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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Disordered, Sub-Nanometer Ru Structures on CeO 2 are Highly Efficient and Selective Catalysts in Polymer Upcycling by Hydrogenolysis

We report non-degradable polyolefin plastics pose severe environmental threats, and thus demand efficient upcycling technologies. In this work, we discovered that low-loading (= 0.25 wt%) Ru/CeO 2 exhibits remarkable catalytic performance in the hydrogenolysis of polypropylene (PP), polyethylene (PE), and n-C 16 H 34 that is superior to high-loading (= 0.5 wt%) Ru/CeO 2 . They possess high PP conversion efficiency (7-fold increase over current literature reports), low selectivity towards undesired CH 4 , and good isomerization ability. In the low-loading range, the intrinsic activity of Ru in PP hydrogenolysis increases as the particle size decreases, opposite of the trend in the high-loading range. Detailed characterization revealed that the abrupt changes in catalytic behaviors coincide with Ru species transitioning from well-defined to highly disordered structures in the low-loading domain. The disordered Ru species were shown to be sub-nanometer in size and cationic. Mechanistically, the regioselectivity and the rate dependence on hydrogen pressure of C-C bond cleavage are different on low- and high-loading Ru/CeO 2 , both explained by the higher coverage of adsorbed hydrogen (*H) on low-loading Ru/CeO 2 . This work uncovers the remarkable catalytic performance of highly disordered, sub-nanometer, cationic Ru species in polyolefin hydrogenolysis, opening immense opportunities to develop effective, selective, and versatile catalysts for plastic upcycling.

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

Mechanisms of Ethylene Epoxidation over Silver from Machine Learning-Accelerated First-Principles Modeling and Microkinetic Simulations

In this work we employed machine learning-augmented density functional theory (DFT) thermodynamic calculations to assess the stability of different AgO x structures under catalytic ethylene epoxidation reaction conditions. We found that there are multiple AgO x surface motifs that could co-exist under the relevant conditions. These included Ag surface oxides (e.g., AgO_p(4 × 4) and Ag 1.83 O) and atomic oxygen-covered Ag(111) surfaces. Furthermore, we employed DFT calculations to evaluate the energetics of different reaction mechanisms by which ethylene and oxygen can react on these surfaces. These studies revealed several energetically viable reaction pathways for ethylene epoxidation. Microkinetic modeling analyses, based on the DFT-calculated reaction pathways, showed that ethylene epoxidation can proceed on all surfaces and that multiple pathways, including those involving Langmuir–Hinshelwood and Eley–Rideal mechanisms, could be involved in selective and unselective reactions. The diversity of mechanisms that we discovered in the context of the relatively simple ethylene epoxidation reaction on Ag suggests that the richness and complexity of surface chemistry are most likely a rule rather than an exception in heterogeneous catalytic chemical transformations on metal surfaces and that the concept of a single or even a dominant mechanism and reaction intermediates might need to be revisited for many reactions.

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

Writing the Programs of Programmable Catalysis

It has long been known that non-steady state and periodic catalytic reactor operation in terms of temperature, pressure, and composition can lead to higher overall productivity and/or product selectivity than the best steady operation. Recently, the emergence of catalysts whose intrinsic properties can be made to oscillate with time, introduces advanced forcing capabilities that can be “programmed” into the catalysts to broaden the scope and applicability of periodic operation to surface chemistry. In this work, an algorithmic approach is implemented to significantly accelerate the discovery and optimization of periodic steady states of catalytic reactors. Decomposition of complex dynamics into fundamental mechanistic fast–slow steps is seen to improve conceptual understanding of the relationship between binding energy oscillation protocols and overall catalytic rates. Finding structured forcing protocols, optimally tailored to the multiple time scales of a given individual mechanism, requires an efficient search of high-dimensional parameter spaces. Here, this is enabled here through active learning (Bayesian optimization, enhanced by our proposed Bayesian continuation). Implementation of these methods is shown to accelerate the evaluation of catalyst programs by up to several orders of magnitude. Faster screening of programmable catalysts to discover periodic steady states enables the optimization of catalytic operating protocols and thus opens the possibility for catalyst engineering based on optimal forcing programs to control rate and product selectivity, even for complex multistep catalytic mechanisms.

catalysis↗
DOE OSTI2024

Data Analytics for Catalysis Predictions: Are We Ready Yet?

Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. Finally, we also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution

Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials' performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications.

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

Origin of Proteolytic Stability of Peptide-Brush Polymers as Globular Proteomimetics

Peptide-brush polymers (PBPs), wherein every side-chain of the polymers is peptidic, represent a new class of proteomimetic with unusually high proteolytic resistance while maintaining bioactivity. Here, we sought to determine the origin of this behavior and to assess its generality via a combined theory and experimental approach. A series of PBPs with various polymer backbone structures were prepared and examined for their proteolytic stability and bioactivity. We discovered that an increase in the hydrophobicity of the polymer backbones is predictive of an elevation in proteolytic stability of the side-chain peptides. Computer simulations, together with small-angle X-ray scattering (SAXS) analysis, revealed globular morphologies for these polymers, in which pendant peptides condense around hydrophobic synthetic polymer backbones driven by the hydrophobic effect. As the hydrophobicity of the polymer backbones increases, the extent of solvent exposure of peptide cleavage sites decreases, reducing their accessibility to proteolytic enzymes. This study provides insight into the important factors driving PBP aqueous-phase structures to behave as globular, synthetic polymer-based proteomimetics.

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

Controllable Phycobilin Modification: An Alternative Photoacclimation Response in Cryptophyte Algae

Cryptophyte algae are well-known for their ability to survive under low light conditions using their auxiliary light harvesting antennas, phycobiliproteins. Mainly acting to absorb light where chlorophyll cannot (500-650 nm), phycobiliproteins also play an instrumental role in helping cryptophyte algae respond to changes in light intensity through the process of photoacclimation. Until recently, photoacclimation in cryptophyte algae was only observed as a change in the cellular concentration of phycobiliproteins; however, an additional photoacclimation response was recently discovered that causes shifts in the phycobiliprotein absorbance peaks following growth under red, blue, or green light. Here, we reproduce this newly identified photoacclimation response in two species of cryptophyte algae and elucidate the origin of the response on the protein level. We compare isolated native and photoacclimated phycobiliproteins for these two species using spectroscopy and mass spectrometry, and we report the X-ray structures of each phycobiliprotein and the corresponding photoacclimated complex. We find that neither the protein sequences nor the protein structures are modified by photoacclimation. We conclude that cryptophyte algae change one chromophore in the phycobiliprotein β subunits in response to changes in the spectral quality of light. Ultrafast pump-probe spectroscopy shows that the energy transfer is weakly affected by photoacclimation.

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

Pendant Group Modifications Provide Graft Copolymer Silicones with Exceptionally Broad Thermomechanical Properties

Graft copolymers offer a versatile platform for the design of self-assembling materials; however, simple strategies for precisely and independently controlling the thermomechanical and morphological properties of graft copolymers remain elusive. Here, using a library of 92 polynorbornene-graft-polydimethylsiloxane (PDMS) copolymers, we discover a versatile backbone-pendant sequence-control strategy that addresses this challenge. Small structural variations of pendant groups, e.g., cyclohexyl versus n-hexyl, of small-molecule comonomers have dramatic impacts on order-to-disorder transitions, glass transitions, mechanical properties, and morphologies of statistical and block silicone-based graft copolymers, providing an exceptionally broad palette of designable materials properties. For example, statistical graft copolymers with high PDMS volume fractions yielded unbridged body-centered cubic morphologies that behaved as soft plastic crystals. By contrast, lamellae-forming graft copolymers provided robust, yet reprocessable silicone thermoplastics (TPs) with transition temperatures spanning over 160 °C and elastic moduli as high as 150 MPa despite being both unentangled and un-cross-linked. Altogether, this study reveals a new pendant-group-mediated self-assembly strategy that simplifies graft copolymer synthesis and enables access to a diverse family of silicone-based materials, setting the stage for the broader development of self-assembling materials with tailored performance specifications.

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

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its “fitness” for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Reproducibility of Radiokrypton in Deep Desert Aquifers: Insights from a Decade of Research

Great technical advances have been achieved since the first atom-trap trace analysis (ATTA) -based radiokrypton application in Egypt, where 1 Myr old groundwater was discovered. Beyond advances in ATTA measurement capabilities, including reduction in sample size, analysis duration, and analytical uncertainty, major progress has been achieved over the past two decades in the sample collection and preparation techniques. These advances paved the expansion of ATTA-based noble gas applications to many other aquifers worldwide, illuminating the nature and flow pattern of deep groundwater systems. While the potential of this new analytical technique for old groundwater dating is well recognized, another important aspect yet to be examined is the reproducibility of radiokrypton in aquifers over time, i.e., how representative is a discrete groundwater sample, collected at a specific time and location, for the natural groundwater system? The likelihood of a negative answer is increased by flow-field disturbance in aquifers following massive groundwater abstraction. Here, in this work, we present repeated 81 Kr sampling and measurements in twenty-one sites over Israel, mostly of deep (up to 1 km) wells tapping confined aquifers in the arid to hyperarid Negev desert. The results demonstrate that radiokrypton measurements are indeed reproducible, even in cases where samples were collected as long as nine years apart and from highly productive (∼1 Mm 3 /yr order) pumping wells. Furthermore, many of the repeated measurements in this study (17 out of the 21 sites) were conducted with different ATTA Instruments in two different laboratories using slightly different sampling, preparation, and analysis techniques, yet with an overall good agreement. The consistency in the ATTA-based 81 Kr-dating results over time highlights the robustness of this state-of-the-art technique as a tool to unravel groundwater flow patterns and encourages further applications to many other yet-to-be-explored deep aquifers.

atom-trap trace analysis↗
DOE OSTI2020

Forecasting the Decay of Hybrid Perovskite Performance Using Optical Transmittance or Reflected Dark-Field Imaging

The practicality and economic viability of hybrid perovskite solar cells hinge on their operational lifetime, and methods for forecasting the performance of perovskites under different operational stresses are urgently needed. Here, we explore the evolution of material-level optoelectronic properties as MAPbI 3 degrades and discover universal behaviors where the carrier diffusion length (L D ) decays before quasi-Fermi-level splitting (ΔE F ), regardless of the specific stress protocol (oxygen, humidity, thermal stress, or a combination). We employ a machine learning greedy feature selection model that uses initially measured properties to predict the time it takes L D to decrease to 85% of its initial value with a prediction accuracy of 12.8%. This model reveals a strong correlation between the initial rate of transmittance change and the time until loss of transport. We translate this material-level finding to photovoltaic device-level forecasting by demonstrating that the rate of change of transmittance is equivalent to the rate of change of the spatial standard deviation of dark-field image intensity (i.e., scattered light intensity) collected in reflection mode (and thus applicable to devices with opaque contacts). Furthermore, this work demonstrates that transmittance and scattering methods are highly effective for accelerated material (and device) stability evaluation and forecasting.

14 SOLAR ENERGY↗
DOE OSTI2021

Quasi-Two-Dimensional Earth-Abundant Bimetallic Electrocatalysts for Oxygen Evolution Reactions

The extremely large surface area offered by quasi-two-dimensional (2D) nanostructures is a great advantage for catalytic applications. While bimetallic materials have been discovered as a group of promising catalysts for electrochemical water splitting, a facile and reliable synthesis approach toward 2D morphology to fully release its electrochemical catalytic potential is still lacking. In this work, bimetallic Co–Mo–O ultrathin nanosheets were synthesized by ionic layer epitaxy with controlled composition ratio. The Co–Mo–O nanosheets exhibited a quadrangle shape with a uniform thickness of ~2.5 nm. This 2D morphology largely enriched the surface catalytic active site ratio. Particularly, nanosheets with 2:1 Co-to-Mo ratio showed a substantially enhanced OER catalytic activity with a 3 orders of magnitude higher mass activity compared to benchmark IrO 2 and RuO 2 in an alkaline environment. Furthermore, this study introduces a promising possibility for creating high-performance electrocatalysts from 2D bimetallic materials with a high efficiency for material utilization.

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

Role of Electrolyte in Overcoming the Challenges of LiNiO 2 Cathode in Lithium Batteries

LiNiO 2 (LNO) is a high-capacity and model cathode first discovered in the 1980s that fell out of favor due to its intrinsic instabilities. However, research activities toward LNO are once again on the rise as the push for higher-energy-density cells marches on. Here we demonstrate here that with appropriate modern electrolytes, major performance improvements can be achieved with LNO with no additional modifications. Cells with a localized high concentration electrolyte (LHCE) deliver 92% capacity retention after 200 cycles compared with 56% capacity retention in a baseline carbonate electrolyte, maintain 94% capacity after high-voltage storage compared with 77% capacity, and display a higher onset temperature of thermal runaway of 244 °C compared with 188 °C. These improvements are attributed to the LHCE’s high oxidative stability and its formation of fluorine-rich interphases. Although further characterization of this new class of electrolyte is necessary, this work demonstrates that modern electrolytes can be drop-in enablers of high-capacity, long-cycle-life cells.

25 ENERGY STORAGE↗
DOE OSTI2024

Local Background Hole Density Drives Nonradiative Recombination in Tin Halide Perovskites

In this study, we use multimodal microscopy to study carrier recombination in semiconducting tin halide perovskite films based on PEA 0.2 FA 0.8 SnI 3 (PEA = phenethylammonium; FA = formamidinium). We use the observation of pseudo-first-order photoluminescence (PL) decay kinetics to establish a method for quantifying the hole dopant level and nonradiative recombination rate constant. We find that untreated PEA 0.2 FA 0.8 SnI 3 films exhibit large hole doping concentrations of p 0 ≈ 10 19 cm –3 , which is reduced to p 0 ≈ 10 16 cm –3 after SnF 2 treatment. While it is well-known that the radiative recombination rates are increased with p 0 , we reveal that the nonradiative rate is also increased. We find that p-type regions in untreated PEA 0.2 FA 0.8 SnI 3 films are centers for nonradiative recombination, which are diminished in films with p 0 ≈ 10 16 cm –3 . We discover significant PL heterogeneity even in PEA 0.2 FA 0.8 SnI 3 films with moderate dopant levels, suggesting that new strategies to eliminate deleterious defects in PEA 0.2 FA 0.8 SnI 3 must be developed.

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

Engineering the Novel Extremophile Alga Chlamydomonas pacifica for High Lipid and High Starch Production as a Path to Developing Commercially Relevant Strains

Microalgae offer a compelling platform for the production of commodity products, due to their superior photosynthetic efficiency, adaptability to nonarable lands and nonpotable water, and their capacity to produce a versatile array of bioproducts, including biofuels and biomaterials. However, the scalability of microalgae as a bioresource has been hindered by challenges such as costly biomass production related to vulnerability to pond crashes during large-scale cultivation. This study presents a pipeline for the genetic engineering and pilot-scale production of biodiesel and thermoplastic polyurethane precursors in the extremophile species Chlamydomonas pacifica. This extremophile microalga exhibits exceptional resilience to high pH (>11.5), high salinity (up to 2% NaCl), and elevated temperatures (up to 42 °C). Initially, we evolved this strain to also have a high tolerance to high light intensity (>2000 μE/m 2 /s) through mutagenesis, breeding, and selection. We subsequently genetically engineered C. pacifica to significantly enhance lipid production by 28% and starch accumulation by 27%, all without affecting its growth rate. We demonstrated the scalability of these engineered strains by cultivating them in pilot-scale raceway ponds and converting the resulting biomass into biodiesel and thermoplastic polyurethanes. This study showcases the complete cycle of transforming a newly discovered species into a commercially relevant commodity production strain. This research underscores the potential of extremophile algae, including C. pacifica, as a key species for the burgeoning sustainable bioeconomy, offering a viable path forward in mitigating environmental challenges and supporting global bioproduct demands.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Potent Inhibition of E. coli DXP Synthase by a gem -Diaryl Bisubstrate Analog

New antimicrobial strategies are needed to address pathogen resistance to currently used antibiotics. Bacterial central metabolism is a promising target space for the development of agents that selectively target bacterial pathogens. 1-Deoxy- D -xylulose 5-phosphate synthase (DXPS) converts pyruvate and d-glyceraldehyde 3-phosphate ( D -GAP) to DXP, which is required for synthesis of essential vitamins and isoprenoids in bacterial pathogens. Thus, DXPS is a promising antimicrobial target. Toward this goal, our lab has demonstrated selective inhibition of Escherichia coli DXPS by alkyl acetylphosphonate (alkylAP)-based bisubstrate analogs that exploit the requirement for ternary complex formation in the DXPS mechanism. Here, we present the first DXPS structure with a bisubstrate analog bound in the active site. Insights gained from this cocrystal structure guided structure–activity relationship studies of the bisubstrate scaffold. A low nanomolar inhibitor (compound 8) bearing a gem-dibenzyl glycine moiety conjugated to the acetylphosphonate pyruvate mimic via a triazole-based linker emerged from this study. Compound 8 was found to exhibit slow, tight-binding inhibition, with contacts to E. coli DXPS residues R99 and R478 demonstrated to be important for this behavior. This work has discovered the most potent DXPS inhibitor to date and highlights a new role of R99 that can be exploited in future inhibitor designs toward the development of a novel class of antimicrobial agents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Confined Crystal Melting in Edgeless Poly( L -lactic acid) Crystalsomes

Polymer single crystals tend to be quasi two-dimensional (2D) lamellae and their small lateral surfaces are the starting points of lamella melting and thickening. However, the recently discovered crystalsomes, which are defined for hollow single crystal-like spherical shells, are edgeless, self-confined, and incommensurate with translational symmetry. Here, this work concerns the structure and melting behavior of these edgeless crystalsomes. Poly(l-lactic acid) crystalsomes were grown using a miniemulsion solution crystallization method. Differential scanning calorimetry and in situ wide-angle X-ray diffraction were used to follow the structural evolution of the crystalsomes upon heating. Our results demonstrated that the structure and melting behavior of crystalsomes are curvature-dependent and significantly different from their flat crystal counterpart.

36 MATERIALS SCIENCE↗