Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Process synthesis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Self-driving thin film laboratory: autonomous epitaxial atomic-layer synthesis via real-time computer vision analysis of electron diffraction

Emerging materials science platforms with the ability to make autonomous decisions on the fly are fundamentally changing the outlook and protocols for materials optimization and discovery. Because AI-driven self-navigating schemes can effectively reduce the total number of iterations needed to arrive at the "answer" (i.e. the best stochiometric composition for a desired physical property, optimum materials processing parameters, etc.) by significant margins, they have the potential to revolutionize materials and chemical manufacturing processes at large in research laboratory settings as well as in industrial plants. Here, we demonstrate a successful implementation of real-time closed-loop autonomous navigation of a multi-dimensional materials synthesis parameter space for fabricating phase-pure epitaxial films of a metastable phase of a functional oxide in a combinatorial pulsed laser deposition chamber. Sequential epitaxial growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision analysis of reflection high-energy electron diffraction (RHEED) images of the unit-cell level film being deposited. The autonomous scheme regularly resulted in > 30-fold reduction in the number of required experiments compared to a comprehensive mapping of the parameter space. The real-time workflow developed here can be readily extended to a variety of thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous optimization of semiconductor manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Decomposition Kinetics of H 2 O 2 on Pd Nanocrystals with Different Shapes and Surface Strains

Direct synthesis of hydrogen peroxide (H 2 O 2 ) from H 2 and O 2 on a Pd-based catalyst has emerged as a promising route to replace the energy-consuming, highly inefficient anthraquinone process. However, Pd is also a good catalyst for the decomposition of H 2 O 2 , thereby compromising the selectivity toward the desired product. The coupling between the formation and decomposition reactions makes it difficult to single out the most important parameter that controls the selectivity toward direct synthesis of H 2 O 2 . Herein, support-free monometallic Pd nanocrystals with different shapes and surface strains are used to investigate their impacts on the decomposition kinetics of H 2 O 2 . The kinetics are analyzed by tracking the concentration of the remaining H 2 O 2 using infrared spectroscopy. The data indicates that both surface structure and strain affect the decomposition kinetics of H 2 O 2 , but their impacts are inferior to that caused by Br – , a surface capping agent for the Pd{100} facets. The experimental results are consistent with the trend obtained through density functional theory calculations. Furthermore, this work helps shed light on the development of Pd-based catalysts for the direct synthesis of H 2 O 2 by offering strategies to mitigate the decomposition of the desired product.

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

Surface Ligand Management Aided by a Secondary Amine Enables Increased Synthesis Yield of CsPbI 3 Perovskite Quantum Dots and High Photovoltaic Performance

Lead-halide perovskite quantum dots (PQDs) or more broadly, nanocrystals possess advantageous features for solution-processed photovoltaic devices. The nanocrystal surface ligands play a crucial role in the transport of photogenerated carriers and ultimately affect the overall performance of PQD solar cells. Here, significantly improved CsPbI 3 PQD synthetic yield and solar-cell performance through surface ligand management are demonstrated. The treatment of a secondary amine, di- n -propylamine (DPA), provides a mild and efficient approach to control the surface ligand density of PQDs, which has an apparently different working mechanism compared to previously reported surface treatments. Using an optimal DPA concentration, the treatment can simultaneously remove both long-chain insulating surface ligands of oleic acid and oleylamine, even for unpurified PQDs with high ligand density. As a result, the electrical coupling between PQDs is enhanced, leading to improved charge transport, reduced carrier recombination, and a high power conversion efficiency approaching 15% for CsPbI 3 -PQD-based solar cells. In addition, the production yield of CsPbI 3 PQDs can be increased by a factor of 8. These results highlight the importance of developing new ligand-management strategies, specifically for emerging PQDs to achieve scalable and high-performance perovskite-based optoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Predicting U 3 O 8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs

High-resolution SEM images of uranium-oxide powders encode micro- and nanoscale clues to their synthesis route and calcination temperature. We trained a ResNet-50 model on 11 commercial-scale U₃O₈ classes, ammonium diuranate (ADU) or uranyl peroxide (H₂O₂) precursors calcined at temperatures ranging from 400 to 750 °C and added a 256-D projection head before the classifier to analyze the learned representation. The best of eight seeds reached 92.4 % accuracy on reserved testing data, but our focus is the structure of the embedding space rather than the accuracy and labels. We quantify class relatedness in the original 256-D space using centroid similarity and distributional distances, and we use Uniform Manifold Approximation Projection (UMAP) for visualization. ‘Unknown’ images from different preparation methods, SEM operators, and from the literature localized near the expected classes under a nearest-centroid analysis without retraining, as well as clustered in similar UMAP space. In conclusion, this embedding-centered workflow complements black-box classification by providing quantitative, similarity-based comparisons of U₃O₈ morphologies and reduces storage space by up to 98 % for image data used in millisecond vector search comparisons.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of a Novel Bioprocess for Synthesizing and Purifying Adipic Acid

Through DOE’s Bioenergy Technologies Office and the Energy Efficiency and Renewable Energy Small Business Voucher (SBV) Pilot, ZymoChem, Inc received support to work with the ABPDU to accelerate the development of a novel adipic acid (AA) bioprocess by evaluating technologies to demonstrate the following: 1) Process development of AA via ZymoChem’s carbon-conserving (C 2 ) pathway in 2L bioreactors, using industrial feedstocks, and as a salt with hexamethylenediamine (HMDA), i.e., the industrially desired form of AA as AA-HMDA is the precursor for nylon 6,6 synthesis. 2) Pilot scale aerobic fermentation at the 300 L scale (stage 1) followed by a high cell density (HCD) anaerobic resting cell biotransformation (RCB) at the 50 L scale (stage 2). 3) Demonstration of a viable recovery process at the 50 L scale for purifying the product (i.e., AA-HMDA salt) and deliver multiple kg-quantity batches of purified product for further testing.

09 BIOMASS FUELS↗
DOE OSTI2021

PS3: The Pheno-Synthesis software suite for integration and analysis of multi-scale, multi-platform phenological data

Phenology is the study of recurring plant and animal life-cycle stages which can be observed across spatial and temporal scales that span orders of magnitude (e.g., organisms to landscapes). The variety of scales at which phenological processes operate is reflected in the range of methods for collecting phenologically relevant data, and the programs focused on these collections. Consideration of the scale at which phenological observations are made, and the platform used for observation, is critical for the interpretation of phenological data and the application of these data to both research questions and land management objectives. However, there is currently little capacity to facilitate access, integration and analysis of cross-scale, multi-platform phenological data. This paper reports on a new suite of software and analysis tools – the “Pheno-Synthesis Software Suite,” or PS3 – to facilitate integration and analysis of phenological and ancillary data, enabling investigation and interpretation of phenological processes at scales ranging from organisms to landscapes and from days to decades. We use PS3 to investigate phenological processes in a semi-aride, mixed shrub-grass ecosystem, and find that the apparent importance of seasonal precipitation to vegetation activity (i.e., “greenness”) is affected by the scale and platform of observation. We end by describing potential applications of PS3 to phenological modeling and forecasting, understanding patterns and drivers of phenological activity in real-world ecosystems, and supporting agricultural and natural resource management and decision-making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluating causal‐based feature selection for fuel property prediction models

Abstract In‐silico screening of novel biofuel molecules based on chemical and fuel properties is a critical first step in the biofuel evaluation process due to the significant volumes of samples required for experimental testing, the destructive nature of engine tests, and the costs associated with bench‐scale synthesis of novel fuels. Predictive models are limited by training sets of few existing measurements, often containing similar classes of molecules that represent just a subset of the potential molecular fuel space. Software tools can be used to generate every possible molecular descriptor for use as input features, but most of these features are largely irrelevant and training models on datasets with higher dimensionality than size tends to yield poor predictive performance. Feature selection has been shown to improve machine learning models, but correlation‐based feature selection fails to provide scientific insight into the underlying mechanisms that determine structure–property relationships. The implementation of causal discovery in feature selection could potentially inform the biofuel design process while also improving model prediction accuracy and robustness to new data. In this study, we investigate the benefits causal‐based feature selection might have on both model performance and identification of key molecular substructures. We found that causal‐based feature selection performed on par with alternative filtration methods, and that a structural causal model provides valuable scientific insights into the relationships between molecular substructures and fuel properties.

Nguyen, Bernard↗
DOE OSTI2024

Tailoring Molecular Space to Navigate Phase Complexity in Cs-Based Quasi-2D Perovskites via Gated-Gaussian-Driven High-Throughput Discovery

Cesium-based quasi-2D halide perovskites (HPs) offer promising functionalities and low-temperature manufacturability, suited to stable tandem photovoltaics. However, the chemical interplays between the molecular spacers and the inorganic building blocks during crystallization cause substantial phase complexities in the resulting matrices. To successfully optimize and implement the quasi-2D HP functionalities, a systematic understanding of spacer chemistry, along with the seamless navigation of the inherently discrete molecular space, is necessary. Herein, by utilizing high-throughput automated experimentation, the phase complexities in the molecular space of quasi-2D HPs are explored, thus identifying the chemical roles of the spacer cations on the synthesis and functionalities of the complex materials. Furthermore, a novel active machine learning algorithm leveraging a two-stage decision-making process, called gated Gaussian process Bayesian optimization is introduced, to navigate the discrete ternary chemical space defined with two distinctive spacer molecules. Through simultaneous optimization of photoluminescence intensity and stability that “tailors” the chemistry in the molecular space, a ternary-compositional quasi-2D HP film realizing excellent optoelectronic functionalities is demonstrated. Finally, this work not only provides a pathway for the rational and bespoke design of complex HP materials but also sets the stage for accelerated materials discovery in other multifunctional systems.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Catalyst synthesis under CO 2 electroreduction favours faceting and promotes renewable fuels electrosynthesis

The electrosynthesis of C 2+ hydrocarbons from CO 2 has garnered recent attention in light of the relatively high market price per unit energy input. Today’s low selectivities and stabilities towards C 2+ products at high current densities curtail system energy efficiency, which limits their prospects for economic competitiveness. Here we present a materials processing strategy based on in situ electrodeposition of copper under CO 2 reduction conditions that preferentially expose and maintain Cu(100) facets, which favour the formation of C 2+ products. We observe capping of facets during catalyst synthesis and achieve control over faceting to obtain a 70% increase in the ratio of Cu(100) facets to total facet area. We report a 90% Faradaic efficiency for C 2+ products at a partial current density of 520 mA cm –2 and a full-cell C 2+ power conversion efficiency of 37%. We achieve nearly constant C2H4 selectivity over 65 h operation at 350 mA cm –2 in a membrane electrode assembly electrolyser.

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

Forest regeneration within Earth system models: current process representations and ways forward

Earth system models must predict forest responses to global change in order to simulate future global climate, hydrology, and ecosystem dynamics. These models are increasingly adopting vegetation demographic approaches that explicitly represent tree growth, mortality, and recruitment, enabling advances in the projection of forest vulnerability and resilience, as well as evaluation with field data. To date, simulation of regeneration processes has received far less attention than simulation of processes that affect growth and mortality, in spite of their critical role maintaining forest structure, facilitating turnover in forest composition over space and time, enabling recovery from disturbance, and regulating climate-driven range shifts. Here, our critical review of regeneration process representations within current Earth system vegetation demographic models reveals the need to improve parameter values and algorithms for reproductive allocation, dispersal, seed survival and germination, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes. These improvements require synthesis of existing data, specific field data-collection protocols, and novel model algorithms compatible with global-scale simulations. Vegetation demographic models offer the opportunity to more fully integrate ecological understanding into Earth system prediction; regeneration processes need to be a critical part of the effort.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

NGEE Arctic Authorship Guidelines

Authorship Guidelines were developed to help facilitate trust among team members as we span multiple institutions, scientific disciplines, and career stages. NGEE Arctic was built on a foundation of open science, data sharing, and collaboration. In Phase 4 of the project, it was particularly important to keep this foundation in mind as we develop new collaborations across the Arctic. Included in this package is one *.pdf. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

Iversen, Colleen [ORNL] (ORCID:0000000182933450)↗
DOE OSTI2025

Comparing approaches for introducing polycyclic aromatic hydrocarbons to Ge(001) to seed graphene nanoribbon synthesis by CH 4 chemical vapor deposition

Here, we evaluate two approaches for introducing polycyclic aromatic hydrocarbons (PAHs) to a graphene catalyst substrate as part of a two-step chemical vapor deposition (CVD) process for growing graphene nanoribbons (GNRs). In this process, PAHs first form graphene-like seeds on Ge, and then GNRs are subsequently evolved from these PAH-derived seeds via substrate-mediated anisotropic growth kinetics during the CVD of CH4. The first seeding approach sublimes controlled doses of PAH thin films into the CVD chamber, while the second delivers PAHs directly from the vapor phase at set concentrations. Using these two approaches, we measure the dependence of GNR density on PAH dose and compare the experimental results with predictions from a rate model of PAH diffusion, desorption, and clustering. We find that PAHs that more strongly adsorb to the catalyst surface (generally larger PAHs) desirably remain more individualized prior to GNR evolution whereas smaller, more weakly bound PAHs aggregate into larger clusters with various sizes on Ge that are undesirable for synthesizing more monodisperse GNRs. As a result, this work is important because it offers a framework that enables the rational selection and design of seed molecules, advancing anisotropic GNR CVD synthesis.

CVD↗
DOE OSTI2020

Synthesis of fluorinated radiopharmaceuticals via electrochemical fluorination

Provided herein are methods and compositions for the electrochemical selective radiofluorination of aromatic molecules. The resulting fluorine-18 labeled compounds are ideal radionuclides for use in Positron Emission Tomography (PET); they are also difficult to radiolabel efficiently and with high specific activity using existing approaches. For example, radiopharmaceuticals such as [F18]L-DOPA, which is indispensable in PET brain disease imaging, may be made electrochemically with high radiochemical yield and high specific activity using 18F-. The invention process described herein opens new possibilities and provides wider access to PET tracers such as 18F-L-Dopa, since 18F- is much more widely available than the 18F2, currently used for synthesis of electron rich substrates.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Synthesis and structure of high-purity BaCe 0.25 Mn 0.75 O 3 : an improved material for thermochemical water splitting

Solar thermochemical hydrogen production (STCH) via redox-active metal oxides is an approach for direct solar-driven hydrogen generation typically using a high-temperature redox cycle involving refractory oxides and steam. Typical cycles involve high-temperature reduction of oxides to form oxygen vacancies, followed by lower temperature reaction between oxygen vacancies and steam where the oxide is re-oxidized and the steam is reduced to hydrogen. Only a few materials have demonstrated reversible cycling under the typically harsh STCH conditions (e.g. 1500°C reduction, 900°C re-oxidation) and critical questions remain on the true reversibility of non-stoichiometric multi-cation oxide systems, significantly hampered by the lack of single-phase samples for these material systems. To date, most STCH processes have relied on CeO 2 as a benchmark active material, but more recently, the 12R phase of BaCe 0.25 Mn 0.75 O 3 (BCM) has demonstrated greater hydrogen-generation potential at lower peak temperatures. However, previous reports of 12R-BCM have included large fractions, > 10 wt%, of secondary phases, which complicate analysis of the stability and performance. A comprehensive understanding of the redox mechanism and reversibility of the process in BCM can only be achieved with nearly single-phase samples which, to date, have been difficult to produce. Here two approaches to BCM synthesis are reported: solid state and sol–gel-based routes. It is demonstrated that both routes can be tuned to produce the 12R structure with > 97 wt% yield when annealed ≥1450°C. Herein synchrotron-based diffraction measurements of rhombohedral 12R-BCM enabled characterization of the anisotropy between thermal expansion along the c-axis and within the ab plane. The impact of high-temperature redox cycling on the stability and phase fraction of the 12R-BCM polytype was also investigated. Our results offer two viable routes for synthesis of high-purity 12R-BCM critically needed for evaluating the efficacy of BCM as a STCH material and validate its ability to split water at lower temperatures over extended numbers of redox cycles.

08 HYDROGEN↗
DOE OSTI2022

A Novel Dynamic Polymer Synthesis via Chlorinated Solvent Quenched Depolymerization

Dynamic polymers with both physical interactions and dynamic covalent bonds exhibit superior performance while achieving such dry polymers in an efficient manner remains a challenge. Herein, we report a novel organic solvent quenched polymer synthesis using a natural molecule, i.e., thioctic acid (TA), which has both dynamic disulfide bond and carboxylic acid. The effects of the solvent types and concentrations along with reaction times on the proposed reaction have been thoroughly explored for polymer synthesis. Solid-state proton nuclear magnetic resonance ( 1 H NMR) and first-principles simulations are carried out to investigate the reaction mechanism. It shows that the chlorinated solvent can efficiently stabilize and mediate the depolymerization of poly(TA) which is more kinetically favorable upon lowering the temperature. Attributed to the numerous dynamic covalent disulfide bonds and noncovalent hydrogen bonding, the obtained poly(TA) shows high extensibility, self-healable and re-processable properties. It can also be employed as an efficient adhesive even on a Teflon surface and 3D printed using the fused deposition modeling (FDM) technique. This new polymer synthesis approach on using organic solvents as catalysts along with the unique reaction mechanism provides a new pathway for efficient polymer synthesis, especially for those multi-functional dynamic polymers.

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

A Closer Look at Acyl-ACPs in Lipid Metabolism

Acyl lipids are a wide range of compounds that have diverse functions in membrane synthesis, energy storage, and signal transduction. Fatty acids are key building blocks of acyl lipids that are synthesized by the fatty acid synthase complex via sequential condensation of two-carbon units to reach lengths of up to 18 carbons (Li-Beisson et al., 2013). During this process, the elongating acyl chains are attached to Acyl Carrier Proteins (ACPs) through a phosphopantetheine group linked to a Ser residue of ACPs to form acyl-ACPs, which are central components in de novo fatty acid synthesis. However, the roles of acyl-ACPs in lipid metabolism are rarely studied due to a lack of methods to accurately quantify individual acyl-ACPs and their cargoes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Microwave-assisted pyrolysis of hydrocarbons using iron-based alumina catalysts obtained via solution combustion synthesis

The demand for hydrogen is growing which makes the development of clean and efficient H2 synthesis technologies imperative. Microwave-assisted, thermocatalytic, dehydrogenation of hydrocarbons has demonstrated the ability to generate H2 with high yield and selectivity, leaving behind valuable solid carbon byproducts. However, this microwave-assisted process is unoptimized which prevents it from being utilized in industry. A critical component of optimization is the development of a catalyst that is catalytically active, a good microwave absorber, and can be regenerated for repeated dehydrogenation cycles. Previous studies that focused on plastic waste decomposition have used iron-based alumina (FeAlxOy) made via solution combustion synthesis (SCS). Unexplored is the effect of tuning SCS parameters on dehydrogenation performance, the use of these materials in hydrocarbon decomposition to H2, and the regeneration of these catalysts. This dissertation has three objectives: (1) characterize the relationship between SCS parameters and the material properties of FeAlxOy, (2) determine how differences in the material properties of FeAlxOy influence their performance as catalysts during microwave-assisted pyrolysis of fossil fuels, and (3) investigate the Boudouard reaction to regenerate the FeAlxOy post-dehydrogenation.

Chanoi, Zachary Aidan↗
DOE OSTI2025

Fluorinated polythiophenes with ester side chains for boosting the V OC and efficiency in non-fullerene polymer solar cells

As the power conversion efficiency (PCE) of organic photovoltaics (OPVs) approximates the 19 % threshold, wide bandgap (WBG) polythiophenes (PTs) have gained increasing attention due to their simple chemical structures and ease of synthesis, making them promising candidates for large-scale production. However, the benchmark polymer poly(3-hexylthiophene) (P3HT) is limited by its high-lying HOMO energy level, which restricts the open-circuit voltage (V OC ) in solar cells. Here, in this study, we introduce a novel series of PT derivatives (PDC8-T, PDC8-3T, PDC16-3T, and PDC16-3T-2F) featuring ester side chains designed to fine-tune electronic properties through a streamlined three-step synthesis. Additionally, we incorporated a π-spacer to reduce steric hindrance and elongated alkyl side chains to improve solubility and processability. Compared to P3HT, these PT derivatives demonstrate a significant reduction in HOMO energy levels, lowering by approximately 0.3–0.4 eV. Among them, PDC16-3T-2F—with fluorine atom substitution—achieves the lowest HOMO energy level, induces a coplanar molecular conformation, and enhances polymer aggregation behavior. We evaluated these PT derivatives in inverted non-fullerene bulk-heterojunction (NFA BHJ) OPVs. The PDC8-3T device showed a relatively low PCE of 0.69 %, with a V OC of 0.76 V, a short-circuit current density (J SC ) of 3.32 mA/cm 2 , and a fill factor (FF) of 27.3 %. In contrast, the PDC16-3T-2F device achieved an impressive PCE of 7.21 %, with a V OC of 0.85 V, a J SC of 14.60 mA/cm 2 , and an FF of 58.4 %. This remarkable improvement is attributed to the fluorine substitution, which not only enhances molecular orientation but also downshifts the HOMO energy level and further boosts the V OC . Hence, these molecular design strategies have led to a fibrillar bicontinuous interpenetrating network with optimal nanoscale phase separation within the active layer.

14 SOLAR ENERGY↗