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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Adsorption Hysteresis Under Control: Tuning Host–Guest Interactions via a Genetic Algorithm

Mesoporous adsorbent materials offer a large volumetric capacity; however, cyclic adsorption/desorption processes in these systems often suffer from hysteresis and may require a significant pressure swing to access this capacity. To mitigate hysteresis, a proposed strategy is to include nucleation sites on the walls of the mesoporous material to facilitate droplet and bubble formation, lowering the free energy barriers to the respective phase transitions. It is unclear, however, what combination of adsorbate− adsorbent interactions and spatial patterning would be beneficial for a given application, considering that improvements to some sorption properties may come at the expense of other attributes. To understand these interconnected observables, we examine two model systems, planar-slit and cylindrical pores with tunable interaction sites, using GPU-accelerated transition matrix Monte Carlo simulations. The simulations provide a free energy map of the pressure−adsorption space in a matter of minutes, which we use to track adsorption isotherm characteristics as a function of adsorbent properties. We then leverage the rapid acquisition of simulation data to construct a genetic algorithm to iteratively modify interaction sites of the slit-pore wall to minimize the hysteresis of this system without sacrificing uptake. We find that the adsorption branch of the isotherm is easily modulated via the average host−guest interaction strength, but desorption is only adjustable if there is a suitable bubble nucleation site. Within the context of a slit-pore system, we identify relative interaction strengths and patch sizes required to gain control over both branches of the hysteresis loop.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

First-Principles Molecular Dynamics Simulations of Ammonia Adsorption onto MFI Zeolite Nanosheets

MFI zeolite nanosheet membranes are promising candidates for ammonia separation from nitrogen and hydrogen gases, yet questions remain on the origin of their high selectivity. Silanols, Si–OH, are present in high concentration at the surface of zeolite nanosheets, and force-field-based simulations indicate that surface adsorption at the silanols contributes to selectivity. Acidic silanol groups can chemically react with ammonia via transfer of a proton to form ammonium ions, which may further contribute to the ability of zeolite nanosheet membranes to separate ammonia from other gases. In this work, we used first-principles molecular dynamics techniques to simulate ammonia’s behavior within stacked MFI zeolite nanosheets. We found that at 523 K and a loading corresponding to 35 bar, conditions desired for the ammonia separation, about 30% of ammonia reacts with surface silanols. Our work explored H-bonding and proton transfer within this system.

ammonia↗
DOE OSTI2026

Membrane Composition Influences Expression Yield of Plant Cytochrome P450s in E. coli Lysate-Based Cell-Free Systems

Plant cytochrome P450 enzymes are central to natural product biosynthesis, but remain difficult to express in microbial hosts due to their transmembrane nature. Lysate-based, cell-free expression systems allow supplementation with artificial membranes to support the expression and translocation of transmembrane proteins. We developed a framework to systematically test liposomal membrane compositions to enhance the plant P450 expression yield. Adjustments to common phospholipid ratios or the addition of plant galactolipids had minimal impact on expression. In contrast, blended liposomes containing Egg PC, sterol-conjugated phospholipids, and PEGylated lipids produced concentration-dependent increases in expression. Expression of an Escherichia coli mechanosensitive channel and three plant P450s improved more than 2-fold, with some P450s showing up to 14-fold enhancement. Furthermore, these findings highlight membrane composition as a key determinant of the P450 expression yield in cell-free expression systems. While P450 activity was not measured, these findings provide a framework for future workflows toward achieving functional plant transmembrane enzymes for the bioproduction of natural products.

TXTL↗
DOE OSTI2024

Experimental Definition of the S = 1 π vs S = 2 σ Reactivity and S = 2 Character in the Ground State of an S = 1 Fe IV O Complex

Iron(IV)-oxo intermediates found in iron enzymes and artificial catalysts are competent for H atom abstraction in catalytic cycles. For S = 2 intermediates, both axial and equatorial approaches are well-established. The mechanism for S = 1 sites is not as well understood: an equatorial approach is more energetically favorable, and an axial approach requires crossing from the S = 1 to the S = 2 surface. Here, in this study, we use 1s2p resonant inelastic X-ray scattering (RIXS) and Fe L-edge X-ray absorption spectroscopy on the S = 1 [Fe IV O(TMC)(CH3CN)] 2+ and observe both S = 2 and S = 1 final states, which enables the experimental evaluation of the energetics of the axial and equatorial reactivity of an S = 1 Fe IV O center on its S = 2 vs S = 1 surface. The observation of S = 2 final states in the RIXS spectrum demonstrates significant S = 2 character spin–orbit mixed into the S = 1 ground state.

energy↗
DOE OSTI2025

Discovery of Stacking Heterogeneity, Layer Buckling, and Residual Water in COF-999-NH 2 and Implications on CO 2 Capture

Covalent organic frameworks (COFs), with their modular architectures and tunable functionalities, provide a versatile platform to design sorbents for the direct capture of CO 2 from air. Here, for this work, we combined density functional theory, molecular dynamics, and grand canonical Monte Carlo simulations with experiment to understand structural factors for furthering COF-999-NH 2 ’s performance as the precursor to COF-999 for direct air CO 2 capture. Small energy differences among laterally shifted stackings suggest intrinsic stacking heterogeneity. The simulations show pronounced layer buckling coupled to extensive amine–nitrile hydrogen bonding and persistent pore water, which initiates undesired polymerization and undermines uptake. The predicted presence of water is confirmed by subsequent experiments. These insights point to a single, actionable design rule: exclude retained water by introducing hydrophobic pore environments to maximize the CO 2 capture efficiency.

adsorption↗
DOE OSTI2026

Room-Temperature Methane Oxidation to Formaldehyde Mediated by CoMoO + Gas-Phase Cations

Formaldehyde (HCHO) is a fundamental chemical feedstock with widespread industrial applications. The direct oxidation of methane by oxygen to formaldehyde (CH 4 + 1/2O 2 → H 2 + HCHO) under mild conditions represents an attractive but challenging transformation, as it requires both activation of the inert C–H bonds of CH 4 and suppression of overoxidation to products such as carbon dioxide. In this work, mass spectrometry experiments combined with theoretical calculations reveal that CoMoO + cations can efficiently mediate this transformation at room temperature. The unique electronic structure of CoMoO + facilitates the formation of a crucial CoMoOCH 2 + intermediate during the reaction with CH 4 and prevents methanol formation. In the subsequent oxidation reaction, the Mo atom in CoMoO + serves as the active site for O 2 adsorption, and both Mo and Co atoms act as electron donors to activate O 2 , leading to the formation of the C–O bond in formaldehyde. This work reports the first gas-phase example of achieving conversion of CH 4 to HCHO and its radical derivatives by O 2 at room temperature using heteronuclear non-noble metal cations. Remarkably, the CoMoOCH 2 + cation maintains high reactivity after adsorbing one or two CH 4 molecules. Finally, these findings provide new mechanistic insights into selective methane activation and conversion.

aldehydes↗
DOE OSTI2025

A Unified Framework to Reconcile Different Approaches of Modeling Transpiration Response to Water Stress: Plant Hydraulics, Supply Demand Balance, and Empirical Soil Water Stress Function

Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply-demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi-arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Vertically Resolved Canopy Improves Chemical Transport Model Predictions of Ozone Deposition to North Temperate Forests

Abstract Dry deposition is the second largest tropospheric ozone (O 3 ) sink and occurs through stomatal and nonstomatal pathways. Current O 3 uptake predictions are limited by the simplistic big‐leaf schemes commonly used in chemical transport models (CTMs) to parameterize deposition. Such schemes fail to reproduce observed O 3 fluxes over terrestrial ecosystems, highlighting the need for more realistic treatment of surface‐atmosphere exchange in CTMs. We address this need by linking a resolved canopy model (1D Multi‐Layer Canopy CHemistry and Exchange Model, MLC‐CHEM) to the GEOS‐Chem CTM and use this new framework to simulate O 3 fluxes over three north temperate forests. We compare results with in situ measurements from four field studies and with standalone, observationally constrained MLC‐CHEM runs to test current knowledge of O 3 deposition and its drivers. We show that GEOS‐Chem overpredicts observed O 3 fluxes across all four studies by up to 2×, whereas the resolved‐canopy models capture observed diel profiles of O 3 deposition and in‐canopy concentrations to within 10%. Relative humidity and solar irradiance are strong O 3 flux drivers over these forests, and uncertainties in those fields provide the largest remaining source of model deposition biases. Flux partitioning analysis shows that: (a) nonstomatal loss accounts for 60% of O 3 deposition on average; (b) in‐canopy chemistry makes only a small contribution to total O 3 fluxes; and (c) the CTM big‐leaf treatment overestimates O 3 ‐driven stomatal loss and plant phytotoxicity in these temperate forests by up to 7×. Results motivate the application of fully online vertically explicit canopy schemes in CTMs for improved O 3 predictions.

Vermeuel, Michael P. [Department of Soil, Water, a↗
DOE OSTI2025

A Comparative Study of Physics‐Informed and Data‐Driven Neural Networks for Compound Flood Simulation at River‐Ocean Interfaces: A Case Study of Hurricane Irene

Simulating compound flooding (CF) at the river-ocean interface within large-scale Earth System Models (ESMs) presents significant challenges due to complex interactions between river discharge, storm surge, and tides. This study assesses the comparative advantages of physics-informed and data-driven machine learning (ML) approaches for enhancing local ESM performance. We systematically compare data-driven neural network models (i.e., CNNs, U-Net, Long Short-Term Memory (LSTM), Gated Recurrent Unit), and physics-informed neural network (PINN) models, including vanilla PINN and a finite-difference-based PINN (FD-PINN). Specifically, FD-PINN is introduced to enhance computational efficiency, accelerating vanilla PINNs by ∼6.5 times while improving accuracy. To enhance data-driven model training, a new data-generation approach is developed to sample historical fluvial and coastal flood events, which ensures a robust data set for extreme event prediction. The models are evaluated using a realistic one-dimensional river domain extracted from an ESM's river mesh and the Hurricane Irene event as an independent test case. Results show that FD-PINN achieves accurate predictions with significantly reduced computational costs relative to vanilla PINNs. Among data-driven models, the best overall performance is achieved by a CNN-LSTM hybrid, which balances accuracy and efficiency. While a fully connected CNN (CNN-FC) provides the best accuracy, it incurs high computational cost. Architectures lacking strong temporal modeling tend to underperform on unseen events. These findings highlight the importance of sequence-aware designs for robust generalization. This study reveals the trade-offs between physics-informed and data-driven models and proposes an adaptive hybrid framework for integrating ML into ESMs to enhance local flood simulations.

Earth Systems Modeling↗
DOE OSTI2025

Colistin resistance plasmids dually enhance bacterial virulence and antibiotic resistance via surface polysaccharide biosynthesis

Plasmids carrying the mobilized colistin-resistance gene mcr-1 are prevalent among multidrug-resistant Gram-negative pathogens, yet their broad impact on bacterial physiology and virulence remains unclear. Here, we demonstrate that acquisition of an mcr-1 plasmid concurrently increases antimicrobial resistance and pathogenicity in Escherichia coli. On the same plasmid, the XRE-family transcriptional regulator EcaR cooperates with MCR-1 to activate the wec operon, driving biosynthesis of two surface polysaccharides: enterobacterial common antigen (ECA) and a high-molecular-weight O-chain. Expression of these surface polysaccharides increases bile resistance and virulence in a murine model and further elevates colistin resistance. MCR-1 enhances transcription of upstream genes in the wec operon, whereas EcaR directly activates an internal promoter (PwecE) to induce downstream gene expression. Thus, both components are required for surface polysaccharide expression, and deletion of either abolishes the phenotype. Genomic analysis of publicly available mcr plasmids reveals widespread co-occurrence of mcr-1 and ecaR on IncI2 and IncX4 plasmids, indicating their functional complementarity. These findings uncover a mechanism by which resistance plasmids remodel the bacterial surface, linking horizontal gene transfer to coordinated regulation of antimicrobial resistance and virulence.

Antimicrobial resistance↗
DOE OSTI2025

Virus-induced gene editing free from tissue culture

Virus-induced gene editing (VIGE) has reached an inflection point. Although conceived as an alternative to traditional methods of producing gene-edited plants, VIGE has historically relied on the very technologies it was meant to supersede—specifically, tissue-culture-mediated transgenesis. Recent VIGE innovations, however, have finally proved its viability as an independent method for plant gene editing. Here we discuss the advances in plant genome engineering VIGE may unlock, what progress has been made towards achieving these advances and the challenges that continue to impede that progress.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Cross-scale covariance for material property prediction

A simulation can stand its ground against an experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale (~10 8 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤10 2 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale “strength-on-predictors” regression model. This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ramification of complex magnetism in Nd 2 Ir 2 O 7 observed by Raman scattering spectroscopy

Using Raman scattering spectroscopy, we uncover a complex magnetic behavior of Nd2Ir2O7, which stands out among magnetic pyrochlores by the lowest temperature of the all-in-all-out (AIAO) Ir moments ordering (${T}_{{\rm{Ir}}}^{{\rm{N}}} = 33$ K) and the highest temperature at which AIAO order of rare-earth Nd ions is detected (${T}_{{\rm{Nd}}}^{{\rm{* }}}$ = 15 K). Detected magnetic Raman scattering and calculations of expected response allow us to demonstrate that the ordering of Ir magnetic moments is accompanied by an appearance of one-magnon Raman modes at 26.3 and 29.6 meV compatible with the AIAO order and allowing to estimate the energies of Ir-Ir interactions. An additional two-magnon excitation of the AIAO Nd order at around 33 meV appears in the spectra below the ordering temperature of Nd moments ${T}_{{\rm{Nd}}}^{{\rm{* }}}$ = 15 K. In the temperature range between 15 K and 33 K we observe a broad mode, which demonstrates strong temperature dependence and shifts on cooling below 20 K from 14 meV to higher frequencies, and disappears at 5 K, when two-magnon excitation of Nd moments becomes prominent. We suggest an interpretation of this excitation in terms of continuum arising from collective fluctuations of Nd moments above the transition. This complex behavior emerges from the interplay of strong spin-orbit coupling, electronic correlations, and geometric frustration on two magnetic pyrochlore sublattices of Nd and Ir ions.

magnetic properties and materials↗
DOE OSTI2026

Recent advances in chemical recycling and upcycling of plastic waste into valuable materials, chemicals, and energy: a comprehensive review

The global plastic waste crisis has increased in severity in recent years: annual plastic production is projected to reach 500 million metric tons by 2025, and plastic waste accumulation is expected to surpass 12 billion metric tons. Despite these growing volumes, only ∼9% of plastic waste is currently recycled; the majority is either landfilled, incinerated, or mismanaged, contributing to escalating greenhouse gas emissions—from 1.7 Gt carbon dioxide equivalent (CO 2 -eq.) in 2015 to an estimated 6.5 Gt CO 2 -eq. by 2050—and physical environmental pollution. This review provides a comprehensive overview of advanced plastic upcycling strategies to address this issue and recover value from diverse plastic waste streams. Recent developments in solvent-based dissolution, chemical depolymerization, and thermochemical conversions are examined for major plastic types, including polyolefins, polycondensation polymers, and PVC. Underlying reaction pathways, catalyst designs, and processing parameters that govern product selectivity, efficiency, and conversion yields are discussed in depth. Emerging techniques such as microwave-assisted depolymerization, tandem catalysis, and co-processing approaches are highlighted for their potential to enhance efficiency under milder conditions. Emphasis is also placed on the production of high-value products such as monomers, naphtha-range hydrocarbons, and syngas, and discussion is provided on catalyst stability, contaminant removal, scalability, life cycle effects on the environment, and technoeconomic viability. Finally, the review outlines future research directions focused on catalyst innovation, integrated process design, supportive policy frameworks, and interdisciplinary collaboration. All recommendations are aimed at accelerating large-scale implementation of plastic upcycling technologies and advancing the global circular plastics economy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Subsurface hydrogen, curvature, and strain: lessons from electro-reduction of benzaldehyde on nano-structured Pd catalysts

The unique ability of palladium (Pd) to absorb hydrogen and form a bulk hydride is vital for chemical transformations that involve hydrogenation reactions. Nano-structured Pd catalysts offer a promise of tuning these reaction rates by exploiting variations of reactant binding energies depending on the surface structure and morphological constraints that result in inhomogeneous strain. However, the interplay between the nano-structure of Pd and the ability of Pd to adsorb (and absorb) hydrogen as well as other reactive species needs to be better understood for a rational understanding of competitive chemical transformations at Pd surfaces. We consider the effects of the surface corrugation, strain, and subsurface Pd hydride on the reduction of benzaldehyde to benzyl alcohol in two qualitatively different samples – Pd nanoparticles and Pd gels formed by quasi-one-dimensional chains of these nanoparticles. Our electrochemical measurements and computational modelling suggest that surface concave sites, inherent to Pd gels, facilitate hydrogen transfer to the Pd subsurface region, thus weakening benzaldehyde binding to the surface. This effect is further modulated by the strain, depending on the local coordination environment on the corrugated surface. Furthermore, these findings demonstrate how structurally complex samples in the form of gels provide degrees of freedom for controlling the behavior of metal catalysts that are not available in isolated nanoparticles, which paves the way for new approaches in the design of catalytic materials and synthesis of metal hydrides.

Padavala, Sri Krishna Murthy [University of Minnes↗
DOE OSTI2025

Disoriented isospin condensates in heavy-ion collisions

Anomalous neutral to charged kaon correlations measured by the ALICE collaboration have defied usual explanations. We propose that the large fluctuations could arise because of a disoriented isospin condensate where there is an imbalance between up and down condensates at the time kaons hadronize. This could happen in heavy-ion collisions when the quark condensate re-forms as the system cools and the approximate chiral symmetry of QCD is broken. Within the linear sigma model, we show that the energy cost of forming a disoriented isospin condensate is small making it very plausible.

Kapusta, Joseph [University of Minnesota, Minneapo↗
DOE OSTI2025

Electrical conductivity of multilayered Cu/Nb composites fabricated by accumulative roll bonding

The electrical conductivity of nanolayered copper/niobium composites fabricated using accumulative roll bonding was investigated as a function of layer thickness. Cu/Nb was used as a model system to evaluate the processing-structure–property relationship stemming from the accumulative roll bonding process. The physical properties were compared against samples of individual average layer heights ranging from 193 to 25 nm. The electrical resistivity was measured over a temperature range of ~3–300 K. Analysis on the role of interfaces on temperature dependence is conducted including the residual resistivity ratio and temperature coefficient of resistivity. It was found that electrical resistivity increases with decreasing layer height.

36 MATERIALS SCIENCE↗
DOE OSTI2026

An information-matching approach to optimal experimental design and active learning

The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applications require inferring parameters only as a means to predict other quantities of interest (QoI). Because models often contain many unidentifiable (sloppy) parameters, QoIs often depend on a relatively small number of parameter combinations. Therefore, we introduce an information-matching criterion based on the Fisher information matrix to select the most informative training data from a candidate pool. This method ensures that the selected data contain sufficient information to learn only those parameters that are needed to constrain downstream QoIs. It is formulated as a convex optimization problem, making it scalable to large models and datasets. Here, we demonstrate the effectiveness of this approach across various modeling problems in diverse scientific fields, including power systems and underwater acoustics. Finally, we use information-matching as a query function within an active learning (AL) loop for materials science applications. In all these applications, we find that a relatively small set of optimal training data can provide the necessary information for achieving precise predictions. These results are encouraging for diverse future applications, particularly AL in large machine-learning models.

Materials science↗