provide new insights into the modes of action of diverse agrichemicals and establish a framework for understanding how agrichemical structure relates to biological function in a vertebrate model.

agrichemical↗

Elastic forces drive nonequilibrium pattern formation in a model of nanocrystal ion exchange

The widely used process of nanocrystal ion exchange operates out of thermodynamic equilibrium and can require mixing components of varying sizes. Here we use theory and computer simulation to study a simple model which captures these two basic features of ion exchange reactions. We show that a strong driving force for exchange among different-sized species creates nonequilibrium patterns within model nanocrystals. We further demonstrate that such patterns can be thermodynamically stable within core/shell nanocrystals. These results help us understand the heterostructures formed in ion-exchanged nanocrystals and suggest strategies for leveraging elasticity to design patterns in nanoscale materials. Chemical transformations, such as ion exchange, are commonly employed to modify nanocrystal compositions. Yet the mechanisms of these transformations, which often operate far from equilibrium and entail mixing diverse chemical species, remain poorly understood. Here we explore an idealized model for ion exchange in which a chemical potential drives compositional defects to accumulate at a crystal’s surface. These impurities subsequently diffuse inward. We find that the nature of interactions between sites in a compositionally impure crystal strongly impacts exchange trajectories. In particular, elastic deformations which accompany lattice-mismatched species promote spatially modulated patterns in the composition. These same patterns can be produced at equilibrium in core/shell nanocrystals, whose structure mimics transient motifs observed in nonequilibrium trajectories. Moreover, the core of such nanocrystals undergoes a phase transition—from modulated to unstructured—as the thickness or stiffness of the shell is decreased. Our results help explain the varied patterns observed in heterostructured nanocrystals produced by ion exchange and suggest principles for the rational design of compositionally patterned nanomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Broadband Circular Polarizers via Coupling in 3D Plasmonic Meta-Atom Arrays

In this work, we report broadband circular polarizers achieved by engineering the electromagnetic coupling between 3D meta-atoms in large-area arrays. The 3D meta-atoms are composed of bulk Au/Au nanocrystal (NC) bilayer helical arms, tailored in their number, length, and curvature through a one-step patterning process and postfabrication chemical and thermal treatments. By tuning the meta-atom array periodicity, hybridization originating from dipole–dipole and quadrupole–quadrupole interactions broadens the chiral response. We demonstrate circular polarizers operating at wavelengths spanning 2.5 to 5 μm, with a maximal transmission difference between left- and right-hand circularly polarized light of 43%.

3D metamaterials↗

Large area nanoscale patterning of functional materials using organosilicate ink based nanotransfer printing

Abstract This paper presents a versatile nanotransfer printing method for achieving large-area sub-micron patterns of functional materials. Organosilicate ink formulations combined with effective release layers have been shown to facilitate patterning of materials through the commonly used patterning approaches—lift off, physical etching and chemical etching. In this paper, we demonstrate that organosilicate ink formulations function as an effective resist owing to its superior physico-chemical stability whereas the release layers ensure clean removal of the resist post patterning. We successfully demonstrate patterning of sub-micron structures (800 nm feature sizes) of chromium metal through the lift off approach, silicon through reactive ion etching technique and silicon dioxide through wet chemical etching technique illustrating the versatility of the reported method. This patterning methodology represents a significant advancement in enabling nanostructure fabrication within resource-constrained laboratories. The approach requires nothing more than a master mold containing the desired structures, a spin coater, a low-temperature hotplate, and a desktop reactive ion etch tool.

Engineering↗

Cohesive phase-field chemo-mechanical simulations of inter- and trans- granular fractures in polycrystalline NMC cathodes via image-based 3D reconstruction

The optimal design and durable utilization of lithium-ion batteries necessitates an objective modeling approach to understand fracture and failure mechanisms. This paper presents a comprehensive chemo-mechanical modeling study focused on elucidating fracture-induced damage and degradation phenomena in the polycrystalline Li $\mathcal{x}$ Ni 0.5 Mn 0.3 Co 0.2 O 2 (NMC532) cathode. An innovative approach that utilizes image-based reconstructed 3D geometry as finite element (FE) mesh input is employed to enhance the precision in capturing the convoluted architecture and morphological features. For accurately representing the intricate crack configurations within the polycrystalline system, we adopted the cohesive phase-field fracture (CPF) model. Through the integration of advanced image-based geometry reconstruction technique and the promising CPF modeling approach, lithium (de)intercalation induced crack evolution (e.g., nucleation, propagation, branching and diverse modes including inter-/trans-(intra-) granular patterns) and the resulting chemical degradation can be precisely captured, which is also compared and validated with numerical predictions using a continuum damage model. In particular, this model predicts fracture induced degradation under varying fracture properties of grain boundaries and charging rates; the conclusion that NMC particles comprised of larger grains are predicted to have less degradation than those with smaller grains can also be drawn. This comprehensive analysis provides valuable insights into the fracture and degradation within polycrystalline NMC cathodes.

25 ENERGY STORAGE↗

Impact of Ligands on the Ion–Ion Selectivity of Ligand-Appended-Pillar[ n ]arene Channels

Ligand-appended pillar[5]arene (LAP) channels are emerging as a promising platform for ion-selective membranes. In this study, we investigated the ion selectivities of three LAP channels functionalized with distinct ligands: diglycolamine, carbamoylmethyl phosphine oxide, and propionamide phosphonic acid. These ligands were chosen based on their demonstrated affinities for lanthanides in solvent extraction studies. We examined the selectivity of each channel toward a series of monovalent, divalent, and trivalent ions: Li + , Na + , K + , Mg 2+ , Ca 2+ , La 3+ , Eu 3+ , and Yb 3+ . To quantify ion-channel interactions and the energetics of translocation, we computed the potential of mean force (PMF) profiles for each ion. These PMFs were subsequently recast into permeability coefficients, enabling a direct evaluation of ion permselectivity. Our results reveal distinct selectivity patterns governed by the chemical nature of the appended ligands. The diglycolamine-functionalized channel exhibits strong interactions with monovalent ions, leading to replication of the Li + bulk hydration shell within the channel. This promotes preferential transport Li + ions while effectively excluding divalent and trivalent species. In contrast, the carbamoylmethyl phosphine oxide-functionalized channel displays a reduced selectivity between monovalent ions while similarly exhibiting an effective rejection of divalent and trivalent ions. This arises from steric hindrance around channel-lining oxygens imposed by the ligand's bulky phenyl groups which limits heavy ion coordination and promotes retention of hydration shells effectively increasing effective ion size. The propionamide phosphonic acid-functionalized channel exhibits a different trend, favoring ions with lower hydration free energies, with K + ions showing enhanced permeation. These findings highlight the critical role of ligand-ion interactions in modulating ion selectivity within LAP channels. In particular, the strong coordination exhibited by diglycolamine and propionamide phosphonic acid ligands significantly influences the ion transport energetics and selectivity profiles.

hydration↗

Edge-Confined Excitons in Monolayer Black Phosphorus

Quantum confinement of two-dimensional excitons in van der Waals materials via electrostatic trapping, lithographic patterning, Moiré potentials, and chemical implantation has enabled significant advances in tailoring light emission from nanostructures. While such approaches rely on complex preparation of materials, natural edges are a ubiquitous feature in layered materials and provide a different approach for investigating quantum-confined excitons. Here, we observe that certain edge sites of monolayer black phosphorus (BP) strongly localize the intrinsic quasi-one-dimensional excitons, yielding sharp spectral lines in photoluminescence, with nearly an order of magnitude line width reduction. Through structural characterization of BP edges using transmission electron microscopy and first-principles GW plus Bethe–Salpeter equation (GW-BSE) calculations of exemplary BP nanoribbons, we find that certain atomic reconstructions can strongly quantum-confine excitons resulting in distinct emission features, mediated by local strain and screening. Here we observe linearly polarized luminescence emission from edge reconstructions that preserve the mirror symmetry of the parent BP lattice, in agreement with calculations. Furthermore, we demonstrate efficient electrical switching of localized edge excitonic luminescence, whose sites act as excitonic transistors for emission. Localized emission from BP edges motivates exploration of nanoribbons and quantum dots as hosts for tunable narrowband light generation, with future potential to create atomic-like structures for quantum information processing applications as well as exploration of exotic phases that may reside in atomic edge structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry

Mass spectrometry is a ubiquitous technique capable of complex chemical analysis. The fragmentation patterns that appear in mass spectrometry are an excellent target for artificial intelligence methods to automate and expedite the analysis of data to identify targets such as functional groups. To develop this approach, we trained models on electron ionization (a reproducible hard fragmentation technique) mass spectra so that not only the final model accuracies but also the reasoning behind model assignments could be evaluated. The convolutional neural network (CNN) models were trained on 2D images of the spectra using transfer learning of Inception V3, and the logistic regression models were trained using array-based data and Scikit Learn implementation in Python. Our training dataset consisted of 21,166 mass spectra from the United States’ National Institute of Standards and Technology (NIST) Webbook. The data was used to train models to identify functional groups, both specific (e.g., amines, esters) and generalized classifications (aromatics, oxygen-containing functional groups, and nitrogen-containing functional groups). We found that the highest final accuracies on identifying new data were observed using logistic regression rather than transfer learning on CNN models. It was also determined that the mass range most beneficial for functional group analysis is 0–100 m/z. We also found success in correctly identifying functional groups of example molecules selected from both the NIST database and experimental data. Beyond functional group analysis, we also have developed a methodology to identify impactful fragments for the accurate detection of the models’ targets. The results demonstrate a potential pathway for analyzing and screening substantial amounts of mass spectral data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Niche expansion for phototrophic sulfur bacteria at the Proterozoic–Phanerozoic transition

Significance Carotenoid pigments afford valuable clues about the chemistry and biology of both modern and ancient aquatic environments. This study reveals that fossil aromatic carotenoids—long considered biomarkers for anoxygenic, phototrophic sulfur bacteria and their physiological requirement for hydrogen sulfide and illumination—can also be biosynthesized by oxygen-producing cyanobacteria. Cyanobacterial aromatic carotenoids, which are distinct in their chemical structures and occurrence patterns, are the most commonly encountered compounds in Proterozoic marine settings as well as in lakes from more recent eras. In contrast, carotenoids diagnostic for green sulfur bacteria of the family Chlorobiaceae became both prevalent and abundant in marine paleoenvironments beginning in the Phanerozoic Eon. This expansion occurs as marine sulfate inventories increased toward the end of the Proterozoic Eon.

59 BASIC BIOLOGICAL SCIENCES↗