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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Interfacial Inversion of Stealth Surfactants

Amphiphilic macromolecular surfactants segregate to liquid–liquid interfaces, thereby reducing the interfacial tension and free energy. Here, we investigated “stealth surfactants” in the form of core–shell bottlebrush polymers comprised of pH-responsive diblock copolymer side chains forming a hydrophilic core and a hydrophobic shell, enabling solubility in oil. At liquid–liquid interfaces, these polymers undergo a structural “inversion”, with hydrophilic blocks segregating into the aqueous phase and hydrophobic blocks residing in the oil phase. The reconfiguration kinetics and surfactant properties are influenced by multiple factors, including the molecular weights of the backbone and side chain components, the hydrophilic-to-hydrophobic balance of the side chains, and the pH of the aqueous phase. An observed nonmonotonic dependence of interfacial tension with time is attributed to a progressive structural inversion, where the projected area of the macromolecule onto the interface decreases. To validate this inversion hypothesis, interfacial properties were characterized by sum-frequency generation vibrational spectroscopy, which revealed configurational changes of the core–shell bottlebrush polymers at the fluid interface and revealed a pH-dependent interfacial coverage. Coarse-grained molecular dynamics simulations supported these experimental findings, showing that the pH-responsive core and hydrophobic shell assume a time-averaged configuration with orientations parallel and perpendicular to the plane of the interface, respectively. These findings open routes to design multistimuli-responsive polymeric surfactants and compatibilizers, expanding their potential applications in advanced interfacial systems.

Stealth surfactants↗
DOE OSTI2026

Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials

Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li + , by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH – dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li + -coupled electron transfer along the PAN backbone, causing ∼10 4 times faster sequential ring-formation of the remaining nitriles. PAN’s extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications.

Chahal-Crockett, Rajni [Oak Ridge National Laborat↗
DOE OSTI2019

Multivalent Cation-Induced Actuation of DNA-Mediated Colloidal Superlattices

Nanoparticles functionalized with DNA can assemble into ordered superlattices with defined crystal habits through programmable DNA “bonds”. Here, we examine the interactions of multivalent cations with these DNA bonds as a chemical approach for actuating colloidal superlattices. Multivalent cations alter DNA structure on the molecular scale, enabling the DNA “bond length” to be reversibly altered between 17 and 3 nm, ultimately leading to changes in the overall dimensions of the micrometer-sized superlattice. The identity, charge, and concentration of the cations each control the extent of actuation, with Ni2+ capable of inducing a remarkable >65% reversible change in crystal volume. In addition, these cations can increase “bond strength”, as evidenced by superlattice thermal stability enhancements of >60 °C relative to systems without multivalent cations. Molecular dynamics simulations provide insight into the conformational changes in DNA structure as the bond length approaches 3 nm and show that cations that screen the negative charge on the DNA backbone more effectively cause greater crystal contraction. Finally, taken together, the use of multivalent cations represents a powerful strategy to alter superlattice structure and stability, which can impact diverse applications through dynamic control of material properties, including the optical, magnetic, and mechanical properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Unraveling the Luminescence Quenching Mechanism in Strong and Weak Quantum-Confined CsPbBr 3 Triggered by Triarylamine-Based Hole Transport Layers

Luminescence quenching by hole transport layers (HTLs) is one of the major issues in developing efficient perovskite light-emitting diodes (PeLEDs), particularly prominent in blue-emitting devices. While a variety of material systems have been used as interfacial layers, the origin of such quenching and the type of interactions between perovskites and HTLs are still ambiguous. Here, we present a systematic investigation of the luminescence quenching of CsPbBr 3 by a commonly employed hole transport polymer, Poly(9,9-dioctylfluorene-alt-N-(4-sec-butylphenyl)-diphenylamine) (TFB) in LEDs. Strong and weakly quantum-confined CsPbBr 3 (nanoplatelets (NPLs)/nanocrystals (NCs)) are rationally selected to study the quenching mechanism by considering the differences in their morphology, energy level alignments, and quantum confinement. The steady-state and time-resolved Stern-Volmer plots unravel the dominance of dynamic and static quenching at lower and higher concentrations of TFB, respectively, with maximum quenching efficiency of 98%. The quenching rate in NCs is faster than in NPLs owing to their longer PL lifetimes and weak quantum confinement. The ultrafast transient absorption results support these dynamics and rule out the involvement of Forster or Dexter energy transfer. Finally, the 1D 1 H and 2D NOESY NMR study confirms the exchange of native ligands at the NCs surface with TFB, leading to dark CsPbBr 3 -TFB ensemble formation accountable for luminescence quenching. This highlights the critical role of the triarylamine functional group on TFB (also the backbone of many HTLs) in the quenching process. These results shed light on the underlying reasons for the luminescence quenching in PeLEDs and will help to choose the interfacial layers rationally for developing efficient LEDs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Design and Evaluation of Pyridinyl Sulfonyl Piperazine LpxH Inhibitors with Potent Antibiotic Activity Against Enterobacterales

Enterobacterales, a large order of Gram-negative bacteria, including Escherichia coli and Klebsiella pneumoniae, are major causes of urinary tract and gastrointestinal infections, pneumonia, and other diseases in healthcare settings and communities. ESBL-producing Enterobacterales and carbapenem-resistant Enterobacterales can break down commonly used antibiotics, with some strains being resistant to all available antibiotics. This public health threat necessitates the development of novel antibiotics, ideally targeting new pathways in these bacteria. Gram-negative bacteria possess an outer membrane enriched with lipid A, a saccharolipid that serves as the membrane anchor of lipopolysaccharides and the active component of the bacterial endotoxin, causing septic shock. The biosynthesis of lipid A is crucial for the viability of Gram-negative bacteria, and as an essential enzyme in this process, LpxH has emerged as a promising target for developing novel antibiotics against multidrugresistant Gram-negative pathogens. Here, we report the development of pyridinyl sulfonyl piperazine LpxH inhibitors. Among them, ortho-substituted pyridinyl compounds significantly boost LpxH inhibition and antibiotic activity over the original phenyl series. Structural and QM/MM analyses reveal that these improved activities are primarily due to the enhanced interaction between F141 of the LpxH insertion lid and the pyridinyl group. Incorporation of the N-methyl-N-phenyl-methanesulfonamide moiety into the pyridinyl sulfonyl piperazine backbone results in JHLPH- 106 and JH-LPH-107, both of which exhibit potent antibiotic activity against wild-type Enterobacterales such as K. pneumoniae and E. coli. JH-LPH-107 exhibits a low rate of spontaneous resistance and a high safety window in vitro, rendering it an excellent lead for further clinical development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

A Robust Methodology to Elucidate Kinetics of Room Temperature Electrochemical Propane Adsorption on Platinum

Electrocatalytic activation of alkanes can further decarbonize chemical manufacturing by leveraging affordable renewable electricity and readily available shale gas reserves in the United States. Earlier works have identified the unique role of Pt in adsorbing and activating alkanes, like propane, at room temperature in acidic, aqueous electrolytes, revealing spontaneous formation of deeply dehydrogenated propane-derived surface species with an intact C 3 - backbone. Although an adsorption mechanism was hypothesized, it has not been explicitly investigated to date, preventing the quantification of kinetic rate parameters. A robust methodology to investigate and benchmark propane adsorption kinetics on Pt is critical for the rational design of electrocatalysts that exhibit higher selectivity toward desired partially oxidized products. Herein, we analyze an oxidative current transience that appears during the adsorption of propane on Pt in aqueous electrochemical conditions and develop a methodology that elucidates the adsorption mechanism and enables quantification of rate parameters such as order dependences and apparent activation barriers. This method yields an expected first-order dependence with respect to propane concentration at low coverage and reveals a second-order dependence with respect to the concentration of surface active sites. Additionally, the apparent activation barrier for propane adsorption was calculated using an Arrhenius analysis of the current transience under temperature control. The experimentally measured activation barrier of 35 kJ mol –1 is in excellent agreement with the theoretical barrier calculated by density functional theory (DFT). The kinetic analysis was extended, via the use of transition state theory, to extract entropy and enthalpy of activation, yielding consistent results with the proposed two-step adsorption mechanism and DFT calculations. These results demonstrate reliable quantification of kinetic parameters for electrocatalytic activation of C–H bonds in alkanes that can be employed for rational catalyst development for a versatile range of electrocatalytic conditions.

alkane activation↗
DOE OSTI2022

An End-To-End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning

Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. Here, in this study, we introduce a new end-to-end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi-station processing of seismic waveforms recorded over a seismic network. This end-to-end architecture consists of three sub-networks: a backbone network that extracts features from raw waveforms, a phase picking sub-network that picks P- and S-wave arrivals based on these features, and an event detection sub-network that aggregates the features from multiple stations to associate and detect earthquakes across a seismic network. We use these sub-networks together with a shift-and-stack module based on back-projection that introduces kinematic constraints on arrival times, allowing the neural network model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end-to-end approach can effectively pick P- and S-wave arrivals and achieve earthquake detection accuracy rivaling that of other state-of-the-art approaches. Because our approach preserves information across tasks in the detection pipeline, it has the potential to outperform approaches that do not.

58 GEOSCIENCES↗
DOE OSTI2022

Differentiable, Learnable, Regionalized Process-Based Models With Multiphysical Outputs can Approach State-Of-The-Art Hydrologic Prediction Accuracy

Predictions of hydrologic variables across the entire water cycle have significant value for water resources management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data-driven deep learning models like long short-term memory (LSTM) showed seemingly insurmountable performance in modeling rainfall runoff and other geoscientific variables, yet they cannot predict untrained physical variables and remain challenging to interpret. Here, we show that differentiable, learnable, process-based models (called δ models here) can approach the performance level of LSTM for the intensively observed variable (streamflow) with regionalized parameterization. We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework, to parameterize, enhance, or replace the process-based model's modules. Without using an ensemble or post-processor, δ models can obtain a median Nash-Sutcliffe efficiency of 0.732 for 671 basins across the USA for the Daymet forcing data set, compared to 0.748 from a state-of-the-art LSTM model with the same setup. For another forcing data set, the difference is even smaller: 0.715 versus 0.722. Meanwhile, the resulting learnable process-based models can output a full set of untrained variables, for example, soil and groundwater storage, snowpack, evapotranspiration, and baseflow, and can later be constrained by their observations. Both simulated evapotranspiration and fraction of discharge from baseflow agreed decently with alternative estimates. The general framework can work with models with various process complexity and opens up the path for learning physics from big data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning

Abstract The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel versions of high‐resolution (∼37 km 2 ) differentiable models (a type of hybrid model): one with implicit, unit‐hydrograph‐style routing and another with explicit Muskingum‐Cunge routing in the river network. The former predicts streamflow at basin outlets whereas the latter presents a discretized product that seamlessly covers rivers in the conterminous United States (CONUS). Both versions use neural networks to provide a multiscale parameterization and process‐based equations to provide a structural backbone, which were trained simultaneously (“end‐to‐end”) on 2,807 basins across the CONUS and evaluated on 4,997 basins. Both versions show great potential to elevate future NWM performance for extensively calibrated as well as ungauged sites: the median daily Nash‐Sutcliffe efficiency of all 4,997 basins is improved to around 0.68 from 0.48 of NWM3.0. As they resolve spatial heterogeneity, both versions greatly improved simulations in the western CONUS and also in the Prairie Pothole Region, a long‐standing modeling challenge. The Muskingum‐Cunge version further improved performance for basins >10,000 km 2 . Overall, our results show how neural‐network‐based parameterizations can improve NWM performance for providing operational flood predictions while maintaining interpretability and multivariate outputs. The modeling system supports the Basic Model Interface (BMI), which allows seamless integration with the next‐generation NWM. We also provide a CONUS‐scale hydrologic data set for further evaluation and use.

Song, Yalan [Civil and Environmental Engineering T↗
DOE OSTI2025

A U.S. Scientific Community Vision for Sustained Earth Observations of Greenhouse Gases to Support Local to Global Action

Managing carbon stocks in the land, ocean, and atmosphere under changing climate requires a globally‐integrated view of carbon cycle processes at local and regional scales. The growing Earth Observation (EO) record is the backbone of this multi‐scale system, providing local information with discrete coverage from surface measurements and regional information at global scale from satellites. Carbon flux information, anchored by inverse estimates from spaceborne Greenhouse Gas (GHG) concentrations, provides an important top‐down view of carbon emissions and sinks, but currently lacks global continuity at assessment and management scales (<100 km). Partial‐column data can help separate signals in the boundary layer from the overlying atmosphere, providing an opportunity to enhance surface sensitivity and bring flux resolution down from that of column‐integrated data (100–500 km). Based on a workshop held in September 2024, the carbon cycle community envisions a carbon observation system leveraging GHG partial columns in the lower and upper troposphere to weave together information across scales from surface and satellite EO data, and integration of top‐down/bottom‐up analyses to link process understanding to global assessment.

Parazoo, Nicholas C. [California Institute of Tech↗
DOE OSTI2020

Structural dynamics in proteins induced by and probed with X-ray free-electron laser pulses

X-ray free-electron lasers (XFELs) enable crystallographic structure determination beyond the limitations imposed upon synchrotron measurements by radiation damage. The need for very short XFEL pulses is relieved through gating of Bragg diffraction by loss of crystalline order as damage progresses, but not if ionization events are spatially non-uniform due to underlying elemental distributions, as in biological samples. Indeed, correlated movements of iron and sulfur ions were observed in XFEL-irradiated ferredoxin microcrystals using unusually long pulses of 80 fs. Here, we report a femtosecond time-resolved X-ray pump/X-ray probe experiment on protein nanocrystals. We observe changes in the protein backbone and aromatic residues as well as disulfide bridges. Simulations show that the latter’s correlated structural dynamics are much slower than expected for the predicted high atomic charge states due to significant impact of ion caging and plasma electron screening. This indicates that dense-environment effects can strongly affect local radiation damage-induced structural dynamics.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

CryoEM structure of the low-complexity domain of hnRNPA2 and its conversion to pathogenic amyloid

hnRNPA2 is a human ribonucleoprotein (RNP) involved in RNA metabolism. It forms fibrils both under cellular stress and in mutated form in neurodegenerative conditions. Previous work established that the C-terminal low-complexity domain (LCD) of hnRNPA2 fibrillizes under stress, and missense mutations in this domain are found in the disease multisystem proteinopathy (MSP). However, little is known at the atomic level about the hnRNPA2 LCD structure that is involved in those processes and how disease mutations cause structural change. Here we present the cryo-electron microscopy (cryoEM) structure of the hnRNPA2 LCD fibril core and demonstrate its capability to form a reversible hydrogel in vitro containing amyloid-like fibrils. Whereas these fibrils, like pathogenic amyloid, are formed from protein chains stacked into β-sheets by backbone hydrogen bonds, they display distinct structural differences: the chains are kinked, enabling non-covalent cross-linking of fibrils and disfavoring formation of pathogenic steric zippers. Both reversibility and energetic calculations suggest these fibrils are less stable than pathogenic amyloid. Moreover, the crystal structure of the disease-mutation-containing segment (D290V) of hnRNPA2 suggests that the replacement fundamentally alters the fibril structure to a more stable energetic state. These findings illuminate how molecular interactions promote protein fibril networks and how mutation can transform fibril structure from functional to a pathogenic form.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Unraveling the influence of non-fullerene acceptor molecular packing on photovoltaic performance of organic solar cells

In non-fullerene organic solar cells, the long-range structure ordering induced by end-group π–π stacking of fused-ring non-fullerene acceptors is considered as the critical factor in realizing efficient charge transport and high power conversion efficiency. Here, we demonstrate that side-chain engineering of non-fullerene acceptors could drive the fused-ring backbone assembly from a π–π stacking mode to an intermixed packing mode, and to a non-stacking mode to refine its solid-state properties. Different from the above-mentioned understanding, we find that close atom contacts in a non-stacking mode can form efficient charge transport pathway through close side atom interactions. The intermixed solid-state packing motif in active layers could enable organic solar cells with superior efficiency and reduced non-radiative recombination loss compared with devices based on molecules with the classic end-group π–π stacking mode. Our observations open a new avenue in material design that endows better photovoltaic performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Tunable, biodegradable grafting-from glycopolypeptide bottlebrush polymers

The cellular glycocalyx and extracellular matrix are rich in glycoproteins and proteoglycans that play essential physical and biochemical roles in all life. Synthetic mimics of these natural bottlebrush polymers have wide applications in biomedicine, yet preparation has been challenged by their high grafting and glycosylation densities. Using one-pot dual-catalysis polymerization of glycan-bearing α-amino acid N-carboxyanhydrides, we report grafting-from glycopolypeptide brushes. The materials are chemically and conformationally tunable where backbone and sidechain lengths were precisely altered, grafting density modulated up to 100%, and glycan density and identity tuned by monomer feed ratios. The glycobrushes are composed entirely of sugars and amino acids, are non-toxic to cells, and are degradable by natural proteases. Inspired by native lipid-anchored proteoglycans, cholesterol-modified glycobrushes were displayed on the surface of live human cells. Our materials overcome long-standing challenges in glycobrush polymer synthesis and offer new opportunities to examine glycan presentation and multivalency from chemically defined scaffolds.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Protein sequence design with a learned potential

The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Structural basis of R-loop recognition by the S9.6 monoclonal antibody

R-loops are ubiquitous, dynamic nucleic-acid structures that play fundamental roles in DNA replication and repair, chromatin and transcription regulation, as well as telomere maintenance. The DNA-RNA hybrid–specific S9.6 monoclonal antibody is widely used to map R-loops. Here, we report crystal structures of a S9.6 antigen-binding fragment (Fab) free and bound to a 13-bp hybrid duplex. We demonstrate that S9.6 exhibits robust selectivity in binding hybrids over double-stranded (ds) RNA and in categorically rejecting dsDNA. S9.6 asymmetrically recognizes a compact epitope of two consecutive RNA nucleotides via their 2'-hydroxyl groups and six consecutive DNA nucleotides via their backbone phosphate and deoxyribose groups. Recognition is mediated principally by aromatic and basic residues of the S9.6 heavy chain, which closely track the curvature of the hybrid minor groove. These findings reveal the molecular basis for S9.6 recognition of R-loops, detail its binding specificity, identify a new hybrid-recognition strategy, and provide a framework for S9.6 protein engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A cryptic third active site in cyanophycin synthetase creates primers for polymerization

Cyanophycin is a nitrogen reserve biopolymer in many bacteria that has promising industrial applications. Made by cyanophycin synthetase 1 (CphA1), it has a poly-L-Asp backbone with L-Arg residues attached to each aspartate sidechain. CphA1s are thought to typically require existing segments of cyanophycin to act as primers for cyanophycin polymerization. In this study, we show that most CphA1s will not require exogenous primers and discover the surprising cause of primer independence: CphA1 can make minute quantities of cyanophycin without primer, and an unexpected, cryptic metallopeptidase-like active site in the N-terminal domain of many CphA1s digests these into primers, solving the problem of primer availability. We present co-complex cryo-EM structures, make mutations that transition CphA1s between primer dependence and independence, and demonstrate that primer dependence can be a limiting factor for cyanophycin production in heterologous hosts. In CphA1, domains with opposite catalytic activities combine into a remarkable, self-sufficient, biosynthetic nanomachine.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Primary radiation damage in bone evolves via collagen destruction by photoelectrons and secondary emission self-absorption

X-rays are invaluable for imaging and sterilization of bones, yet the resulting ionization and primary radiation damage mechanisms are poorly understood. Here we monitor in-situ collagen backbone degradation in dry bones using second-harmonic-generation and X-ray diffraction. Collagen breaks down by cascades of photon-electron excitations, enhanced by the presence of mineral nanoparticles. We observe protein disintegration with increasing exposure, detected as residual strain relaxation in pre-stressed apatite nanocrystals. Damage rapidly grows from the onset of irradiation, suggesting that there is no minimal ‘safe’ dose that bone collagen can sustain. Ionization of calcium and phosphorous in the nanocrystals yields fluorescence and high energy electrons giving rise to structural damage that spreads beyond regions directly illuminated by the incident radiation. Our findings highlight photoelectrons as major agents of damage to bone collagen with implications to all situations where bones are irradiated by hard X-rays and in particular for small-beam mineralized collagen fiber investigations.

36 MATERIALS SCIENCE↗